bluuebunny/crossref_metadata_2025 huggingface dataset:
Accounting Information System for Calculating Remaining Profits from Cooperative Operations Using the Direct Method at Primkoppol Belawan Port Resort
Authors: Widiasih Zhuria
Published: June 2023

Primkoppol Belawan Port Resort is the Agency and Implementer of Cooperatives at the Polres Level, also assists in the implementation of the Cooperative work program and policies underlined by the North Sumatra Police and Puskoppolda Leaders in an effort to assist Leaders in realizing the welfare of members of the Police and Civil Servants of the Police and their families, which is carried out by raising funds from members and channeling/financing in the real business sector. there are also those who say that Primkoppol Belawan Harbor Resort is a microfinance institution that can and is able to serve the needs of members based on a profit sharing system. As happens in every company, both government-owned and private companies have relatively large numbers of transactions, so a database is needed that can store and process the data efficiently, quickly and can be used at any time when needed. Primkoppol Belawan Harbor Resort is a private financial institution engaged in savings and loan services. Primkoppol Belawan Resort Resort's activities are inseparable from members' savings and loan transactions, so a computerized system is needed so that transactions run well. This information system was built using Visual Basic 2010 and SQL Server 2008 as the database. Primkoppol Belawan Resort Resort's activities are inseparable from members' savings and loan transactions, so a computerized system is needed so that transactions run well. This information system was built using Visual Basic 2010 and SQL Server 2008 as the database. Primkoppol Belawan Resort Resort's activities are inseparable from members' savings and loan transactions, so a computerized system is needed so that transactions run well. This information system was built using Visual Basic 2010 and SQL Server 2008 as the database
PENGARUH PENDAPATAN TERHADAP KONSUMSI MELALUI MENTAL ACCOUNTING PADA MASYARAKAT DESA TUTUARU PULAU LETTI KABUPATEN MALUKU BARAT DAYA
Authors: Leunupun Engrith Grafelia
Published: June 2023

This writing aims to empirically determine the relationship between income and consumption through mental accounting. This research was conducted by taking samples from the Tutuaru village community, Letti Island, Southwest Maluku Regency. The number of samples selected was 43, the samples were selected randomly based on anyone who could be found at the time of data collection. The results of the study are that income has a significant effect on the level of public consumption, while simultaneously mental accounting cannot influence income on consumption by the people of Tuturu Village.
Prensa y discurso político en los albores de la República en el Perú. Aproximaciones al estudio de El Loro (1822)
Authors: Jara Katherine , Morán Daniel
Published: June 2023

The historiography on the independence process has been renewed and diversified in recent decades. Special interest has been taken in the study of the press as a representative of a political faction and, consequently, the analysis of its political discourse, as well as the identification of the communication networks and the spaces of sociability in which it circulated. For this reason, the political discourse of El Loro (1822) is analyzed to reflect on the political culture at the dawn of the Republic, because this historical source was one of the many ephemeral and rebellious newspapers, which formed an opposition faction of the republican model and He showed himself as a political actor in the intense ideological debate that took place in the public space on the government model that Peru should opt for after the departure of San Martín. The work is divided into two sections, it began with a historiographical balance on the press in the process of Independence and will culminate with the analysis of the following thematic axes: freedom of the press and public debate, the defense of a monarchical government, the delivery of Guayaquil and the representative government.
PEMANFAATAN TEKNOLOGI INTERNET OF THINGS (IOT) PADA BIDANG PERTANIAN
Authors: Heru Sandi Ganesa , Fatma Yulia
Published: June 2023

Kemajuan teknologi diera saat ini membuat bermacam - macam hal wajib mengutamakan efesiensi serta kemudahan dalam melaksanakan pekerjaan yang sering dilakukan tiap hari, hal itu menjadikan banyak manusia menghasilkan bermacam jenis teknologi yang otomatis dan dapat membantu memudahkan pekerjaan serta tidak perlu banyak menyita waktu. Internet of Things (IoT) merupakan sebuah teknologi yang dibangun untuk memberikan kemudahan didalamnya, dengan memungkinkan kita dapat terhubung dengan alat melalui internet. Karya ilmiah ini di tulis bertujuan untuk mengetahui apa saja pemanfaatan IoT pada salah satu sector ekonomi di Indonesia yaitu pertanian. Sector pertanian merupakan sumber penghasilan yang cukup memberikan dampak besar bagi perekonomian di Indonesia, contohnya seperti padi, jagung, kelapa sawit, lada, kopi, teh dan lain lain. Pada karya ilmiah yang di tulis oleh penulis digunakan metode penulisan literatur review / studi pustaka. Hasil dari penulisan karya ilmiah ini adalah gambaran mengenai apa saja pemanfaatan teknologi IoT di bidang pertanian yang nantinya dapat membantu masyarakat untuk meningkatkan hasil dan kualitas pertaniannya. Dan di setiap pembahasan masing - masing pemanfaatan akan ada citation yang berguna sebagai rujukan untuk lebih memahami mengenai masing – masing pemanfaatan teknologi IoT
EVALUASI ARSITEKTUR JARINGAN KOMPUTER PEMERINTAH DAERAH DALAM PENYELENGGARAAN SISTEM PEMERINTAH BERBASIS EKEKTRONIK (SPBE)
Authors: Sa`di Ahmad , Andriani Ria , Ghozali Bahrun
Published: June 2023

Penyelengggaraan jaringan komputer di pemerintah daerah belum memenuhi standar ketersediaan. Permasalahan yang sering dijumpai yaitu perluasan jaringan dilakukan asal terhubung, sehingga berkibat bencana. Ketika terjadi masalah pada jalur jaringan atau perangkat sehingga berdampak besar pada layanan atau aplikasi di jaringan, protokol yang mendukung redundansi juga tidak digunakan, dan beberapa gedung pemerintahan tidak terhubung ke jaringan. Kondisi tersebut belum mendukung terwujudnya Sistem Pemerintahan Berbasis Elektronik.

Peneliti mengusulkan, Pemerintah yang memiliki  wewenang menyediakan jaringan privat pemerintah menggunakan konsep connected, scalability, high availability, security, dan integrated. Metode yang digunakan yaitu pendekatan audit yaitu pre-audit, perform audit dan pasca-audit. Hasilnya yaitu berupa rekomendasi arsitektur jaringan intra pemerintah untuk mewujudkan sistem pemerintah berbasis elektronik yang mumpuni.

Untuk menaikkan level ke skalability, dengan menggunakan pendekatan jaringan hierarki. Kuncinya yaitu dengan memperhatikan konsep desain jaringan, kualitas dan kapabilitas perangkat di layer edge, core, distribusi dan access. Pada level handal yaitu dengan mempertimbangkan layer fisik dan layer logis. Memastikan jalur redundant dan protokol yang digunakan harus mendukung failover. Protocol yang digunakan pada layer edge, core distribusi menggunakan protorokol mendukung failover. Di tingkatan selanjutnya yaitu terintegrasi. Jaringan dapat mendukung integrasi data di pusat data dengan data di DRC
PENGENALAN EKSPRESI MIKRO WAJAH DENGAN EKSTRAKSI FITUR PADA KOMPONEN WAJAH MENGGUNAKAN METODE LOCAL BINARY PATTERN HISTOGRAM
Authors: Nur Cahyo Dwi , Zulfia Zahro' Hani , Vendyansyah Nurlaily
Published: June 2023

Ekspresi merupakan bentuk komunikasi dengan cara berinteraksi atau ketika sedang berdialog. Mengenal ekspresi untuk saat ini banyak menggunakan berbagai macam metode seperti menunjukan ekspresi didepan kamera, atau komputer. Namun untuk melakukan analisa terhadap ekspresi membutuhkan banyak data dan bervariasi selain itu posisi wajah dapat mempengaruhi kecepatan memprediksi yang bersifat realtime. Dalam hal tersebut dilakukan metode facial landmark sebagai deteksi area komponen wajah seperti alis, mata, dan mulut untuk diekstraksi agar menghasilkan nilai ciri tanpa harus memproses keseluruhan wajah. Ekstraksi yang dibutuhkan dalam mengelola citra yaitu ekstraksi ciri tekstur yang menggunakan metode Local Binary Pattern Histogram. Metode ini merupakan bentuk interpretasi dari citra tekstur, selanjutnya nilai ciri dari pemrosesan tersebut akan diklasifikasi menggunakan K-Nearest Neighbor dengan penerapan jarak antara data uji dan data latih dengan formula Euclidean Distance. Hasil menunjukan dalam mengambil citra wajah secara ideal yaitu 25-75 cm, menangkap citra dengan kondisi terang dengan intensitas cahaya diatas 150 - 2530 lux dapat mengklasifikasikan ekspresi secara realtime dan menghasilkan akurasi sebesar 92.50%.
SISTEM PENDUKUNG KEPUTUSAN UNTUK PENENTUAN PELAKSANAAN PRAKTIKUM TERBAIK MENGGUNAKAN METODE TOPSIS
Authors: Farhan Abiyyu Ivan , Zulfia Zahro’ Hani , Dedy Irawan Joseph
Published: June 2023

Laboratorium Jaringan Komputer Teknik Informatika S-1 ITN Malang melaksanakan praktikum tiap semesternya. Pada akhir pelaksanaan praktikum, pihak laboratorium melaksanakan evaluasi yang datanya diambil dari survey kepada peserta praktikum. Keputusan yang diambil dirasa kurang objektif karena hanya mengacu pada satuan kriteria dan hanya menimbang pada praktikum yang sedang dievaluasi. Selain itu, tidak adanya standar penilaian untuk menentukan praktikum terbaik. Serta, hasil evaluasi yang dihasilkan tidak disimpan pada suatu sistem yang mengakibatkan lamanya keputusan yang akan diambil. Penelitian ini bermaksud untuk membuat sistem pendukung keputusan menggunakan metode TOPSIS yang dibuat dalam bentuk Website guna membantu mencari praktikum terbaik sebagai evaluasi dan analisis praktikum yang dilaksanakan pada tempat studi kasus. Metode ini berlandaskan pada konsep yang mana alternatif yang terpilih bukan hanya memiliki jarak terpendek dari solusi ideal positif, tetapi juga memiliki jarak terpanjang dari solusi ideal negatif. Hasil pengujian pengguna menunjukan 79 suara memilih Setuju dengan persentase sebesar 88% yang membuktikan bahwa sistem telah berjalan dengan baik serta dapat diterima oleh pengguna dan berhasil melakukan perhitungan TOPSIS dalam menentukan pelaksanaan praktikum terbaik. Serta, 12 dari 15 asisten laboratorium setuju bahwa sistem dapat membantu menyimpan dan merekap data praktikum.
SISTEM MONITORING DAN CONTROLLING ALAT PENGURAS AIR OTOMATIS PADA KOLAM LOBSTER BERBASIS IOT
Authors: Prasetyo Nugroho Bagas , Fahrudi Setiawan Ahmad , Rudhistiar Deddy
Published: June 2023

Lobster air tawar (Cherax) ini merupakan hewan yang berasal dari negara Australia yang masuk dalam genus dari famili parastacidae, di Indonesia para petani ikan mulai membudidayakan lobster air tawar (Cherax) sejak tahun 2000. Dari hasil pengamatan penulis, para petani lobster air tawar (Cherax) di Indonesia khususnya dalam studi kasus GAS FARM ini sedikit kurang efektif dalam segi waktu, maka dibutuhkan sistem berbasis android dan alat untuk memonitoring kualitas air dalam kolam dari jarak jauh untuk membuat waktu para peternak lebih efektif. Tujuan dari penelitian ini yaitu menciptakan alat dan sistem guna membantu para peternak memonitoring dan controlling kondisi air dalam kolam lobster dengan mengimplementasikan metode Fuzzy Logic sugeno untuk pengolahan data. Fuzzy logic sugeno digunakan untuk mengolah hasil nilai yang dihasilkan dari sensor ph dan sensor kekeruhan, yang dimana dari hasil pengolahan yang telah diproses menggunakan fuzzy logic sugeno ini akan menentukan terjadinya pengurasan air dalam kolam. Hasil pengujian fungsional aplikasi android menunjukkan bahwa semua fitur berhasil 100%. Hasil pengujian sensor PH menunjukkan bahwa sensor PH mendapatkan akurasi sebesar 93.5% dengan error sebesar 6.5% yang telah dibandingkan dengan PH meter lakmus. Hasil pengujian aplikasi oleh user menunjukkan bahwa 90% menilai baik dan 10% orang menilai cukup.
ANALISA POLA TRANSAKSI PEMBELIAN KONSUMEN PADA TOKO RITEL KESEHATAN MENGGUNAKAN ALGORITMA FP-GROWTH
Authors: Achmad Fandi , Nurdiawan Odi , Arie Wijaya Yudhistira
Published: June 2023

Persaingan dalam dunia bisnis ritel saat ini menuntut perusahaan untuk terus menemukan berbagai macam strategi dan kreatifitas dalam menjalankan bisnis. CV Harmoni Medicine Indonesia merupakan perusahaan ritel yang bergerak dalam penjualan produk kesehatan dan kecantikan. Salah satu strategi bisnis yang dapat dilakukan adalah Cross-selling atau menawarkan produk lain yang memiliki keterkaitan dengan produk yang dibeli sehingga dapat meningkatkan sales penjualan. Penelitian ini bertujuan untuk melakukan analisa pembelian konsumen dengan melihat keterkaitan antara produk yang sering dibeli. Data yang digunakan dalam penelitian ini merupakan data transaksi penjualan CV Harmoni Medicine Indonesia selama 3 tahun dari 1 Januari 2020 hingga 31 Desember 2023 yang mempunyai 9.844 baris data dan 10 atribut. Metode yang digunakan pada penelitian ini menggunakan pendekatan data mining asosiasi dengan algoritma Frequent Pattern Growth (FP-Growth) yang mempunyai tahap proses di mulai pengumpulan data transaksi penjualan, pemilihan atribut yang relevan, preprocessing data, proses asosiasi dataset, dan evaluasi pola terbentuk. Penentuan pola asosiasi menggunakan nilai minimum support, nilai minimum confidence, dan nilai minimum lift. Berdasarkan hasil pengujian didapatkan 9 aturan asosiasi yang terbentuk menggunakan nilai minimum Support = 0.005, nilai minimum Confidence = 0.1, dan nilai minimum Lift = 1.0, dengan 9 produk pembentuk. Aturan asosiasi yang terbentuk menggunakan algoritma algoritma FP-Growth dapat membantu strategi penjualan cross-selling lebih mudah dan efesien dengan memberi informasi pola pembelian produk konsumen secara detail dengan besaran tingkat peluang keberhasilan.
IMPLEMENTASI AUGMENTED REALITY SEBAGAI MEDIA BELAJAR ALAT-ALAT OTOMOTIF
Authors: Taukhid Abdul , Astuti Rini , Purnamasari Ade Irma
Published: June 2023

Berubahnya kehidupan manusia tidak luput dari berkembangnya teknologi dan seiring berjalannya waktu teknologi sangat berkembang pesat misalnya di perangkat lunak, teknologi mempunyai fungsi sangat penting sebagai pendukung aktivitas kehidupan manusia contohnya adalah perkembangan smartphone yang dilengkapi fitur yang sangat canggih. Media pembelajaran merupakan hal yang berpengaruh terhadap hasil belajar siswa, media pembelajaran yang ada di SMK AL-JABBAR Ciledug khususnya jurusan Teknik Kendaran Ringan masih dengan cara lama yang menyebabkan siswa sulit untuk mengerti pelajaran dan cenderung cepat bosan dikarenakan terlalu monoton, kompleks dan sering terjadi alat-alat yang hilang ataupun rusak ditiap komponen yang digunakan untuk materi belajar. Kebutuhan alat dalam bidang otomotif sangat diperlukan namum masih banyak kendala dalam kepemilikan alat karena mahalnya alat tersebut. Hal tersebut membuat gagasan untuk mengembangkan media belajar alat-alat otomotif dengan memanfaatkan teknologi Augmented Reality (AR). Metode Waterfall merupakan pendekatan SDLC paling awal yang digunakan untuk pengembangan perangkat lunak. Urutan dalam Metode Waterfall bersifat serial yang dimulai dari proses perencanaan, analisa, desain, dan implementasi pada sistem. Penelitian ini bertujuan untuk membuat sebuah media pembalajaran baru menerapkan Augmented Reality (AR) pada aplikasi pengenalan alat-alat otomotif, objek dibuat dengan 3 dimensi yang menyerupai kondisi nyata disajikan secara visual dan interaktif. Augmented Reality merupakan teknologi yang berkembang pada saat ini dimana teknologi tersebut memanfaatkan kecanggihan dari fitur yang dimiliki oleh smarphone sehingga dapat menampilkan objek dari dua dimensi menjadi 3 dimensi. Hasil dari penelitian ini adalah sebuah aplikasi pengenalan alat-alat otomotif yang dapat dimanfaatkan untuk membantu siswa untuk memahami materi yang diajarkan, mengatasi permasalahan komponen-komponen yang hilang dan rusak sebagai materi belajar, dan diharapkan siswa lebih kretif dalam media pembelajaran. Hasil ini juga sebagai evaluasi siswa dalam pembelajaran serta mendorong sekolah untuk lebih meningkatkan kualitas belajar siswa dan fasilitas.
RANCANG BANGUN ADMINISTRASI SURAT PERINTAH PERJALANAN DINAS BERBASIS WEB
Authors: Fitriyani Nur Sifa , Nurdiawan Odi
Published: June 2023

Hampir semua aktivitas atau pekerjaan saat ini dilakukan dengan menggunakan teknologi, sehingga komputer kini menjadi pengganti untuk melakukan aktivitas atau bekerja seiring dengan perkembangan manusia saat ini. Komputer itu sendiri memiliki banyak manfaat, terutama hanya untuk divisi administrasi dan keuangan suatu instansi, yang dapat ditangani dengan lebih efektif dan efisien. Agensi harus menyiapkan surat perintah perjalanan dinas untuk perjalanan dinas. Surat penugasan yang disebut SPPD diberikan kepada pegawai pemerintah, pegawai tidak tetap, dan pejabat negara untuk digunakan dalam perjalanan dinas. Setiap Anggaran yang dikeluarkan harus mengajukan laporan pertanggungjawaban setelah perjalanan dinas. Penulis mengatakan bahwa Dinas Pemuda dan Olahraga di Kota Cirebon terus menggunakan aplikasi Microsoft Office Excel, yang akan mengakibatkan kesalahan kecil yang akan terus terjadi, seperti penumpukan berkas dan dokumen. Dengan mengembangkan sistem yang menggunakan bahasa pemrograman PHP, phpMyAdmin sebagai sistem administrasi basis data, dan XAMPP menggunakan metode waterfall, penulis mencapai tujuan tersebut dan mengangkat judulnya. Hasil sistem, yang juga mencakup penyimpanan data yang tepat, peningkatan keamanan data, dan akses dari setiap tempat kapan saja, dapat membantu efisiensi kinerja dalam pembuatan laporan pertanggung jawaban perjalanan dinas.
PENERAPAN DATA MINING PADA PENJUALAN PRODUK PAKAIAN DAMEYRA FASHION MENGGUNAKAN METODE K-MEANS CLUSTERING
Authors: Pii Iwan , Suarna Nana , Rahaningsih Nining
Published: June 2023

Dameyra Fashion adalah toko online yang menjual produk Pakaian lewat E-Commerce Lazada. Dameyra Fashion berlokasi di Kabupaten Cirebon lebih tepatnya di desa arjawinangun. Metode K-Means Clustering bisa diterapkan pada Toko Dameyra Fashion untuk mengetahui Penjualan Pakaian yang laku dan tidak laku. Permasalahan yang dihadapi oleh toko dameyra fashion yaitu masih belum bisa mengelola persediaan barang. Tujuan tugas akhir ini yaitu untuk mengelompokkan data penjualan dameyra fashion menggunakan metode k-means clustering serta bagaimana Penerapan teknik K-Means Clustering oleh Dameyra Fashion Stores Pada RapidMiner, K-Means Clustering bekerja dengan cara membuat dataset dengan memasukkan data penjualan pakaian dari dameyra yang akan diubah. Setelah itu, penambangan data, pemilihan data, pembersihan data, preprocessing data, dan pengetahuan Temuan dari cluster 2. Maka dapat diketahui penjualan yang laris ada 289 item dan tidak laris ada 9 item. Dengan rincian produk laris yaitu Tunik Polos, Tunik Rempel, Tunik Dzuvia, Tunik Leopard, Kemeja Polos dan yang tidak laris Daster Kaos, Daster Kaos OZ, Daster Kimono, Daster Pretty Girl, Daster Kerah V-Neck, Daster Andin Rayon, Daster Batik, Gamis Crinkle, Gamis Benecia Allana.
KOMPARASI ALGORITMA MACHINE LEARNING DALAM MEMPREDIKSI HARGA RUMAH
Authors: Haryanto Cep , Rahaningsih Nining , Muhammad Basysyar Fadhil
Published: June 2023

Manusia memiliki kebutuhan mendasar akan sebuah tempat yang bisa disebut sebagai rumah atau tempat berteduh. Dengan banyaknya pertumbuhan penduduk di Indonesia dari tahun ke tahun membuat tidak semua golongan masyarakat memiliki tempat tinggal yang layak huni. Maka dari itu agar setiap keluaga dapat memiliki tempat tinggal pribadi yang layak diperlukan perencanaan yang matang. Dalam perencanaan tersebut diperlukan perencanaan atau prediksi harga di masa mendatang. Salah satu metode yang bisa digunakan adalah dengan menggunakan algoritma machine learning. Adapun beberapa faktor yang mempengaruhi harga rumah diantaranya luas lahan, luas bangunan berdiri, banyaknya kamar tidur, kamar mandi, dan garasi. Metode yang digunakan dalam penelitian ini adalah multiple linear regression dan random forest regression. Tujuan dari penelitian ini adalah mencari hasil prediksi terbaik antara multiple linear regression dan random forest regression. Untuk mendapatkan nilai prediksi yang tinggi penelitian dilakukan secara berulang kali dengan 80% dataset untuk training dan 20% dataset untuk testing guna mencapai hasil prediksi yang tinggi. Hasil dari penelitian ini diperoleh bahwa algoritma random forest regression merupakan algoritma terbaik dalam penelitian ini dengan nilai akurasi sebesar 81,6%.
IMPLEMENTASI DATA MINING MENGGUNAKAN METODE K-MEANS CLUSTERING UNTUK MENENTUKAN STATUS KEMATIAN BAYI DI JAWA BARAT
Authors: Pujianti Anisa , Mulyawan Mulyawan
Published: June 2023

Angka Kematian Bayi (AKB) digunakan sebagai indikator untuk mengukur tingkat kesehatan di suatu daerah karena bayi adalah anak-anak berusia antara 0 dan 12 bulan, yang sangat rentan terhadap kesehatan dan kesejahteraan yang buruk. Provinsi Jawa Barat perlu mendapat perhatian khusus untuk menekan tingginya AKB karena memiliki jumlah kasus kematian bayi yang cukup besar—menempati urutan ketiga di Indonesia dalam hal ini. Teknik K-Means Clustering membagi data menjadi dua kelompok yang hampir identik satu sama lain dan berbeda satu sama lain. Tujuan dari penelitian ini adalah untuk mengevaluasi efektivitas metode K-Means Clustering dalam klasterisasi kematian bayi berbasis desa-desa di Jawa Barat. Berdasarkan informasi dari desa dan volume kejadian, pengukuran ini dilihat. Data penelitian ini berasal dari data.jabarprov.go.id dan berdasarkan statistik kematian bayi di Jawa Barat. 27 dari 601 data yang dikumpulkan dari 2019 hingga 2021, berjumlah 601, berasal dari Jawa Barat.
Parallel and Distributed Hybrid Beamforming for Multicell Millimeter Wave MIMO Full Duplex
Authors: Sheemar Chandan Kumar , Chatzinotas Symeon , Slock Dirk , lagunas Eva , Querol Jorge
Published: June 2023

Full duplex (FD) is an auspicious wireless technology that holds the potential to double data rates through simultaneous transmission and reception. This paper proposes two innovative designs of hybrid beamforming (HYBF) for a multicell massive multiple-input-multiple-output (mMIMO) millimeter wave (mmWave) FD system. Initially, we introduce a novel centralized HYBF (C-HYBF) scheme, which employs the minorization-maximization (MM) method. However, while centralized beamforming designs offer superior performance, they suffer from high computational complexity, substantial communication overhead, and demand expensive computational resources. To surmount these challenges, we present a framework that facilitates per-link parallel and distributed HYBF (P$\&$D-HYBF) in the mmWave frequency band. This cooperative approach enables each base station (BS) to independently solve its local, low-complexity sub-problems in parallel, resulting in a substantial reduction in communication overhead and computational complexity. Simulation results demonstrate that P$\&$D-HYBF achieves comparable performance to C-HYBF, and with only a few radio-frequency (RF) chains, both designs surpass the capabilities of fully digital half duplex (HD) systems.
Understanding of P vs NP Problem: With Extension of the Continuum Hypothesis
Authors: Yoon Jihyeon
Published: June 2023

P vs NP problem turns out to be P=NP in quantum computing but remains questions of optimization in classical computing.
A Conceptual Insight Into Achieving Interoperability Between Heterogeneous Blockchain Enabled Interconnected Smart Microgrids
Authors: Dinesha Disha Lagadamane , Patil Balachandra
Published: June 2023

Blockchain technology (BCT) has emerged as a game-changer for many industries since its inception in 2008. Its application in the energy industry as blockchain enabled interconnected smart microgrids (BSMG) is on the rise as it can execute energy trading, automate the market operations, manage the grid, and facilitate real-time payments. With the increase in usage of BSMGs, different types of BCT will emerge, making the system heterogeneous in nature. BCT is also limited currently due to its scalability and low transaction rate. Interoperability between heterogeneous BSMGs can counteract the drawbacks and improve the functionality and, thereby, adoption of BCT in energy. This paper recognises the need for interoperability and thoroughly reviews the different methods of interoperability that currently exist (i.e., notary mechanism, relay or side chains, hashed time-locked contracts, and blockchain routers). Two relay mechanisms - Polkadot and Cosmos with Inter Blockchain Communication Protocol (IBC) are reviewed further to determine the usability of the protocols. Finally, a conceptual architecture of interconnection of heterogeneous BSMGs is proposed. Conceptual solution to connecting Ethereum and Hyperledger Fabric to the Cosmos Hub through IBC is explored. While interoperability between heterogeneous users is addressed in Decentralized Finance (DeFi), it has not yet been addressed in energy. A conceptual solution is provided for this research gap for the first time in energy domain. 
Interpretation idea of dynamical topological phase in concept of ordinal
Authors: Yoon Jihyeon
Published: June 2023

Recent study suggests dynamical topological phase could be derived into additional dimension of time. By adding interpretation of this phenomenon with a topological interaction within simplified model, possibility of approaching many-timeline in real world could be suggested.
Generalized Prime Sequence Conjecture with Application
Authors: Yoon Jihyeon
Published: June 2023

Prime number could be formalized with using notation of sequence. And by utilizing the sequence, approximation in generating prime number could be achieved.
The Generation of Semantics in Natural Language and the Formation of Brain Intelligence
Authors: Tang Lei
Published: June 2023

This paper first discussed that based on the radial basis function principle, the brain neuron system has achieved the primary representations to color, sound, and object shape. Note that this is a kind of distributed representation approach, and through combining these primary representations, the neuron system is able to generate a large number of semantics particles in the higher representation layers, which exist on a large number of neuron fiber bundle nodes in the brain. In addition, as the three primary colors in the color representation, we have found that through utilizing the three neuron groups that respectively have most sensitive responses to the directions 45o, 90o, and 135o, the vision neuron system is able to achieve the effective representation to line segment direction. In addition, based on our more understandings to the characteristics of the brain processing information, we are able to better explain the core principle of intelligence behavior. Just like the appearance of steam engine liberated man-power, and from now on, through further researches to the brain intelligence we expect to be able to liberate the computing-power involving in creating machine intelligence, and achieve more powerful intelligence.
Selective Multi-Source Domain Adaptation Network for Cross-Subject Motor Imagery Discrimination
Authors: Lee Juho , Choi Jin Woo , Jo Sungho
Published: June 2023

Discriminating motor imagery with electroencephalogram (EEG)-based brain-computer interface (BCI) poses a challenge as it involves an extensive data acquisition phase that demands a substantial amount of effort from the user. To address this issue, one approach is to use unsupervised domain adaptation, where classification models are constructed using data from multiple subjects, and only the unlabeled data from the target user is used for model calibration. However, since brain patterns from motor imagery vary between individuals, the reliability of each subject must be considered when multiple subjects are used to build the classification model. Thus in this paper, we propose Selective-MDA that performs domain adaptation on each source subject and selectively limits influences based on their domain discrepancies. To evaluate our approach, we assess our results with two public datasets, BCI Competition IV IIa and the Autocalibration and Recurrent Adaptation datasets. We further investigate the effect of source selection by comparing the discrimination performance when different numbers of source domains are selected based on discrepancy measures. Our results demonstrate that Selective-MDA not only integrates multi-source domain adaptation to cross-subject motor imagery discrimination but also highlights the impact of source domain selection when using data from multiple subjects for model training.
Exploring Legal Ownership of AI-Generated Images: Insights from the Indian Context
Authors: Dubey Ayush , A Soumya
Published: June 2023

Artificial Intelligence has taken the world by storm, and its advancements have opened up new possibilities in various fields. One such field is image generation through AI technology that can produce images with remarkable detail and accuracy. However, as exciting as these developments are, they also raise some critical questions regarding the ownership of AI-generated images. AI-generated arts are disrupting social media, print media, and mass media whereas the Indian copyright office is faced with dilemmas and turmoil regarding the implementation of the strict action. Currently, there is no legal framework governing the right to AI-generated images, which creates several potential issues. For example, if an AI-generated image is used without the creator's consent, it can be considered copyright. The ownership of these images and the overall consideration of these images as intellectual property is strictly under question. This paper aims to explore and provide insight into this topic. The author will delve into the legal framework surrounding AI-generated image ownership in India while considering different stakeholders' perspectives on the issue. The author will also provide policy recommendations that would benefit all parties involved, such as creators, licensees, and the general public. Overall, this goal is to provide readers with a comprehensive understanding of the legal issues surrounding AI-generated images in India. The paper will conclude by providing recommendations for policymakers who must consider all relevant factors when creating policies regarding this complex matter.
Exploring Distributed PV Power Measurements for Real-Time Potential Power Estimation in Utility-Scale PV Plants
Authors: Gostein Michael , Hobbs William
Published: June 2023

We explore the use of distributed PV power measurements for real-time short-term forecasting of the maximum potential power output of a utility-scale PV power plant, to support future incorporation of PV plants in automatic generation control (AGC) systems. PV plants operating under AGC may run in a curtailed state but must be capable of accurately forecasting their maximum potential non-curtailed power output, or potential high limit (PHL), 10-20 seconds in advance – despite variable environmental conditions. One forecast approach estimates PHL from a subset of inverters, designated reference inverters, which are never curtailed. We propose an alternative approach using in-situ I V tracers to measure maximum power point of reference modules distributed throughout a PV plant. This would allow for greater sampling of variations in irradiance, module temperature, soiling, and albedo, permitting better PHL prediction accuracy. To study potential benefits of this method, we analyzed a one-year data set from an approximately 100 MW PV power plant in the US Southeast, using non-curtailed string combiner outputs as a proxy for distributed module-level power measurements. We compare the reference inverter and distributed power measurement approaches. Results indicate that for highly flexible operation, substantial improvement in prediction accuracy can be achieved using distributed power measurements compared to using a low fraction of reference inverters. We review the study methodology, conclusions, and shortcomings and discuss prospective future work.
Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
Authors: Jokhio Sarang , Olleja Pierluigi , Bärgman Jonas , Yan Fei , Baumann Martin
Published: June 2023

Lane changing is complex, yet it is one of the most common manoeuvres a driver performs in everyday driving. Before initiating a lane change, drivers should indicate their intention using a turn signal. When drivers are changing lanes, their use of a turn signal could provide meaningful insights about their behaviour. Therefore, we investigated the behaviour of drivers initiating a lane change using realistic data. In particular, time-to-lane-change-initiation (TTLCI) was investigated. TTCLI is the time from when a driver signals to when the driver initiates the lane change. We used the Kaplan-Meier method and mixed effect Cox Proportional Hazard (CPH) model to analyze the lane change data obtained from a large dataset collected as part of the L3Pilot project. The results indicate that most lane changes were initiated within 2 seconds of signalling. Furthermore, the results of the mixed-effect CPH model revealed that the speed of the lane-changing vehicle, the type and the direction of the lane change, and the presence of either a lead or lag vehicle and the lag gap significantly affected the TTLCI. The findings of this paper provide detailed insights into pre-lane change behaviour and pave the way for future studies. The findings of this study have important  implications for the development of regulations concerning the usage of turn signals by human drivers. Additionally, they have implications for the development of advanced algorithms for autonomous vehicles, particularly in improving the detection of lane changes of surrounding vehicles and estimation of the remaining time to it.
Transformer Model Based Soft Actor-Critic Learning for HEMS
Authors: Ludolfinger Ulrich , Peric Vedran S. , Hamacher Thomas , Hauke Sascha , Martens Maren
Published: June 2023

The transition to weather dependent renewable energy generators requires the electric loads to be adjusted to generation. This is made possible by demand response programs and home energy management systems. However, practically easy to use rule-based control systems often miss many optimization potentials. Self-learning alternatives employing reinforcement learning often ignore the partial observability of the building control problem and consequently neglect the importance of the observation history. Adaptive control systems that do consider that history often rely on policies that suffer from catastrophic forgetting, which makes them unable to fully grasp long histories.

As an alternative, we present a new reinforcement learning method for autonomous building energy management control based on the soft actor-critic method and the transformer deep neural network architecture. For the control of a heat pump and an the inlet port of a thermal storage, under consideration of photovoltaic generations and dynamic electricity prices, we formulate the problem as partially observable and use the history of observations to determine the control signals. We show, based on a validated building simulation, that our method outperforms rule-based as well as reinforcement learning methods that use multi layer perceptrons or recurrent neural networks as policy.
The Expression of Happiness in Social Media of Individuals Diagnosed With Depression
Authors: Bucur Ana-Maria , Chulvi Berta , Cosma Adrian , Rosso Paolo
Published: June 2023

Depression has long been studied in the NLP field, with most works focusing on individuals' negative emotions. People with depression also experience happiness, but this was not extensively studied.Previous works have shown that approaches relying on sentiment or emotion classification are unsuitable for extracting the expressions of feelings that bring happiness to an individual because they may not be expressed in positive words only. In this work, we conduct a large-scale study of happy moments from social media texts of depressed and non-depressed individuals. We develop an extensive deep learning-based framework to extract happy moments from text, and annotate them with semantic topics, gender labels, and agency and sociality measures. We analyze over 400,000 happy moments and show significant differences in topics, agency, and sociality of depressed and not-depressed users, varying by gender. Both male and female users with depression expressed less sociality in their happy moments than control users. Male users' agency was not impaired in depression, while female users with depression expressed fewer happy moments with agency than the control group. Our research can inform psychology interventions, which can foster feelings of longer-lasting happiness and represent a promising path of collaboration between computational linguistics and psychology.
Graphical Consensus-based Sharding for Efficient and Secure Sharings in Blockchain-enabled Internet of Vehicles
Authors: Li Wenqi , Zhao Zheng , Ma Pingchuan , Xie Zeqiang , Palade Vasile , Liu Hongbo
Published: June 2023

Blockchain-enabled Internet of Vehicles provides a reliable collaboration environment for traffic entities and promotes road safety and traffic efficiency through real-time sharings between vehicles and infrastructure. However, this is usually confronted by a bottleneck as the Internet of Vehicles scales up. In this paper, we propose a novel Graphical Consensus-based Sharding (GCS) framework, which is underpinned by four important strategies: 1) A graphical consensus is adopted as its intra-shard consensus, where the consensus group is set up according to its maximal connected subgraph, and the leader is elected by its reliability weight. The consensus group is refreshed intermittently by alternating the role of leader. 2) Within GCS, the intra-shard data are stored in the local chain, while a block-based directed acyclic graph, rather than a chained structure, is employed as the main chain. The local chain is used to respond to requests within each shard, and the main chain supports the cross-shard sharings. GCS will parallelly optimize the throughput of the blockchain-enabled Internet of Vehicles. 3) GCS further introduces the shard backup and node scheduling to handle shard failure and overheating by using new backup strategies and temporal-spatial graph convolutional network prediction model, respectively. 4) An off-chain transmission algorithm is presented for secure sharings between the infrastructure and the vehicles. Simulation results show that the number of Transactions Per Second is 1.69 times higher than that of the non-sharding blockchain, and the pending time is dramatically reduced compared to the mainstreaming sharding approach, which is 1.02s. 
A Comparative Study of Optimizers for Deep Learning Model used for Evaluating the Signal to Noise Ratio (SINR) of Large Intelligent Surfaces
Authors: Desai Jai , Markande Shriram D.
Published: June 2023

The next generation communication systems demand a tremendous increase in the existing data rate for various applications. One solution to this issue is the use of Large Intelligent Surfaces (LIS) which consists of a panel on which reflecting elements are mounted. Their main role is to redirect the incident electromagnetic signal to the desire user. This in turn increases the strength of the received signal which increases the quality of reception, providing an enhanced Quality of Service (QoS). The Machine Learning Algorithms have been implemented for various aspects of LIS implementation like channel estimation, calculation of discrete phase shifts to name a few. Here, a Deep Learning (DL) model is proposed to evaluate the Signal to Interference Noise Ratio (SINR) for a LIS assisted communication system. The effect of Adam, SGD, RMSProp, Adamax and Adadelta optimizers on the DL model is studied. The loss function considered is Mean Squared Error (MSE). The simulation results indicate that the DL model with Adam Optimizer gives accuracy of 97.45% which is better as compared to other optimizers.
The test validity of the Bluefrog Structured Query Language assessment
Authors: Kunenborg Ronald , den Adel Stephan , Kappen Teus , Pasma Wietze
Published: June 2023

The article discusses the need for an objective assessment of SQL skills for employees and the shortcomings of current tests on the market. The authors developed and validated a new assessment that tests for insight and found it to be a good objective measure of skill level for the intended target population. The assessment offers a solution to mitigate risks associated with errors in SQL queries and provides an objective means to assess skills and knowledge. However, there is still some uncertainty in the interpretation of test results.
Performance Analysis of Multi-user OTFS, OTSM, and Single carrier in Uplink
Authors: Reddy B V Sudhakar , Velampalli Chaithanya , Das suvra
Published: June 2023

In this work, we investigate multiple access schemes for orthogonal time frequency space (OTFS), orthogonal time sequency multiplexing modulation (OTSM), and block-based single-carrier (SC) systems. We also develop two iterative receivers for multi-user reception in the uplink for these waveforms, considering practical pulse shapes, based on the principles of successive interference cancellation (SIC) and turbo decoding, respectively. We show an improvement in coded performance in multi-user scenarios compared to single-user scenarios for all three waveforms using both receivers. This contrasts with earlier reported results of uncoded error performance for multi-user OTFS, where degradation is observed between single-user and multi-user scenarios. We finally compare these waveforms in the presence of nonlinear effects of high-power amplifiers (HPAs) and we observe that the block SC performs best and stands to challenge the OTFS and OTSM.
A Decoupling Paradigm with Prompt Learning for Remote Sensing Image Change Captioning
Authors: Liu Chenyang , Zhao Rui , Chen Jianqi , Qi Zipeng , Zou Zhengxia , Shi Zhenwei
Published: June 2023

Remote sensing image change captioning (RSICC) is a novel task that aims to describe the differences between bi-temporal images by natural language. Previous methods ignore a significant specificity of the task: the difficulty of RSICC is different for unchanged and changed image pairs. They process the unchanged and changed image pairs in a coupled way, which usually causes confusion for change captioning. In this paper, we decouple the task into two issues to ease it: whether and what changes have occurred. An image-level classifier performs binary classification to address the first issue. A feature-level encoder contributes to extracting discriminative features to help the caption generation module address the second issue. For caption generation, we utilize prompt learning to introduce pre-trained large language models (LLMs) into the RSICC task. A multi-prompt learning strategy is proposed to generate a set of unified prompts and a class-specific prompt conditioned on the image-level classifier's results. It can prompt a pre-trained LLM to know whether changes exist and generate captions. Finally, the multiple prompts and the features of the feature-level encoder are fed into a frozen LLM for captioning. Compared with previous methods, our method can leverage the powerful abilities of the pre-trained LLM in language to generate plausible captions, which is free of training. Extensive experiments show that our method is effective and achieves state-of-the-art performance. Besides, an additional experiment demonstrates that our decoupling paradigm is more promising than the previous coupled paradigm for the RSICC task. We will make our codebase publicly available to facilitate future research at https://github.com/Chen-Yang-Liu/PromptCC
A Reference Architecture for Quantum Computing as a Service
Authors: Ahmad Aakash , Altamimi Ahmed B. , Aqib Jamal
Published: June 2023

Quantum computers (QCs) aim to disrupt the status-quo of computing – replacing traditional systems and platforms that are driven by digital circuits and modular software – with hardware and software that operates on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve ‘quantum computational supremacy’ over traditional, i.e., digital computing systems. This research contributes by (i) developing a reference architecture for enabling quantum computing as a service, (ii) implementing microservices with the quantum-classic split pattern as an architectural use-case, and (iii) evaluating the reference architecture based on feedback by 22 practitioners. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting and implementing Quantum Computing as a Service (QCaaS). 
A Channel Weight Re-Scaling Module for Spectral-Spatial Feature Extraction-Based Motor Imagery Discrimination
Authors: Choi Jin Woo , Jo Sungho
Published: June 2023

Decoding motor imagery, an imagination of body movement, is an essential component for electroencephalogram (EEG) based brain-computer interface (BCI) applications. Recent studies have shown that acquiring brain signals within immersive VR environments can enhance motor imagery performance with more discriminant neural patterns. However, applying such signals directly to train classification models for real-life BCI systems may have limitations, as exposure to different environments may produce varying neural activity. To reduce the influence from such environmental causes, we propose a channel weight re-scaling module that refines EEG features depending on the learned separability of each electrode channel. As many pre-existing EEG classification models have their own strengths in different perspectives, our module is designed to be incorporated with previously studied convolutional neural network (CNN) based models that extract spectral-spatial features, preserving their characteristics. To analyze the performance of our module, we integrated our module with three widely used CNN models and performed evaluation using two datasets, which include our dataset containing resting state, left hand, and right hand motor imagery EEG data of participants within immersive VR and non-immersive environments. For all three baseline models, our module was able to improve classification performance. Furthermore, the re-scaled channel weights incremented towards theoretically important electrode positions. These results illustrate the potential of our module to be used not only to reduce unwanted features but also to inspect which channels were more influential to the model for classification.
IRS-Assisted Rate-Splitting Multiple Access for Overloaded Multiuser MISO Transmission
Authors: Elganimi Taissir , Mahmodi Hamza M. , Almqadim Hosam , Rabie Khaled
Published: June 2023

This paper is accepted and presented in IEEE ICC 2023.