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"This update of the 1987 title of the same name is an examination of what is currently known about the probabilistic method, written by one of its principal developers. Based on the notes from Spencer's 1986 series of ten lectures, this new edition contains an additional lecture: The Janson Inequalities. These inequalities allow accurate approximation of extremely small probabilities. A new algorithmic approach to the Lovasz Local Lemma, attributed to Jozsef Beck, has been added to Lecture 8, as well. Throughout the monograph, Spencer retains the informal style of his original lecture notes and emphasizes the methodology, shunning the more technical "best possible" results in favor of clearer exposition. The book is not encyclopedic--it contains only those examples that clearly display the methodology. The probabilistic method is a powerful tool in graph theory, combinatorics, and theoretical computer science. It allows one to prove the existence of objects with certain properties (e.g., colorings) by showing that an appropriately defined random object has positive probability of having those properties."

"White Matter Hyperintensities (WMH) adalah area di otak yang memiliki intensitas yang lebih tinggi dibandingkan dengan area normal lainnya pada hasil pemindaian Magnetic Resonance Imaging (MRI). WMH seringkali terkait dengan penyakit pembuluh kecil di otak, sehingga deteksi dini WMH sangat penting. Namun, terdapat dua masalah umum dalam mendeteksi WMH, yaitu ambiguitas yang tinggi dan kesulitan dalam mendeteksi WMH yang berukuran kecil. Dalam penelitian ini, kami mengusulkan metode yang disebut Probabilistic TransUNet untuk mengatasi masalah segmentasi objek WMH yang berukuran kecil dan ambiguitas yang tinggi pada citra medis. Kami melakukan eksperimen K-fold cross validation untuk mengukur kinerja model. Berdasarkan hasil eksperimen, model berbasis Transformer (TransUNet dan Probabilistic TransUNet) lebih baik dan presisi dalam melakukan segmentasi pada obyek WMH yang berukuran kecil, hal ini ditunjukkan oleh nilai Dice Similarity Coefficient (DSC) yang dihasilkan lebih tinggi dibandingkan dengan model berbasis Convolutional Nueral Networks (CNN) (U-Net dan Probabilistic U-Net). Penambahan probabilistic model dan pendekatan berbasis transformer berhasil mendapatkan performa yang lebih baik. Metode yang kami usulkan berhasil mendapatkan nilai DSC sebesar 0,744 dalam 5-fold cross validation, lebih baik dari metode sebelumnya. Dalam melakukan segmentasi objek kecil metode usulan kami mendapatkan nilai DSC sebesar 0,51.

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White Matter Hyperintensities (WMH) are areas of the brain that have a higher intensity than other normal brain regions on Magnetic Resonance Imaging (MRI) scans. WMH is often associated with small vessel disease in the brain, making early detection of WMH important. However, there are two common issues in detecting WMH: high ambiguity and difficulty detecting small WMH. In this study, we propose a method called Probabilistic TransUNet to address the precision of small object segmentation and the high ambiguity of medical images. We conducted a k-fold cross-validation experiment to measure model performance. Based on the experiments, Transformer-based models (TransUNet and Probabilistic TransUNet) were found to provide more precise and better segmentation results, as demonstrated by the higher DSC scores obtained compared to CNN-based models (U-Net and Probabilistic U-Net) and their ability to segment small WMH objects. The proposed method obtained a DSC score of 0742 in k-fold cross-validation, better than the previous method. In conducting segmentation of small objects, our proposed method achieved a DSC score of 0,51."

"ABSTRAKPrediksi durasi banyak dilakukan oleh para pemangku kepentingan berdasarkan pengalaman atau intuisi mereka. Hal ini menyebabkan perkiraan durasi memiliki risiko kesalahan yang dapat menggangu proses pelaksanaan konstruksi. Penelitian ini mencoba mengembangkan suatu model pendekatan prediksi durasi proyek berdasarkan metode prediksi durasi akhir proyek Earned Schedule, yang merupakan pengembangan metode Earned Value, dan memanfaatkan faktor-faktor pengaruh eksternal yang tersedia dalam informasi resmi. Selain itu penelitian ini mengakomodasi kondisi ketidak pastian yang umum terjadi di lingkungan negara berkembang semisal Indonesia. Ketiga hal tersebut dirangkum dalam suatu model pengembangan prediksi durasi probabilistik berdasarkan faktor pengaruh eksternal. Hasil penelitian menunjukan adanya hubungan antara beberapa faktor pengaruh terukur dengan durasi probabilistik proyek konstruksi. 

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ABSTRACT

The prediction of project duration is mostly calculated based on the experience or intuition of the estimator. This causes the estimated duration to have an error risk that could disrupt the construction process. This research tries to develop a project duration prediction approach model based on the Earned Schedule project's final prediction method, which is the development of the Earned Value Method, and utilizes the external influencing factors available in official information. In addition, this study accommodates uncertainty conditions that are common in developing countries such as Indonesia. These three matters are summarized in a probabilistic duration prediction development model based on external influencing factors. The result of the research shows that there is a sufficient correlation between several factors of measured influence with the probabilistic duration of the construction project. 

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"Reservoir karbonat diperkirakan mengandung hampir 60% dari total cadangan hidrokarbon dunia dan diperkirakan memiliki 50% dari total produksi hidrokarbon. Hidrokarbon umumnya terdapat pada batuan berpori. Porositas batuan karbonat umumnya memiliki heterogenitas yang tinggi, kompleksitas, dan random. Salah satu metode yang efektif untuk mengatasi heterogenitas adalah metode neural network. Sehingga penelitian ini bertujuan untuk menetukan distribusi porositas dengan neural network pada batuan karbonat dengan menggunakan 2 data sumur dan data seismik 2D post stack time migration (PSTM) pada lapangan T. Seismik atribut yang digunakan sebagai input proses probabilistic neural network berupa data seismik dan hasil inversi serta log yang akan diprediksi penyebarannya. Digunakan step wise regression dan validation error untuk menentukan atribut terbaik yang akan digunakan. Hasil prediksi nilai porositas menggunkan probabilistic neural network dengan input atribut terbaik yang telah terpilih menghasilkan korelasi yang lebih baik 0.81 dengan error 0.03 dibanding dengan metode multiatribut yang menggunakan persamaan linier yaitu 0.66 dengan error 0.04 dan hasil model log prediksi mendekati log aktual. Hasil distribusi porositas dapat dianilisis bahwa nilai porositas pada sumur C1 memiliki nilai porositas efektif yang rendah dibandingkan dengan sumur C4.

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Reservoir carbonate mostly contains 60% of total hydrocarbon preserves in the world, and it is predicted about 50% which is produced hydrocarbon. Commonly, hydrocarbon is found in the rock pores. The porosity of carbonate, generally, has high heterogeneity, complexity, and random. One of effective methods to solve the problem is neural network. The aim of this study is to determine the distribution of porosity using neural network for carbonate in T field. Seismic attribute is used as input in neural network process which is seismic data, inversion result, and well log. Step wise regression and validation error are used to determine the best attributes that will be used to.

The prediction result of porosity using probabilistic neural network with the best attribute has better correlation than using multi attributes for linier method. The correlation and error value using neural network are 0.08% and 0.03%, while the value of correlation and error using multi attribute for linier method are 0.06% and 0.04%, respectively. The predicted log model is approaching the actual log. The result of porosity distribution shows that the porosity value of well C1 has lower effective porosity than well C4."

"the present study examined gender differences among Japanese students in the effect of closeness on conflict regardless of conflict avoidance. we predicted that male participants would take assertive strategies in conflicts regardless of whether they were in close relationship or not, whereas female participants would take avoidance in conflicts with non-close others, but assertive strategic with close ones. 79 Japanese university students (33 males and 46 females) were assigned into one of two friend conditions (non-close condition vs. close condition). and were measured of the extent to which they took avoidance in 3 conflict situation =s (scenario). the results supported the prediction only with females, and suggested that Japanese males attempt to send non-verbal messages to close friends while taking avoidance"

"Lapangan ‘B’ merupakan lapangan prospek hidrokarbon yang berlokasi di offshorecekungan Kutai, Kalimantan Timur. Untuk mengetahui karakterisasi reservoir lapangan‘B’, dilakukan pemodelan porositas dan saturasi air menggunakan inversi AI, multiatributseismik dan probabilistic neural network. Penelitian ini menggunakan data seismik 3DPSTM dan data sumur (AND-1, AND-2, AND-3 dan AND-4). Pada data seismik dan datasumur dilakukan inversi AI untuk mengetahui sifat litologi area penelitian. Kemudian,hasil AI ditransformasikan untuk mendapatkan model porositas. Metode multiatributseismik menggunakan beberapa atribut untuk memprediksi model porositas dan saturasiair. Setelah itu, diaplikasikan sifat non-linear dari probabilistic neural network sehinggamenghasilkan model porositas dan saturasi air hasil probabilistic neural network (PNN).Model porositas dan saturasi air transformasi AI, multiatribut seismik dan PNN divalidasidengan nilai porositas dan saturasi air data sumur untuk mengetahui apakah modelporositas dan saturasi air tersebut merepresentatifkan nilai data sumur. Validasi dilakukanpada sumur AND-1 dan AND-2. Nilai porositas dan saturasi air data sumur untuk AND-1 adalah 25.3 – 35.9% dan 45 – 60%, dan nilai porositas dan saturasi air AND-2 adalah11 – 35% dan 15 – 82%. Nilai porositas AND-1 hasil transformasi AI sekitar 16 – 67%,multiatribut seismik sekitar 11.5 – 27% dan PNN sekitar 11.5 – 27%. Nilai saturasi airAND-1 hasil multiatribut seismik sekitar 4 – 63% dan PNN sekitar 18 – 63%. Nilaiporositas AND-2 hasil transformasi AI sekitar 52 – 72%, multiatribut seismik sekitar 11– 21.5% dan PNN sekitar 11 – 21.5%. Nilai saturasi air AND-2 hasil multiatribut seismiksekitar 63 – 85% dan PNN sekitar 63 – 85%. Kemudian, metode multiatribut seismik danPNN didapatkan nilai korelasi antara parameter target dengan parameter prediksi. Modelporositas multiatribut seismik memiliki korelasi 0.840836 dan PNN memiliki korelasi0.936868. Model saturasi air multiatribut seismik memiliki korelasi 0.915254 dan PNNmemiliki korelasi 0.994566. Model porositas transformasi AI memiliki rentang yanglebih tinggi dibandingkan dengan data sumur. Model porositas dan saturasi air metodePNN memiliki rentang nilai yang cukup dekat dengan data sumur dan memiliki korelasiyang lebih tinggi dibandingkan dengan metode multiatribut seismik. Oleh sebab itu,model porositas dan saturasi air metode PNN merupakan model prediksi terbaik.Berdasarkan model PNN, reservoir zona target lapangan ‘B’ memiliki nilai impedansiakustik 25384 – 26133 ((ft/s)*(g/cc)), porositas sekitar 15 – 27% dan nilai saturasi airsekitar 11 – 63%.

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The 'B' field is a hydrocarbon prospect field located in the offshore Kutai Basin, East

Kalimantan. To determine the characterization of the ‘B’ field reservoir, porosity and

water saturation modeling was carried out using AI inversion, seismic multiattribute and

probabilistic neural network. This study uses 3D PSTM seismic data and wells data

(AND-1, AND-2, AND-3 and AND-4). In seismic data and wells data, AI inversion was

carried out to determine the lithological characteristics of the research area. Then, the AI

results were transformed to obtain a porosity model. The seismic multiattribute method

uses several attributes to predict the porosity and water saturation model. After that, the

non-linear properties of the probabilistic neural network were applied to produce the

porosity and water saturation model of the probabilistic neural network (PNN). The

porosity and water saturation model of AI transformation, seismic multiattribute and PNN

were validated with the porosity and water saturation values of the wells data to determine

whether the porosity and water saturation models represent the wells data values.

Validation was carried out on AND-1 and AND-2 wells. The porosity and water

saturation value of the well data for AND-1 around 25.3 - 35.9% and 45 - 60%, and the

porosity and water saturation value of AND-2 around 11 - 35% and 15 - 82%. The

porosity value of AND-1 as a result of AI transformation is around 16 - 67%, the seismic

multiattribute about 11.5 - 27% and the PNN about 11.5 - 27%. The water saturation value

of AND-1 resulted from seismic multiattribute around 4 - 63% and PNN around 18 - 63%.

The porosity value of AND-2 transformed by AI around 52 - 72%, the seismic

multiattribute around 11 - 21.5% and the PNN around 11 - 21.5%. The water saturation

value of AND-2 result from the seismic multiattribute around 63 - 85% and PNN around

63 - 85%. Then, the multiattribute seismic and PNN methods obtained the correlation

value between the target parameter and the predicted parameter. The seismic

multiattribute porosity model has a correlation of 0.840836 and PNN has a correlation of

0.936868. The multiattribute seismic water saturation model has a correlation of 0.915254

and PNN has a correlation of 0.994566. The AI transformation porosity model has a

higher range than the wells data. The PNN method of porosity and water saturation model

has a fairly close range of values to wells data and has a higher correlation than the

multiattribute seismic method. Therefore, the porosity and water saturation model of the

PNN method is the best prediction model. Based on the PNN model, the field target zone

reservoir 'B' has an acoustic impedance value about 25384 – 26133 ((ft/s) * (g/cc)), a

porosity of 15 - 27% and a water saturation of 11 - 63%."

"Tomato fruit is one of agroproducts that has high-economic value in the world particularly inIndonesia. To compete in a worldwide market a tomato fruit producer must produce fresh or processedtomato with high quality. High quality tomato products are influenced by the application of post-harvesttreatment or processing. One of the vital process in post-harvest treatment is sortation. Mannualsortation introduces subjectivity (bias), inaccuracy, slowness and inconsistency. This needs moreintelligent sortation methods and tools that overcome the sort comings of manual process. ProbabilisticNeural Network (PNN) is one of Artificial Neural Network (ANM variants that can be to develop acomputer-based sortation engine for tomato fruits. However, to accelerate the sortation process, parallelcomputation is employed allowing multiple processors to execute simultaneously the sortation process.This research is aimed towards the implementation and testing of a parallel computation algorithm withPNN to perform sortation for tomato fruits. Some criteria being observed and tested include accuracy,total execution time, speedup, and efficiency compared to sequential algorithm. The experimental resultsshow that the application of parallel computation algorithm with PNN introduces the increase ofaccuracy, total execution time, speedup, and efficiency with the same accuracy."

"Telah dirancang prototype motor imagery dengan memanfaatkan perintah sinyal otak yang dihasilkan oleh Electroencephalography EEG . Sinyal EEG digunakan untuk memberikan informasi sinyal motorik. Bentuk unik dari sinyal EEG menggambarkan perintah untuk menggerakkan lengan. Pada kondisi lumpuh sekalipun, informasi motorik pada sinyal EEG masih akan ditemukan saat seseorang membayangkan menggerakkan lengannya. Dalam penelitian ini informasi motorik pada sinyal EEG digunakan sebagai umpan balik dengan menggabungkan 4 elektrode input F3, F4, FC5, FC6 . Akuisisi sinyal EEG menggunakan Emotiv EPOC portable. Probabilistic Neural Network PNN berfungsi sebagai pemrosesan sinyal. Fungsi ini digunakan untuk pengenalan sinyal motor imagery membayangkan gerakan lengan tangan . Karakteristik komputasi yang dilakukan oleh PNN secara parallel mampu mempersingkat waktu pemrosesan sinyal. Hasil pengolahan PNN adalah power maksimum sinyal mu, Power maksimum sinyal beta, frekuensi mu dan frekuensi beta. Kombinasi keempat fitur ini memberikan nilai akurasi yang cukup tinggi. Hasil percobaan menunjukkan bahwa akurasi untuk training rata-rata adalah 85,49 - 91,32 sedangkan nilai untuk testing 82,6 - 87,6 . Alat terapi yang digunakan nBETTER Upper Limb Feedback. Alat terapi akan aktif, bila nilai testing sinyal EEG lebih besar dari 80 . Ke depan, prototype motor imagery ini dapat dikembangkan sebagai alat terapi pasien stroke yang mampu mengurangi ketergantungan pada seorang fisioterapis saat proses terapi.

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A modeling arms post stroke therapy used command brain signals generated by Electroencephalography EEG has been designed. EEG signals used to provide motorics information. The unique form of signal EEG describe commands to move the limbs. On condition paralyzed, motorics information on the EEG signals will still be found when someone tried to move his limbs.

In this research, we aim used the motorics information on the EEG signals as neuro feedback with combine 4 input electrode F3, F4, FC5, FC6. EEG signal acquisition using the Emotiv EPOC portable. Probabilistic Neural Network PNN function as signal processing. This function was applied to the recognition research of motor imagery EEG signals imagining arms movement . The parallel computing characteristic of PNN not only improved the generation ability for network, but also shorted the operation time.

The result of PNN are maximum mu power, maximum beta power, mu frequency and beta frequency that provided value to calculate classification accuracy. The experimental results show that the accuracy for training on average is 85.49 91.32 while the value for testing is 82.6 87.6. Therapy tool used nBETTER Upper Limb Feedback. The therapeutic tool will be active, when the value of the EEG signal testing is greater than 80. In the future, this modeling post stroke therapy can be reduced dependency from physiotherapist."