Reviewerly/exHarmony huggingface dataset:
An Empirical Study of the Dependency Networks of Deep Learning Libraries

Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory study on the dependency networks of deep learning libraries, namely, Tensorflow, PyTorch, and Theano. We study the project purposes, application domains, dependency degrees, update behaviors and reasons as well as version distributions of deep learning projects that depend on Tensorflow, PyTorch, and Theano. Our study unveils some commonalities in various aspects (e.g., purposes, application domains, dependency degrees) of deep learning libraries and reveals some discrepancies as for the update behaviors, update reasons, and the version distributions. Our findings highlight some directions for researchers and also provide suggestions for deep learning developers and users.
Adoption of the Software-Defined Perimeter (SDP) Architecture for Infrastructure as a Service

The use of cloud Infrastructure as a Service (IaaS) for enterprise applications is at an all-time high and is charted to continue growing to approximately 73% by 2022. IaaS suffers from several security concerns, such as hypervisor hijacking, virtual machine (VM) hopping, and account hijacking. With such a large percentage of enterprise traffic on the cloud, a strong security framework is demanded. To secure IaaS, this article proposes a software-defined perimeter (SDP) as a solution. SDP provides a logical perimeter to restrict access to services with a layer of authentication and authorization to allow. Only authorized clients may connect to services hidden by SDP gateways. SDP is implemented and verified in an AWS cloud environment. Port scanning is used to verify SDP behavior as well. The results demonstrate the SDP's ability to “darken” services behind a gateway. The performance of SDP against a denial-of-service (DoS) attack is demonstrated in a local environment. The test results demonstrate that SDP is indeed capable of resisting DoS attacks while allowing legitimate user traffic even under the duration of the attack. These results lead to a discussion on future research for SDP in IaaS.
Analyzing the sustainability of 28 ‘Blockchain for Good’ projects via affordances and constraints

Proponents of ‘Blockchain for Good’ – blockchain efforts seeking to enable benefits to humans and the environment – have suggested that the technology can support sustainability. However, while previous research has addressed aspects of the sustainability affordances of Blockchain for Good projects, the constraints that these projects impose have not faced equal consideration. Furthermore, the theoretical concepts of sustainability ‘problems’ and ‘solutions’ implicit in these projects have not been made clear. In this exploratory study, we evaluate the sustainability of 28 Blockchain for Good projects that use cryptocurrencies or tradable tokens with regard to the UN sustainability goals. These projects span a range of goals, such as supply chain tracking, transparent charity, and fairer voting. Despite their admirable goals, we find that current Blockchain for Good projects are unlikely to contribute to a sustainable future due to technical limitations and a conceptual framing that favors the status quo rather than transformative change.
Reversible data hiding method based on combining IPVO with bias-added Rhombus predictor by multi-predictor mechanism

The well-known pixel-value-ordering (PVO) framework based reversible data hiding (RDH) methods like improved pixel-value-ordering (IPVO) take fully advantages of sorting and achieve an outstanding performance. In this paper, we merge the advantages of sorting and averaging by combining IPVO and conventional Rhombus predictor through the multi-predictor mechanism. Furthermore, in order to better utilize the advantages of multi-predictor mechanism, a bias is added to improve the conventional Rhombus predictor. By adding adaptive biases for the pixels with different complexities, our proposed bias-added Rhombus predictor can reduce the embedding distortion significantly. Moreover, a two-step optimization is proposed to save the running time. Experimental results verify that the proposed method outperforms state-of-the-art PVO based RDH methods.
Method and dataset entity mining in scientific literature: A CNN + BiLSTM model with self-attention

The traditional literature analysis mainly focuses on the literature metadata such as topics, authors, keywords, references, and rarely pays attention to the main content of papers. However, in many scientific domains such as science, computing, engineering, the methods and datasets involved in the papers published carry important information and are quite useful for domain analysis and recommendation. Method and dataset entities have various forms, which are more difficult to recognize than common entities. In this paper, we propose a novel Method and Dataset Entity Recognition model (MDER), which is able to effectively extract the method and dataset entities from the main textual content of scientific papers. The model is the first to combine rule embedding, a parallel structure of Convolutional Neural Network (CNN) and a two-layer Bi-directional Long Short-Term Memory (BiLSTM) with the self-attention mechanism. We evaluate the proposed model on datasets constructed from the published papers of different research areas in computer science. Our model performs well in multiple areas and features a good capacity for cross-area learning and recognition. Ablation study indicates that building different modules collectively contributes to the good entity recognition performance as a whole. The data augmentation positively contributes to model training, making our model much more robust. We finally apply the proposed model on PAKDD papers published from 2009–2019 to mine insightful results over a long time span.1
A Novel Algebraic Technique for Design of Computational Substitution-Boxes Using Action of Matrices on Galois Field

Cryptography entails the practice of designing mathematical algorithms to secure data communication over insecure networks in the presence of adversaries.In this aspect, a cryptographic algorithm encrypts the confidential data and converts it into a non-readable text for adversaries.Advanced Encryption Standard (AES) is the most effective encryption algorithm proposed till now.Substitution-box (S-box) is the most crucial and only nonlinear component in AES (or any cryptographic algorithm), which provides data confusion.A highly nonlinear S-box offers high confidentiality and security against cryptanalysis attacks; hence, the design of S-box is very crucial in any encryption algorithm.To address this challenge, we propose a novel algebraic technique for S-box construction to generate highly nonlinear 8 × 8 S-boxes based on the action of matrices (conforming to the basis of P 7 [Z 2 ]) on the Galois field GF 2 8 .Consequently, by our proposed algorithm, we obtain 1.324×10 14 different S-boxes.Standard S-box tests analyze the cryptographic strength of our proposed S-boxes.The examined results show that the proposed S-boxes possess state-of-the-art cryptographic properties.Moreover, we also demonstrate the effectiveness of the proposed S-boxes in image encryption applications using the majority logic criterion.
Unsupervised Deep K-Means Hashing for Efficient Image Retrieval and Clustering

Recent studies show that hashing technology can achieve efficient similarity searching and many works have been done on supervised deep hash learning. However, under unsupervised scenarios, there are several issues to be solved when learning hashing codes based on visual features for image retrieval and clustering. In this article, we propose a simple but effective Unsupervised Deep K-means Hashing (UDKH) method to simultaneously alleviate the problems of image retrieval and clustering within a single learning framework. UDKH progressively improves the quality of cluster labels and binary hash codes by minimizing pair-wise supervision loss and optimizing the binary K-means to generate discriminative hash codes under the supervision of the learned cluster labels for effective image retrieval. Since the learned hash codes are discriminative, UDKH also improves the image clustering accuracy. Experiments on test datasets demonstrate its effectiveness for image retrieval and clustering.
DeepAirSig: End-to-End Deep Learning Based in-Air Signature Verification

In-air signature verification is vital for biometric user identification in contact-less mode. The state-of-the-art methods use heuristics for signature acquisition, and provide insufficient data to train neural networks for the verification. In this article, we present a novel method for end-to-end deep learning based in-air signature verification using a depth sensor. In this regard, we propose a new medium-scale in-air signature dataset which is created using an accurate convolutional neural network (CNN) based 3D hand pose estimation algorithm. The proposed dataset offers a total of 1800 signatures collected from 40 subjects. So far, dynamic time warping (DTW) has been the most effective and commonly used method for verification. Keeping in view the significant advancement in deep learning, we present a more accurate deep learning based alternative which outperforms DTW by 67.6%. To this end, we train a personalized autoencoder to reconstruct the signature of each subject. Thereafter, the signature is verified by thresholding the reconstruction loss. We perform extensive experiments by formulating spatial and depth features of the signature in images and point clouds based representations. Moreover, for comparisons, we implement several deep learning algorithms (i.e. linear autoencoder, convolutional autoencoder, and Deep One-Class classifier). Our verification approach achieves an EER of 0.055%. The dataset is available at https://bit.ly/2mEJzOw.
OWMA: An improved self-regulatory woodpecker mating algorithm using opposition-based learning and allocation of local memory for solving optimization problems

Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the Woodpecker Mating Algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the Hα parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems.
The Impact of Missing Fingers in Virtual Reality

Avatars in virtual reality (VR) can have body structures that differ from the physical self. Game designers, for example, often stylize virtual characters by reducing the number of fingers. Previous work found that the sensation of presence in VR depends on avatar realism and the number of limbs. However, it is currently unknown how the removal of individual fingers affects the VR experience, body perception, and how fingers are used instead. In a study with 24 participants, we investigate the effects of missing fingers and avatar realism on presence, phantom pain perception, and finger usage. Our results show that particularly missing index fingers decrease presence, show the highest phantom pain ratings, and significantly change hand interaction behavior. We found that relative usage of thumb and index fingers in contrast to middle, ring, and little finger usage was higher with abstract hands than with realistic ones – even when the fingers were missing. We assume that dominant fingers are firstly integrated into the own body schema when an avatar does not resemble one's own appearance. We discuss cognitive mechanisms in experiencing virtual limb loss.
Learning Dual Encoding Model for Adaptive Visual Understanding in Visual Dialogue

Different from Visual Question Answering task that requires to answer only one question about an image, Visual Dialogue task involves multiple rounds of dialogues which cover a broad range of visual content that could be related to any objects, relationships or high-level semantics. Thus one of the key challenges in Visual Dialogue task is to learn a more comprehensive and semantic-rich image representation that can adaptively attend to the visual content referred by variant questions. In this paper, we first propose a novel scheme to depict an image from both visual and semantic views. Specifically, the visual view aims to capture the appearance-level information in an image, including objects and their visual relationships, while the semantic view enables the agent to understand high-level visual semantics from the whole image to the local regions. Furthermore, on top of such dual-view image representations, we propose a Dual Encoding Visual Dialogue (DualVD) module, which is able to adaptively select question-relevant information from the visual and semantic views in a hierarchical mode. To demonstrate the effectiveness of DualVD, we propose two novel visual dialogue models by applying it to the Late Fusion framework and Memory Network framework. The proposed models achieve state-of-the-art results on three benchmark datasets. A critical advantage of the DualVD module lies in its interpretability. We can analyze which modality (visual or semantic) has more contribution in answering the current question by explicitly visualizing the gate values. It gives us insights in understanding of information selection mode in the Visual Dialogue task. The code is available at https://github.com/JXZe/Learning_DualVD.
Virtual Mouse Control Using Colored Finger Tips and Hand Gesture Recognition

In human-computer interaction, virtual mouse implemented with finger tip recognition and hand gesture tracking based on image in a live video is one of the studies. In this paper, virtual mouse control using finger tip identification and hand gesture recognition is proposed. This study consists of two methods for tracking the fingers, one is by using colored caps and other is by hand gesture detection. This includes three main steps that are finger detection using color identification, hand gesture tracking and implementation on on-screen cursor. In this study, hand gesture tracking is generated through the detection of the contour and formation of a convex hull around it. Features of hands are extracted with the area ratio of contour and hull formed. Detailed tests are performed to check this algorithm in real world scenarios.
Reducing the Amount of Single‐Qubit Rotations in VQE and Related Algorithms

With the advent of hybrid quantum classical algorithms using parameterized quantum circuits the question of how to optimize these algorithms and circuits emerges. In this paper we show that the number of single-qubit rotations in parameterized quantum circuits can be decreased without compromising the relative expressibility or entangling capability of the circuit. We also show that the performance of a variational quantum eigensolver is unaffected by a similar decrease in single-qubit rotations. We compare relative expressibility and entangling capability across different number of qubits in parameterized quantum circuits. High-dimensional qudits as a platform for hybrid quantum classical algorithms is a rarity in the literature. Therefore we consider quantum frequency comb photonics as a platform for such algorithms and show that we can obtain an relative expressibility and entangling capability comparable to the best regular parameterized quantum circuits.
Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability

A key capability of an intelligent system is deciding when events from past experience must be remembered and when they can be forgotten. Towards this goal, we develop a predictive model of human visual event memory and how those memories decay over time. We introduce Memento10k, a new, dynamic video memorability dataset containing human annotations at different viewing delays. Based on our findings we propose a new mathematical formulation of memorability decay, resulting in a model that is able to produce the first quantitative estimation of how a video decays in memory over time. In contrast with previous work, our model can predict the probability that a video will be remembered at an arbitrary delay. Importantly, our approach combines visual and semantic information (in the form of textual captions) to fully represent the meaning of events. Our experiments on two video memorability benchmarks, including Memento10k, show that our model significantly improves upon the best prior approach (by 12% on average).
Where to Look and How to Describe: Fashion Image Retrieval With an Attentional Heterogeneous Bilinear Network

Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extract both appearance (i.e., “how to describe”) and localization (i.e., “where to look”) information, and their interactions. To this end, we propose a biologically inspired framework for image-based fashion product retrieval, which mimics the hypothesized two-stream visual processing system of human brain. The proposed attentional heterogeneous bilinear network (AHBN) consists of two branches: a deep CNN branch to extract fine-grained appearance attributes and a fully convolutional branch to extract landmark localization information. A joint channel-wise attention mechanism is further applied to the extracted heterogeneous features to focus on important channels, followed by a compact bilinear pooling layer to model the interaction of the two streams. Our proposed framework achieves satisfactory performance on three image-based fashion product retrieval benchmarks.
SNF

Our work addresses how a cloud provider can offer Network Functions (NF) as a Service, or NFaaS, using the emerging serverless computing paradigm. Serverless computing has the right NFaaS building blocks - usage-based billing, event-driven programming model and elastic scaling. But we identify two core limitations of existing serverless platforms that undermine support for NFaaS - coupling of the billing and work assignment granularities, and state sharing via an external store. Our framework, SNF, overcomes these limitations via two ideas. SNF allocates work at the granularity of flowlets observed in network traffic, whereas billing and programming occur at a finer level. SNF embellishes serverless platforms with ephemeral local state that lasts for the flowlet duration and supports high performance state operations. We demonstrate that our SNF prototype matches utilization closely with demand and reduces tail packet processing latency substantially compared to alternatives.
Integer Programming for Learning Directed Acyclic Graphs from Continuous Data

Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and in practice, because the number of possible DAGs scales superexponentially with the number of nodes. In this paper, we study the problem of learning an optimal DAG from continuous observational data. We cast this problem in the form of a mathematical programming model that can naturally incorporate a superstructure to reduce the set of possible candidate DAGs. We use a negative log-likelihood score function with both ℓ0 and ℓ1 penalties and propose a new mixed-integer quadratic program, referred to as a layered network (LN) formulation. The LN formulation is a compact model that enjoys as tight an optimal continuous relaxation value as the stronger but larger formulations under a mild condition. Computational results indicate that the proposed formulation outperforms existing mathematical formulations and scales better than available algorithms that can solve the same problem with only ℓ1 regularization. In particular, the LN formulation clearly outperforms existing methods in terms of computational time needed to find an optimal DAG in the presence of a sparse superstructure.
Expressive Telepresence via Modular Codec Avatars

VR telepresence consists of interacting with another human in a virtual space represented by an avatar. Today most avatars are cartoon-like, but soon the technology will allow video-realistic ones. This paper aims in this direction, and presents Modular Codec Avatars (MCA), a method to generate hyper-realistic faces driven by the cameras in the VR headset. MCA extends traditional Codec Avatars (CA) by replacing the holistic models with a learned modular representation. It is important to note that traditional person-specific CAs are learned from few training samples, and typically lack robustness as well as limited expressiveness when transferring facial expressions. MCAs solve these issues by learning a modulated adaptive blending of different facial components as well as an exemplar-based latent alignment. We demonstrate that MCA achieves improved expressiveness and robustness w.r.t to CA in a variety of real-world datasets and practical scenarios. Finally, we showcase new applications in VR telepresence enabled by the proposed model.