bluuebunny/crossref_metadata_2025 huggingface dataset:
SARS-CoV-2 Detection from Lung CT-Scan Images Using Fine Tuning Concept on Deep-CNN Pretrained Model
Authors: Prasetyo Simeon Yuda
Published: August 2023

The problem of the spread of COVID-19 (SARS-CoV-2) is spreading fleetly and worldwide. Beforehand discovery and opinion of complaint is veritably important to ensure the right remedy so that it needs to be enforced through various practical approaches. In former studies, complaint discovery through medical imaging has started to appear and get a good delicacy of around 80 to 90 percent using machine learning. In the deep learning era, some trials get better accuracy of 95 percent using the traditional deep learning approach. Now, deep learning has developed more fleetly, especially for image classification. therefore, it's necessary to experiment with a pretrained model approach to medical images. In addition, the fine tuning approach will also be an aspect of the approach that will be carried out in this trial to be compared and to find out its effect, specifically on CT-Scan images of the lungs for the discovery of COVID 19. The results of this experiment showed that the pretrained model approach can get high accuracy. Relatively high accuracy, the smallest testing accuracy in this trial reached 94.78 percent of the Xception without fine tuning phase, this result has beaten the machine learning approach which is didn't reach 90 percent of accuracy. The best experiment testing accuracy get 97.59 percet on the VGG 16 by applying fine tuning. The results of this trial also show that the fine tuning stage (for the top 10th layers) can increase the accuracy of the model.
Comparasion KNN, Decision Tree and Naïve Bayes for Sentimen Analysis Marketplace Bukalapak
Authors: Halim Elisa Nathania , Huda Baenil , Elanda Anggi
Published: August 2023

The number of platforms currently makes it easier for users to provide reviews, one of which is the Bukalapak application on the Google Play Store. While both negative and positive reviews can influence the value of the app, users can also be affected by the app's sentiment reviews. Therefore it is necessary to carry out sentiment analysis to classify negative and positive reviews. This research uses review data of 1000 reviews and then classifies them using the RapidMiner application using three methods, namely KNN, Naive Bayes, and also the Decision Tree. The results of the KNN method obtained accuracy values of 85.03%, precision of 84.98%, and recall of 100.00%, then for the Naive Bayes method obtained accuracy values of 73.95%, precision of 100.00%, and recall of 69.26%, and for the Decision Tree method obtained 89.12% accuracy value, 88.62% precision, and 100.00% recall. this can prove that the Decision Tree method is superior to the KNN method and also the Naive Bayes method.
Implementation of Digital Libraries in Book Management and Donation in the Nyala Aksara Community
Authors: Saragih Saut Pintubipar , Silalahi Mesri
Published: August 2023

Web Based Applications has been implemented to organize bookstore and it is reuired to handle a wider library system, especially in a non-profit organization such as Nyala Aksara community library in Batam City. All library sources especially book collections come from donations from the community or charity, with this situation it is expected for the commitee to be able provide a system to reach more people to donate with easiness. The E-library Web application is expected to be able to provide solutions for the Nyala Aksara organization management. The main problems is the donations systems that have not been systematically organized, and then borrowing and returning system for users. E-library application will be built using web-based programming like HTML PHP, MySQL database and designing method with UML Method. The e-library will provide services for those who want to donate books, this application will also provide a system that is able to manage borrowers and how to process the book returning. The results of this research is producing a solution for the organization called E-libarary which is a web-based library management.
Cyber Security Audit using CIS CSC, NIST CSF and COBIT 2019 Framework
Authors: Fadila Viny , Mutiah Nurul , Sari Renny Puspita
Published: August 2023

Tingginya penggunaan teknologi dan informasi saat ini mengakibatkan peningkatan risiko dan ancaman keamanan data dan informasi. Dinas Komunikasi dan Informatika Kota Pontianak, dinas pemerintahan yang memanfaatkan dan menggunakan banyak teknologi informasi. Untuk mengetahui sejauh mana kemampuan Dinas Komunikasi dan Informatika Kota Pontianak dalam mengelola keamanan siber, maka diperlukan audit keamanan siber. Audit dapat dilakukan dengan menggabungkan framework CIS CSC (Center for Internet Security Critical Security Controls) untuk membatasi focus area keamanan siber aset TI serta menggunakan NIST CSF (National Institute of Standards and Technology Cybersecurity Framework) dan COBIT 2019 (Control Objective for Information Technologies) untuk melakukan perhitungan level kapabilitas. Perhitungan level kapabilitas menggunakan metode CPM (COBIT Performance Model). Hasil perhitungan level kapabilitas keamanan siber Dinas Komunikasi dan Informatika Kota Pontianak pada Identify (ID) mencapai level 3.9, Protect (PR) mencapai level 3.4, Detect (DE) mencapai level 2.5, dan Respond (RS) mencapai level 4. Terdapat 19 rekomendasi aktivitas untuk dilakukan agar mencapai level keamanan siber yang diinginkan, kemudian dilakukan pemetaan aktivitas rekomendasi ke dalam action priority matrix, 10 aktivitas masuk ke dalam kuadran Quick Wins, dan 9 aktivitas yang masuk ke dalam kuadran Major Projects. The frequent use of technology and information today impacts the increased risk and threats to data and information security. Department of Information and Communications of Pontianak is the department that utilizes and uses a lot of information technology. To find out how far the Pontianak City Communication and Informatics Office is capable of managing cyber security, a cyber security audit is needed. Audits can be conducted by combining the CIS CSC (Center for Internet Security Critical Security Controls) framework to define the cybersecurity focus areas of IT assets and using the NIST CSF (National Institute of Standards and Technology Cybersecurity Framework) and COBIT 2019 (Control Objective for Information Technologies) to calculate the capability level. Capability level calculation uses the CPM (COBIT Performance Model) method. The results of calculating the level of cyber security capability of the Pontianak City Communication and Informatics Service for Identification (ID) reaches level 3.9, Protect (PR) reaches level 3.4, Detect (DE) reaches level 2.5, and Respond (RS) reaches level 4. There are 19 activity recommendations to be carried out in order to achieve the desired level of cybersecurity, then capture recommendation activities into the action priority matrix, 10 activities included in the Quick Wins quadrant, and 9 activities entered into the Major Projects quadrant.
Use of the SAW (Simple Additive Weighting) Decision Making System in Determining the OSIS Board of Senior High Schools
Authors: Hutagalung Dini M , Panjaitan Christin Erniati
Published: August 2023

Dalam mengambil keputusan melalui sistem sangatlah diperlukan. Karena dengan menggunakan sistem yang benar, maka keputusan dapat diperoleh dengan akurat dalam waktu yang singkat. Sistem Pengambilan Keputusan metode SAW digunakan pada penelitian ini untuk menolong pihak sekolah dalam menentukan keputusan terhadap siswa-siswi yang tepat sebagai anggota pengurus OSIS. Kriteria-kriteria yang ditentukan pada pengambilan keputusan ditentukan berlandaskan kriteria yang cocok dan berhubungan dengan akademik, karakter, serta kedisiplinan siswa-siswi. Adapun kriteria-kriteria yang dipakai adalah : Prestasi Belajar (Akademik), Kedisiplinan (Karakter), Kerjasama (Karakter), Absensi (Komitmen dan Karakter) serta Tanggungjawab (Karakter). Pemberian bobot pada setiap kriteria didasarkan pada urutan daripada kepentingan atau kebutuhan dari yang paling dibutuhkan sampai yang paling butuh. Dari hasil penggunaan Sistem Pengambilan Keputusan SAW, menunjukkan adanya keakuratan dan ketepatan dalam pemilihan pengurus OSIS di sekolah menengah atas. User Interface dan Sistem Database yang dipakai menggunakan MySQL dan PHP. Desain sistem untuk menggambarkan fungsi-fungsi pada sistem tersebut menggunakan UML.It is important to use decision support system  applicaiton to help  management to improve efficiency in making decision. Decision Support System SAW (Simple Additive Weighting) method is use to help the school to decide which students are appropriate to be the Students Executive Board of Studen Union in secondary school.  The SAW method use some criteria based on academic achievement, characters and discipline. The criteria that are used in the system are : Academic Achievement, Disciplince, Teamwork, Attendance, and Responsibility. Each of the criteria is given certain weight based on the most to the least important list criteria. The result of using SAW method shows that the top list of the students are the most appropriate students to be sitting in the Executive Boards of Student Union. UML is used to design the model of the system. The system application GUI are build by using MySQL and PHP
Evaluation Scorring Game Application Construct Based Learning Arabic Letter with Analytical Heuristic Processing
Authors: Hayati Ariadi Retno
Published: August 2023

Pada penelitian ini mengimplementasikan evaluasi penilaian dengan berbasis metode Analytical Heuristic Process (AHP) yang diimplementasikan untuk menilai aplikasi belajar huruf Arab Hijaiyah yang bertujuan menilai lebih sesuai dengan permainan yang terselesaikan. Terdapat beberapa level permainan dan setiap level memiliki desain permainan yang berbeda, dimana pada level 1 adalah kuis soal, level 2 adalah drag and drop, level 3 adalah mencari kata dimana karakter fani akan bergerak atas bawah kanan kiri dengan animasi, level 4 adalah mengumpulkan poin dengan karakter fani meloncat objek dan bergerak kanan kiri atas bawah. Hasil penilaian scor permainan user dihasilkan dengan akumulasi nilai pada semua level permainan dengan metode AHP dengan variabel level, variabel nilai, dan variabel waktu sebagai dasar penghitungan weight dari metode AHP.In this research implementation evaluation use Analytical Heuristic Process (AHP) for analysis application game education for learning Arabic Letter Hijaiyah the purpose is evaluated user score actually as level user finished. Application there is few level in this application game and every level has game desain which different. Level 1 user should solve game with quizioner game, level 2 user should drag and drop the game, level 3 user should find word where fani character can move to up,bottom, right and left whith animation, level 4 user should collect poin value with fani character can jump object and move to right,left, uo and bottom. Evaluation scor for user result from accumulation value from all level game use AHP method with level variabel, score variabel and time variable as basic weight from AHP method.
Analysis of Indonesian Netizens' Dissent on President Jokowi before and after Fuel Price Increase
Authors: Mulia Adi , Dzikrillah Akhmad Rizal
Published: August 2023

Penelitian ini dilakukan dengan tujuan yang didasarkan pada masalah yang muncul di masyarakat, yaitu kenaikan harga BBM. Metode klasifikasi sentimen yang diterapkan dalam penelitian ini adalah dengan menggunakan kamus korpus leksikon yang memperhitungkan nilai sentimen positif dan negatif. Peneliti lalu melakukan komparasi sentiment antara sebelum dan sesuadah kebijakan kenaikan harga BBM. Selanjutnya, peneliti menerapkan topik pemodelan Latent Dirichlet Allocation atau (LDA) untuk mengetahui apakah perbincangan kenaikan harga BBM menjadi topik utama saat BBM naik. Hasil dari penelitian ini menunjukkan bahwa setelah mengumumkan kenaikan harga BBM pada bulan September 2022, Persentase tweet bersentimen negatif yang mengarah kepada Presiden Jokowi mengalami kenaikan jika dibandingkan dengan sebelum mengumumkan kenaikan harga BBM. Persentase tweet bersentimen positif yang mengarah kepada presiden Jokowi mengalami penurunan jika dibandingkan dengan sebelum menaikkan harga BBM. Pada bulan saat presiden Jokowi mengumumkan kebijakan kenaikan harga BBM yaitu pada bulan September 2022, topik perbincangan yang berkaitan dengan kebijakan kenaikan harga BBM merupakan topik perbincangan terpopuler pada tweet yang mengarah kepada Presiden Jokowi. 33,8 % twit yang memperbincangkan kenaikan harga bahan bakar minyak adalah twit yang bersentimen negatif dengan topik perbincangan terpopuler netizen bersentimen negatif adalah topik-topik yang berelasi kritik pada pemerintahan Jokowi.This research was based on a problem that arose in the community, which was the increase in fuel prices. The sentiment classification method applied by the researcher is by using a lexicon corpus dictionary that considers positive and negative sentiment values. The researcher then compares sentiment between before and after the fuel price increase policy. Furthermore, the researcher applied Latent Dirichlet Allocation or (LDA) topic modeling to find out whether discussion of fuel price increase became the main topic when fuel rose. The results of this study show that after announcing the fuel price increase in September 2022, the percentage of negative tweets directed at President Jokowi has increased when compared to before announcing fuel price increase. The percentage of positive tweets directed at President Jokowi decreased when compared before raising fuel prices. In the month when President Jokowi announced fuel price increase policy, namely in September 2022, the topic of conversation related to fuel price increase policy was the most popular topic of conversation in tweets directed at President Jokowi. 33.8% of tweets that discussed fuel price increase negative tweets with the most popular topics of discussion netizens with negative sentiments topics related to criticism of Jokowi government.
Implementation of Naive Bayes Classification Algorithm in Determining Appropriate Help Targets of Unlimited Houses (RTLH) in Bojonegoro District
Authors: Tussholikhah Anissa Nurul Farida , Ulinnuha Nurissaidah , Utami Wika Dianita
Published: August 2023

Rumah merupakan salah satu kebutuhan primer bagi setiap individu, dan termasuk kedalam aset terpenting yang harus dimiliki. Kelayakan rumah yang layak huni dan tidak layak huni harus dipertimbangkan. Rumah yang tidak memenuhi kecukupan minimum dari segi ruang dan luas ruangan dianggap sebagai Rumah tidak layak huni (RTLH). Untuk mengatasi terjadinya peningkatan RTLH maka pemerintah menanggulanginya dengan memberikan bantuan kepada masyarakat yang layak menerima dengan tepat sasaran. Penelitian ini bertujuan untuk menerapkan metode Naïve Bayes dalam menentukan bantuan tepat sasaran menggunakan dua kelas penelitian, yakni layak menerima bantuan RTLH dan tidak layak menerima bantuan RTLH. Dari analisis klasifikasi menggunakan confusion matrix didapatkan hasil akurasi sebesar 63%, recall 100% dan presisi 25% untuk 400 data training dan 100 data testing dari total 500 data dengan 10 atribut pengujian.The house is one of the primary needs for every individual, and is included in the most important asset that must be owned. The livability of the livable and uninhabitable houses should be considered. A house that does not meet the minimum adequacy in terms of space and room area is considered an uninhabitable house (RTLH). To overcome the increase in RTLH, the government overcomes it by providing assistance to people who deserve to receive it on target. This study aims to apply the Naïve Bayes method in determining targeted assistance using two research classes, namely eligible to receive RTLH assistance and not eligible to receive RTLH assistance. From the classification analysis using the confusion matrix, the results obtained are 63% accuracy, 100% recall and 25% precision for 400 training data and 100 testing data from a total of 500 data with 10 test attributes.
Expert System for Damage Detection on Sony Cameras Using the Case Based Reasoning (CBR) Method with the 3W-Jaccard Algorithm
Authors: Pratama Disa , Putri Raissa Amanda
Published: August 2023

Di zaman sekarang ini terutama di era digital, fotografi dan videografi sudah semakin melekat di kehidupan kita. perkembangan teknologi membuat pengguna kamera terutama kamera digital semakin banyak namun tidak diiringi dengan media informasi. Minimnya media informasi mengenai kerusakan kamera terkhusus kamera dengan merk Sony yang merupakan salah satu merek kamera digital yang terkenal di Indonesia membuat seseorang sulit untuk mengetahui kerusakan yang dialami oleh kameranya. Selain itu kurangnya jumlah pakar yang tersebar menjadi salah satu kendala dalam mengetahui kerusakan yang sedang dialami. Untuk itu sistem pakar deteksi kerusakan kamera dibuat agar mengetahui jenis kerusakan dan kemudian diberikan alternative pemecahan masalah. Dengan demikian metoda dan algoritma yang digunakan adalah Case Based Reasoning (CBR) dengan menggunakan persamaan perhitungan 3W-Jaccard. Metode dan algoritma ini dapat digunakan untuk mendeteksi kerusakan berdasarkan gejala yang ada pada database. Setiap gejala baru yang di masukkan akan dibandingkan dengan gejala lama yang ada pada tabel kemudian akan diberikan hasil deteksi dan solusi terhadap kerusakan yang dialami.In this day and age, especially in the digital era, photography and videography are increasingly embedded in our lives. Technological developments make camera users, especially digital cameras more and more, but not accompanied by information media. The lack of media information regarding camera damage, especially cameras with the Sony brand, which is one of the well-known digital camera brands in Indonesia, makes it difficult for someone to know about the damage experienced by their camera. Besides that, the lack of scattered experts is one of the obstacles in knowing the damage that is being experienced. For this reason, an expert camera damage detection system was created to find out the type of damage and then provide alternative solutions to the problem. Thus the method and algorithm used is Case Based Reasoning (CBR) using the 3W-Jaccard calculation equation. These methods and algorithms can be used to detect damage based on the symptoms in the database. Each new symptom that is entered will be compared with the old existing symptom there is a table and then the detection results and solutions for the damage will be given.
El supuesto complot nipo-mexicano contra Estados Unidos durante la Revolución
Authors: Kerber Víctor
Published: August 2023

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Leslie Dienes. Soviet Asia, economic development and national policy choices. Boulder: Westview Press, 1987, 289 pp.
Authors: Connelly Marisela
Published: August 2023

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John L. Sorenson y Martin H. Raish (comps.). Pre-Columbian Contact with the Americas across the oceans, An Annotated Bibliography. Provo, Utah: Research Press, 1990, 2 vols. no paginados.
Authors: Maeth Ch. Russell
Published: August 2023

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Yü, Ch'ang-chiang y Hou Chien (comps.). Chung-Kuo li-tai shih-ko ming-p'ien chien-shang tz'u-tien (Diccionario para la apreciación de obras renombradas de la poesía china a través de las eras). Pekín: 1989, 34 + 1987 pp.
Authors: Maeth Ch. Russell
Published: August 2023

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La revolución en el tiempo etnológico
Authors: Trautmann Thomas R.
Published: August 2023

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Geopolítica e identidades histórico-culturales: fuerzas divergentes en el mundo actual
Authors: Devalle Susana B. C.
Published: August 2023

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Jocelyn Linnekin y Lin Poyer (comps.). Cultural Identity and Ethnicity in the Pacific. Honolulu: University of Hawaii Press, 1990, 323 pp.
Authors: Devalle Susana B. C.
Published: August 2023

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Gyan Prakash. Bonded Histories: Genealogies of Labor Servitude in Colonial India. Cambridge: Cambridge University Press, 1990. (Ilustraciones, mapas y tablas)
Authors: Devalle Susana B. C.
Published: August 2023

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Nirmal Sengupta. Managing Common Property. Irrigation in India and the Philippines. Nueva Delhi/Newbury Park/ Londres: Sage, 1991, 283 pp. (Indo-dutch studies on develpment alternatives-6) (cuadros, diagramas y mapas).
Authors: Devalle Susana B. C.
Published: August 2023

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El encuentro de las concepciones de la vida orgánica en Occidente y Oriente y sus posibles aprovechamientos en la medicina moderna
Authors: Tsutomu Nagao
Published: August 2023

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GUISHI WADYIN DEN (Sobre el pueblo de Wa) Registros chinos sobre Japón
Authors: Ogi Yasuko Rocío , Sato Amalia
Published: August 2023

Traducción completa del original chino, cotejado con las versiones japonesas, Yasuko Rocío Ogi y Amalia Sato. Introducción de Amalia Sato.
Katsu Kokichi. Musui's story. The autobiography of a Tokugawa Samurai. Traducción, introducción y notas de Terkuko Craig. Tucson, Arizona: University of Arizona Press, 1988, 178 pp.
Authors: Santillana Daniel
Published: August 2023

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Tarjetas de Año Nuevo. Bellos impresos elaborados por artistas del siglo XVIII para expresar sentimientos amistosos mediante símbolos
Authors: Tablada José Juan
Published: August 2023

Traducción del inglés: Guillermo Quartucci.
Daniel Chirot (comp.). The Crisis of Leninism and the Decline of the Left, the Revolutions of 1989. Seattle y Londres: University of Washington Press, 1991, 245 pp.
Authors: Connelly Marisela
Published: August 2023

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Pembelajaran PPR Untuk Meningkatkan Literasi Numerasi Pada Konsep Bilangan Mahasiswa
Authors: Anugrahana Andri , Hasthiolivia Cintya
Published: August 2023

This study aims to determine whether PPR learning can improve numeracy literacy in the concept of numbers in students. This type of research was descriptive qualitative research with a sample of 24 Elementary School Teacher Study Program students. Pretest and posttest are used to find out whether there is an increase in student knowledge, and observation is used to find out the implementation of lectures with PPR dynamics. The results of this study indicate that: (1) students attend lectures with PPR dynamics; (2) numeracy literacy in the number concept of students. The results of the study showed that there was an increase in numeracy literacy in the number concept of students by 28%.
PERSEPSI GURU TERHADAP PERUBAHAN KARAKTER SOPAN SANTUN SISWA PASCA PEMBELAJARAN DARING
Authors: Damayanti Atika
Published: August 2023

Peralihan pembelajaran daring ke luring pasca pandemic membutuhkan penyesuaian diri yang baik. Adapun dampak dari pembelajaran daring menyebabkan perubahan di bidang pendidikan diantaranya terjadi pergeseran nilai-nilai  karakter. Penelitian ini bertujuan untuk melihat bagaimana persepsi guru terhadap perubahan karakter sopan santun siswa pasca pembelajaran daring. Penelitian ini menggunakan metode penelitian kualitatif deskriptif berupa wawancara. Subjek pada penelitian ini yakni 3 orang guru di SMA X. Kajian ini menemukan bahwa pendidikan karakter sopan-santun siswa yang terlihat di sekolah pasca pandemi covid-19 cukup memprihatinkan. Berdasarkan  pengamatan guru, ternyata cukup banyak bentuk perubahan  karakter sopan-santun yang terlihat saat di sekolah. (1) Bentuk perubahan karakter sopan santun seperti tidak menyapa ketika berpapasan dengan guru, berani menjawab ksaat dinasehati atau diberi teguran oleh guru, berbicara dengan nada yang cukup tinggi, tidak ramah dengan guru atau tenaga pendidik di sekolah; (2) Perubahan karakter dapat terjadi karena kurangnya pengawasan baik dari orang tua dan guru, perkembangan teknologi yang pesat dan pengaruh budaya luar; (3) Dampaknya adalah rasa ketidaknyamanan yang dirasakan baik dari guru atau lingkungan sekitar. Selain itu berpengaruh pada citra sekolah dan lain sebagainya; (4) Sejauh ini strategi yang dilakukan dari pihak guru maupun sekolah dalam menangani atau mengatasi perubahan karakter sopan santun yakni dengan diberikan peneguran/hukuman serta memberikan modeling atau contoh yang baik terkait perilaku sopan santun.
Penerapan Model Problem-based Learning Berbantu Media Video Pembelajaran untuk Meningkatkan Hasil Belajar IPA Siswa Kelas IV Sekolah Dasar
Authors: Nurmasari Ira
Published: August 2023

Penelitian ini bertujuan untuk mengetahui peningkatan hasil belajar siswa kelas IV SD Muhammadiyah 16 Surabaya pada tahun ajaran 2022/2023 dengan menerapkan model problem based learning disertai media video. Penelitian ini merupakan Penelitian Tindakan Kelas (PTK) yang dilaksanakan dalam dua siklus. Tiap siklus terdiri atas perencanaan, pelaksanaan, pengamatan, dan refleksi. Subjek penelitian adalah peserta didik kelas IV SD Muhammadiyah 16 Surabaya dengan jumlah peserta didik 28 orang. Instrumen penelitian berupa tes uraian dan lembar observasi. Hasil penelitian menunjukkan pencapaian hasil belajar pengetahuan pada siklus I dengan ketuntasan klasikal 73,39% meningkat pada siklus II yakni 90,25%. Berdasarkan hasil tersebut dapat disimpulkan bahwa penerapan model problem based learning berbantu media video dapat meningkatkan hasil belajar pada materi transportasi dan cara menghemat energi.
Electrodepósito de nanopartículas bimetálicas (Paladio–Níquel y Paladio–Cobalto), utilizando disolventes eutécticos profundos, como centros catalíticos en la electro–oxidación de ácido fórmico
Authors:   , Landa Castro Midori
Published: August 2023

The present study focuses on the synthesis and characterization of bimetallic nanoparticles (NPs) (Pd-Ni and Pd-Co) as an alloy using deep eutectic solvents (DES’s) formed from choline chloride and urea (reline) or ethylene glycol (ethaline) and choline chloride), and their evaluation in the electrochemical formic acid (FAOR) oxidation reaction. These DES are eutectic mixtures of a Lewis acid and a Lewis base containing cationic and anionic species, also called green solvents. Both reline and ethaline have been increasingly used in electrochemistry, as a medium for the electrodeposition of NPs, due to their excellent properties, such as: low cost, biodegradability, high electrical conductivity, almost no presence of protons and water. It is also worth underlining their excellent ability to solvate metal ions, thus allowing the synthesis of binary and ternary alloys, with control over morphologies and sizes. The precursor salts of Pd, Ni and Co were dissolved in each DES to study through electrochemical techniques, such as: Cyclic Voltammetry (VC) and Chronoamperometry (CA) the nucleation and growth kinetics of the NPs on the glassy carbon electrode. The modified electrode with the bimetallic NPs (NPs-BM) was used forFAO R, which is a key reaction in direct formic acid fuel cells. Electrodeposits were characterized by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX) and X-ray Photoelectron Spectroscopy (XPS) to determine diameters, composition, morphology, and oxidation states of elements that make up the deposits bimetallic NPs.
10 powodów, dla których warto stosować montelukast
Authors: Poziomkowska-Gęsicka Iwona , Gazdowska Marta
Published: August 2023

Montelukast, najczęściej stosowany z leków antyleukotrienowych, wykorzystywany jest w terapii astmy (zalecenia GINA) i astmy z alergicznym nieżytem nosa. Z uwagi na zróżnicowanie: mnogość fenotypów i endotypów astmy, warto pamiętać o możliwości terapii monteluka-stem, zwłaszcza w przypadkach: pacjentów z astmą aspirynową, astmy u osób otyłych, astmy u osób palących tytoń, astmy, która pogarsza się w dokuczliwy sposób w przebiegu infekcji, astmy pacjentów pediatrycznych, kiedy terapia inhalacyjna nastręcza trudności.
10 powodów, dzięki którym terapia astmy i przewlekłej obturacyjnej choroby płuc salmeterolem z propionianem flutykazonu w inhalatorze DPI może być bardzo skuteczna i bezpieczna
Authors: Kowal Krzysztof
Published: August 2023

Długo działające β2-mimetyki i wziewne glikokortykosteroidy są szeroko stosowane w leczeniu astmy i przewlekłej obturacyjnej choroby płuc od wielu lat. W terapii astmy i przewlekłej obturacyjnej choroby płuc dostępna jest szeroka gama długo działających β2-mimetyków i wziewnych glikokortykosteroidów oraz ich połączeń. Od prawie 20 lat stosuje się połączenie salmeterolu z propionianem flutykazonu dostarczane z inhalatora suchego proszku. Charakteryzuje się ono unikalnymi cechami tych składników, w tym wysoką selektywnością i siłą działania, które u chorych na astmę i przewlekłą obturacyjną chorobę płuc wywierają synergistyczny wpływ na czynność płuc i proces zapalny w drogach oddechowych. W osiągnięciu wysokiej skuteczności klinicznej pomaga również bardzo wygodny system podawania, dzięki niewielkiej liczbie błędów popełnianych podczas inhalacji i znacznemu stopniowi akceptacji inhalatora przez pacjentów.
Justiça restaurativa versus justiça penal negocial e sua aplicação no acordo de não persecução penal
Authors: Augusto Fernandes David
Published: August 2023

Atendendo a comutação de paradigma existente no cenário Internacional onde a visão punitivista de aplicação da pena sofreu um deslocamento para uma visão negocial e de aplicabilidade da Justiça Restaurativa, esta pesquisa objetiva especificamente verificar se as Resoluções nº 118, de 1º de dezembro de 2014 do CNMP, nº 225, de 31 de maio de 2016 e Resolução nº 288, de 25 de junho de 2019, ambas do CNJ, que tratam da Justiça Restaurativa possuem aplicabilidade no acordo de não persecução penal, implementado pelo artigo 28-A do Código de Processo Penal. A metodologia utilizada foi a pesquisa bibliográfica, bem como a pesquisa de campo, por meio de questionário direcionado aos advogados, defensores públicos, representantes do parquet e juízes que atuam nas Varas Criminais de Macaé. Como resultado da pesquisa, foi detectada a não adequação do ANPP com a Justiça Restaurativa, mas sim com a justiça penal negocial.
ALL-E: Aesthetics-guided Low-light Image Enhancement
Authors: Li Ling , Liang Dong , Gao Yuanhang , Huang Sheng-Jun , Chen Songcan
Published: August 2023

Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions, i.e., its corresponding adjustment curve is estimated sequentially. Extensive experiments show that integrating aesthetic assessment improves both subjective experience and objective evaluation. Our results on various benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods. Source code: https://dongl-group.github.io/project pages/ALLE.html
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View
Authors: Li Peizheng , Ding Shuxiao , Chen Xieyuanli , Hanselmann Niklas , Cordts Marius , Gall Juergen
Published: August 2023

Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic. While bird’s-eye view (BEV) representations are commonplace in perception for autonomous driving, their potential in a motion prediction setting is less explored. Existing approaches for BEV instance prediction from surround cameras rely on a multi-task auto-regressive setup coupled with complex post-processing to predict future instances in a spatio-temporally consistent manner. In this paper, we depart from this paradigm and propose an efficient novel end-to-end framework named PowerBEV, which differs in several design choices aimed at reducing the inherent redundancy in previous methods. First, rather than predicting the future in an auto-regressive fashion, PowerBEV uses a parallel, multi-scale module built from lightweight 2D convolutional networks. Second, we show that segmentation and centripetal backward flow are sufficient for prediction, simplifying previous multi-task objectives by eliminating redundant output modalities. Building on this output representation, we propose a simple, flow warping-based post-processing approach which produces more stable instance associations across time. Through this lightweight yet powerful design, PowerBEV outperforms state-of-the-art baselines on the NuScenes Dataset and poses an alternative paradigm for BEV instance prediction. We made our code publicly available at: https://github.com/EdwardLeeLPZ/PowerBEV.
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification
Authors: Xiong Conghao , Chen Hao , Sung Joseph J.Y. , King Irwin
Published: August 2023

Multiple Instance Learning (MIL) and transformers are increasingly popular in histopathology Whole Slide Image (WSI) classification. However, unlike human pathologists who selectively observe specific regions of histopathology tissues under different magnifications, most methods do not incorporate multiple resolutions of the WSIs, hierarchically and attentively, thereby leading to a loss of focus on the WSIs and information from other resolutions. To resolve this issue, we propose a Hierarchical Attention-Guided Multiple Instance Learning framework to fully exploit the WSIs. This framework can dynamically and attentively discover the discriminative regions across multiple resolutions of the WSIs. Within this framework, an Integrated Attention Transformer is proposed to further enhance the performance of the transformer and obtain a more holistic WSI (bag) representation. This transformer consists of multiple Integrated Attention Modules, which is the combination of a transformer layer and an aggregation module that produces a bag representation based on every instance representation in that bag. The experimental results show that our method achieved state-of-the-art performances on multiple datasets, including Camelyon16, TCGA-RCC, TCGA-NSCLC, and an in-house IMGC dataset. The code is available at https://github.com/BearCleverProud/HAG-MIL.
Pyramid Diffusion Models for Low-light Image Enhancement
Authors: Zhou Dewei , Yang Zongxin , Yang Yi
Published: August 2023

Recovering noise-covered details from low-light images is challenging, and the results given by previous methods leave room for improvement. Recent diffusion models show realistic and detailed image generation through a sequence of denoising refinements and motivate us to introduce them to low-light image enhancement for recovering realistic details. However, we found two problems when doing this, i.e., 1) diffusion models keep constant resolution in one reverse process, which limits the speed; 2) diffusion models sometimes result in global degradation (e.g., RGB shift). To address the above problems, this paper proposes a Pyramid Diffusion model (PyDiff) for low-light image enhancement. PyDiff uses a novel pyramid diffusion method to perform sampling in a pyramid resolution style (i.e., progressively increasing resolution in one reverse process). Pyramid diffusion makes PyDiff much faster than vanilla diffusion models and introduces no performance degradation. Furthermore, PyDiff uses a global corrector to alleviate the global degradation that may occur in the reverse process, significantly improving the performance and making the training of diffusion models easier with little additional computational consumption. Extensive experiments on popular benchmarks show that PyDiff achieves superior performance and efficiency. Moreover, PyDiff can generalize well to unseen noise and illumination distributions. Code and supplementary materials are available at https://github.com/limuloo/PyDIff.git.
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Authors: Deng Jiewen , Deng Jinliang , Jiang Renhe , Song Xuan
Published: August 2023

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e.g., transportation demands and air pollutants). Compared to modeling time series or multivariate time series, which has received much attention and achieved tremendous progress in recent years, tensor time series has been paid less effort. Properly coping with the tensor time series is a much more challenging task, due to its high-dimensional and complex inner structure. In this paper, we develop a novel TTS forecasting framework, which seeks to individually model each heterogeneity component implied in the time, the location, and the source variables. We name this framework as GMRL, short for Gaussian Mixture Representation Learning. Experiment results on two real-world TTS datasets verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/beginner-sketch/GMRL.
Denoised Self-Augmented Learning for Social Recommendation
Authors: Wang Tianle , Xia Lianghao , Huang Chao
Published: August 2023

Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling. Recently, Self-Supervised Learning (SSL) has proven to be remarkably effective in addressing data sparsity through augmented learning tasks. Inspired by this, researchers have attempted to incorporate SSL into social recommendation by supplementing the primary supervised task with social-aware self-supervised signals. However, social information can be unavoidably noisy in characterizing user preferences due to the ubiquitous presence of interest-irrelevant social connections, such as colleagues or classmates who do not share many common interests. To address this challenge, we propose a novel social recommender called the Denoised Self-Augmented Learning paradigm (DSL). Our model not only preserves helpful social relations to enhance user-item interaction modeling but also enables personalized cross-view knowledge transfer through adaptive semantic alignment in embedding space. Our experimental results on various recommendation benchmarks confirm the superiority of our DSL over state-of-the-art methods. We release our model implementation at: https://github.com/HKUDS/DSL.
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks
Authors: Han Bing , Zhao Feifei , Zeng Yi , Pan Wenxuan , Shen Guobin
Published: August 2023

Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing continual learning frameworks are usually applicable to Deep Neural Networks (DNNs) and lack the exploration on more brain-inspired, energy-efficient Spiking Neural Networks (SNNs). Drawing on continual learning mechanisms during child growth and development, we propose Dynamic Structure Development of Spiking Neural Networks (DSD-SNN) for efficient and adaptive continual learning. When learning a sequence of tasks, the DSD-SNN dynamically assigns and grows new neurons to new tasks and prunes redundant neurons, thereby increasing memory capacity and reducing computational overhead. In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task). We validate the effectiveness of the proposed model on multiple class incremental learning and task incremental learning benchmarks. Extensive experiments demonstrated that our model could significantly improve performance, learning speed and memory capacity, and reduce computational overhead. Besides, our DSD-SNN model achieves comparable performance with the DNNs-based methods, and significantly outperforms the state-of-the-art (SOTA) performance for existing SNNs-based continual learning methods.
On the Paradox of Learning to Reason from Data
Authors: Zhang Honghua , Li Liunian Harold , Meng Tao , Chang Kai-Wei , Van den Broeck Guy
Published: August 2023

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning. We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has, in fact, learned statistical features that inherently exist in logical reasoning problems. We also show that it is infeasible to jointly remove statistical features from data, illustrating the difficulty of learning to reason in general. Our result naturally extends to other neural models (e.g. T5) and unveils the fundamental difference between learning to reason and learning to achieve high performance on NLP benchmarks using statistical features.
A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram
Authors: Zhang Ming-Liang , yin Fei , Liu Cheng-Lin
Published: August 2023

Geometry problem solving (GPS) is a high-level mathematical reasoning requiring the capacities of multi-modal fusion and geometric knowledge application. Recently, neural solvers have shown great potential in GPS but still be short in diagram presentation and modal fusion. In this work, we convert diagrams into basic textual clauses to describe diagram features effectively, and propose a new neural solver called PGPSNet to fuse multi-modal information efficiently. Combining structural and semantic pre-training, data augmentation and self-limited decoding, PGPSNet is endowed with rich knowledge of geometry theorems and geometric representation, and therefore promotes geometric understanding and reasoning. In addition, to facilitate the research of GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K, labeled with both fine-grained diagram annotation and interpretable solution program. Experiments on PGPS9K and an existing dataset Geometry3K validate the superiority of our method over the state-of-the-art neural solvers. Our code, dataset and appendix material are available at \url{https://github.com/mingliangzhang2018/PGPS}.
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Authors: Chen Yuhan , Luo Yihong , Tang Jing , Yang Liang , Qiu Siya , Wang Chuan , Cao Xiaochun
Published: August 2023

Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs.
Deep Multi-view Subspace Clustering with Anchor Graph
Authors: Cui Chenhang , Ren Yazhou , Pu Jingyu , Pu Xiaorong , He Lifang
Published: August 2023

Deep multi-view subspace clustering (DMVSC) has recently attracted increasing attention due to its promising performance. However, existing DMVSC methods still have two issues: (1) they mainly focus on using autoencoders to nonlinearly embed the data, while the embedding may be suboptimal for clustering because the clustering objective is rarely considered in autoencoders, and (2) existing methods typically have a quadratic or even cubic complexity, which makes it challenging to deal with large-scale data. To address these issues, in this paper we propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG). To be specific, DMCAG firstly learns the embedded features for each view independently, which are used to obtain the subspace representations. To significantly reduce the complexity, we construct an anchor graph with small size for each view. Then, spectral clustering is performed on an integrated anchor graph to obtain pseudo-labels. To overcome the negative impact caused by suboptimal embedded features, we use pseudo-labels to refine the embedding process to make it more suitable for the clustering task. Pseudo-labels and embedded features are updated alternately. Furthermore, we design a strategy to keep the consistency of the labels based on contrastive learning to enhance the clustering performance. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods.
SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations
Authors: Kim Chan , Cho Jaekyung , Bobda Christophe , Seo Seung-Woo , Kim Seong-Woo
Published: August 2023

Robotic agents trained using reinforcement learning have the problem of taking unreliable actions in an out-of-distribution (OOD) state. Agents can easily become OOD in real-world environments because it is almost impossible for them to visit and learn the entire state space during training. Unfortunately, unreliable actions do not ensure that agents perform their original tasks successfully. Therefore, agents should be able to recognize whether they are in OOD states and learn how to return to the learned state distribution rather than continue to take unreliable actions. In this study, we propose a novel method for retraining agents to recover from OOD situations in a self-supervised manner when they fall into OOD states. Our in-depth experimental results demonstrate that our method substantially improves the agent’s ability to recover from OOD situations in terms of sample efficiency and restoration of the performance for the original tasks. Moreover, we show that our method can retrain the agent to recover from OOD situations even when in-distribution states are difficult to visit through exploration. Code and supplementary materials are available at https://github.com/SNUChanKim/SeRO.
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
Authors: Meng Ziqiao , Zhao Peilin , Yu Yang , King Irwin
Published: August 2023

Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of electrons, resulting in state-of-the-art top-1 accuracy, and enabling parallel sampling. However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule. This violation of the physical constraints of chemical reactions impairs model performance. In this work, we propose a new framework called ReactionSink that combines two doubly stochastic self-attention mappings to obtain electron redistribution predictions that follow both constraints. We further extend our solution to a general multi-head attention mechanism with augmented constraints. To achieve this, we apply Sinkhorn's algorithm to iteratively update self-attention mappings, which imposes doubly conservative constraints as additional informative priors on electron redistribution modeling. We theoretically demonstrate that our ReactionSink can simultaneously satisfy both rules, which the current decoder mechanism cannot do. Empirical results show that our approach consistently improves the predictive performance of non-autoregressive models and does not bring an unbearable additional computational cost.