Reviewerly/exHarmony huggingface dataset:
R-YOLO: A Real-Time Text Detector for Natural Scenes with Arbitrary Rotation

Accurate and efficient text detection in natural scenes is a fundamental yet challenging task in computer vision, especially when dealing with arbitrarily-oriented texts. Most contemporary text detection methods are designed to identify horizontal or approximately horizontal text, which cannot satisfy practical detection requirements for various real-world images such as image streams or videos. To address this lacuna, we propose a novel method called Rotational You Only Look Once (R-YOLO), a robust real-time convolutional neural network (CNN) model to detect arbitrarily-oriented texts in natural image scenes. First, a rotated anchor box with angle information is used as the text bounding box over various orientations. Second, features of various scales are extracted from the input image to determine the probability, confidence, and inclined bounding boxes of the text. Finally, Rotational Distance Intersection over Union Non-Maximum Suppression is used to eliminate redundancy and acquire detection results with the highest accuracy. Experiments on benchmark comparison are conducted upon four popular datasets, i.e., ICDAR2015, ICDAR2013, MSRA-TD500, and ICDAR2017-MLT. The results indicate that the proposed R-YOLO method significantly outperforms state-of-the-art methods in terms of detection efficiency while maintaining high accuracy; for example, the proposed R-YOLO method achieves an F-measure of 82.3% at 62.5 fps with 720 p resolution on the ICDAR2015 dataset.
Characterizing IOTA Tangle with Empirical Data

IOTA organizes transactions in the ledger as a Directed Acyclic Graph (DAG) called Tangle, instead of a hash chain of transaction blocks used by most of traditional blockchains. IOTA is considered a promising platform to support Internet-of-Things (IoT) applications with its key features such as micropayment support and absence of transaction fees. While prior art shows extensive analysis based on synthetic data generated through simulations, an analysis based on empirical data from a deployed IOTA network is still missing. In this paper, we provide the first comprehensive analysis by using real transaction data officially published by IOTA Foundation. Our key finding is that neither the tangle's topological features nor the actual observed performance is consistent with the main conclusions from the literature. In particular, most of transactions take roughly 10 minutes to be officially confirmed, which is not exactly instant as commonly assumed; yet, what is arguably worse is that there is a certain amount (5%) of transactions experiencing exceptionally long confirmation time. This shows that IOTA still has gaps to meet the stringent requirements of IoT applications that are delay sensitive.
Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles

Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights into the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path following and collision avoidance, decision making becomes nontrivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques to develop autonomous agents capable of achieving this hybrid objective without having a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path following and avoiding collisions towards achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.
Context-aware graph-based recommendations exploiting Personalized PageRank

In this article we present a context-aware recommendation method that exploits graph-based data models and Personalized PageRank to provide users with recommendations. In particular, our approach extends the basic graph-based representation that relies on users and items nodes by introducing a third class of nodes, that is to say, context nodes, whose goal is to model the different contextual situations in which an item can be consumed. Given such a data model, we used Personalized PageRank to identify the most suitable recommendations for each user: in a nutshell, our model is based on the intuition that context nodes shall be used to influence random walks, in order to assist the algorithm in identifying the items that are relevant in a particular contextual setting. In the experimental evaluation we investigated the effectiveness of the approach on three different datasets. The results showed that our context-aware graph-based approach overcame the baselines in most of the experimental settings and obtained the best overall results in cold-start situations, thus confirming the validity of the methodology.
Agile Collaboration for Distributed Teams [Software Technology]

Editor Introduction: Today software engineering is characterized by two strong trends: agile and distributed. Both together are increasingly demanded and challenge teams and projects due to lack of discipline, insufficient transparency, agile "ping pong" and thus overheads and rework. Authors Fabio Calefato and I describe current technologies and tools for agile collaboration. I look forward to hearing from both readers and prospective column authors about this column and the technologies you want to know more about. -- Christof Ebert
Heterogeneous Stacked Ensemble Classifier for Software Defect Prediction

Software defect prediction (SDP) is vital to enhance the software quality with reduced testing cost. It stresses to put more testing efforts on those modules which are susceptible to defects. Hence, the focused testing saves resources and increases the probability to deliver failure-free software product exhibiting desired quality levels. One major hinderance in accurate performance of SDP classifiers is class imbalance. This paper proposes to use stacked ensemble to deal with the class imbalance and enhance the performance of SDP classifiers. The experimental study utilizes the publicly available NASA corpus. The study makes a statistical comparison between the proposed model and baseline models. The results show that the proposed model performs better than baseline models over area under the curve (AUC) and accuracy evaluation metrics.
Statistical complexity of quantum circuits

In theoretical machine learning, the statistical complexity is a notion that measures the richness of a hypothesis space. In this work, we apply a particular measure of statistical complexity, namely the Rademacher complexity, to the quantum circuit model in quantum computation and study how the statistical complexity depends on various quantum circuit parameters. In particular, we investigate the dependence of the statistical complexity on the resources, depth, width, and the number of input and output registers of a quantum circuit. To study how the statistical complexity scales with resources in the circuit, we introduce a resource measure of magic based on the $(p,q)$ group norm, which quantifies the amount of magic in the quantum channels associated with the circuit. These dependencies are investigated in the following two settings: (i) where the entire quantum circuit is treated as a single quantum channel, and (ii) where each layer of the quantum circuit is treated as a separate quantum channel. The bounds we obtain can be used to constrain the capacity of quantum neural networks in terms of their depths and widths as well as the resources in the network.
Modeling the Conditional Distribution of Co-Speech Upper Body Gesture Jointly Using Conditional-GAN and Unrolled-GAN

Co-speech gestures are a crucial, non-verbal modality for humans to communicate. Social agents also need this capability to be more human-like and comprehensive. This study aims to model the distribution of gestures conditioned on human speech features. Unlike previous studies that try to find injective functions that map speech to gestures, we propose a novel, conditional GAN-based generative model to not only convert speech into gestures but also to approximate the distribution of gestures conditioned on speech through parameterization. An objective evaluation and user study show that the proposed model outperformed the existing deterministic model, indicating that generative models can approximate real patterns of co-speech gestures better than the existing deterministic model. Our results suggest that it is critical to consider the nature of randomness when modeling co-speech gestures.
The parameters of minimal linear codes

Let k≤n be two positive integers and q a prime power. The basic question in minimal linear codes is to determine if there exists an [n,k]q minimal linear code. The first objective of this paper is to present a new sufficient and necessary condition for linear codes to be minimal. Using this condition, it is easy to construct minimal linear codes or to prove some linear codes are minimal. The second objective of this paper is to use the new sufficient and necessary condition to partially give an answer to the basic question in minimal linear codes. The third objective of this paper is to present four classes of minimal linear codes, which generalize the results about the binary case given in X. Li and Q. Yue (2020) [13]. One can find that our method is much easier and more effective.
Preparing Dicke States on a Quantum Computer

Exact requirement of controlled NOT (CNOT) and single-qubit gates to implement a quantum algorithm in a given architecture is one of the central problems in this computational paradigm.In this article, we take a tutorial approach in explaining the preparation of Dicke states (|D n k ) using concise realizations of partially defined unitary transformations.We show how to efficiently implement the state-of-the-art deterministic Dicke state preparation circuits and in turn optimize them in terms of CNOT and single-qubit gate counts.We explain theoretical ideas in reducing the gate counts and observe how these improvements are reflected in actual implementation of the circuits.To emphasize the advantages, we describe the circuit for preparing |D 4 2 on the "ibmqx2" machine of the IBM quantum experience (QX) service.Our approach shows that the error induced due to noise in the system is lesser in comparison to the existing works.We conclude by describing the CNOT map of the generic |D n k preparation circuit and analyze different ways of distributing the CNOT gates in the circuit and its effect on the induced error in the Noisy Intermediate Scale Quantum environment.
ResLT: Residual Learning for Long-tailed Recognition

Deep learning algorithms face great challenges with long-tailed data distribution which, however, is quite a common case in real-world scenarios. Previous methods tackle the problem from either the aspect of input space (re-sampling classes with different frequencies) or loss space (re-weighting classes with different weights), suffering from heavy over-fitting to tail classes or hard optimization during training. To alleviate these issues, we propose a more fundamental perspective for long-tailed recognition, i.e., from the aspect of parameter space, and aims to preserve specific capacity for classes with low frequencies. From this perspective, the trivial solution utilizes different branches for the head, medium, tail classes respectively, and then sums their outputs as the final results is not feasible. Instead, we design the effective residual fusion mechanism - with one main branch optimized to recognize images from all classes, another two residual branches are gradually fused and optimized to enhance images from medium+tail classes and tail classes respectively. Then the branches are aggregated into final results by additive shortcuts. We test our method on several benchmarks, i.e., long-tailed version of CIFAR-10, CIFAR-100, Places, ImageNet, and iNaturalist 2018. Experimental results manifest the effectiveness of our method. Our code is available at https://github.com/jiequancui/ResLT.
Human pose recognition via adaptive distribution encoding for action perception in the self-regulated learning process

Human pose recognition (HPR) under infrared imaging is considered a significant role for action perception in the self-regulated learning process. In this article, we propose a novel elliptical distribution encoding (EDE) method for understanding the human behavior through body poses under infrared imaging. Firstly, to reveal the relationship between the adjacent joint points, the elliptical Gaussian coordinate coding is proposed for the human skeleton direction description. Then, the KL divergence is introduced to measure the different between the predicated heatmap and the groundtruth one. Finally, the EDE method is executed on the infrared human pose images in the classroom. The proposed pose recognition is performed in the Python tool. Experimental results are tested on two standard datasets like IRPSRL and MS COCO, and it provides very well classification accuracy. The performances of the proposed method are higher in all performances as related to other existing schemes. The developed EDE method will promote the development of computer-assisted technology in the self-regulated learning process. Our code is publicly available at https://github.com/hai-LIU/EDE.
Micro-expression recognition using advanced genetic algorithm

In recent years, numerous facial expression recognition related applications had been commercialized in the market. Many of them achieved promising and reliable performance results in real-world applications. In contrast, the automated micro-expression recognition system relevant research analysis is still greatly lacking. This is because of the nature of the micro-expression that is usually appeared with relatively lesser duration and lower intensity. However, due to its uncontrollable, subtlety, and spontaneity properties, it is capable to reveal one’s concealed genuine feelings. Therefore, this paper attempts to improve the performance of current micro-expression recognition systems by introducing an efficient and effective algorithm. Particularly, we employ genetic algorithms (GA) to discover an optimal solution in order to facilitate the computational process in producing better recognition results. Prior to the GA implementation, the benchmark preprocessing method and feature extractors are directly adopted herein. Succinctly, the complete proposed framework composes three main steps: the apex frame acquisition, optical flow approximation, and feature extraction with CNN architecture. Experiments are conducted on the composite dataset that is made up of three publicly available databases, viz, CASME II, SMIC, and SAMM. The recognition performance tends to prevail the state-of-the-art methods by attaining an accuracy of 85.9% and F1-score of 83.7%.
Fusion Chain: A Decentralized Lightweight Blockchain for IoT Security and Privacy

As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI).
A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.
More Efficient Digital Signatures with Tight Multi-user Security

We construct the currently most efficient signature schemes with tight multi-user security against adaptive corruptions. It is the first generic construction of such schemes, based on lossy identification schemes (Abdalla et al.; JoC 2016), and the first to achieve strong existential unforgeability. It also has significantly more compact signatures than the previously most efficient construction by Gjøsteen and Jager (CRYPTO 2018). When instantiated based on the decisional Diffie–Hellman assumption, a signature consists of only three exponents.We propose a new variant of the generic construction of signatures from sequential OR-proofs by Abe, Ohkubo, and Suzuki (ASIACRYPT 2002) and Fischlin, Harasser, and Janson (EUROCRYPT 2020). In comparison to Fischlin et al., who focus on constructing signatures in the non-programmable random oracle model (NPROM), we aim to achieve tight security against adaptive corruptions, maximize efficiency, and to directly achieve strong existential unforgeability (also in the NPROM). This yields a slightly different construction and we use slightly different and additional properties of the lossy identification scheme.Signatures with tight multi-user security against adaptive corruptions are a commonly-used standard building block for tightly-secure authenticated key exchange protocols. We also show how our construction improves the efficiency of all existing tightly-secure AKE protocols.
Edit Distance in Near-Linear Time: it's a Constant Factor

We present an algorithm for approximating the edit distance between two strings of length n in time n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1+ε</sup> , for any , up to a constant factor. Our result completes a research direction set forth in the recent breakthrough paper [1], which showed the first constant-factor approximation algorithm with a (strongly) sub-quadratic running time. The recent results [2], [3] have shown near-linear complexity only under the restriction that the edit distance is close to maximal (equivalently, there is a near-linear additive approximation). In contrast, our algorithm obtains a constant-factor approximation in near-linear running time for any input strings.
A Comprehensive Review on Email Spam Classification using Machine Learning Algorithms

Email is the most used source of official communication method for business purposes. The usage of the email continuously increases despite of other methods of communications. Automated management of emails is important in the today's context as the volume of emails grows day by day. Out of the total emails, more than 55 percent is identified as spam. This shows that these spams consume email user time and resources generating no useful output. The spammers use developed and creative methods in order to fulfil their criminal activities using spam emails, Therefore, it is vital to understand different spam email classification techniques and their mechanism. This paper mainly focuses on the spam classification approached using machine learning algorithms. Furthermore, this study provides a comprehensive analysis and review of research done on different machine learning techniques and email features used in different Machine Learning approaches. Also provides future research directions and the challenges in the spam classification field that can be useful for future researchers.
MVDF-RSC: Multi-view data fusion via robust spectral clustering for geo-tagged image tagging

Image tag recommendation, aiming at assigning a set of relevant tags for images, is a useful way to help users organize images’ content. Early methods in image tagging mainly demonstrated using low-level visual features. However, two visually similar photos may have different concepts (semantic gap). Although different multi-view tagging methods are proposed to learn the discriminative features, they usually do not consider the geographical correlation among images. Moreover, geographical-based image tagging models generally focused on the relevance criterion, i.e., how well the suggested tags describe image content. Diversity and redundancy should be controlled to guarantee the recommendation models’ effectiveness and promote complementary information among tags. This paper proposes a robust multi-view image tagging method, termed MVDF-RSC, which considers the relevance, diversity, and redundancy criteria. Precisely, the proposed method consists of two phases: training and prediction. We propose a new robust optimization problem in the training phase to determine the similarity between data via the early fusion of multiple views of images and obtain clusters. In the prediction phase, relevant tags are recommended to each test data using a search-based method and a late fusion strategy. Comprehensive experiments on two geo-tagged image datasets demonstrate the proposed method’s effectiveness over state-of-the-art alternatives.