TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN

Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach

Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a higher dimensional space. However, since these manifolds belong to non-Euclidean topological spaces, exploiting their structures is computationally expensive, especially when one considers the clustering analysis of massive amounts of data. To this end, we propose an efficient framework to address the clustering problem on Riemannian manifolds. This framework implements random projections for manifold points via kernel space, which can preserve the geometric structure of the original space, but is computationally efficient. Here, we introduce three methods that follow our framework. We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds. Experimental results demonstrate that our framework maintains the performance of the clustering whilst massively reducing computational complexity by over two orders of magnitude in some cases

The Influence of DER, RTO, CR, TATO, And EPS on Stock Prices in Wholesale Sub Sector Companies

This study analyzes the impact of DER, RTO, CR, TATO, and EPS on share prices of companies in the wholesale sub-sector listed on IDX in the 2016-2020 period. The object of this research are companies that are members of the wholesale sub-sector. The method used is a quantitative method with literature review and documentation techniques. The total number of companies are 47 companies, so there are 12 companies that meet the criteria made by the author. In this study using several analytical techniques in the form of classical assumption test, multiple linear regression test, multiple correlation test and hypothesis testing namely ttest and Ftest. The results of data analysis prove &nbsp

Matching Image Sets via Adaptive Multi Convex Hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

MOTIVASI BELAJAR DITINJAU DARI IKLIM KELAS PADA SISWA/SISWI JURUSAN IPS

The purpose of this research is to determine the relationship between classroom climate and learning motivation. The hypothesis submitted in this research is, there is a positive relationship between classroom climate and learning motivation, with assumption the more conducive classroom climate perceived by students, the higher learning motivation of students and vice versa. The research subjects used in this study were high school students in Methodist 2 with a major of Social Sciences as many as 182 people selected by the disproportionate stratified random sampling method. The method of analysis data used for this research is Product Moment correlation through SPSS 17 for Windows. The results of data analysis showed r = 0.575 and p = 0.000 (p <0.05) which means that there is a positive relationship between classroom climate and learning motivation. The results of this research show that the contribution (R2) given the classroom climate variable to learning motivation was 33.1 percent, the rest 66.9 percent was influenced by other factors not examined in this research. From the results of this research it can be concluded that the research hypothesis there is a positive relationship between classroom climate with learning motivation, can be accepted. Keywords: Classroom Climate; Learning Motivation; StudentsPenelitian ini bertujuan untuk mengetahui hubungan antara iklim kelas dan motivasi belajar. Hipotesis yang diajukan dalam penelitian ini adalah bahwa ada hubungan positif antara iklim kelas dan motivasi belajar, dengan asumsi iklim kelas yang lebih kondusif dirasakan oleh siswa, semakin tinggi motivasi belajar siswa dan sebaliknya. Subjek penelitian yang digunakan dalam penelitian ini adalah siswa/i SMA di Methodist 2 dengan jurusan IPS sebanyak 182 orang yang dipilih dengan metode disproportionate stratified random sampling. Analisis data yang digunakan adalah dengan menggunakan korelasi Product Moment melalui SPSS 17 untuk Windows. Hasil analisis data menunjukkan r = 0,575 dan p = 0,000 (p <0,05) yang menunjukkan bahwa ada hubungan positif antara iklim kelas dan motivasi belajar. Hasil penelitian ini menunjukkan bahwa kontribusi (R2) yang diberikan variabel iklim kelas terhadap motivasi belajar adalah 33,1 persen, sisanya 66,9 persen dipengaruhi oleh faktor lain yang tidak diteliti. Dari hasil penelitian ini dapat disimpulkan bahwa hipotesis penelitian ada hubungan positif antara iklim kelas dengan motivasi belajar, dapat diterima. Kata kunci: Iklim Kelas; Motivasi Belajar; Sisw

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126

Does Interference Exist When Training a Once-For-All Network?

The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Once trained, a subnet can be derived from the supernet (both architecture and trained weights) and deployed directly to the target platform with little to no retraining or fine-tuning. To train the subnet population, OFA uses a novel training method called Progressive Shrinking (PS) which is designed to limit the negative impact of interference during training. It is believed that higher interference during training results in lower subnet population accuracies. In this work we take a second look at this interference effect. Surprisingly, we find that interference mitigation strategies do not have a large impact on the overall subnet population performance. Instead, we find the subnet architecture selection bias during training to be a more important aspect. To show this, we propose a simple-yet-effective method called Random Subnet Sampling (RSS), which does not have mitigation on the interference effect. Despite no mitigation, RSS is able to produce a better performing subnet population than PS in four small-to-medium-sized datasets; suggesting that the interference effect does not play a pivotal role in these datasets. Due to its simplicity, RSS provides a $1.9\times$ reduction in training times compared to PS. A $6.1\times$ reduction can also be achieved with a reasonable drop in performance when the number of RSS training epochs are reduced. Code available at https://github.com/Jordan-HS/RSS-Interference-CVPRW2022.Comment: Accepted to CVPR Embedded Vision Workshop 202

Kajian Aktivitas Antikanker Ekstrak Teripang (Bohadschia Argus) Pada Cell Line Kanker Payudara T47d

Kanker merupakan suatu keadaan yang ditandai dengan pertumbuhan sel atau jaringan yang tidak terkontrol akibatnya sel tidak dapat melakukan fungsinya dengan baik. Hal ini mempengaruhi beberapa hal seperti proliferasi sel, siklus sel, apoptosis sel, diferensiasi sel dan metastasis. Sel kanker dapat menekan apoptosis dengan mengekspresi protein Antiapoptosis seperti Bcl-2 maupun dengan menurunkan regulasi atau mutasi protein Proapoptosis Bcl-2 seperti BAX. Salah satu alternatif pengobatan kanker yaitu dengan memanfaatkan biota laut seperti teripang. Teripang jenis Bohadshia argus memiliki nilai ekonomi yang tinggi dan juga memiliki kandungan bioaktif seperti Tryterpene Glycoside, Glikosaminoglikan maupun Glikoprotein yang berpotensi sebagai antikanker dengan menghambat perkembangan siklus sel, proliferasi sel dan menginduksi terjadinya apoptosis baik secara in vitro maupun in vivo. Tujuan penelitian ini adalah untuk mengetahui aktivitas antikanker ekstrak teripang B. argus pada cell line kanker payudara T47D. Teripang yang digunakan dalam penelitian ini diperoleh dari perairan Desa Kamal Kabupaten Seram Bagian Barat Provinsi Maluku. Ekstraksi teripang menggunakan dua jenis ekstraksi yaitu air dan metanol. Komponen Kimia ekstrak air dianalisis B. argus menggunakan LC-MS dan SDS-PAGE, sedangkan ekstrak metanol B.argus dianalisis menggunakan GC-MS dan SDS-PAGE. Nilai IC50 ekstrak air dan metanol teripang pada cell line Kanker Payudara T47D dilakukan dengan dosis 0, 50, 100, 250 dan 500 μg/mL. Analisis sitotosik sel dilakukan menggunakan WST-1 assay. IC50 yang didapatkan untuk ekstrak air adalah 480 μg/mL, sedangkan IC50 untuk ekstrak metanol adalah 146 μg/mL. IC50 yang diperoleh digunakan untuk penelitian tahap berikutnya. Uji aktivitas antikanker ekstrak teripang pada cell line kanker payudara T47D dilakukan dengan perlakuan kontrol (tanpa perlakuan ekstrak), cisplatin (obat anti kanker), kelompok perlakuan ekstrak air dosis 480 μg/mL dan ekstrak metanol dosis 146 μg/mL. Semua kelompok perlakuan dikultur pada media RPMI, suhu 37ºC, kadar CO2 5%, diinkubasi selama 24 jam dan 48 jam. Pengamatan Jumlah apoptosis sel, siklus Sel, proliferasi sel dan ekspresi Bcl-2 dilakuan dengan menggunakan flow citometry. Hasil penelitian menunjukan bahwa protein dominan yang ditemukan pada Ekstrak Metanol adalah protein dengan berat molekul 55 kDa, sedangkan protein dominan yang ditemukan pada Ekstrak air adalah protein dengan berat molekul 55 kDa, 70 kDa, dan 80 kDa. Berdasarkan hasil LC-MS dan GC-MS ekstrak teripang jenis B. argus memiliki senyawa Tryterpene Glycoside dan Ethyl Acetate. Ekstrak air teripang dengan dosis 480 μg/mL dan ekstrak metanol teripang dengan dosis 146 μg/mL mampu menginduksi apoptosis, menghambat fase transisi G1 ke fase S, menghambat proliferasi dan menurunkan ekspresi protein Bcl-2 pada cell line kanker payudara T47D. Dari hasil penelitian ini dapat disimpulkan bahwa ekstrak teripang B. argus memilki aktivitas antikanker pada cell line kanker payudara T47D

SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions

High-quality training data is essential for enhancing the robustness of object detection models. Within the maritime domain, obtaining a diverse real image dataset is particularly challenging due to the difficulty of capturing sea images with the presence of maritime objects , especially in stormy conditions. These challenges arise due to resource limitations, in addition to the unpredictable appearance of maritime objects. Nevertheless, acquiring data from stormy conditions is essential for training effective maritime detection models, particularly for search and rescue, where real-world conditions can be unpredictable. In this work, we introduce SafeSea, which is a stepping stone towards transforming actual sea images with various Sea State backgrounds while retaining maritime objects. Compared to existing generative methods such as Stable Diffusion Inpainting~\cite{stableDiffusion}, this approach reduces the time and effort required to create synthetic datasets for training maritime object detection models. The proposed method uses two automated filters to only pass generated images that meet the criteria. In particular, these filters will first classify the sea condition according to its Sea State level and then it will check whether the objects from the input image are still preserved. This method enabled the creation of the SafeSea dataset, offering diverse weather condition backgrounds to supplement the training of maritime models. Lastly, we observed that a maritime object detection model faced challenges in detecting objects in stormy sea backgrounds, emphasizing the impact of weather conditions on detection accuracy. The code, and dataset are available at https://github.com/martin-3240/SafeSea.Comment: Accepted to WACV 2024 workshop on Maritime Computer Visio

Kepentingan Dan Implementasi Green Construction Dari Sisi Pandang Kontraktor

Kegiatan konstruksi berdampak negatif terhadap lingkungan dengan adanya pengurangan lahan bebas/hijau, penggunaan material yang sebagian besar diperoleh dari sumber daya alam, penggunaan alat berat dan transportasi selama proses konstruksi yang menyebabkan polusi. Dampakdampak negatif ini menjadi perhatian masyarakat sehingga perencana dan ahli konstruksi mengembangkan konsep sustainable construction yang salah satunya dikenal dengan konsep green construction. Dalam konsep ini, kontraktor tidak hanya bertanggung jawab untuk mendirikan bangunan yang kuat dan efisien saja, tetapi juga perlu memperhatikan lingkungan. Penelitian ini dimaksudkan untuk menganalisa kepentingan dan implementasi green construction oleh kontraktor mulai dari pekerjaan persiapan hingga finishing. Metode pengolahan data menggunakan statistik untuk mengetahui nilai ratarata dan varians dari setiap poin-poin green construction untuk setiap tahap pekerjaan, yang kemudian dibuat ranking untuk setiap poin tersebut. Dari hasil penelitian ini diketahui bahwa secara keseluruhan kontraktor di Surabaya sudah memperhatikan dan mengimplementasikan green construction dengan baik