bluuebunny/crossref_metadata_2025 huggingface dataset:
Cultural Harmony in Motion: Revealing the Communication Prowess of Saung Angklung Udjo Community in Enriching Local Traditions
Authors: Angelo Eldorado Aluzio , Mujiono Mujiono , Alvin Silvanus
Published: September 2023

The research explores Saung Angklung Udjo's strategic communication practices, highlighting their successful preservation and promotion of traditional Indonesian culture in the digital age. Aligned with Machfoedz's communication principles, Saung Angklung Udjo crafts a dynamic strategy merging cultural preservation with modern marketing techniques. The study underscores the significance of Saung Angklung Udjo's strategy, harmonizing content and form to engage diverse audiences. Kang Puji Yanandra's insights reveal a meticulous process emphasizing clear objectives, target markets, and tailored messaging. Saung Angklung Udjo's storytelling coupled with diverse media platforms practically translates theoretical principles. Furthermore, the research underscores Saung Angklung Udjo's adeptness in addressing consumer needs through precise information communication. Merging emotional appeal and factual data, Saung Angklung Udjo effectively conveys cultural significance, creating a meaningful connection between audience and heritage. The creative strategy and media selection exemplify Machfoedz's theories, creatively engaging audiences and countering cultural appropriation. The @InfoWisataBandung Instagram case study showcases the synergy of traditional and digital channels in reinforcing the cultural message. Beyond Saung Angklung Udjo, the study offers a model for cultural communities to safeguard heritage and engage wider audiences. By merging traditional and contemporary communication, Saung Angklung Udjo demonstrates the power of strategic communication in preserving cultural identity. Amid evolving digital landscapes, their success illustrates effective communication's potential in celebrating and sustaining cultural heritage.
Analysis of Factors Influencing Intention to Purchase “Portable Fire Extinguisher” for Residential House in DKI Jakarta
Authors: Oktovani Theresia , Arief Bowo Prayoga Kasmo
Published: September 2023

This study aims to analyze the factors that influence the intention to buy Fire Extinguisher products for residential homes in DKI Jakarta area. These factors include Awareness, Knowledge, and Perceived Price as independent variables. Attitude as an intervening variable, and Purchase Intention as the dependent variable. The population of this study is households living in DKI Jakarta, with a total sample of 150 people who will be taken through a questionnaire instrument. The data analysis method uses Structural Equation Model–Partial Least Square (SEM-PLS) which will be processed using Smart-PLS software. The results of the study show that there is a positive and significant influence between the independent variables namely Awareness, Knowledge, and Perceived Price on the dependent variable Purchase Intention. Attitude as an intervening variable can mediate the independent variables on the dependent variable.
The Effect of Product Quality and Store Atmosphere on Puchase Decisions mediated By Purchase Intention as Intervening Variable (Study at Café Ma’Ani Rengat)
Authors: Rianti Tranita , Taufiqurrahman Taufiqurrahman , Firli Musfar Tengku
Published: September 2023

This study aims to determine the effect of product quality and store atmosphere on purchase decisions mediated by purchase intention as intervening variable. The type of research conducted is quantitative research with primary data using questionnaire as an instrument to prove the result of the research. The population in this study is all the customers of Café Ma’Ani Rengat. To test the hypothesis in this study is using path analysis with Statistical Program for Social Science (SPSS) 20. The sampling method used a non-probability sampling method with purposive sampling technique (102 respondents). The result of the study indicate that product quality and store atmosphere have a significant ang positive effect on purchase intention. Product quality and store atmosphere have a positive and not significant effect on purchase decision. Product quality and store atmosphere have a positive significant effect towards purchase decision through purchase intention. And purchase intention has a positive significant effect towards purchase decision.
LITERATURE REVIEW DETERMINASI STRUKTUR ORGANISASI: TEKNOLOGI, LINGKUNGAN DAN STRATEGI ORGANISASI
Authors: Julia Mela , Jiddal Masyruroh Alifah
Published: September 2023

Pengembangan Organisasi merupakan sebuah pendekatan sistematik serta terencana guna meningkatkan efektivitas organisasi dan membantu untuk memecahkan masalah.  Beberapa yang termasuk Manajemen Perubahan ialah Individu, Tim Kerja, Organisasi kerja sama/kooperasi, desentralisasi yang berlebihan dan kurang cepatnya komunikasi  yang mempengaruhi dalam efisiensi pengoperasian pada semua tingkatan. Perubahan strategis organisasi ialah sebuah pengaruh baru terhadap evolusi pengembangan organisasi. Struktur organisasi dan teknologi, strategi organisasi dan sosial lingkungan semakin kompleks mendorong perubahan organisasi semakin meningkat. Suatu organisasi akan mengalami sebuah perkembangan jika organisasi tersebut dapat dinyatakan berhasil saat berkembang ke arah yang positif, sehingga karyawan yang bergabung dalam organisasi tersebut dapat mencapai tujuannya. Perkembangan struktur organisasi membutuhkan penyesuaian sistem dalam organisasi tersebut agar dapat mengikuti seiring terjadinya perubahan waktu. Perkembangan suatu organisasi juga dapat dijadikan skala bagi organisasi tersebut, apakah organisasi tersebut tetap aktif dan mengayomi karyawan dalam organisasi tersebut juga dalam menghadapi perkembangan jaman. Tujuan penulisan artikel ini guna membangun hipotesis pengaruh antar variabel untuk digunakan pada riset selanjutnya.
KORELASI KEPUTUSAN PEMBELIAN: KUALITAS PRODUK, PERSEPSI HARGA DAN PROMOSI (LITERATURE REVIEW MANAJEMEN PEMASARAN)
Authors: Tri Nuryani Fajar , Nurkesuma Nurkesuma  , Baruna Hadibrata
Published: September 2023

Perilaku Konsumen adalah topik yang sangat menarik untuk dibahas dan tidak pernah habis. Sesuai dengan perkembangan dan perubahan zaman, maka perilaku konsumen pun mengalami perubahan. Salah satu perilaku konsumen yang banyak diteliti adalah megenai faktor-faktor yang mempengaruhi Konsumen dalam melakukan pengambilan keputusan sebelum membeli suatu produk. Sepanjang tahun 1998-2009 saja ditemukan 60 penelitian terkait dengan pengambilan keputusan Konsumen (Peighambari at al., 2016). Beberapa penelitian kuantitatif terdahulu di Indonesia terkait pengambilan keputusan ini menunjukkan bahwa terdapat hubungan positif yang simultan antara kualitas produk, persepsi harga dan promosi dari suatu produk terhadap keputusan Konsumen dalam menentukan pilihannya membeli suatu barang. Artikel ini mereview beberapa Jurnal terkait Korelasi Keputusan Pembelian dengan Kualitas Produk, Persepsi Harga dan Promosi, Literature Review Manajemen Pemasaran. Adapun artikel ini disusun bukan untuk mengevaluasi hasil penelitian melainkan bertujuan untuk membangun hipotesis pengaruh antar variable untuk digunakan pada riset selanjutnya. Dari penelitian (Sari, I. R. & Harti, 2021) menghasilkan secara simultan kualitas produk, harga dan promosi berpengaruh positif terhadap keputusan pembelian. Sedangkan dari penelitian (Brata et al., 2017) hasilnya menunjukkan bahwa kualitas produk, harga, promosi, dan lokasi mempengaruhi keputusan pembelian, baik sebagian maupun bersamaan.
FAKTOR-FAKTOR YANG MEMPENGARUHI PENJUALAN DALAM UNIT YANG DIPERLUKAN UNTUK MENCAPAI TARGET LABA PADA PERUSAHAAN DAGANG: PRODUK, HARGA DAN PROMOSI (LITERATURE REVIEW AKUNTANSI MANAJEMEN)
Authors: Achmad Fauzi  , Prayogo Adi , Hanna Luthfiana  , Nurul Aulia Pertama  , Putri Setyawati  , Sabrina Luna Rahellea
Published: September 2023

Artikel ini memberi Anda gambaran umum tentang faktor-faktor yang memengaruhi keuntungan Anda. Yaitu, penelusuran literatur tentang produk, harga, promosi, dan manajemen sumber daya manusia. Tujuan penulisan artikel ini untuk membangun hipotesis pengaruh antar variabel untuk digunakan pada riset selanjutnya. Hasil artikel literature ini adalah sebagai berikut:  1) Produk  berpengaruh terhadap Laba; 2) Harga berpengaruh terhadap Laba; dan 3) Promosi berpengaruh terhadap Laba. Akuntansi adalah proses yang menghasilkan laporan keuangan. Informasi akuntansi harus disiapkan dan dilaporkan secara objektif untuk melayani pengguna. Oleh karena itu, akuntansi keuangan harus didasarkan pada standar atau pedoman yang telah terbukti. Titik impas adalah metode analisis yang mengkaji hubungan antara biaya total, laba yang diharapkan, dan volume penjualan. Secara umum analisis ini juga memberikan informasi headroom yang berguna sebagai indikasi dan arah besarmya penurunan yang dapat diperkirakan sehingga aktivitas yang sedang berjalan tidak mengalami kerugian.
Job Characteristics: Pengaruhnya Terhadap Kinerja Karyawan Dengan Employee Engagement Sebagai Variabel Intervening Pada UPTD. Pengelolaan Sampah Talang Gulo Kota Jambi
Authors: Sutejo Bambang , Amin Shofia , Yacob Syahmardi
Published: September 2023

Penelitian  ini bertujuan untuk memperjelas Pengaruh Karakteristik Pekerjaan terhadap kinerja karyawan yang di mediasi oleh Keterikatan Karyawan. Dalam perspektif global dan perubahan tenaga kerja , keterlibatan karyawan sekarang dianggap sebagai indikator yang berharga untuk keberhasilan organisasi dan menjadi issue penting dalam sumber daya manusia. Metode pengumpulan data dilakukan dengan survei dan kuisioner / angket. Teknik  sampling yang digunakan yaitu menggunakan teknik Saturation Sampling (sampel jenuh), dengan jumlah sampel sebanyak 99 responden. Model penelitian ini menggunakan Structural Equation Modelling (SEM). Alat untuk mengolah data menggunakan Partial Least Square (PLS), dengan menggunakan bantuan software SmartPLS 3.3. Hasil dari penelitian ini adalah : 1) Job Characteristics memberikan pengaruh yang positif dan tidak signifikan terhadap Kinerja Karyawan UPTD. Pengelolaan Sampah TPA. Sanitary Landfill Talang Gulo Kota Jambi. 2) Job Characteristics memberikan pengaruh positif dan signifikan terhadap Employee Engagement UPTD. Pengelolaan Sampah TPA. Sanitary Landfill Talang Gulo Kota Jambi. 3) Employee Engagement juga memberikan pengaruh positif dan signifikan terhadap Kinerja Karyawan UPTD. Pengelolaan Sampah TPA. Sanitary Landfill Talang Gulo Kota Jambi. 4) Hasil analisis pengaruh tidak langsung Job Characteristics terhadap Kinerja Karyawan UPTD. Pengelolaan Sampah TPA. Sanitary Landfill Talang Gulo Kota Jambi, bahwa pengaruh Job Characteristics terhadap Kinerja Karyawan dengan Employee Engagement sebagai variabel intervening adalah positif dan signifikan. Dari variabel yang diteliti diharapkan dapat memberikan kontribusi baik secara langsung maupun tidak langsung terhadap kinerja karyawan.
Pengaruh Indikator Makroekonomi Terhadap Harga Saham Perusahaan (Studi pada Perusahaan yang Tercatat dalam Indeks LQ45)
Authors: Ristia Ilyas Suci
Published: September 2023

Penelitian ini bertujuan untuk melihat pengaruh variabel indikator makroekonomi yaitu inflasi, nilai tukar rupiah, BI rate, dan produk domestik bruto (PDB) terhadap harga saham perusahaan baik secara parsial maupun simultan. Penelitian dilakukan dengan mengambil 14 perusahaan sebagai objek penelitian dengan waktu penelitian dari tahun 2011-2021, yaitu kelompok perusahaan yang terdaftar pada Indeks saham LQ45 dengan teknik purposive sampling. Metode yang digunakan adalah regresi data panel dengan model fixed effect. Hasil penelitian menunjukkan bahwa secara parsial variabel inflasi dan produk domestik bruto (PDB) tidak ada pengaruh dan tidak signifikan terhadap harga saham perusahaan, sedangkan nilai tukar rupiah dan BI rate berpengaruh dan signifikan terhadap harga saham perusahaan. Secara simultan ada pengaruh inflasi, nilai tukar rupiah, BI rate, dan produk domestik bruto (PDB) terhadap harga saham perusahaan pada Indeks saham LQ45 di Bursa Efek Indonesia. Korelasi variabel pada penelitian ini terhadap harga saham perusahaan adalah 33.107%, sedangkan lainnya 66.90% dapat dijelaskan oleh variabel lain yang tidak digunakan dalam penelitian ini.
Faktor-Faktor yang Mempengaruhi Pencegahan Fraud Pada Pengadaan Barang dan Jasa: Sistem Pengendalian Internal, E-Procurement, Sistem dan Prosedur
Authors: Supriyanto Dadang
Published: September 2023

Ada banyak faktor yang dapat memicu terjadinya kecurangan atau fraud khususnya pelaksanaan pengadaan barang dan jasa di instansi pemerintah, faktor yang pertama adalah Sistem Pengendalian Internal. Pelaksanaan Sistem Pengendalian Intern Pemerintah (SPIP) diatur dalam Peraturan Pemerintah (PP) Nomor 60 Tahun 2008 merupakan bentuk salah satu upaya yang dilakukan oleh pemerintah untuk meningkatkan kinerja, dengan diterapkannya sistem pengendalian intern ini diharapkan dapat mengurangi segala bentuk fraud (kecurangan) atau bahkan kecurangan yang mungkin akan terjadi dalam mendukung efektifitas kerja pemerintah dalam mencapai tujuannya. Faktor yang lain yaitu pengadaan secara elektronik (e-procurement). Telah banyak penelitian sebelumnya yang menunjukkan bahwa e-procurement berpengaruh positif terhadap pencegahan kecurangan. Selain itu, Sistem dan Prosedur juga merupakan faktor yang menyebabkan terjadinya fraud karena sistem merupakan arah serta tujuan, sedangkan prosedur merupakan serangkaian langkah yang harus dijalankan dalam rangka memulai, melaksanakan, mengendalikan dan menyelesaikan suatu kegiatan.
Faktor-Faktor Yang Mempengaruhi Kinerja Karyawan: Motivasi, Komitmen Dan Kualitas Sdm (Literature Review Metodologi Riset Bisnis)
Authors: Adam Alfin , Rizky Gemis Muhammad , Arief Syahputra Pradhana , Ahmadi Rif’an , Edi Nurdiansyah Rizky , Pangestu Sandy
Published: September 2023

Riset yang relevan sangat penting dalam suatu artikel ilmiah. Riset yang relevan berfungsi untuk memperkuat teori dan fenomena hubungan atau pengaruh antar variable. Artikel ini mereview  Faktor-faktor yang mempengaruhi Kinerja Karyawan, yaitu: Motivasi, Komitmen dan Kualitas SDM, suatu studi literatur METODOLOGI RISET BISNIS. Tujuan penulisan artikel ini guna membangun hipotesis pengaruh antar variabel untuk digunakan pada riset selanjutnya.   Hasil artikel literature review ini adalah:  1) Motivasi  berpengaruh terhadap Kinerja Karyawan; 2) Komitmen berpengaruh terhadap Kinerja Karyawan; dan 3) Kualitas SDM berpengaruh terhadap Kinerja Karyawan.
Pengaruh Kualitas Kehidupan Kerja Terhadap Perilaku Kewarganegaraan Dalam Organisasi dan Dampaknya Terhadap Kinerja Pegawai di Sekretariat DPRD Kota Sungai Penuh
Authors: Ubelsa Afenda , Edward Edward , Machpudin Asep
Published: September 2023

Penelitian ini bertujuan menganalisis pengaruh Quality of Work Life melalui Organizational Citizenshiip Behavior terhadap kinerja pegawai di Sekretariat DPRD Kota Sungai Penuh. Populasi pada penelitian ini adalah pegawai di Sekretariat DPRD Kota Sungai Penuh yang berjumlah 40 orang pegawai. Dimana teknik penarikan sampel dalam penelitian ini menggunakan Sampling Jenuh (Sampling Sensus). Penelitian ini menggunakan pendekatan kuantitatif dengan metode survey dan menggunakan analisis data Partial Least Square (PLS). Hasil penelitian menunjukkan bahwa Quality of Work Life secara langsung berpengaruh positif dan signifikan terhadap Organizational Citizenshiip Behavior, sedangkan Quality of Work Life tidak berpengaruh signifikan terhadap Organizational Citizenship Behavior. Kemudian Organizational Citizenship Behavior berpengaruh positif dan signifikan terhadap kinerja pegawai. Selanjutnya Quality of Work Life berpengaruh signifikan dan positif terhadap kinerja pegawai dengan Organizational Citizenship Behavior sebagai variabel mediasi.
Pengaruh Motivasi, Lingkungan Kerja, dan Budaya Organisasi terhadap Kinerja Pegawai Melalui Kepuasan Kerja sebagai Variabel Mediasi (Kajian Studi Literatur Manajemen Sumber Daya Manusia)
Authors: C.H Dethan Selvi , E. H. J. Foeh John , A. Manafe Henny
Published: September 2023

Writing this article is also meant to be able to determine the hypothesis that has an impact on each variable so that it can be used for subsequent writing. The results of this study are 1) Motivation has a positive influence and is quite important for job satisfaction; 2) The work environment has a positive influence and is quite important for job satisfaction; 3) Organizational culture has a positive influence and is quite important for job satisfaction; 4) Motivation has a positive effect and is quite important for employee performance; 5) The work environment has a positive influence and is quite crucial for performance; 6) Organizational culture has a positive influence and is quite essential for employee performance. 7) Job satisfaction has a positive and significant influence on employee performance. 8) Job satisfaction mediates the influence of motivation, work environment, and organizational culture on employee performance.
Efektivitas Penggunaan Teknologi Informasi dan Komunikasi Terhadap Tata Persuratan Elektronik (Literature Review Manajemen Sekuriti)
Authors: Tiara Ananda , Fauzi Achmad , Dayanti Helmi , Sari Novita , Khotimmah Nurul , Roliyanah Tesalonika
Published: September 2023

Penelitian ini bertujuan untuk menganalisis efektivitas penggunaan teknologi informasi dalam komunikasi elektronik untuk menilai sejauh mana penggunaan teknologi informasi dalam komunikasi elektronik dapat meningkatkan efektivitas dan efisiensi proses komunikasi dalam suatu organisasi atau lembaga. Penting bagi suatu organisasi atau perusahaan untuk memiliki kebijakan dan prosedur yang jelas mengenai penggunaan komunikasi elektronik, termasuk tata cara penggunaan komunikasi elektronik, penyimpanan file dan keamanan informasi.
Tingkat Kesuksesan Rekam Medis Elektronik berdasarkan Perspektif Perawat di RS Hermina Sukabumi: Studi Metode Campuran
Authors: Ramdani Ridha , Gilang Genta , Sandinirwan Indra
Published: September 2023

Tujuan penelitian adalah untuk mengetahui tingkat kesuksesan implementasi Rekam Medis Elektronik berdasarkan perspektif perawat di RSU Hermina Sukabumi, serta mengetahui hubungan antara 6 faktor Information Success System Model (ISSM) dengan tingkat kesuksesan RME. Studi ini menggunakan metode campuran, yaitu metode kuantitatif menggunakan kuesioner ISSM yang dilanjutkan dengan metode kualitatif melalui wawancara mendalam. Data dianalisis secara kuantitatif melalui metode analisis regresi multivariat, serta secara kualitatif. Tingkat kesuksesan RME berdasarkan hasil kuesioner ISSM pada 108 responden adalah 75,82% (berhasil). Seluruh faktor ISSM (Teknologi, SDM, Organisasi, Kemudahan Penggunaan, Kebermanfaatan, dan Net Benefit) memiliki pengaruh signifikan terhadap tingkat kesuksesan RME (p<0,05). Penggunaan RME disukai karena kemudahan akses dan fleksibilitas, akan tetapi kesulitan yang ditemukan adalah RME belum terintegrasi dengan sistem. Rekomendasi: Studi menemukan bahwa tingkat kesuksesan RME mendapatkan angka keberhasilan cukup tinggi berdasarkan kuesioner ISSM. Akan tetapi, ditemukan juga beberapa elemen pada sistem RME belum sepenuhnya terintegrasi, sehingga mengakibatkan kesulitan bagi para perawat. Sehingga, perlu diperhatikan agar alokasi waktu kerja perawat tetap sesuai dan dapat meningkatkan kesuksesan implementasi RME.
Faktor-faktor yang mempengaruhi Kinerja, Pengambilan Keputusan : Kepemimpinan, Komunikasi dan Motivasi (Literature Review Pengambilan Keputusan Manajerial)
Authors: Dwi Wahyuni Aulia , Sapto Bagaskoro Dwi , Ramadhani Nauval , Nur Pangestu Ridwan , Ramadhan Surya , Zen Agustian
Published: September 2023

Dalam hal penelitian atau pendidikan, penelitian atau pendidikan sebelumnya sangatlah penting. Penelitian sebelumnya telah memfokuskan pada teori dan fenomena yang berkaitan dengan interaksi variabel. Faktor-faktor berikut dibahas dalam artikel ini: kepemimpinan, komunikasi, dan motivasi. Proyek ini bertujuan untuk mengembangkan hipotesis tentang pengaruh variabel pada peramalan jangka panjang. Hasil artikel kajian pustaka ini adalah: 1) Kepemimpinan berpengaruh terhadap kinerja; 2) Komunikasi berpengaruh terhadap Kinerja; 3) Motivasi berpengaruh terhadap Kinerja, 4) Kepemimpinan berpengaruh terhadap Pengambilan Keputusan, 5) Komunikasi berpengaruh terhadap Pengambilan Keputusan, 6) Motivasi berpengaruh terhadap Pengambilan Keputusan, dan 7) Pengambilan Keputusan berpengaruh terhadap Kinerja.
Analisis Pengaruh Kemudahan Penggunaan, Pengaruh Sosial Dan Risiko Terhadap Minat Penggunaan Financial Technology (Fintech) (Studi Kasus Pada Generasi Milenial Mahasiswa Universitas Dian Nusantara)
Authors: Damayanti Silvani , Fathihani Fathihani
Published: September 2023

This study aims to see the influence of the Ease of Use, Social Influence and Risk factors on the interest in transacting using financial technology in the millennial generation of Dian Nusantara University students. This research includes causal associative research and has independent and dependent variables. This type of research is a quantitative research. Where data collection techniques in the form of primary. The collection of data obtained is by means of a questionnaire. The sample in this study focused on the millennial generation of Dian Nusantara University students who used Fintech, namely 105 people. The analysis used is the SEM – PLS (Structural Equation Modeling – Partial Least Square) method with smartPLS software version 3.0.
Diversification of entrepreneurial activities in the agricultural sector of Ukraine’s economy in the post-war period
Authors: Ilchuk Mykola , Nikitchenko Serhii , Sytnyk Oleksandr , Pavlenko Vitaliy
Published: September 2023

In the context of modern development of agricultural producers, assessing the level of their activity efficiency and searching for an appropriate strategy for further development is essential. The aim of the article was to formulate a concept of sustainable development for agricultural producers based on identifying the most viable development strategy. The article’s methodological framework included the following methods of scientific inquiry: descriptive method, measurement and comparison methods, economic-mathematical modelling, induction, deduction, analysis and synthesis, system-structural analysis. The essence of diversification of activities in the agricultural sector was examined and revealed, defining their role in forming sustainable development. To achieve this, theoretical concepts of Ukrainian and foreign scholars, as well as the categories of “diversification” and “sustainable development,” were synthesized. Subsequently, an analysis and forecast of land use by agricultural enterprises were conducted. The dynamics of changes in the structure of cultivated areas from 1991 to 2021 were determined, as this period marked the implementation of agricultural reform aimed at finding effective agricultural product producers. Changes in the magnitude of costs for primary resources in farm enterprises of the Kyiv region on the eve of the war were defined, and the economic efficiency of cultivating major agricultural crops was analysed. Based on established data, a development strategy was modelled for an average farm in the Forest-Steppe zone of Ukraine, considering the concept of diversification through the implementation and development of animal husbandry. Furthermore, directions for further development of agricultural entities were substantiated by deepening the diversification of activities through optimizing cultivated areas. The research outcomes could be considered in shaping the development strategy of entrepreneurial structures and evaluating their impact on sustainable development in the post-war period.
On the Effectiveness of Compact Strategies for Opinion Diffusion in Social Environments
Authors: Adornetto Carlo , Fionda Valeria , Greco Gianluigi
Published: September 2023

An opinion diffusion scenario is considered where two marketers compete to diffuse their own opinions over a social network. In particular, they implement social proof marketing approaches that naturally give rise to a strategic setting, where it is crucial to find the appropriate order for targeting the individuals to which provide the incentives to adopt their opinions. The setting is extensively studied from the theoretical and empirical viewpoint, by considering strategies defined in a compact way, such as those that can be defined by selecting the individuals according to their degree of centrality in the underlying network. In addition to depicting a clear picture of the complexity issues arising in the setting, several compact strategies are empirically compared on real-world social networks. Results suggest that the effectiveness of compact strategies is moderately influenced by the characteristic of the network, with some centrality measures naturally emerging as good candidates to define heuristic approaches for marketing campaigns.
Hierarchical Text Classification Using Contrastive Learning Informed Path Guided Hierarchy
Authors: Agrawal Neeraj , Kumar Saurabh , Bhatt Priyanka , Agarwal Tanishka
Published: September 2023

Hierarchical Text Classification (HTC) has recently gained traction given the ability to handle complex label hierarchy. This has found applications in domains like E- commerce, customer care and medicine industry among other real-world applications. Existing HTC models either encode label hierarchy separately and mix it with text encoding or guide the label hierarchy structure in the text encoder. Both approaches capture different characteristics of label hierarchy and are complementary to each other. In this paper, we propose a Hierarchical Text Classification using Contrastive Learning Informed Path guided hierarchy (HTC-CLIP), which learns hierarchy-aware text representation and text informed path guided hierarchy representation using contrastive learning. During the training of HTC-CLIP, we learn two different sets of class probabilities distributions and during inference, we use the pooled output of both probabilities for each class to get the best of both representations. Our results show that the two previous approaches can be effectively combined into one architecture to achieve improved performance. Tests on two public benchmark datasets showed an improvement of 0.99 – 2.37% in Macro F1 score using HTC-CLIP over the existing state-of-the-art models.
Cooperative Thresholded Lasso for Sparse Linear Bandit
Authors: Barghi Hanieyh , Cheng Xiaotong , Maghsudi Setareh
Published: September 2023

We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension d whereas the reward function depends on only a limited set of features - precisely s0 ≪ d. Furthermore, the learning follows under information-sharing constraints. The proposed method employs Lasso regression for dimension reduction, allowing each agent to independently estimate an approximate set of main dimensions and share that information with others depending on the network’s structure. The information is then aggregated through a specific process and shared with all agents. Each agent then resolves the problem with ridge regression focusing solely on the extracted dimensions. We represent algorithms for both a star-shaped network and a peer-to-peer network. The approaches effectively reduce communication costs while ensuring minimal cumulative regret per agent. Theoretically, we show that our proposed methods have a regret bound of order O(s0 log d + s0 √T) with high probability, where T is the time horizon. To our best knowledge, it is the first algorithm that tackles row-wise distributed data in sparse linear bandits, achieving comparable performance compared to the state-of-the-art single and multi-agent methods. Besides, it is widely applicable to high-dimensional multi-agent problems where efficient feature extraction is critical for minimizing regret. To validate the effectiveness of our approach, we present experimental results on both synthetic and real-world datasets.
On the Structural Complexity of Grounding – Tackling the ASP Grounding Bottleneck via Epistemic Programs and Treewidth
Authors: Besin Viktor , Hecher Markus , Woltran Stefan
Published: September 2023

Answer Set Programming is widely applied research area for knowledge representation and for solving industrial domains. One of the challenges of this formalism focuses on the so-called grounding bottleneck, which addresses the efficient replacement of first-order variables by means of domain values. Recently, there have been several works in this direction, ranging from lazy grounding, hybrid solving, over translational approaches. Inspired by a translation from non-ground normal programs to ground disjunctive programs, we attack the grounding bottleneck from a more general angle. We provide a polynomial reduction for grounding disjunctive programs of bounded domain size by reducing to propositional epistemic logic programs (ELPs). By slightly adapting our reduction, we show new complexity results for non-ground programs that adhere to the measure treewidth. We complement these results by matching lower bounds under the exponential time hypothesis, ruling out significantly better algorithms.
Efficiently Computing Smallest Agreeable Sets
Authors: Bredereck Robert , Fluschnik Till , Talmon Nimrod
Published: September 2023

We study the computational complexity of identifying a small agreeable subset of items. A subset of items is agreeable if every agent does not prefer its complement set. We study settings in which agents either can assign arbitrary utilities to the items; can approve or disapprove the items; or can rank the items (in which case we consider Borda utilities). We prove that deciding whether an agreeable set exists is NP-hard for all variants; and we perform a parameterized analysis regarding the following natural parameters: the number of agents, the number of items, and the upper bound on the size of the agreeable set in question.
Selective Learning for Sample-Efficient Training in Multi-Agent Sparse Reward Tasks
Authors: Chen Xinning , Liu Xuan , Ba Yanwen , Zhang Shigeng , Ding Bo , Li Kenli
Published: September 2023

Learning effective strategies in sparse reward tasks is one of the fundamental challenges in reinforcement learning. This becomes extremely difficult in multi-agent environments, as the concurrent learning of multiple agents induces the non-stationarity problem and sharply increased joint state space. Existing works have attempted to promote multi-agent cooperation through experience sharing. However, learning from a large collection of shared experiences is inefficient as there are only a few high-value states in sparse reward tasks, which may instead lead to the curse of dimensionality in large-scale multi-agent systems. This paper focuses on sparse-reward multi-agent cooperative tasks and proposes an effective experience-sharing method, Multi-Agent Selective Learning (MASL), to boost sample-efficient training by reusing valuable experiences from other agents. MASL adopts a retrogression-based selection method to identify high-value traces of agents from the team rewards, based on which some recall traces are generated and shared among agents to motivate effective exploration. Moreover, MASL selectively considers information from other agents to cope with the non-stationarity issue while enabling efficient training for large-scale agents. Experimental results show that MASL significantly improves sample efficiency compared with state-of-the-art MARL algorithms in cooperative tasks with sparse rewards.
Region-Aware Dynamic Filtering Network for 3D Hand Reconstruction
Authors: Chen Yuchen , Ren Pengfei , Wang Jingyu , Sun Haifeng , Qi Qi , Wang Jing , Liao Jianxin
Published: September 2023

3D hand reconstruction from RGB image has attracted a lot of attention due to its crucial role in human-computer interaction. Nevertheless, it is still challenging to perform 3D hand reconstruction under conditions of hand-object interaction due to severe mutual occlusion. Previous methods usually adopt fixed convolution kernel to extract features. We argue that simply sharing the static filter for all regions is impertinent, given that the occlusion degree varies across different regions, resulting in inconsistent visual representations. To address this issue, we proposed Region-aware Dynamic Filtering Network (RDFNet), which dynamically generates convolution kernels based on the features of different regions, thereby adaptively extracting region-related information. Furthermore, we introduce a dynamic receptive field selection mechanism to determine the most appropriate scale for the convolution kernel. For the severely occluded regions, larger receptive field is needed to capture semantic-related features, while the visible regions are mainly concerned with their own local pattern to accumulate spatial-related features and avoid the interference of irrelevant information. Our proposed RDFNet outperforms state-of-the-art methods by a large margin on several challenging hand-object interaction datasets.
Learning Logic Programs by Combining Programs
Authors: Cropper Andrew , Hocquette Céline
Published: September 2023

The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples. Inducing programs with many rules and literals is a major challenge. To tackle this challenge, we introduce an approach where we learn small non-separable programs and combine them. We implement our approach in a constraint-driven ILP system. Our approach can learn optimal and recursive programs and perform predicate invention. Our experiments on multiple domains, including game playing and program synthesis, show that our approach can drastically outperform existing approaches in terms of predictive accuracies and learning times, sometimes reducing learning times from over an hour to a few seconds.
Worrisome Properties of Neural Network Controllers and Their Symbolic Representations
Authors: Cyranka Jacek , Church Kevin E.M. , Lessard Jean-Philippe
Published: September 2023

We raise concerns about controllers’ robustness in simple reinforcement learning benchmark problems. We focus on neural network controllers and their low neuron and symbolic abstractions. A typical controller reaching high mean return values still generates an abundance of persistent low-return solutions, which is a highly undesirable property, easily exploitable by an adversary. We find that the simpler controllers admit more persistent bad solutions. We provide an algorithm for a systematic robustness study and prove existence of persistent solutions and, in some cases, periodic orbits, using a computer-assisted proof methodology.
Aggregating Correlated Estimations with (Almost) no Training
Authors: Delemazure Théo , Durand François , Mathieu Fabien
Published: September 2023

Many choice problems cannot be solved exactly and use several estimation algorithms that assign scores to the different available options. The estimation errors can have various correlations, from low (e.g. between two very different approaches) to high (e.g. when using a given algorithm with different hyperparameters). Most aggregation rules would suffer from this diversity of correlations. In this article, we introduce Embedded Voting (EV), an aggregation rule designed to take correlations into account, and we compare it to other aggregation rules in various experiments based on synthetic data. Our results show that when sufficient information about the correlations between errors is available, a maximum likelihood aggregation should be preferred. Otherwise, typically with limited training data, EV outperforms the other approaches.
Learning in Teams: Peer Evaluation for Fair Assessment of Individual Contributions
Authors: Duzhin Fedor
Published: September 2023

We develop a game-theoretical model of a classroom scenario, where n students collaborate on a common task. We assume that there exists an objective truth known to the students but not to the course instructor. Each of the students estimates the contributions of all team members and reports her estimates to the instructor. Thus, a matrix A of peer evaluations arises and the instructor’s task is to grade students individually based on peer evaluations. The method of deriving individual grades from the matrix A is supposed to be psychometrically valid and reliable. We argue that mathematically it means that 1) the collective truth-telling is a strict Nash equilibrium and 2) individual grade of student i does not depend on the true contribution of student j for j ≠ i. Existing methods of peer evaluation commonly used in educational practice fail to satisfy at least one of these properties. We construct a new method of peer evaluation satisfying both desired properties for n ≥ 5. We share a large dataset (1201 students, 220 teams, 6619 evaluations) of peer evaluations collected in undergraduate courses taught by the author, outline some practical challenges, and show how these challenges can be addressed.
Structured Sparse Multi-Task Learning with Generalized Group Lasso
Authors: Fei Luhuan , Sun Lu , Kudo Mineichi , Kimura Keigo
Published: September 2023

Multi-task learning (MTL) improves generalization by sharing information among related tasks. Structured sparsity-inducing regularization has been widely used in MTL to learn interpretable and compact models, especially in high-dimensional settings. These methods have achieved much success in practice, however, there are still some key limitations, such as limited generalization ability due to specific sparse constraints on parameters, usually restricted in matrix form that ignores high-order feature interactions among tasks, and formulated in various forms with different optimization algorithms. Inspired by Generalized Lasso, we propose the Generalized Group Lasso (GenGL) to overcome these limitations. In GenGL, a linear operator is introduced to make it adaptable to diverse sparsity settings, and helps it to handle hierarchical sparsity and multi-component decomposition in general tensor form, leading to enhanced flexibility and expressivity. Based on GenGL, we propose a novel framework for Structured Sparse MTL (SSMTL), that unifies a number of existing MTL methods, and implement its two new variants in shallow and deep architectures, respectively. An efficient optimization algorithm is developed to solve the unified problem, and its effectiveness is validated by synthetic and real-world experiments.
EFx Budget-Feasible Allocations with High Nash Welfare
Authors: Garbea Marius , Gkatzelis Vasilis , Tan Xizhi
Published: September 2023

We study the problem of allocating indivisible items to budget-constrained agents, aiming to provide fairness and efficiency guarantees. Specifically, our goal is to ensure that the resulting allocation is envy-free up to any item (EFx) while minimizing the amount of inefficiency that this needs to introduce. We first show that there exist two-agent problem instances for which no EFx allocation is Pareto-efficient. We, therefore, turn to approximation and use the (Pareto-efficient) maximum Nash welfare allocation as a benchmark. For two-agent instances, we provide a procedure that always returns an EFx allocation while achieving the best possible approximation of the optimal Nash social welfare that EFx allocations can achieve. For the more complicated case of three-agent instances, we provide a procedure that guarantees EFx, while achieving a constant approximation of the optimal Nash social welfare for any number of items.
Do Not Trust Me: Explainability Against Text Classification
Authors: Gniewkowski Mateusz , Walkowiak Paweł , Syga Piotr , Klonowski Marek , Walkowiak Tomasz
Published: September 2023

Explaining artificial intelligence models can be utilized to launch targeted adversarial attacks on text classification algorithms. Understanding the reasoning behind the model’s decisions makes it easier to prepare such samples. Most of the current text-based adversarial attacks rely on brute-force by using SHAP approach to identify the importance of tokens in the samples, we modify the crucial ones to prepare targeted attacks. We base our results on experiments using 5 datasets. Our results show that our approach outperforms TextBugger and TextFooler, achieving better results with 4 out of 5 datasets against TextBugger, and 3 out of 5 datasets against TextFooler, while minimizing perturbation introduced to the texts. In particular, we managed to outperform the efficacy of TextFooler by over 3100% and TextBugger by over 420% on the WikiPL dataset, additionally keeping high cosine similarity between the original text sample and the adversarial example. The evaluation of the results was additionally supported through a survey to assess their quality and ensure that the text perturbations did not change the intended class according to subjective, human classification.
Group Activity Recognition in Basketball Tracking Data – Neural Embeddings in Team Sports (NETS)
Authors: Hauri Sandro , Vucetic Slobodan
Published: September 2023

Like many team sports, basketball involves two groups of players who engage in collaborative and adversarial activities to win a game. Players and teams are executing various complex strategies to gain an advantage over their opponents. Defining, identifying, and analyzing different types of activities is an important task in sports analytics, as it can lead to better strategies and decisions by the players and coaching staff. The objective of this paper is to automatically recognize basketball group activities from tracking data representing locations of players and the ball during a game. We propose a novel deep learning approach for group activity recognition (GAR) in team sports called NETS. To efficiently model the player relations in team sports, we combined a Transformer-based architecture with LSTM embedding, and a team-wise pooling layer to recognize the group activity. Training such a neural network generally requires a large amount of annotated data, which incurs high labeling cost. To alleviate this problem, we pretrain the neural network on a self-supervised trajectory prediction task and fine-tune it using a mix of strong and weak labels. We used a large tracking data set from 632 NBA games to evaluate our approach. The results show that NETS is capable of learning group activities with high accuracy, and that self- and weak-supervised training in NETS have a positive impact on GAR accuracy.
DTBS: Dual-Teacher Bi-Directional Self-Training for Domain Adaptation in Nighttime Semantic Segmentation
Authors: Huang Fanding , Yao Zihao , Zhou Wenhui
Published: September 2023

Due to the poor illumination and the difficulty in annotating, nighttime conditions pose a significant challenge for autonomous vehicle perception systems. Unsupervised domain adaptation (UDA) has been widely applied to semantic segmentation on such images to adapt models from normal conditions to target nighttime-condition domains. Self-training (ST) is a paradigm in UDA, where a momentum teacher is utilized for pseudo-label prediction, but a confirmation bias issue exists. Because the one-directional knowledge transfer from a single teacher is insufficient to adapt to a large domain shift. To mitigate this issue, we propose to alleviate domain gap by incrementally considering style influence and illumination change. Therefore, we introduce a one-stage Dual-Teacher Bi-directional Self-training (DTBS) framework for smooth knowledge transfer and feedback. Based on two teacher models, we present a novel pipeline to respectively decouple style and illumination shift. In addition, we propose a new Re-weight exponential moving average (EMA) to merge the knowledge of style and illumination factors, and provide feedback to the student model. In this way, our method can be embedded in other UDA methods to enhance their performance. For example, the Cityscapes to ACDC night task yielded 53.8 mIoU (%), which corresponds to an improvement of +5% over the previous state-of-the-art. The code is available at https://github.com/hf618/DTBS.
Combating Short Circuit Behavior in Natural Language Reasoning: Crossover and Mutation Operations for Enhanced Robustness
Authors: Huang Shanshan , Ren Siyu , Zhu Kenny Q.
Published: September 2023

In this study, we delve into the “short circuit” phenomenon observed in multiple-choice natural language reasoning tasks, where models tend to make accurate choices without properly considering the context of the question. To better understand this phenomenon, we propose white-box and black-box proxy tests as investigative tools to detect short circuit behavior, confirming its presence in fine-tuned NLU reasoning models. To tackle the short circuit issue, we introduce biologically inspired “crossover” and “mutation” operations. These operations are applied to augment the training data for popular models such as BERT, XLNet, and RoBERTa. Our results demonstrate that these data augmentation techniques effectively enhance the models’ robustness and mitigate the short circuit problem.
Grouped Multi-Task Learning with Hidden Tasks Enhancement
Authors: Jin Jiachun , Wang Jiankun , Sun Lu , Zheng Jie , Kudo Mineichi
Published: September 2023

In multi-task learning (MTL), multiple prediction tasks are learned jointly, such that generalization performance is improved by transferring information across the tasks. However, not all tasks are related, and training unrelated tasks together can worsen the prediction performance because of the phenomenon of negative transfer. To overcome this problem, we propose a novel MTL method that can robustly group correlated tasks into clusters and allow useful information to be transferred only within clusters. The proposed method is based on the assumption that the task clusters lie in the low-rank subspaces of the parameter space, and the number of them and their dimensions are both unknown. By applying subspace clustering to task parameters, parameter learning and task grouping can be done in a unified framework. To relieve the error induced by the basic linear learner and robustify the model, the effect of hidden tasks is exploited. Moreover, the framework is extended to a multi-layer architecture so as to progressively extract hierarchical subspace structures of tasks, which helps to further improve generalization. The optimization algorithm is proposed, and its effectiveness is validated by experimental results on both synthetic and real-world datasets.
The Leximin Approach for a Sequence of Collective Decisions
Authors: Kahana Ido , Hazon Noam
Published: September 2023

In many situations, several agents need to make a sequence of decisions. For example, a group of workers that needs to decide where their weekly meeting should take place. In such situations, a decision-making mechanism must consider fairness notions. In this paper, we analyze the fairness of three known mechanisms: round-robin, maximum Nash welfare, and leximin. We consider both offline and online settings, and concentrate on the fairness notion of proportionality and its relaxations. Specifically, in the offline setting, we show that the three mechanisms fail to find a proportional or approximate-proportional outcome, even if such an outcome exists. We thus introduce a new fairness property that captures this requirement, and show that a variant of the leximin mechanism satisfies the new fairness property. In the online setting, we show that it is impossible to guarantee proportionality or its relaxations. We thus consider a natural restriction on the agents’ preferences, and show that the leximin mechanism guarantees the best possible additive approximation to proportionality and satisfies all the relaxations of proportionality.