Reviewerly/exHarmony huggingface dataset:
Forensic Recovery of SQL Server Database: Practical Approach

Database forensics is becoming more important for investigators with the increased use of the information system. Although various database forensic methods such as log analysis and investigation model development have been studied, among the database forensic methods, recovering deleted data is a key technique in database investigation for DB tampering and anti-forensics. Previous studies mainly focused on transaction or journal log to recover deleted data, but if logs are set to be deleted periodically or logs containing critical evidence are overwritten by new logs, the log-based recovery method can not be used practically. For this reason, an engine-based recovery method that analyzes data file at a raw level has been also introduced. There is research to recover small-sized databases such as SQLite and EDB, but there is no prior work describing the structure of data file and technology to recover deleted data of large databases used by enterprises or large organizations. In this context, we investigate Microsoft SQL Server (MSSQL), which is one of the most used large databases. Our method focuses on a storage engine of MSSQL. Through analyzing the storage engine, we identify the internal structure of MSSQL data files and the storage mechanism. Based on these findings, a method to recover tables and records is presented by empirical examination. It is compatible with various versions of MSSQL because it accesses data at the raw level. Our proposed method is verified by a comparative experiment with forensic tools implemented to recover deleted MSSQL data. The experimental results show that our method recovers all deleted records from the unallocated area. It recovers all data types including multimedia data, called Large Objects (LOB) in the database field. To contribute digital forensic community, we also provide the source code of the implementation; it facilitates the knowledge sharing of database forensics.
Development of an Android-Based for Math E-Module by using Adobe Flash Professional CS6 for Grade X Students of Senior High School

Abstract Learning in the industrial revolution 4.0 requires the optimal use of technology in the learning, one of which is by learning to use mathematical e-modules that can be operated on smartphones using an android operating system. This study aimed to create valid, practical, and effective android-based e-modules. This research was research and development (R&amp;D) with the Plomp development model, which consists of the preliminary research phase, the development or prototyping phase, and the assessment phase. The subjects of this study were students of grade X in SMA N 5 Pekanbaru. The results of this study were the android-based e-module was valid, practical, and effective. The validity of e-modules was classified based on the results of the experts' evaluation. The practicality of the e-module was classified based on the questionnaire responses of teachers and students, who can use the e-module well, can help students in understanding the material, and interested in using it. The effectiveness of e-modules was classified by the percentage of student's completeness learning after using e-modules more than 70%.
Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks

In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks.The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions.We test the method on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien.Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation.The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted.In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.
Dual Camera-Based Supervised Foreground Detection for Low-End Video Surveillance Systems

Deep learning-based algorithms showed promising prospects in the computer vision domain. However, their deployment in real-time systems is challenging due to their computational complexity, high-end hardware prerequisites, and the amount of annotated data for training. This paper proposes an efficient foreground detection (EFDNet) algorithm based on deep spatial features extracted from an RGB input image using VGG-16 convolutional neural networks (CNN). The VGG-16 CNN is modified by concatenated residual (CR) blocks to learn better global contextual features and recover lost feature information due to several convolution operations. Anew upsampling network is designed using bilinear interpolation sandwiched between 3 × 3 convolutions to upsample and refine feature maps for pixel-wise prediction. This helps to propagate loss errors from the upsampling network during backpropagation. The experiments showed the effectiveness of the EFDNet in outperforming top-ranked foreground detection algorithms. EFDNet trains faster on low-end hardware and demonstrated promising results with a minimum of 50 training frames with binary ground-truth.
Improved Conditional VRNNs for Video Prediction

Predicting future frames for a video sequence is a challenging generative modeling task. Promising approaches include probabilistic latent variable models such as the Variational Auto-Encoder. While VAEs can handle uncertainty and model multiple possible future outcomes, they have a tendency to produce blurry predictions. In this work we argue that this is a sign of underfitting. To address this issue, we propose to increase the expressiveness of the latent distributions and to use higher capacity likelihood models. Our approach relies on a hierarchy of latent variables, which defines a family of flexible prior and posterior distributions in order to better model the probability of future sequences. We validate our proposal through a series of ablation experiments and compare our approach to current state-of-the-art latent variable models. Our method performs favorably under several metrics in three different datasets.
IDGCP: Image Dehazing Based on Gamma Correction Prior

This paper introduces a novel and effective image prior, i.e., gamma correction prior (GCP), which leads to an efficient image dehazing method, i.e., IDGCP. A step-by-step procedure of the proposed IDGCP is as follows. First, an input hazy image is preprocessed by the proposed GCP, resulting in a homogeneous virtual transformation of the hazy image. Then, from the original input hazy image and its virtual transformation, the depth ratio is extracted based on atmospheric scattering theory. Finally, a “global-wise” strategy and a vision indicator are employed to recover the scene albedo, thus restoring the hazy image. Unlike other image dehazing methods, IDGCP is based on the “global-wise” strategy, and it only needs to determine one unknown constant without any refining process to attain a high-quality restoration, thereby leading to significantly reduced processing time and computation cost. Each step of IDGCP is tested experimentally to validate its robustness. Moreover, a series of experiments are conducted on a number of challenging images with IDGCP and other state-of-the-art technologies, demonstrating the superiority of IDGCP over the others in terms of restoration quality and implementation efficiency.
Randomness improvement of chaotic maps for image encryption in a wireless communication scheme using PIC-microcontroller via Zigbee channels

Recently, a lot of research has been done in chaotic cryptography field using different kinds of chaotic systems, like chaotic maps, which are being considered as one of the secure and efficient methods to protect confidential information. This article highlights that the main cryptography requirements demand that the new embedded cryptosystems have to be more efficient and secure, it means that they must be faster and offer greater security. For instance, the new cryptosystems require to be compatible with the new telecommunication protocols and, in addition, to be efficient in energy consumption. In this manner, this article introduces a process to improve the randomness of five chaotic maps that are implemented on a PIC-microcontroller. The improved chaotic maps are tested to encrypt digital images in a wireless communication scheme, particularly on a machine-to-machine (M2M) link, via ZigBee channels. We show that function mod 255 improves the randomness of the pseudo-random number generators (PRNG), which is verified performing NIST SP 800-22 statistical tests, histograms, phase-plane analysis, entropy, correlation of adjacent pixels, differential attacks, and using digital images of size 256 × 256 and 512 × 512 pixels. A comparative analysis is presented versus related works that also use chaotic encryption and classic algorithms, such as: AES, DES, 3DES and IDEA. The security analysis confirms that the proposed process to improve the randomness of chaotic maps, is appropriate to implement an encryption scheme that is secure and robust against several known attacks and other statistical tests. Finally, it was experimentally verified that this chaotic encryption scheme can be used in practical applications such as M2M and Internet of things (IoT).
Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks

In this paper, we study fast training of adversarially robust models. From the analyses of the state-of-the-art defense method, i.e., the multi-step adversarial training [34], we hypothesize that the gradient magnitude links to the model robustness. Motivated by this, we propose to perturb both the image and the label during training, which we call Bilateral Adversarial Training (BAT). To generate the adversarial label, we derive an closed-form heuristic solution. To generate the adversarial image, we use one-step targeted attack with the target label being the most confusing class. In the experiment, we first show that random start and the most confusing target attack effectively prevent the label leaking and gradient masking problem. Then coupled with the adversarial label part, our model significantly improves the state-of-the-art results. For example, against PGD100 white-box attack with cross-entropy loss, on CIFAR10, we achieve 63.7% versus 47.2%; on SVHN, we achieve 59.1% versus 42.1%. At last, the experiment on the very (computationally) challenging ImageNet dataset further demonstrates the effectiveness of our fast method.
Learning to Adapt Invariance in Memory for Person Re-identification

This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among target samples. This paper investigates into the intra-domain variations of the target domain and proposes a novel adaptation framework w.r.t three types of underlying invariance, i.e., Exemplar-Invariance, Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar memory is introduced to store features of samples, which can effectively and efficiently enforce the invariance constraints over the global dataset. We further present the Graph-based Positive Prediction (GPP) method to explore reliable neighbors for the target domain, which is built upon the memory and is trained on the source samples. Experiments demonstrate that 1) the three invariance properties are complementary and indispensable for effective domain adaptation, 2) the memory plays a key role in implementing invariance learning and improves the performance with limited extra computation cost, 3) GPP can facilitate the invariance learning and thus significantly improves the results, and 4) our approach produces new state-of-the-art adaptation accuracy on three re-ID large-scale benchmarks.
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images “In the Wild”

We present the first method to perform automatic 3D pose, shape and texture capture of animals from images acquired in-the-wild. In particular, we focus on the problem of capturing 3D information about Grevy's zebras from a collection of images. The Grevy's zebra is one of the most endangered species in Africa, with only a few thousand individuals left. Capturing the shape and pose of these animals can provide biologists and conservationists with information about animal health and behavior. In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other. To overcome these challenges, we integrate the recent SMAL animal model into a network-based regression pipeline, which we train end-to-end on synthetically generated images with pose, shape, and background variation. Going beyond state-of-the-art methods for human shape and pose estimation, our method learns a shape space for zebras during training. Learning such a shape space from images using only a photometric loss is novel, and the approach can be used to learn shape in other settings with limited 3D supervision. Moreover, we couple 3D pose and shape prediction with the task of texture synthesis, obtaining a full texture map of the animal from a single image. We show that the predicted texture map allows a novel per-instance unsupervised optimization over the network features. This method, SMALST (SMAL with learned Shape and Texture) goes beyond previous work, which assumed manual keypoints and/or segmentation, to regress directly from pixels to 3D animal shape, pose and texture.
An overview of recent multi-view clustering

With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more useful, however more complex. Since traditional clustering algorithms cannot handle such data, multi-view clustering has become a research hotspot. In this paper, we review some of the latest multi-view clustering algorithms, which are reasonably divided into three categories. To evaluate their performance, we perform extensive experiments on seven real-world data sets. Three mainstream metrics are used, including clustering accuracy, normalized mutual information and purity. Based on the experimental results and a large number of literature reading, we also discuss existing problems in current multi-view clustering and point out possible research directions in the future. This research provides some insights for researchers in related fields and may further promote the development of multi-view clustering algorithms.
Evaluation of machine learning methods for lithology classification using geophysical data

Specific computational tools assist geologists in identifying and sorting lithologies in well surveys and reducing operational costs and practical working time. This allows for the management of professional output, the efficient interpretation of data, and completion of scientific research on data collected in geologically distinct regions. Machine learning methods and applications integrate large sets of information with the goal of efficient pattern recognition and the capability of leveraging accurate decision making. The objective of this study is to apply machine learning methods to the supervised classification of lithologies using multivariate log parameter data from offshore wells from the International Ocean Discovery Program (IODP). According to the analysis of the lithologies proposed in the IODP Expeditions and for the application of our methods, the lithologies were divided into four groups. The IODP Expeditions were organized into four templates for better results in analyzing the set of expeditions and practical application of the methods. The templates were submitted to training, validation, and testing by multilayer perceptron (MLP), decision tree, random forest, and support vector machine (SVM) methods. The evaluation was randomly divided into training (70%), validation (10%), and testing (20%) using the classification methods as an evaluation of the results. In the results, it was observed that Template1 (IODP Expedition 362) obtained better results with the MLP method, Template2 (IODP Expeditions 354, 355, and 359) and Template3 (IODP Expeditions 354, 355, 359, and 362) obtained better results with the random forest method with greater than 80.00% accuracy. For cross-validation, the random forest method performed well in all scenarios. In the practical template, the G2 group obtained a better result with the MLP method with an average accuracy above 85.00%. It is expected that machine learning methods can help improve the study of geology with accurate and rapid answers related to interpreting collected data in different study regions.
Deep Residual Learning based Enhanced JPEG Compression in the Internet of Things

With the development of big data and network technology, there are more use cases, such as edge computing, that require more secure and efficient multimedia big data transmission. Data compression methods can help achieving many tasks like providing data integrity, protection, as well as efficient transmission. Classical multimedia big data compression relies on methods like the spatial-frequency transformation for compressing with loss. Recent approaches use deep learning to further explore the limit of the data compression methods in communication constrained use cases like the Internet of Things (IoT). In this article, we propose a novel method to significantly enhance the transformation-based compression standards like JPEG by transmitting much fewer data of one image at the sender's end. At the receiver's end, we propose a two-step method by combining the state-of-the-art signal processing based recovery method with a deep residual learning model to recover the original data. Therefore, in the IoT use cases, the sender like edge device can transmit only 60% data of the original JPEG image without any additional calculation steps but the image quality can still be recovered at the receiver's end like cloud servers with peak signal-to-noise ratio over 31 dB.
Meltdown

Lessons learned from Meltdown's exploitation of the weaknesses in today's processors.
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy

Data augmentation is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for high-level vision tasks (e.g., classification) and few are studied for low-level vision tasks (e.g., image restoration). In this paper, we provide a comprehensive analysis of the existing augmentation methods applied to the super-resolution task. We find that the methods discarding or manipulating the pixels or features too much hamper the image restoration, where the spatial relationship is very important. Based on our analyses, we propose CutBlur that cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of CutBlur is to enable a model to learn not only "how" but also "where" to super-resolve an image. By doing so, the model can understand "how much", instead of blindly learning to apply super-resolution to every given pixel. Our method consistently and significantly improves the performance across various scenarios, especially when the model size is big and the data is collected under real-world environments. We also show that our method improves other low-level vision tasks, such as denoising and compression artifact removal.
Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

Students' feedback is an effective mechanism that provides valuable insights about teaching-learning process. Handling opinions of students expressed in reviews is a quite labour-intensive and tedious task as it is typically performed manually by the human intervention. While this task may be viable for small-scale courses that involve just a few students' feedback, it is unpractical for large-scale cases as it applies to online courses in general, and MOOCs, in particular. Therefore, to address this issue, we propose in this paper a framework to automatically analyzing opinions of students expressed in reviews. Specifically, the framework relies on aspect-level sentiment analysis and aims to automatically identify sentiment or opinion polarity expressed towards a given aspect related to the MOOC. The proposed framework takes advantage of weakly supervised annotation of MOOC-related aspects and propagates the weak supervision signal to effectively identify the aspect categories discussed in the unlabeled students' reviews. Consequently, it significantly reduces the need for manually annotated data which is the main bottleneck for all deep learning techniques. A large-scale real-world education dataset containing around 105k students' reviews collected from Coursera and a dataset comprising of 5989 students' feedback in traditional classroom settings are used to perform experiments. The experimental results indicate that our proposed framework attains inspiring performance with respect to both the aspect category identification and the aspect sentiment classification. Moreover, the results suggest that the framework leads to more accurate results than the expensive and labour-intensive sentiment analysis techniques relying heavily on manually labelled data.
Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion

The main aim of this paper is to develop a framelet representation of the tensor nuclear norm for third-order tensor completion. In the literature, the tensor nuclear norm can be computed by using tensor singular value decomposition based on the discrete Fourier transform matrix, and tensor completion can be performed by the minimization of the tensor nuclear norm which is the relaxation of the sum of matrix ranks from all Fourier transformed matrix frontal slices. These Fourier transformed matrix frontal slices are obtained by applying the discrete Fourier transform on the tubes of the original tensor. In this paper, we propose to employ the framelet representation of each tube so that a framelet transformed tensor can be constructed. Because of framelet basis redundancy, the representation of each tube is sparsely represented. When the matrix slices of the original tensor are highly correlated, we expect the corresponding sum of matrix ranks from all framelet transformed matrix frontal slices would be small, and the resulting tensor completion can be performed much better. The proposed minimization model is convex and global minimizers can be obtained. Numerical results on several types of multi-dimensional data (videos, multispectral images, and magnetic resonance imaging data) have tested and shown that the proposed method outperformed the other testing methods.