Generative Models for Low-Dimensional Video Representation and Reconstruction

Finding compact representation of videos is an essential component in almost every problem related to video processing or understanding. In this paper, we propose a generative model to learn compact latent codes that can efficiently represent and reconstruct a video sequence from its missing or under-sampled measurements. We use a generative network that is trained to map a compact code into an image. We first demonstrate that if a video sequence belongs to the range of the pretrained generative network, then we can recover it by estimating the underlying compact latent codes. Then we demonstrate that even if the video sequence does not belong to the range of a pretrained network, we can still recover the true video sequence by jointly updating the latent codes and the weights of the generative network. To avoid overfitting in our model, we regularize the recovery problem by imposing low-rank and similarity constraints on the latent codes of the neighboring frames in the video sequence. We use our methods to recover a variety of videos from compressive measurements at different compression rates. We also demonstrate that we can generate missing frames in a video sequence by interpolating the latent codes of the observed frames in the low-dimensional space

Compressive Sensing with Tensorized Autoencoder

Deep networks can be trained to map images into a low-dimensional latent space. In many cases, different images in a collection are articulated versions of one another; for example, same object with different lighting, background, or pose. Furthermore, in many cases, parts of images can be corrupted by noise or missing entries. In this paper, our goal is to recover images without access to the ground-truth (clean) images using the articulations as structural prior of the data. Such recovery problems fall under the domain of compressive sensing. We propose to learn autoencoder with tensor ring factorization on the the embedding space to impose structural constraints on the data. In particular, we use a tensor ring structure in the bottleneck layer of the autoencoder that utilizes the soft labels of the structured dataset. We empirically demonstrate the effectiveness of the proposed approach for inpainting and denoising applications. The resulting method achieves better reconstruction quality compared to other generative prior-based self-supervised recovery approaches for compressive sensing

Generative Models for Low-Rank Video Representation and Reconstruction

Finding compact representation of videos is an essential component in almost every problem related to video processing or understanding. In this paper, we propose a generative model to learn compact latent codes that can efficiently represent and reconstruct a video sequence from its missing or under-sampled measurements. We use a generative network that is trained to map a compact code into an image. We first demonstrate that if a video sequence belongs to the range of the pretrained generative network, then we can recover it by estimating the underlying compact latent codes. Then we demonstrate that even if the video sequence does not belong to the range of a pretrained network, we can still recover the true video sequence by jointly updating the latent codes and the weights of the generative network. To avoid overfitting in our model, we regularize the recovery problem by imposing low-rank and similarity constraints on the latent codes of the neighboring frames in the video sequence. We use our methods to recover a variety of videos from compressive measurements at different compression rates. We also demonstrate that we can generate missing frames in a video sequence by interpolating the latent codes of the observed frames in the low-dimensional space

Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis

In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, including computers, mobile devices, and IoT devices. Conventional methods like heuristic-based or signature-based systems struggle against this type of malware, as it leaves no discernible traces on the system. In this research, we propose a simple and cost-effective obfuscated malware detection system through memory dump analysis, utilizing diverse machine-learning algorithms. The study focuses on the CIC-MalMem-2022 dataset, designed to simulate real-world scenarios and assess memory-based obfuscated malware detection. We evaluate the effectiveness of machine learning algorithms, such as decision trees, ensemble methods, and neural networks, in detecting obfuscated malware within memory dumps. Our analysis spans multiple malware categories, providing insights into algorithmic strengths and limitations. By offering a comprehensive assessment of machine learning algorithms for obfuscated malware detection through memory analysis, this paper contributes to ongoing efforts to enhance cybersecurity and fortify digital ecosystems against evolving and sophisticated malware threats. The source code is made open-access for reproducibility and future research endeavours. It can be accessed at https://bit.ly/MalMemCode.Comment: Accepted and Presented at IEEE-ICTP2023, Dhaka, Banglades

Pemberdayaan Masyarakat Melalui Program Life Skills Berbasis Potensi Lokal Untuk Meningkatkan Produktivitas Keluarga Di Desa Lero Kecamatan Suppa Kabupaten Pinrang

Adapun tujuan kajian ini adalah untuk mengetahui tingkat keberhasilan pemberdayaan masyarakat melalui program life skills berbasis potensi lokal di Desa Lero Kecamatan Suppa Kabupaten Pinrang. Jumlah subyek (peserta pelatihan) sebanyak 121 terdiri atas 60 peserta pelatihan pembuatan ikan asin (kering) dan 61 peserta pelatihan pembuatan minyak kelapa fermentasi. Metode yang digunakan yaitu workshop, penyuluhan, dan pelatihan. Teknik pengumpulan data yaitu pengamatan (penilaian proses), angket, dan wawancara. Teknik analisis data/evaluasi program digunakan analisis deskriptif. Hasil pelaksanaan program pemberdayaan masyarakat menunjukkan (1) jumlah peserta yang berpartisifasi aktif dalam program pemberdayaan melalui pelatihan pembuatan ikan asin (kering) dan minyak kelapa fermentasi yang higienis dan berkualitas sebanyak 121 orang, (2) peserta pelatihan telah memiliki pengetahuan dan keterampilan dalam membuat ikan asin (kering) dan minyak kelapa fermentasi yang higienis dan berkualitas, dan (3) terbentuknya 3 (tiga) kelompok USAha kecil produksi ikan asin (kering) dan minyak kelapa fermentasi di Desa Lero Kecamatan Suppa Kabupaten Pinrang. Direkomendasikan kepada pemerintah agar pemberdayaan masyarakat melalui program pelatihan life skills perlu ditingkatkan dalam rangka peningkatan taraf hidup masyarakat

Analysis of a U.S. Fashion Brand\u27s Outsourcing from Bangladesh: Problems and Proposed Solution

This research focused on exploring the role of apparel outsourcing from Bangladesh for the U.S. and the ways to reduce the effect of inefficiency factors to make this outsourcing long-term sustainable. In this research, a sequential mixed case study was conducted. Gary Teng and Jaramillo\u27s (2005) supplier evaluation model was used to evaluate the outsourcing performance of Bangladeshi and Vietnamese suppliers for a U.S. fashion brand (i.e., Phillips Van-Heusen (PVH). The supplier evaluation model has 20 factors under five clusters (i.e., delivery, flexibility, cost, quality & reliability). On the other part of the research, in-depth interviews were taken from three buyers in the Bangladeshi local office of PVH and three Bangladeshi suppliers from three different companies. The supplier evaluation scores revealed that the Bangladeshi supplier had a very competitive position compared to its competitor (Vietnamese supplier). Bangladesh scored 0.410 while Vietnam scored 0.307 out of 1.0. In terms of the five clusters, Bangladesh (0.106) has a great advantage in the cost cluster over Vietnam (0.281). Bangladesh (0.204) is also more advanced in the quality cluster than Vietnam (0.192). For the remaining three clusters, Vietnam has marginal advancement than Bangladesh. Specifically, the Bangladeshi supplier performed higher than expected in the capacity, negotiability, continuous improvement, and certification factors. The Bangladeshi supplier just met the buyer\u27s expectations in information sharing, customization, customer service, feeling of trust, and country\u27s political situation factors. This research gives an overview of the apparel business practice in Bangladesh to the unexplored part of the U.S. fashion industry

Effect of foliar application of urea and planofix on the foliage yield of coriander

Note: The authorship was changed on 23.02.2016 FROM Abdur Rakib, M. Kamruzzaman1*, Soyema Khatun1 and M. Moniruzzaman2  TO Abdur Rakib1*, M. Moniruzzaman2, M. Hasan3 and M.M. Rahman3. The authorship is changed due to request of the principal author to add two co-authors of his major professor and advisory committee member of postgraduate study and two co-authors are excluded from the authorship and acknowledged them for their contribution in the manuscript.______________________________________________________________Abstract:An experiment on coriander (Coriandrum sativum L.) was conducted at the experimental field of Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur during November 2012 to April 2013 to find out the suitable foliar doses and application frequency of urea and planofix (NAA). The experiment was laid out in factorial randomized complete block design with three replications. The treatment consisted of six foliar dozes viz. T1 (Tap water as control), T2 (0.10 % urea), T3 (0.25 % urea), T4 (0.40 % urea), T5 (5 ppm planofix) and T6 (10 ppm planofix) and three application frequencies viz. F1 [20 days after sowing (20 DAS)], F2 (30 DAS) and F3 (20 and 30 DAS). Maximum foliage yield (6.94 t/ha) was recorded in 10 ppm planofix coupled with its twice application at 20 and 30 DAS which was closely followed the foliage yield (6.33 t/ha) by 5 ppm planofix with the same application frequency. The foliage yield was increased with the increase in urea concentration. The highest foliage yield (5.37 t/ha) was also recorded from twice application of urea and planofix at 20 and 30 DAS, respectively. Planofix 10 ppm with its twice application at 20 and 30 DAS gave the highest benefit-cost of ration 2.51.Int. J. Agril. Res. Innov. & Tech. 5 (1): 40-44, June, 201