Reviewerly/exHarmony huggingface dataset:
Invisible Semi-Fragile Watermarking and Steganography of Digital Videos for Content Authentication and Data Hiding

In the current digital age, the piracy of digital media content has become rampant. Illegal distribution of movies and video clips on a global scale causes a significant loss to the media industry. To prevent such theft and distribution of content, we use a watermarking technique for videos where copyright information is hidden inside the original video in the form of a watermark video. Using a video as the watermark facilitates the user in hiding a large amount of information. The watermarking scheme used in this paper is semi-fragile, such that tampering of videos can be detected with relative ease. To improve the robustness of the watermark, we embed the watermark in frequency domain, where we use DWT+DCT+SVD to embed the watermark. The original video and watermark video are transformed by using the DWT and DCT sequentially, then the singular values of the watermark with some embedding strength are added to the singular values of the original video thus obtaining a watermarked video. Some detection tools which are available today cannot detect the watermark video inside the original video. This method equalizes the frames of the watermark and original video to reduce time consumed as well as complexity. The effects of various attacks on the watermarked video have been analyzed using the calculated PSNR values.
Deep Multiple Instance Learning for Zero-Shot Image Tagging

In-line with the success of deep learning on traditional recognition problem, several end-to-end deep models for zero-shot recognition have been proposed in the literature. These models are successful to predict a single unseen label given an input image, but does not scale to cases where multiple unseen objects are present. In this paper, we model this problem within the framework of Multiple Instance Learning (MIL). To the best of our knowledge, we propose the first end-to-end trainable deep MIL framework for the multi-label zero-shot tagging problem. Due to its novel design, the proposed framework has several interesting features: (1) Unlike previous deep MIL models, it does not use any off-line procedure (e.g., Selective Search or EdgeBoxes) for bag generation. (2) During test time, it can process any number of unseen labels given their semantic embedding vectors. (3) Using only seen labels per image as weak annotation, it can produce a bounding box for each predicted label. We experiment with large-scale NUS-WIDE dataset and achieve superior performance across conventional, zero-shot and generalized zero-shot tagging tasks.
Predicting Novel Views Using Generative Adversarial Query Network

The problem of predicting a novel view of the scene using an arbitrary number of observations is a challenging problem for computers as well as for humans. This paper introduces the Generative Adversarial Query Network (GAQN), a general learning framework for novel view synthesis that combines Generative Query Network (GQN) and Generative Adversarial Networks (GANs). The conventional GQN encodes input views into a latent representation that is used to generate a new view through a recurrent variational decoder. The proposed GAQN builds on this work by adding two novel aspects: First, we extend the current GQN architecture with an adversarial loss function for improving the visual quality and convergence speed. Second, we introduce a feature-matching loss function for stabilizing the training procedure. The experiments demonstrate that GAQN is able to produce high-quality results and faster convergence compared to the conventional approach
Equi-affine differential invariants for invariant feature point detection

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.
Recognition of Hand Gestures and Conversion of Voice for Betterment of Deaf and Mute People

Around 5% of people across the globe have difficulty in speaking or are unable to speak. So, to overcome this difficulty, sign language came into the picture. It is a method of non-verbal communication which is usually used by deaf and mute people. Another problem that arises with sign language is that people without hearing or speaking problems do not learn this language. This problem is severe as it creates a barrier between them. To resolve this issue, this paper makes use of computer vision and machine learning along with Convolutional Neural Network. The objective of this paper is to facilitate communication among deaf and mute and other people. For achieving this objective, a system is built to convert hand gestures to voice with gesture understanding and motion capture. This system will be helpful for deaf and mute people as it will increase their communication with other people.
Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units

In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due, in part, to computational complexity in parameter optimization. As an alternative, a learning algorithm to train a memory in real time is proposed, named the Markov chain Hebbian learning algorithm. The algorithm pursues efficient use in memory during training in that: 1) the weight matrix has ternary elements (-1, 0, 1) and 2) each update follows a Markov chain-the upcoming update does not need past weight values. The algorithm was verified by two proof-of-concept tasks: image (MNIST and CIFAR-10 datasets) recognition and multiplication table memorization. Particularly, the latter bases multiplication arithmetic on memory, which may be analogous to humans' mental arithmetic. The memory-based multiplication arithmetic feasibly offers the basis of factorization, supporting novel insight into memory-based arithmetic.
A multi-scale face detection algorithm based on improved SSD model

At present, the face detection model based on single convolutional neural network has the problem of the low accuracy of small-scale face detection when solving the problem of face detection at different scales. So, we propose an improved multi-scale face detection method based on SSD. The method adopts the feature-dense connection strategy to improve the network structure of the basic network in the SSD model, strengthening the information mobility between different convolutional layers and improving the feature description ability of the basic network. Then, the detection accuracy of small-scale faces is improved by introducing context information into shallow features. We evaluate our proposed architecture on WIDER FACE dataset, and it achieves a high average precision (AP) of 73.1%, 90% and 92% for different data sets ("difficult", "medium" and "simple") respectively, which is higher than several other methods.
Synthesizing simulation and field data of solar irradiance

Predicting the intensity and amount of sunlight as a function of location and time is an essential component in identifying promising locations for economical solar farming. Although weather models and irradiance data are relatively abundant, these have yet, to our knowledge, been hybridized on a continental scale. Rather, much of the emphasis in the literature has been on short‐term localized forecasting. This is probably because the amount of data involved in a more global analysis is prohibitive with the canonical toolkit, via the Gaussian process (GP). Here we show how GP surrogate and discrepancy models can be combined to tractably and accurately predict solar irradiance on time‐aggregated and daily scales with measurements at thousands of sites across the continental United States. Our results establish short‐term accuracy of bias‐corrected weather‐based simulation of irradiance, when realizations are available in real space‐time (eg, in future days), and provide accurate surrogates for smoothing in the more common situation where reliable weather data is not available (eg, in future years).
A Collaborative Virtual Reality System (VRS) with X3D Visualization for RAPID

A collaborative Virtual Reality System (VRS) software with X3D interactive visualization has been developed for the RAPID Code System. The software is named "VRS-RAPID".
An Improved Learning Framework for Covariant Local Feature Detection

Learning feature detection has been largely an unexplored area when compared to handcrafted feature detection. Recent learning formulations use the covariant constraint in their loss function to learn covariant detectors. However, just learning from covariant constraint can lead to detection of unstable features. To impart further, stability detectors are trained to extract pre-determined features obtained by hand-crafted detectors. However, in the process they lose the ability to detect novel features. In an attempt to overcome the above limitations, we propose an improved scheme by incorporating covariant constraints in form of triplets with addition to an affine covariant constraint. We show that using these additional constraints one can learn to detect novel and stable features without using pre-determined features for training. Extensive experiments show our model achieves state-of-the-art performance in repeatability score on the well known datasets such as Vgg-Affine, EF, and Webcam.
A Reliable and Accurate Multiple Choice Question Answering System for Due Diligence

The problem of answering multiple choice questions, based on the content of documents has been studied extensively in the machine learning literature. We pose the due diligence problem, where lawyers study legal contracts and assess the risk in potential mergers and acquisitions, as a multiple choice question answering problem, based on the text of the contract. Existing frameworks for question answering are not suitable for this task, due to the inherent scarcity and imbalance in the legal contract data available for training. We propose a question answering system which first identifies the excerpt in the contract which potentially contains the answer to a given question, and then builds a multi-class classifier to choose the answer to the question, based on the content of this excerpt. Unlike existing question answering systems, the proposed system explicitly handles the imbalance in the data, by generating synthetic instances of the minority answer categories, using the Synthetic Minority Oversampling Technique. This ensures that the number of instances in all the classes are roughly equal to each other, thus leading to more accurate and reliable classification. We demonstrate that the proposed question answering system outperforms the existing systems with minimal amount of training data.
Fuzzy C-Means in Lower Dimensional Space for Topics Detection on Indonesian Online News

One of the automated methods for textual data analysis is topic detection. Fuzzy C-Means is a soft clustering-based method for topic detection. Textual data usually has a high dimensional data, which make Fuzzy C-Means fails for topic detection. An approach to overcome the problem is transforming the textual data into lower dimensional space to identify the memberships of the textual data in clusters and use these memberships to generate topics from the high dimensional textual data in the original space. In this paper, we apply the Fuzzy C-Means in lower dimensional space for topic detection on Indonesian online news. Our simulations show that the Fuzzy C-Means gives comparable accuracies than nonnegative matrix factorization and better accuracies than latent Dirichlet allocation regarding topic interpretation in the form of coherence values.
Efficiently Factoring Polynomials Modulo p4

Polynomial factoring has famous practical algorithms over fields-- finite, rational and p-adic. However, modulo prime powers, factoring gets harder because there is non-unique factorization and a combinatorial blowup ensues. For example, x^2+p \bmod p^2 is irreducible, but x^2+px \bmod p^2 has exponentially many factors! We present the first randomized poly(\deg f, łog p) time algorithm to factor a given univariate integral f(x) modulo p^k, for a prime p and k łeq 4. Thus, we solve the open question of factoring modulo p^3 posed in (Sircana, ISSAC'17). Our method reduces the general problem of factoring f(x) mod p^k to that of \em root finding in a related polynomial E(y) \bmodłangle p^k, \varphi(x)^\ell \rangle for some irreducible \varphi \bmod p. We can efficiently solve the latter for kłe4, by incrementally transforming E(y). Moreover, we discover an efficient refinement of Hensel lifting to lift factors of f(x) \bmod p to those \bmod\ p^4 (if possible). This was previously unknown, as the case of repeated factors of f(x) \bmod p forbids classical Hensel lifting.
Large universe attribute based encryption enabled secured data access control for cloud storage with computation outsourcing

Cloud data access control is a very important issue especially when data are outsourced into a third-party cloud server. Recently, some researchers are working to enhance the trust level of cloud users by developing the secured access control to store the data into a third-party server. CP-ABE (Cip hertext-Policy Attribute-Based Encryption) is a very fruitful technology for such situation providing a capability of encrypted data access control. The existing mechanisms for outsourced data access control are mostly centralized, where a single key distribution center works as attribute authority and generates attributes along with corresponding keys for all users. In some cases, multiple key distribution centers used to generate user attributes along with user secret keys to make the system decentralized. However, all such mechanisms are based on small universe CP-ABE, where attribute set need to define in the initial setup phase and therefore public parameters and ciphertext size are increased linearly with attribute numbers in the system. As the attribute numbers in an access control system in the cloud should not be defined initially or if new attribute required to add time to time in the system and the existing attribute may be required to revoke form the system, then small universe enabled CP-ABE is not scalable and efficient compared to large universe system. In a large universe system, initially the size of parameters is large but it makes the size of ciphertext short in the system. In the paper, we proposed an access control scheme using large universe CP-ABE with user revocation and to make more efficient using decryption computation outsourcing into the cloud. The proposed system helps efficiently decrypt the data using a lightweight device. We have analyzed the security, scalability, and performance of our proposed scheme with existing approaches with respect to communication and computation cost.
On the cluster admission problem for cloud computing

Cloud computing providers must handle heterogeneous customer workloads for resources such as (virtual) CPU or GPU cores. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads' ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low average utilization of the cluster. In this paper, we propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design heuristic admission policies that estimate moments of each workload's distribution of future resource usage. Via simulations we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then evaluate how much further this can be improved with learned or elicited prior information and how to incentivize users to provide this information.
Object and Text-guided Semantics for CNN-based Activity Recognition

Many previous methods have demonstrated the importance of considering semantically relevant objects for carrying out video-based human activity recognition, yet none of the methods have harvested the power of large text corpora to relate the objects and the activities to be transferred into learning a unified deep convolutional neural network. We present a novel activity recognition CNN which co-learns the object recognition task in an end-to-end multitask learning scheme to improve upon the baseline activity recognition performance. We further improve upon the multitask learning approach by exploiting a text-guided semantic space to select the most relevant objects with respect to the target activities. To the best of our knowledge, we are the first to investigate this approach.
Accelerate DNN Performance with Sparse Matrix Compression in Halide

Machine learning nowadays is profoundly impacting every aspect of our lives. With the evolution of the machine learning, many techniques, such as deep learning, improve the accuracy and performance of machine learning. Deep learning is a set of ML techniques that use layers of transformation and consist of neural networks. The power consumption of deep learning becomes a serious problem when it comes to edge computing. One of the most computationally demanding operation of DNN is convolution which preserve the image arrangement and obtain partial image as an input feature. Our goal is to find an effective way for programmers to improve the performance of convolution operation. In this paper, we proposed the design of sparse matrix compression schedule primitives in Halide and find a way to improve convolution operation with im2col method. Halide is an image processing programming language that separates algorithm from its schedule. With this design, we can compress the result of im2col matrix to achieve performance improvements. In our experiments, results show that convolution operation can achieve 20X speedup with our implementation.
Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects

When performing Bayesian data analysis using a general linear mixed model, the resulting posterior density is almost always analytically intractable. However, if proper conditionally conjugate priors are used, there is a simple two-block Gibbs sampler that is geometrically ergodic in nearly all practical settings, including situations where p>n (Abrahamsen and Hobert, 2017). Unfortunately, the (conditionally conjugate) multivariate Gaussian prior on β does not perform well in the high-dimensional setting where p≫n. In this paper, we consider an alternative model in which the multivariate Gaussian prior is replaced by the normal-gamma shrinkage prior developed by Griffin and Brown (2010). This change leads to a much more complex posterior density, and we develop a simple MCMC algorithm for exploring it. This algorithm, which has both deterministic and random scan components, is easier to analyze than the more obvious three-step Gibbs sampler. Indeed, we prove that the new algorithm is geometrically ergodic in most practical settings.
A Software Framework and Datasets for the Analysis of Graph Measures on RDF Graphs

As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. In this work, we propose two resources: (i) a software framework (Resource URL of the framework: https://doi.org/10.5281/zenodo.2109469 ) able to acquire, prepare, and perform a graph-based analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets (Resource URL of the datasets: https://doi.org/10.5281/zenodo.1214433 ) from the LOD Cloud with values for 28 graph measures computed with the framework. We present a preliminary analysis based on the proposed resources and point out implications for synthetic dataset generators. Finally, we identify a set of measures, that can be used to characterize graphs in the Semantic Web.
High confidence detection for moving target in aerial video

The moving target detection and tracking in aerial video is a challenge task because of its moving background, smaller target sizes, lower resolution and limited onboard computing resources. In this study, a high confidence detection method based on background compensation and three-frame-difference method is designed, which can detect moving objects in a dynamic background accurately. First, the authors use local feature extraction and matching for image registration and demonstrate that speed-up robust feature key points are suitable for the stabilisation task. Then, they estimate the global camera motion parameters using affine transformation which are obtained by the random sample consensus algorithm. Finally, they detect moving object by three-frame-difference method. As the detection results of the frame-difference method generally exists ‘empty’ and noise, in order to select the two higher-quality differential images to perform the logic AND operation, they add image quality assessment to the three-frame-difference method to obtain more accurate moving objects. Moreover, the edge detection algorithm and morphological processing are integrated together to further boost the overall detecting performance. The extensive empirical evaluations on aerial videos demonstrate that the proposed detector is very promising for the various challenging scenarios.
Composing photomosaic images using clustering based evolutionary programming

Photomosaic images are a type of images consisting of various tiny images. In the past, many approaches have been proposed trying to automatically compose photomosaic images. To obtain a better visual sense and satisfy some commercial requirements, a constraint that a tiny image should not be repeatedly used many times is usually added. With the constraint, algorithms using greedy mechanism fail to solve it. In this paper, we present an approach called clustering based evolutionary programming to deal with the problem. Our new approach has a similar mechanism to that of evolutionary programming (we adopt mutation, selection and fitness evaluation) and uses the normalized color histogram information as the prior information. The two characteristics make our approach converges fast and performs well. In our experiment, the proposed algorithm is compared with the state of the art algorithms and software. The results indicate that our algorithm is able to generate higher quality photomosaic images.