Reviewerly/exHarmony huggingface dataset:
Compressed Animated Light Fields with Real-Time View-Dependent Reconstruction

We propose an end-to-end solution for presenting movie quality animated graphics to the user while still allowing the sense of presence afforded by free viewpoint head motion. By transforming offline rendered movie content into a novel immersive representation, we display the content in real-time according to the tracked head pose. For each frame, we generate a set of cubemap images per frame (colors and depths) using a sparse set of of cameras placed in the vicinity of the potential viewer locations. The cameras are placed with an optimization process so that the rendered data maximise coverage with minimum redundancy, depending on the lighting environment complexity. We compress the colors and depths separately, introducing an integrated spatial and temporal scheme tailored to high performance on GPUs for Virtual Reality applications. A view-dependent decompression algorithm decodes only the parts of the compressed video streams that are visible to users. We detail a real-time rendering algorithm using multi-view ray casting, with a variant that can handle strong view dependent effects such as mirror surfaces and glass. Compression rates of 150:1 and greater are demonstrated with quantitative analysis of image reconstruction quality and performance.
Exploiting an HMAC-SHA-1 Optimization to Speed up PBKDF2

PBKDF2 [27] is a well-known password-based key derivation function.In order to slow attackers down, PBKDF2 introduces CPUintensive operations based on an iterated pseudorandom function (in our case HMAC-SHA-1).If we are able to speed up a SHA-1 or an HMAC implementation, we are able to speed up PBKDF2-HMAC-SHA-1.This means that a performance improvement might be exploited by regular users and attackers.Interestingly, suggests that it is possible to precompute first message block of a keyed hash function only once, store such a value and use it each time is needed [43].Therefore the computation of first message block does not contribute to slowing attackers down, thus making the computation of second message block crucial.In this paper we focus on the latter, investigating the possibility to avoid part of the HMAC-SHA-1 operations.We show that some CPU-intensive operations may be replaced with a set of equivalent, but less onerous, instructions.We identify useless XOR operations exploiting and extending Intel optimizations [26], and applying the Boyar-Peralta heuristic [12].In addition, we provide an alternative method to compute the SHA-1 message scheduling function and explain why attackers might exploit these findings to speed up a brute force attack.
Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN

Blind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy, respectively. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO scheme simultaneously. By restructuring the received signals, we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy. Then, a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features, and a minimal weighted norm-1 distance metric is utilized for decision making. In particular, our approach can identify additional MIMO schemes, which most previous works have not considered, and is compatible with both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. Simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.
Evaluating Management Actions to Mitigate Disruptive Scenario Impacts in an E-Commerce Systems Integration Project

E-commerce and other software-related projects are subject to disruptions that result in schedule delays, cost overruns, and degraded quality. Systems integration projects must address and mitigate the most disruptive scenarios resulting in the potential to miss predefined programmatic targets. In this paper, we present a method that aids in the selection of management actions that mitigate the effects of two concurrent modes of project disruption: 1) changes in the emphasis placed by decision-makers on project management success criteria, and 2) changes to the schedule network and its constituent activities. We identify disruptive emergent and future conditions and quantify those scenarios that are the most disruptive to the project. We then evaluate several management actions in mitigating project disruptions, accounting for benefits and costs. A systems integration of an e-commerce system with multiple functionalities is described in an example.
IterGANs: Iterative GANs to learn and control 3D object transformation

We are interested in learning visual representations which allow for 3D manipulations of visual objects based on a single 2D image. We cast this into an image-to-image transformation task, and propose Iterative Generative Adversarial Networks (IterGANs) which iteratively transform an input image into an output image. Our models learn a visual representation that can be used for objects seen in training, but also for never seen objects. Since object manipulation requires a full understanding of the geometry and appearance of the object, our IterGANs learn an implicit 3D model and a full appearance model of the object, which are both inferred from a single (test) image. Two advantages of IterGANs are that the intermediate generated images can be used for an additional supervision signal, even in an unsupervised fashion, and that the number of iterations can be used as a control signal to steer the transformation. Experiments on rotated objects and scenes show how IterGANs help with the generation process.
From similarity perspective: a robust collaborative filtering approach for service recommendations

Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions.We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.
A Multigranularity Forensics and Analysis Method on Privacy Leakage in Cloud Environment

The problem of cloud forensics aims at processing multidimensional, massive, and heterogeneous data to collect and recover evidence in cloud environment. Existing approaches focus on excavating all suspicious behaviors from data and ignore privacy leakage details and behavioral characteristics. In order to conduct privacy leakage analysis in cloud specifically, we propose a multigranularity privacy leakage forensics method to analyze privacy violations caused by malware in cloud environment. By simulating the target virtual machine environment, our method can detect privacy leakage behaviors of malware without touching user's privacy data. We combine continuous RAM mirroring technology and dynamic taint analysis to assist the forensics investigation. To demonstrate the efficacy and utility of our method, we evaluate its performance with some real-world malware samples by comparing with some state-of-the-art malware analysis systems. Experimental results indicate that our method can identify more privacy leakage paths and behaviors.
Understanding information needs of agile teams to improve requirements communication

For traditional software development processes, we have detailed empirical knowledge about role-specific information needs that have to be satisfied in requirements documents. Particularly, we gained detailed knowledge about priorities of a set of traditional requirements artifacts in a series of empirical studies from the viewpoint of software architects, usability engineers, and testers. However, little is known about such information needs from the viewpoint of agile team members. Motivated by the widespread use of agile development approaches and existing challenges faced by requirements engineering in agile projects, our current research aims at investigating whether agile teams could also benefit from the empirical results regarding information needs that we gained in our previous research. We envision the development of guidelines based on empirical data in order to support the extensive collaboration and communication necessary in agile projects. In this paper, we present our research agenda as well as first results of our work. This includes a mapping analyis by the authors where the scope and content of the outcome of agile RE practices (e.g., user stories) was compared to previously investigated traditional RE artifacts. This analysis shows that artifacts resulting from agile practices cover key requirements information, but at a lower level of detail than in traditional RE artifacts. Based on these first findings, we conducted interviews and performed surveys with agile team members in order to better understand current challenges and the possible impacts of these challenges. The results reveal that, for instance, less detailed documentation leads to problems such as communication lapses, rework and product inconsistencies in agile settings. We also gained first insights regarding the relevance of agile RE practices from the viewpoint of different agile team members. Finally, we elaborated initial guidelines to be used and evaluated within the scope of industry and research projects in the near future.
Skew quasi cyclic codes over 𝔽q + v𝔽q

In this work, skew quasi cyclic codes over [Formula: see text], where [Formula: see text] are considered. The generating set for one generator skew quasi cyclic codes over [Formula: see text] is also determined. We discuss a sufficient condition for one generator skew quasi cyclic codes to be free. Furthermore, a BCH type bound is given for free one generator skew quasi cyclic codes. We investigate the dual of skew quasi cyclic codes over [Formula: see text]. We give a necessary and sufficient condition for skew cyclic codes over [Formula: see text] to contain its dual. Moreover, we construct quantum codes from skew cyclic codes over [Formula: see text]. By using computer search we give some examples about skew quasi cyclic codes and list some quantum parameters in the table.
Sampling and Super Resolution of Sparse Signals Beyond the Fourier Domain

Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super-resolution. In many cases, however, Fourier domain may not be the natural choice. For example, in holography, low-pass projections of sparse signals are obtained in the Fresnel domain. Similarly, time-varying system identification relies on low-pass projections on the space of linear frequency modulated signals. In this paper, we study the recovery of sparse signals from low-pass projections in the Special Affine Fourier Transform domain (SAFT). The SAFT parametrically generalizes a number of well known unitary transformations that are used in signal processing and optics. In analogy to the Shannon's sampling framework, we specify sampling theorems for recovery of sparse signals considering three specific cases: (1) sampling with arbitrary, bandlimited kernels, (2) sampling with smooth, time-limited kernels and, (3) recovery from Gabor transform measurements linked with the SAFT domain. Our work offers a unifying perspective on the sparse sampling problem which is compatible with the Fourier, Fresnel and Fractional Fourier domain based results. In deriving our results, we introduce the SAFT series (analogous to the Fourier series) and the short time SAFT, and study convolution theorems that establish a convolution--multiplication property in the SAFT domain.
Automatic Object Searching and Behavior Learning for Mobile Robots in Unstructured Environment by Deep Belief Networks

Automatic object searching is one of the essential skills for domestic robots to operate in unstructured human environments. It involves concatenation of several capabilities, including object identification, obstacle avoidance, path planning, and navigation. In this paper, we propose an automatic object searching framework for a mobile robot equipped with a single RGB-D camera. The obstacle avoidance is achieved by a behavior learning algorithm based on deep belief networks. The target object is recognized using scale-invariant feature transform descriptors and the relative position between the target and mobile robot is estimated from the RGB-D data. Subsequently, the mobile robot makes a path planning to the target location using an improved bug-based algorithm. The framework is tested in indoor environments and requires the robot to perform obstacle avoidance and automatically search and approach the target object. The results indicate that the system is collision free and reliable in performing searching tasks. This system's functions make itself have the potential of being used for local navigation in unstructured environments.
Unifying Visual Attribute Learning with Object Recognition in a Multiplicative Framework

Attributes are mid-level semantic properties of objects. Recent research has shown that visual attributes can benefit many typical learning problems in computer vision community. However, attribute learning is still a challenging problem as the attributes may not always be predictable directly from input images and the variation of visual attributes is sometimes large across categories. In this paper, we propose a unified multiplicative framework for attribute learning, which tackles the key problems. Specifically, images and category information are jointly projected into a shared feature space, where the latent factors are disentangled and multiplied to fulfil attribute prediction. The resulting attribute classifier is category-specific instead of being shared by all categories. Moreover, our model can leverage auxiliary data to enhance the predictive ability of attribute classifiers, which can reduce the effort of instance-level attribute annotation to some extent. By integrated into an existing deep learning framework, our model can both accurately predict attributes and learn efficient image representations. Experimental results show that our method achieves superior performance on both instance-level and category-level attribute prediction. For zero-shot learning based on visual attributes and human-object interaction recognition, our method can improve the state-of-the-art performance on several widely used datasets.
Underwater image restoration algorithm for free-ascending deep-sea tripods

A Free-Ascending Tripod (FAT) was deployed at a water depth of 2100 m to measure the currents and sediment movement at the seafloor. FAT is used to better understand how and where deep-seafloor sediment moves and accumulates. We also use FATs to study deep-sea biology. In the images obtained by the camera, biological animals can hardly be distinguished. In this paper, we use image processing technology to uncover the real deep-sea scene. We propose four methods for improving the underwater image quality. First, we use the deep-sea optical imaging model to determine the properties of water in different sea areas and then remove the haze from underwater images using the underwater dual dark channel model. Next, we remove the footprint of artificial light through halo-estimation devignetting. Then, we obtain the real deep-sea scene color based on the color temperature of the camera and the inherent optical properties of water. Finally, we propose a semi-self-similarity-based super resolution for super-resolving the low-quality images. The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods.
Differential evolution using homeostasis adaption based mutation operator and its application for software cost estimation

Among meta-heuristic algorithms, differential evolution (DE) is one of the most powerful nature-inspired algorithm used to solve the complex problems in various application areas. In DE algorithm at higher generations, there is an increase in the computational cost because existing mutation operator may not provide more diversity. In this paper, a new variant of DE has been proposed by incorporating the homeostasis adaption based mutation operator (HABMO), which maintains the diversity when it stuck to the local optimum problem. This operator with DE is applied for the cost estimation in software development, where proposed optimization technique is used with constructive cost model (COCOMO) for optimizing the tuning parameters. The main objective of this work is accurate prediction and minimization of the error like MMRE, MMER, MSE and RMSE in less number of iteration, for COCOMO model. Further, the proposed variant of DE has been compared with different versions of DE and it has been concluded that the proposed HABDE is able to improve the performance of DE algorithm.
Weakly-paired deep dictionary learning for cross-modal retrieval

Many multi-modal data suffers from significant weak-pairing characteristics, i.e., there is no sample-to-sample correspondence between modalities, rather classes of samples in one modality correspond to classes of samples in the other modality. This provides great challenges for the cross-modal learning for retrieval. In this work, our focus is learning cross-modal representations with minimal class label supervision and without correspondences between samples. To tackle this challenging problem, we establish a scalable hierarchical learning architecture to deal with the extensive weakly-paired heterogeneous multi-modal data. A shared classifier across different modalities is used to effectively deal with label supervision information, and a multi-modal low-rank model is introduced to encourage the modal-invariant representation. Finally, some cross-modal validations on publicly available datasets are performed to show the advantages of the proposed method.
Multiview, Few-Labeled Object Categorization by Predicting Labels With View Consistency

The categorization accuracies of objects have been greatly improved in recent years. However, large quantities of labeled images are needed. many methods fail when only few labeled images are available. To tackle the few-labeled object categorization problem, we need to represent and classify them from multiple views. In this paper, we propose a novel multiview, few-labeled object categorization algorithm by predicting the labels of images with view consistency (MVFL-VC). We use labeled images along with other unlabeled images in a unified framework. A mapping function is learned to model the correlations of images with their labels. Since there are no labeling information for unlabeled images, we simultaneously learn the mapping function and image labels by classification error minimization. We make use of multiview information for joint object categorization. Although different views represent different aspects of images, for one image, the predicted categories of multiple views should be consistent with each other. We learn the mapping function by minimizing the summed classification losses along with the discrepancy of predicted labels between different views in an alternative way. We conduct object categorization experiments on five public image datasets and compare with other semi-supervised methods. Experimental results well demonstrate the effectiveness of the proposed MVFL-VC method.
Deep Architectures and Ensembles for Semantic Video Classification

This paper addresses the problem of accurate semantic labeling of short videos. To this end, a multitude of three different deep nets, ranging from traditional recurrent neural 4 networks (LSTM, GRU), temporal agnostic networks (FV, VLAD, BoW), fully connected neural networks mid-stage AV fusion, and others were considered. Additionally, we also propose a residual architecture-based deep neural network (DNN) for video classification, with state-of-the-art classification performance at significantly reduced complexity. Furthermore, we propose four new approaches to diversity-driven multi-net ensembling, one based on fast correlation measure and three incorporating a DNN-based combiner. We show that significant performance gains can be achieved by ensembling diverse nets and we investigate factors contributing to high diversity. Based on the extensive YouTube8M dataset, we provide an in-depth evaluation and analysis of their behavior. We show that the performance of the ensemble is state-of-the-art achieving the highest accuracy on the YouTube8M Kaggle test data. The performance of the ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets, and show that the resulting method achieves comparable accuracy with the state-of-the-art methods using similar input features.
Efficient Algorithm for Secure Outsourcing of Modular Exponentiation with Single Server

Outsourcing computation allows an outsourcer with limited resource to delegate the computation load to a powerful server without the exposure of true inputs and outputs. It is well known that modular exponentiation is one of the most expensive operations in public key cryptosystems. Currently, most of outsourcing algorithms for modular exponentiation are based on two untrusted servers or have small checkability with single server. In this paper, we first propose an efficient outsourcing algorithm of modular exponentiation based on two untrusted servers, where the outsourcer can detect the error based on Euler theorem with a probability of 1 if one of the servers misbehaves. We then present an outsourcing algorithm of modular exponentiation with single server, and the outsourcer can also check the failure with a probability of 1. Therefore, the proposed algorithm with single server improves efficiency and checkability simultaneously compare with the previous ones. Finally, we provide the experimental evaluations to demonstrate that the proposed two algorithms are the most efficient ones in all of the outsourcing algorithms for an outsourcer.