Reviewerly/exHarmony huggingface dataset:
Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

Monocular depth estimation is a basic task in machine vision.In recent years, the performance of monocular depth estimation has been greatly improved.However, most depth estimation networks are based on a very deep network to extract features that lead to a large amount of information lost.The loss of object information is particularly serious in the encoding and decoding process.This information loss leads to the estimated depth maps lacking object structure detail and have non-clear edges.Especially in a complex indoor environment, which is our research focus in this paper, the consequences of this loss of information are particularly serious.To solve this problem, we propose a Dense feature fusion network that uses a feature pyramid to aggregate various scale features.Furthermore, to improve the fusion effectiveness of decoded object contour information and depth information, we propose an adaptive depth fusion module, which allows the fusion network to fuse various scale depth maps adaptively to increase object information in the predicted depth map.Unlike other work predicting depth maps relying on U-NET architecture, our depth map predicted by fusing multi-scale depth maps.These depth maps have their own characteristics.By fusing them, we can estimate depth maps that not only include accurate depth information but also have rich object contour and structure detail.Experiments indicate that the proposed model can predict depth maps with more object information than other prework, and our model also shows competitive accuracy.Furthermore, compared with other contemporary techniques, our method gets state-of-the-art in edge accuracy on the NYU Depth V2 dataset.
Research on comprehensive evaluation of digital economy development level in typical areas of China

The development of digital economy has become the basic national policy of China. In order to explore its development level, this paper first expounds the research status of digital economy, analyzes the influence mechanism of digital economy, and analyzes the current situation of China's digital economy from the perspectives of digital infrastructure, digital industrialization, industrial digitization, digital innovation ability and digital investment. According to the data of the China Statistical Yearbook 2018, this paper measures and ranks the development of digital economy by principal component analysis. According to the empirical results, it is concluded that the development of digital economy in China is greatly affected by digital infrastructure and digital investment, and the national development level is very uneven. Finally, some suggestions are given.
Optimal Text Document Clustering Enabled by Weighed Similarity Oriented Jaya With Grey Wolf Optimization Algorithm

Abstract Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency–inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis.
Pauli Matrix based Quantum Communication Protocol

Quantum Computing is being pitched as the next revolution which is set to overwhelm the IT and Computing sector with its unique abilities and features. The properties of Quantum Computing like Entanglement, Super-position of the state of its quantum bits, No Cloning and Wave Collapse are some of the properties that sets it apart from the conventional computers. The research in this paper is directed towards introducing and explaining some of the unique properties of quantum computers. Further the paper aims at proposing a Pauli Matrix based Quantum Communication Protocol with ability to preserve the received qubits for future reference. The novelty in the proposed protocol is its ability to send two bits worth of information on a single bit. The inherent features of Quantum Mechanics prohibit any kind of eavesdropping or cloning of the qubits thereby bringing in in-inbuilt security to the communication protocol.
Cybersecurity of consumer products against the background of the EU model of cyberspace protection

Abstract The entry into force of Regulation 2019/881 heralded a new stage in the construction of the EU cybersecurity model. At present, at the level of both EU institutions and individual Member States, preparatory work is underway to create the first ICT certification programmes relating to the area of cybersecurity. To date, the role of national competition and consumer protection authorities in helping to build a coherent cybersecurity model has not been sufficiently highlighted. The problem of the cybersecurity of products intended for the consumer market is a pressing issue. Furthermore, its significance is growing due in no small part to the increasing number of so-called smart connected consumer products and the mass expansion of the IoT market. As a result, threats to security or privacy increasingly stem not from cyberattacks on leading providers of online services but from the exploitation of vulnerabilities in commonly-used consumer products. This article aims to discuss the possible role of competition and consumer protection authorities in shaping a future model of EU cybersecurity. We discuss the existing mechanisms in EU law that allow supervisory authorities to shape the consumer product safety market and consider whether these measures can also be considered adequate for cybersecurity purposes. Particular attention is paid to identifying what legislative steps would be necessary to effectively synthesize the new EU cybersecurity regulations (including the planned cybersecurity certification framework) with existing consumer product safety laws.
ImageDataset2Vec: An image dataset embedding for algorithm selection

Convolutional Neural Networks (CNNs) have become the main solution for image classification tasks in different applications. Although several CNN architectures are available, there is no best architecture regardless the problem at hand. The selection of the most suitable CNN architecture is usually performed by trial and error, which may take much time and computational resources. Meta-learning (MtL) is a framework developed in machine learning to perform algorithm selection based on the meta-features of each task being solved. Such meta-features are usually descriptive characteristics extracted from the training dataset available in the task at hand. Despite the increasing attention of MtL for algorithm selection, its success strongly depends on defining relevant meta-features to represent the classification tasks of interest. This paper proposes the ImageDataset2Vec method for extracting meta-features to describe image classification datasets. ImageDataset2Vec adopts a pre-trained deep neural network to extract features from images datasets, embedding them in a single feature vector. The derived meta-features are employed by MtL to select CNN architectures for image classification. The proposed approach was evaluated for selecting among six CNN algorithms in 45 two-classes image datasets. The results showed that MtL using ImageDataset2Vec overcame different baseline methods, selecting the best possible CNN algorithm in 84.45% of the datasets. Furthermore, the proposal was statistically equivalent to the ground truth when the best CNN is recommended, i.e., when MtL does not select the best CNN, it selects a competitive algorithm. These results show that the proposal was able to extract representative features from image datasets.
Visual Quality Optimization for View-Dependent Point Cloud Compression

The video-based point cloud compression (V-PCC) is the state-of-the-art dynamic point cloud compression technique. V-PCC projects the 3D point cloud data patch by patch to its bounding box and organizes projected patches into a video frame, making full use of the well-developed video coding tools. Despite its high efficiency, cracks easily exist in the reconstructed point cloud in various viewing angles, which seriously degrades the visual quality. In this paper, we propose an efficient method to improve the visual quality of dynamic point cloud, especially for the main view from the content provider. The relationship between patches and views is exploited, and an algorithm intelligently reserving points that may be discarded in V-PCC is proposed. According to our subjective and perceptual objective evaluation experiments, compared with V-PCC, the overall visual quality of the reconstructed point could is evidently improved. In particular, cracks are mended with our proposed method. The Bjontegaard delta bit-rate reduction of up to 3.1% is achieved with respect to Point Cloud Quality Metric (PCQM), which partially verifies the improvement of subjective quality when adopting the proposed method.
Knowledge-driven description synthesis for floor plan interpretation

Image captioning is a widely known problem in the area of AI. Caption generation from floor plan images has applications in indoor path planning, real estate, and providing architectural solutions. Several methods have been explored in the literature for generating captions or semi-structured descriptions from floor plan images. Since only the caption is insufficient to capture fine-grained details, researchers also proposed descriptive paragraphs from images. However, these descriptions have a rigid structure and lack flexibility, making it difficult to use them in real-time scenarios. This paper offers two models, description synthesis from image cue (DSIC) and transformer-based description generation (TBDG), for text generation from floor plan images. These two models take advantage of modern deep neural networks for visual feature extraction and text generation. The difference between both models is in the way they take input from the floor plan image. The DSIC model takes only visual features automatically extracted by a deep neural network, while the TBDG model learns textual captions extracted from input floor plan images with paragraphs. The specific keywords generated in TBDG and understanding them with paragraphs make it more robust in a general floor plan image. Experiments were carried out on a large-scale publicly available dataset and compared with state-of-the-art techniques to show the proposed model's superiority.
Zeroth-order methods for noisy Hölder-gradient functions

In this paper, we prove new complexity bounds for zeroth-order methods in non-convex optimization with inexact observations of the objective function values. We use the Gaussian smoothing approach of Nesterov and Spokoiny(Found Comput Math 17(2): 527–566, 2015. https://doi.org/10.1007/s10208-015-9296-2 ) and extend their results, obtained for optimization methods for smooth zeroth-order non-convex problems, to the setting of minimization of functions with Hölder-continuous gradient with noisy zeroth-order oracle, obtaining noise upper-bounds as well. We consider finite-difference gradient approximation based on normally distributed random Gaussian vectors and prove that gradient descent scheme based on this approximation converges to the stationary point of the smoothed function. We also consider convergence to the stationary point of the original (not smoothed) function and obtain bounds on the number of steps of the algorithm for making the norm of its gradient small. Additionally we provide bounds for the level of noise in the zeroth-order oracle for which it is still possible to guarantee that the above bounds hold. We also consider separately the case of $$\nu = 1$$ and show that in this case the dependence of the obtained bounds on the dimension can be improved.
Interactive imitation learning for spacecraft path-planning in binary asteroid systems

Exploration of small body systems poses the problem of designing path planning strategies for possibly uncharted environments. Traditional methods aimed at developing rigorous trajectory baselines may suffer inefficiencies, or turn infeasible when confronted with unknown dynamics. In strongly non-linear dynamics, mapping point design solutions from one dynamical regime to another may be hindered by underlying chaotic behavior. Rather than relying on baseline driven approaches, more generalized strategies may be found by observing human pilots controlling spacecraft motion within varying dynamical environments; the resultant data can then be utilized to initialize machine learning agents to provide more autonomous solutions. A previous numerical experiment resulted in a technical dataset comprising of human-based path planning strategies across a range of binary asteroid systems. This dataset is now used to train various imitation learning agents, and initiate the creation of a framework that integrates human–machine cooperation into the early training phases of artificial intelligent agents; the current application is for spacecraft guidance in binary asteroid systems, as a prototype of complex, potentially unknown, orbit dynamics. An interactive training architecture, based on the DAgger algorithm, is designed and employed to train both original and interactively coached agents, the latter stemming from both corrective and evaluative feedback by a real time human interactor. All agents were interactively trained for a predefined time period. The results from this investigation may provide the first, empirical observations of behavioral cloning within multi-body dynamics with largely randomized parameters, with some notable contributions including early characterization of training time, initial evidence of an autonomous agent learning meaningful policy features via imitation, and early identification of challenges in training fully autonomous agents for a multi-body dynamics path planning problem of this complexity and high dimensional state space.
Towards an efficient rule-based framework for legal reasoning

A rule based knowledge system consists of three main components: a set of rules, facts to be fed to the reasoning corresponding to the data of a case, and an inference engine. In general, facts are stored in (relational) databases that represent knowledge in a first-order based formalism. However, legal knowledge uses defeasible deontic logic for knowledge representation due to its particular features that cannot be supported by first-order logic. In this work, we present a unified framework that supports efficient legal reasoning. In the framework, a novel inference engine is proposed in which the Semantic Rule Index can identify candidate rules with their corresponding semantic rules if any, and an inference controller is able to guide the executions of queries and reasoning. It can eliminate rules that cannot be fired to avoid unnecessary computations in early stages. The experiments demonstrated the effectiveness and efficiency of the proposed framework.
Progressive Mimic Learning: A new perspective to train lightweight CNN models

Knowledge distillation (KD) builds a lightweight Student Model (SM) and trains it to approximate a large Teacher Model (TM) by exploring knowledge learned by the TM, which shows effectiveness to train lightweight CNN models. However, training a small SM to achieve better performance remains a challenging problem. Recent researches on human learning behaviors show that both the knowledge from teachers and the knowledge learning processes of teachers are significant for students. Inspired by this characteristic, in this paper, we propose a new perspective, called Progressive Mimic Learning (PML), to train lightweight CNN models by mimicking the learning trajectory of the TM. In order to obtain a more powerful SM, the useful hints in the learning process of the TM are explored. To start with, the TM learning process is divided into multiple stages, and the last state of the TM in each stage is recorded as a landmark. The learning trajectory of the TM is composed of these landmarks. Then, a landmark loss is defined to constrain the SM to progressively mimic the learning process of the TM, by employing landmarks in the learning trajectory as a training hint of the SM. Several experiments are conducted on four benchmark data sets, CIFAR-10, CIFAR-100, Fashion-MNIST, and ImageNet-10, to investigate the performance of the PML. The results show that the PML can make SMs generate more accurate predictions than SMs trained by its counterparts.
Blocklist Babel: On the Transparency and Dynamics of Open Source Blocklisting

Blocklists constitute a widely-used Internet security mechanism to filter undesired network traffic based on IP/domain reputation and behavior. Many blocklists are distributed in open source form by threat intelligence providers who aggregate and process input from their own sensors, but also from third-party feeds or providers. Despite their wide adoption, many open-source blocklist providers lack clear documentation about their structure, curation process, contents, dynamics, and inter-relationships with other providers. In this paper, we perform a transparency and content analysis of 2,093 free and open source blocklists with the aim of exploring those questions. To that end, we perform a longitudinal 6-month crawling campaign yielding more than 13.5M unique records. This allows us to shed light on their nature, dynamics, inter-provider relationships, and transparency. Specifically, we discuss how the lack of consensus on distribution formats, blocklist labeling taxonomy, content focus, and temporal dynamics creates a complex ecosystem that complicates their combined crawling, aggregation and use. We also provide observations regarding their generally low overlap as well as acute differences in terms of liveness (i.e., how frequently records get indexed and removed from the list) and the lack of documentation about their data collection processes, nature and intended purpose. We conclude the paper with recommendations in terms of transparency, accountability, and standardization.
Chessboard-Like High-Frequency Patterns for 3D Measurement of Reflective Surface

Surface reflections severely degrade the performance of the structured-light 3D measurement. In this article, we introduce a novel structured-light 3D measurement technique that works in the presence of reflective highlights. The focus of this article is on designing chessboard-like coding schema and crossover-based decoding schemes. First, we design chessboard-like fringe patterns that are resistant to reflective highlights by using simple logical operations. Compared with conventional coding schemes that inevitably have low-frequency patterns, the chessboard-like coding schemes use all-high-frequency patterns to provide a better highlights-suppression effect. Second, we propose an inverse-pattern method to extract crossover positions for decoding. The chessboard-like patterns have abundant crossover information. The crossover information is more localizable and better preserved than image intensity, which makes the decoding strategy more robust. Moreover, both positive and negative patterns are used to precisely localize the crossover positions. The proposed method can be readily incorporated into existing binary structured-light measurement techniques without significant overhead in terms of capture time or hardware. Extensive experiments are shown to demonstrate the high performance of the proposed method.
A fast algorithm based on gray level co-occurrence matrix and Gabor feature for HEVC screen content coding

To reduce the computational complexity of screen content video coding (SCC), a fast algorithm based on gray level co-occurrence matrix and Gabor feature model for HEVC-SCC, denoted as GGM, is proposed in this paper. By studying the correlation of non-zero number in gray level co-occurrence matrix with different partitioning depth, the coding unit (CU) size of intra coding can be prejudged, which selectively skips the intra prediction process of CU in other depth. With Gabor filter, the edge information reflecting the features of screen content images to the human visual system (HVS) are extracted. According to Gabor feature, CUs are classified into natural content CUs (NCCUs), smooth screen content CUs (SSCUs) and complex screen content CUs (CSCUs), with which, the calculation and judgment of unnecessary intra prediction modes are skipped. Under all-intra (AI) configuration, experimental results show that the proposed algorithm GGM can achieve encoding time saving by 42.13% compared with SCM-8.3, and with only 1.85% bit-rate increasement.
Subversion-Resilient Public Key Encryption with Practical Watchdogs

Restoring the security of maliciously implemented cryptosystems has been widely considered challenging due to the fact that the subverted implementation could arbitrarily deviate from the official specification. Achieving security against adversaries that can arbitrarily subvert implementations seems to inherently require trusted component assumptions and/or architectural properties. At ASIACRYPT 2016, Russell et al. proposed an attractive model where a watchdog is used to test and approve individual components of an implementation before or during deployment. Such a detection-based strategy has been useful for designing various cryptographic schemes that are provably resilient to subversion.We consider Russell et al.’s watchdog model from a practical perspective regarding watchdog efficiency. We find that the asymptotic definitional framework, while permitting strong positive theoretical results, does not yet guarantee practical watchdogs, due to the fact that the running time of a watchdog is only bounded by an abstract polynomial. Hence, in the worst case, the running time of the watchdog might exceed the running time of the adversary, which seems impractical for most applications. We adopt Russell et al.’s watchdog model to the concrete security setting and design the first subversion-resilient public-key encryption scheme which allows for extremely efficient watchdogs with only linear running time.At the core of our construction is a new variant of a combiner for key encapsulation mechanisms (KEMs) by Giacon et al. (PKC’18). We combine this construction with a new subversion-resilient randomness generator that also can be checked by an efficient watchdog, even in constant time, which could be of independent interest for the design of other subversion-resilient cryptographic schemes. Our work thus shows how to apply Russell et al.’s watchdog model to design subversion-resilient cryptography with efficient watchdogs. We insist that this work does not intend to show that the watchdog model outperforms other defense approaches, but to demonstrate that practical watchdogs are practically achievable.