Reviewerly/exHarmony huggingface dataset:
Quantum Random Oracle Model with Auxiliary Input

The random oracle model (ROM) is an idealized model where hash functions are modeled as random functions that are only accessible as oracles. Although the ROM has been used for proving many cryptographic schemes, it has (at least) two problems. First, the ROM does not capture quantum adversaries. Second, it does not capture non-uniform adversaries that perform preprocessings. To deal with these problems, Boneh et al. (Asiacrypt’11) proposed using the quantum ROM (QROM) to argue post-quantum security, and Unruh (CRYPTO’07) proposed the ROM with auxiliary input (ROM-AI) to argue security against preprocessing attacks. However, to the best of our knowledge, no work has dealt with the above two problems simultaneously. In this paper, we consider a model that we call the QROM with (classical) auxiliary input (QROM-AI) that deals with the above two problems simultaneously and study security of cryptographic primitives in the model. That is, we give security bounds for one-way functions, pseudorandom generators, (post-quantum) pseudorandom functions, and (post-quantum) message authentication codes in the QROM-AI. We also study security bounds in the presence of quantum auxiliary inputs. In other words, we show a security bound for one-wayness of random permutations (instead of random functions) in the presence of quantum auxiliary inputs. This resolves an open problem posed by Nayebi et al. (QIC’15). In a context of complexity theory, this implies $$ \mathsf {NP}\cap \mathsf {coNP} \not \subseteq \mathsf {BQP/qpoly}$$ relative to a random permutation oracle, which also answers an open problem posed by Aaronson (ToC’05).
Evaluating Existing China Military Camouflage Designs using Camouflage Similarity Index (CSI)

China military has been one of the most powerful in the world. The purpose of the current study was to evaluate the existing China military camouflages using camouflage algorithm, Camouflage Similarity Index (CSI). CSI ranges from 0 to 1 and the best value 0 is achieved if the selected camouflage perfectly merges with the environment. Eight existing China military camouflages were evaluated under six different backgrounds (20x50 pixels). Post-hoc Tukey test found that there was no significant difference between each existing military camouflage indicating that all camouflages had the same level of concealment in the selected backgrounds. This study represents the first attempt to investigate the effectiveness of China military and the results can be very useful for China military organizations, textile manufacturers, and academicians particularly related to the enhancement of the existing design.
A QUBO Formulation of Minimum Multicut Problem Instances in Trees for D-Wave Quantum Annealers

Abstract Quantum annealing algorithms were introduced to solve combinatorial optimization problems by taking advantage of quantum fluctuations to escape local minima in complex energy landscapes typical of NP − hard problems. In this work, we propose using quantum annealing for the theory of cuts, a field of paramount importance in theoretical computer science. We have proposed a method to formulate the Minimum Multicut Problem into the QUBO representation, and the technical difficulties faced when embedding and submitting a problem to the quantum annealer processor. We show two constructions of the quadratic unconstrained binary optimization functions for the Minimum Multicut Problem and we review several tradeoffs between the two mappings and provide numerical scaling analysis results from several classical approaches. Furthermore, we discuss some of the expected challenges and tradeoffs in the implementation of our mapping in the current generation of D-Wave machines.
Quantum Lyapunov control with machine learning

Quantum state engineering is a central task in Lyapunov-based quantum control. Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning. Specifically, artificial neural networks are used to learn the relationship between the optimal control parameters and initial states through supervised learning with samples. Two designs are presented where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. We demonstrate the performance of the designs with a three-level quantum system for an eigenstate control problem. Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented for new initial states without much increase of computational resources.
Augmented Reality and Industry 4.0

Augmented Reality is widely emerging in almost every field whether it is entertainment (gaming), the field of visual art, photography, cinema, interactive digital media, healthcare or industry. This innovation is sprouting in a previous couple of years with a developing number of luxury and affordable AR gadgets getting to be accessible to the overall population. AR systems have exhibited the ability to advance undertaking productivity in an expansive scope of ventures. It is one of the most promising fields in the context of Industry 4.0. A number of researchers are working on this emerging technology as it is still a challenging area. This chapter discusses the basics of Augmented Reality, its history, types, working, applications and its emergence into the Industry 4.0.
DORY: Lightweight memory hierarchy management for deep NN inference on IoT endnodes

IoT endnodes often couple a small and fast L1 scratchpad memory with higher-capacity but lower bandwidth and speed L2 background memory. The absence of a coherent hardware cache hierarchy saves energy but comes at the cost of labor-intensive explicit memory management, complicating the deployment of algorithms with large data memory footprint, such as Deep Neural Network (DNN) inference. In this work, we present DORY, a lightweight software-cache dedicated to DNN Deployment Oriented to memoRY. DORY leverages static data tiling and DMA-based double buffering to hide the complexity of manual L1-L2 memory traffic management. DORY enables storage of activations and weights in L2 with less than 4% performance overhead with respect to direct execution in L1. We show that a 142 kB DNN achieving 79.9% on CIFAR-10 runs 3.2X faster compared to its execution directly from L2 memory while consuming 1.9X less energy.
ViCo: Word Embeddings From Visual Co-Occurrences

We propose to learn word embeddings from visual co-occurrences. Two words co-occur visually if both words apply to the same image or image region. Specifically, we extract four types of visual co-occurrences between object and attribute words from large-scale, textually-annotated visual databases like VisualGenome and ImageNet. We then train a multi-task log-bilinear model that compactly encodes word “meanings” represented by each co-occurrence type into a single visual word-vector. Through unsupervised clustering, supervised partitioning, and a zero-shot-like generalization analysis we show that our word embeddings complement text-only embeddings like Glove by better representing similarities and differences between visual concepts that are difficult to obtain from text corpora alone. We further evaluate our embeddings on five downstream applications, four of which are vision-language tasks. Augmenting Glove with our embeddings yields gains on all tasks. We also find that random embeddings perform comparably to learned embeddings on all supervised vision-language tasks, contrary to conventional wisdom.
Convex Optimization for Shallow Neural Networks

We consider non-convex training of shallow neural networks and introduce a convex relaxation approach with theoretical guarantees. For the single neuron case, we prove that the relaxation preserves the location of the global minimum under a planted model assumption. Therefore, a globally optimal solution can be efficiently found via a gradient method. We show that gradient descent applied on the relaxation always outperforms gradient descent on the original non-convex loss with no additional computational cost. We then characterize this relaxation as a regularizer and further introduce extensions to multineuron single hidden layer networks.
Dilated Skip Convolution for Facial Landmark Detection

Facial landmark detection has gained enormous interest for face-related applications due to its success in facial analysis tasks such as facial recognition, cartoon generation, face tracking and facial expression analysis. Many studies have been proposed and implemented to deal with the challenging problems of localizing facial landmarks from given images, including large appearance variations and partial occlusion. Studies have differed in the way they use the facial appearances and shape information of input images. In our work, we consider facial information within both global and local contexts. We aim to obtain local pixel-level accuracy for local-context information in the first stage and integrate this with knowledge of spatial relationships between each key point in a whole image for global-context information in the second stage. Thus, the pipeline of our architecture consists of two main components: (1) a deep network for local-context subnet that generates detection heatmaps via fully convolutional DenseNets with additional kernel convolution filters and (2) a dilated skip convolution subnet-a combination of dilated convolutions and skip-connections networks-that are in charge of robustly refining the local appearance heatmaps. Through this proposed architecture, we demonstrate that our approach achieves state-of-the-art performance on challenging datasets-including LFPW, HELEN, 300W and AFLW2000-3D-by leveraging fully convolutional DenseNets, skip-connections and dilated convolution architecture without further post-processing.
Circular Clustering in Fuzzy Approximation Spaces for Color Normalization of Histological Images

One of the foremost and challenging tasks in hematoxylin and eosin stained histological image analysis is to reduce color variation present among images, which may significantly affect the performance of computer-aided histological image analysis. In this regard, the paper introduces a new rough-fuzzy circular clustering algorithm for stain color normalization. It judiciously integrates the merits of both fuzzy and rough sets. While the theory of rough sets deals with uncertainty, vagueness, and incompleteness in stain class definition, fuzzy set handles the overlapping nature of histochemical stains. The proposed circular clustering algorithm works on a weighted hue histogram, which considers both saturation and local neighborhood information of the given image. A new dissimilarity measure is introduced to deal with the circular nature of hue values. Some new quantitative measures are also proposed to evaluate the color constancy after normalization. The performance of the proposed method, along with a comparison with other state-of-the-art methods, is demonstrated on several publicly available standard data sets consisting of hematoxylin and eosin stained histological images.
Permissioned Blockchain Model for End-to-End Trackability in Supply Chain Management

Supply chain management or logistics is a crucial aspect of any business, be it agriculture, food, healthcare, and more. However, it also involves a communication among a host of actors performing different roles in the chain. Integration of these actors in a transparent yet secure way is needed, which blockchain technology has the potential to do so. In this article, different models of blockchain application in logistics are discussed and how the permissioned blockchains can be used in realizing the business case. An overview of different frameworks which enable permissioned blockchains are discussed in this article. Based on this, the authors propose an asset transfer model which can be employed to enhance supply chain trackability using permissioned blockchain.
View-LSTM: Novel-View Video Synthesis Through View Decomposition

We tackle the problem of synthesizing a video of multiple moving people as seen from a novel view, given only an input video and depth information or human poses of the novel view as prior. This problem requires a model that learns to transform input features into target features while maintaining temporal consistency. To this end, we learn an invariant feature from the input video that is shared across all viewpoints of the same scene and a view-dependent feature obtained using the target priors. The proposed approach, View-LSTM, is a recurrent neural network structure that accounts for the temporal consistency and target feature approximation constraints. We validate View-LSTM by designing an end-to-end generator for novel-view video synthesis. Experiments on a large multi-view action recognition dataset validate the proposed model.
Code Summarization with Abstract Syntax Tree

Code summarization, which provides a high-level description of the function implemented by code, plays a vital role in software maintenance and code retrieval. Traditional approaches focus on retrieving similar code snippets to generate summaries, and recently researchers pay increasing attention to leverage deep learning approaches, especially the encoder-decoder framework. Approaches based on encoder-decoder suffer from two drawbacks: (a) Lack of summarization in functionality level; (b) Code snippets are always too long (more than ten words), regular encoders perform poorly. In this paper, we propose a novel code representation with the help of Abstract Syntax Trees, which could describe the functionality of code snippets and shortens the length of inputs. Based on our proposed code representation, we develop Generative Task, which aims to generate summary sentences of code snippets. Experiments on large-scale real-world industrial Java projects indicate that our approaches are effective and outperform the state-of-the-art approaches in code summarization.
Cyberdream VR

Cyberdream VR is a short artistic virtual reality (VR) experience, which is based on the concept of visualizing the imaginative worlds suggested by rave music and vaporwave as symbolic, spatial, virtual environments. The piece is conceptualized as a virtual hallucination through the broken techno-utopias of cyberspace. Aesthetically, the work adapts the forms of 1990s VJ performance, demoscene animations, and the visual language of rave fliers from this era, constructing these forms as virtual spaces that the user can enter into through VR. Drawing upon the Internet-borne music subculture vaporwave, the piece also deconstructs the visual language of 1990s technoutopian computer culture. By transporting the user into the imaginative worlds suggested by rave music and vaporwave, Cyberdream VR more broadly indicates a possible approach to visualizing music that prioritizes symbolic representation. In the future, this approach could be applied for other types of music and yield new transformative approaches to music visualization that may eventually be automated.
An Intelligent Subtype Fuzzy Cluster based Relevant User Data Retrieval Model for Effective Classification

Information retrieval is the process of collecting the data or information from the large volume of data that are available in a local repository or internet according to the user query. Many information retrieval approaches are used by various organizations to facilitate the internet users. Even though, these approaches are consuming more time for the huge data. For reducing the retrieval time, many techniques proposed including clustering techniques for reducing the time. However, they failed to retrieve the more relevant data with less time. For overcoming these drawbacks, this paper proposes an intelligent subtype fuzzy cluster based relevant user data retrieval (ISFCDR) model for effective relevant data retrieval and classification for performing better performance in data recommendation. The proposed model developed by using the existing fuzzy clustering algorithm and classification. Moreover, the existing mutual information based preprocessing also has been done for enhancing the performance of the proposed model. The proposed system is evaluated by conducting various experiments and proved that is better than other existing works in terms of time taken and the content quality.
PCFNet: Deep neural network with predefined convolutional filters

Convolutional neural network (CNN) is a common framework that has been widely applied in many computer vision tasks. However, dealing with many real-world problems, especially problems of medical image analysis, requires the addition of some other task-specific information, which we call as domain knowledge. In this study, we propose a new framework named as Predefined Convolutional Filters Network (PCFNet), which replaces the kernel in the first layer convolution of conventional CNN with some learnable predefined filters. This framework is a modified version of the conventional CNN and, when using proper image filters, outperforms conventional CNN in solving many specific problems, such as medical image classification and handwritten digit recognition problems. As expected, this framework achieves state-of-the-art results on publicly available medical image (IDRiD) dataset and USPS dataset. In addition, PCFNet has fewer learnable parameters in the first layer convolution, meaning less training data will be required, and this effectiveness has been validated by experimental results on CIFAR10/100.
Comprehending Test Code: An Empirical Study

Developers spend a large portion of their time and effort on comprehending source code. While many studies have investigated how developers approach these comprehension tasks and what factors influence their success, less is known about how developers comprehend test code specifically, despite the undisputed importance of testing. In this paper, we report on the results of an empirical study with 44 developers to understand which factors influence developers when comprehending Java test code. We measured three dependent variables: the total time spent reading a test suite, the ability to identify the overall purpose of a test suite, and the ability to produce additional test cases to extend a test suite. The main findings of our study, with several implications for future research and practitioners, are that (i) prior knowledge of the software project decreases the total reading time, (ii) experience with Java affects the proportion of time spent on the Arrange and Assert sections of test cases, (iii) experience with Java and prior knowledge of the software project positively influence the ability to produce additional test cases of certain categories, and (iv) experience with automated tests is an influential factor towards understanding and extending an automated test suite.
Rethinking sketching as sampling: A graph signal processing approach

Abstract Sampling of signals belonging to a low-dimensional subspace has well-documented merits for dimensionality reduction, limited memory storage, and online processing of streaming network data. When the subspace is known, these signals can be modeled as bandlimited graph signals. Most existing sampling methods are designed to minimize the error incurred when reconstructing the original signal from its samples. Oftentimes these parsimonious signals serve as inputs to computationally-intensive linear operators. Hence, interest shifts from reconstructing the signal itself towards approximating the output of the prescribed linear operator efficiently. In this context, we propose a novel sampling scheme that leverages graph signal processing, exploiting the low-dimensional (bandlimited) structure of the input as well as the transformation whose output we wish to approximate. We formulate problems to jointly optimize sample selection and a sketch of the target linear transformation, so when the latter is applied to the sampled input signal the result is close to the desired output. Similar sketching as sampling ideas are also shown effective in the context of linear inverse problems. Because these designs are carried out off line, the resulting sampling plus reduced-complexity processing pipeline is particularly useful for data that are acquired or processed in a sequential fashion, where the linear operator has to be applied fast and repeatedly to successive inputs or response signals. Numerical tests showing the effectiveness of the proposed algorithms include classification of handwritten digits from as few as 20 out of 784 pixels in the input images and selection of sensors from a network deployed to carry out a distributed parameter estimation task.
AI at the Edge for Sign Language Learning Support

In the field of multimodal communication, sign language is and continues to be, one of the most understudied areas. Thanks to the recent advances in the field of deep learning, there are far-reaching implications and applications that neural networks can have for sign language mastering. This paper describes a method for ASL alphabet recognition using Convolutional Neural Networks (CNN), which allows to monitor user's learning progress. American Sign Language (ASL) alphabet recognition by computer vision is a challenging task due to the complexity in ASL signs, high interclass similarities, large intraclass variations, and constant occlusions. We produced a robust model that classifies letters correctly in a majority of cases. The experimental results encouraged us to investigate the adoption of AI techniques to support learning of a sign language, as a natural language with its own syntax and lexicon. The challenge was to deliver a mobile sign language training solution that users may adopt during their everyday life. To satisfy the indispensable additional computational resources to the locally connected end-user devices, we propose the adoption of a Fog-Computing Architecture.
Dynamic Material-Aware Object Tracking in Hyperspectral Videos

Traditional color trackers tend to fail in some challenging scenarios such as object deformation, rotation, background clutter and varying illumination. In this paper, we take advantages of the material identification ability of hyperspectral images to tackle the object tracking problem. Abundances and spectral-spatial histogram of oriented gradients (SSHOG) are adopted as material features for tracking. Abundances extract the material distribution by performing dynamic joint online unmixing, in which the temporal information is used to suppress the effect of spectral variability between adjacent frames. SSHOG summarizes the local spectral-spatial oriented gradients to describe the local 3D textures of the target. These features are further embedded to correlation filters, yielding a novel dynamic material-aware tracking (DMT) method. Experimental results on hyperspectral benchmark show the superiority of DMT over other trackers.