Reviewerly/exHarmony huggingface dataset:
Learning reinforced attentional representation for end-to-end visual tracking

Although numerous recent tracking approaches have made tremendous advances in the last decade, achieving high-performance visual tracking remains a challenge. In this paper, we propose an end-to-end network model to learn reinforced attentional representation for accurate target object discrimination and localization. We utilize a novel hierarchical attentional module with long short-term memory and multi-layer perceptrons to leverage both inter- and intra-frame attention to effectively facilitate visual pattern emphasis. Moreover, we incorporate a contextual attentional correlation filter into the backbone network to make our model trainable in an end-to-end fashion. Our proposed approach not only takes full advantage of informative geometries and semantics but also updates correlation filters online without fine-tuning the backbone network to enable the adaptation of variations in the target object’s appearance. Extensive experiments conducted on several popular benchmark datasets demonstrate that our proposed approach is effective and computationally efficient.
Face Detection Based on YOLOv3

Face detection is the precondition of various research fields, involving face recognition, face identification, face expression analysis, etc. The existing object detection methods, whether two-stage methods or one-stage ones, expect to balance speed and accuracy. Meanwhile, YOLOv3, as a popular object detection algorithm, has gained obvious advantages in both speed and accuracy. Thus, we migrated YOLOv3 to the face detection area and made some improvements to adjust it to the face detection problem, including changing the detection layer to detect smaller faces, choosing the Softmax as the loss function instead of the logistic classifier to maximize the difference of inter-class features, and decreasing the dimension of features on detection layers to improve the speed. This method was trained on the WIDER FACE database and the CelebA database and tested on the FDDB database. Experimental results have shown that the face detection method based on YOLOv3 has obtained great performance on small face, speed, and accuracy for the face detection task.
GPT-NeoX-20B: An Open-Source Autoregressive Language Model

We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license.It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of submission.In this work, we describe GPT-NeoX-20B's architecture and training, and evaluate its performance on a range of language-understanding, mathematics and knowledge-based tasks.We open-source the training and evaluation code, as well as the model weights, at https://github.com/
Accelerated gradient boosting

Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov’s accelerated descent to design a new algorithm, which we call AGB (for Accelerated Gradient Boosting). Substantial numerical evidence is provided on both synthetic and real-life data sets to assess the excellent performance of the method in a large variety of prediction problems. It is empirically shown that AGB is less sensitive to the shrinkage parameter and outputs predictors that are considerably more sparse in the number of trees, while retaining the exceptional performance of gradient boosting.
Image Encryption Based on Pixel-Level Diffusion with Dynamic Filtering and DNA-Level Permutation with 3D Latin Cubes

Image encryption is one of the essential tasks in image security. In this paper, we propose a novel approach that integrates a hyperchaotic system, pixel-level Dynamic Filtering, DNA computing, and operations on 3D Latin Cubes, namely DFDLC, for image encryption. Specifically, the approach consists of five stages: (1) a newly proposed 5D hyperchaotic system with two positive Lyapunov exponents is applied to generate a pseudorandom sequence; (2) for each pixel in an image, a filtering operation with different templates called dynamic filtering is conducted to diffuse the image; (3) DNA encoding is applied to the diffused image and then the DNA-level image is transformed into several 3D DNA-level cubes; (4) Latin cube is operated on each DNA-level cube; and (5) all the DNA cubes are integrated and decoded to a 2D cipher image. Extensive experiments are conducted on public testing images, and the results show that the proposed DFDLC can achieve state-of-the-art results in terms of several evaluation criteria.
A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization

State-of-the-art studies have demonstrated the superiority of joint modeling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two processes. To exploit these benefits in a more sophisticated way, we propose a novel deep neural multi-task learning framework with explicit feedback strategies to jointly model recognition and normalization. On one hand, our method benefits from the general representations of both tasks provided by multi-task learning. On the other hand, our method successfully converts hierarchical tasks into a parallel multi-task setting while maintaining the mutual supports between tasks. Both of these aspects improve the model performance. Experimental results demonstrate that our method performs significantly better than state-of-theart approaches on two publicly available medical literature datasets.
Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.
Prediction Models — Development, Evaluation, and Clinical Application

Prediction models’ newfound importance and the emergence of model development based on machine learning raise questions about how to ensure their safety and efficacy, given their growing role in risk stratification, care pathways, and clinical outcomes.
Virus tinged? Exploring the facets of virtual reality use in tourism as a result of the COVID-19 pandemic

Several studies have investigated the use of virtual reality (VR) in tourism, but none has taken an epidemiological outlook. This research examined the use of VR in tourism through the lenses of an extended TAM model in times of COVID-19 pandemic. The premise was that, in this context, people would prefer less risky experiences and would see VR as a substitute for traditional travel. The data used was collected through a within-subjects experiment, which proved that intention to use VR in tourism increased under the COVID-19 effect. This study tested a conceptual model that showed this intention was influenced by the perceived ease of use, perceived usefulness, and perceived substitutability of VR, all mediated by people's interest in VR use in tourism. The perceived authenticity of VR experience determined the perceived substitutability of VR. This paper has theoretical and practical implications. In the long term, promoting tourism-related VR activities might reduce the risk of virus spreading, lessen the pressure imposed on this sector by such epidemic episodes, and increase its sustainability.
Blockchain for decentralization of internet: prospects, trends, and challenges

Blockchain has made an impact on today's technology by revolutionizing the financial industry through utilization of cryptocurrencies using decentralized control. This has been followed by extending Blockchain to span several other industries and applications for its capabilities in verification. With the current trend of pursuing the decentralized Internet, many methods have been proposed to achieve decentralization considering different aspects of the current Internet model ranging from infrastructure and protocols to services and applications. This paper investigates Blockchain's capacities to provide a robust and secure decentralized model for Internet. The paper conducts a critical review on recent Blockchain-based methods capable for the decentralization of the future Internet. We identify and investigate two research aspects of Blockchain that provides high impact in realizing the decentralized Internet with respect to current Internet and Blockchain challenges while keeping various design in considerations. The first aspect is the consensus algorithms that are vital components for decentralization of the Blockchain. We identify three key consensus algorithms including PoP, Paxos, and PoAH that are more adequate for reaching consensus for such tremendous scale Blockchain-enabled architecture for Internet. The second aspect that we investigated is the compliance of Blockchain with various emerging Internet technologies and the impact of Blockchain on those technologies. Such emerging Internet technologies in combinations with Blockchain would help to overcome Blockchain's established flaws in a way to be more optimized, efficient and applicable for Internet decentralization.
HABITAT: An IoT Solution for Independent Elderly

In this work, a flexible and extensive digital platform for Smart Homes is presented, exploiting the most advanced technologies of the Internet of Things, such as Radio Frequency Identification, wearable electronics, Wireless Sensor Networks, and Artificial Intelligence. Thus, the main novelty of the paper is the system-level description of the platform flexibility allowing the interoperability of different smart devices. This research was developed within the framework of the operative project HABITAT (Home Assistance Based on the Internet of Things for the Autonomy of Everybody), aiming at developing smart devices to support elderly people both in their own houses and in retirement homes, and embedding them in everyday life objects, thus reducing the expenses for healthcare due to the lower need for personal assistance, and providing a better life quality to the elderly users.
Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement

Recent years have witnessed remarkable success of deep learning methods in quality enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective quality enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video quality enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video quality enhancement in terms of both accuracy and efficiency.
A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation

Human gaze is essential for various appealing applications. Aiming at more accurate gaze estimation, a series of recent works propose to utilize face and eye images simultaneously. Nevertheless, face and eye images only serve as independent or parallel feature sources in those works, the intrinsic correlation between their features is overlooked. In this paper we make the following contributions: 1) We propose a coarse-to-fine strategy which estimates a basic gaze direction from face image and refines it with corresponding residual predicted from eye images. 2) Guided by the proposed strategy, we design a framework which introduces a bi-gram model to bridge gaze residual and basic gaze direction, and an attention component to adaptively acquire suitable fine-grained feature. 3) Integrating the above innovations, we construct a coarse-to-fine adaptive network named CA-Net and achieve state-of-the-art performances on MPIIGaze and EyeDiap.
Multiple grayscale image encryption using cross-coupled chaotic maps

This paper proposes a newly developed multiple grayscale image encryption scheme using cross-coupling of two Piece-wise Linear Chaotic Maps (PWLCM). In this scheme, cross-coupled PWLCM systems are used to perform both the permutation and diffusion operation. The sorted iterated sequences of cross-coupled PWLCM systems are used to perform the row-column diffusion operation, whereas their corresponding indexed sequences are used to execute the row-column permutation operation. The cross-coupled chaotic map improves the discrete dynamics of chaos and avoids the weakness of using a single chaotic map for permutation-diffusion operation. The use of a cross-coupled chaotic map increases the difficulty of cryptanalysis of the cipher image because the cipher output is generated by the mixing of different chaotic orbits. In addition to this, the cross-coupling of a single type of chaotic map (PWLCM system) increases the software and hardware efficiency of the algorithm. As well as, the use of the PWLCM system increases the encryption speed of the algorithm. The proposed technique also uses the Secure Hash Algorithm SHA-256 to resist the algorithm against the known-plaintext attack and the chosen-plaintext attack. Simulation results and security analysis reveals that the proposed algorithm is more encryption efficient and enhances security in multiple cipher images.
RAMBleed: Reading Bits in Memory Without Accessing Them

The Rowhammer bug is a reliability issue in DRAM cells that can enable an unprivileged adversary to flip the values of bits in neighboring rows on the memory module. Previous work has exploited this for various types of fault attacks across security boundaries, where the attacker flips inaccessible bits, often resulting in privilege escalation. It is widely assumed however, that bit flips within the adversary's own private memory have no security implications, as the attacker can already modify its private memory via regular write operations.We demonstrate that this assumption is incorrect, by employing Rowhammer as a read side channel. More specifically, we show how an unprivileged attacker can exploit the data dependence between Rowhammer induced bit flips and the bits in nearby rows to deduce these bits, including values belonging to other processes and the kernel. Thus, the primary contribution of this work is to show that Rowhammer is a threat to not only integrity, but to confidentiality as well.Furthermore, in contrast to Rowhammer write side channels, which require persistent bit flips, our read channel succeeds even when ECC memory detects and corrects every bit flip. Thus, we demonstrate the first security implication of successfully-corrected bit flips, which were previously considered benign.To demonstrate the implications of this read side channel, we present an end-to-end attack on OpenSSH 7.9 that extracts an RSA-2048 key from the root level SSH daemon. To accomplish this, we develop novel techniques for massaging memory from user space into an exploitable state, and use the DRAM rowbuffer timing side channel to locate physically contiguous memory necessary for double-sided Rowhammering. Unlike previous Rowhammer attacks, our attack does not require the use of huge pages, and it works on Ubuntu Linux under its default configuration settings.
Optimization of Skewed Data Using Sampling-Based Preprocessing Approach

In the past few years’ classification has undergone some major evolution. With constant surge of the amount of data gathered from different sources efficient processing and analysis of data is becoming difficult. Due to the uneven distribution of data among classes data classification with machine learning techniques has become more tedious. While most algorithms focus on major data samples they ignore the minor class data. Thus data skewing issue is one of the critical problems that need attention of researchers. The paper stresses upon data preprocessing using sampling techniques to overcome the data skewing problem. Here three different sampling techniques such as Resampling, SpreadSubSampling and SMOTE are implemented to reduce this uneven data distribution issue and classified with K-Nearest Neighbour algorithm. The performance of classification is evaluated with various performance metrics to determine the efficiency of classification.
Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.
TextMountain: Accurate scene text detection via instance segmentation

In this paper, we propose a novel scene text detection method named TextMountain. The key idea of TextMountain is making full use of border-center information. Different from previous works that treat center-border as a binary classification problem, we predict text center-border probability (TCBP) and text center-direction (TCD). The TCBP is just like a mountain whose top is text center and foot is text border. The mountaintop can separate text instances which cannot be easily achieved using semantic segmentation map and its rising direction can plan a road to top for each pixel on mountain foot at the group stage. The TCD helps TCBP learning better. Our label rules will not lead to the ambiguous problem with the transformation of angle, so the proposed method is robust to multi-oriented text and can also handle well curved text. In inference stage, each pixel at the mountain foot needs to search the path to the mountaintop and this process can be efficiently completed in parallel, yielding the efficiency of our method compared with others. The experiments on MLT, ICDAR2015, RCTW-17 and SCUT-CTW1500 datasets demonstrate that the proposed method achieves better or comparable performance in terms of both accuracy and efficiency. It is worth mentioning our method achieves an F-measure of 76.85% on MLT which outperforms the previous methods by a large margin. Code will be made available.
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.A total of 30 teams participated in at least one of the tracks.This paper introduces each track's goal, data and evaluation metrics, and reports the results of the received submissions.
Estimation of spatial distribution and health risk by arsenic and heavy metals in shallow groundwater around Dongting Lake plain using GIS mapping

Potable groundwater has become the primary water source for the local population because of the serious pollution of As and heavy metals in the surface water around the Dongting Lake Plain. A comprehensive research on the shallow groundwater was performed in this study via geographical information system (GIS) and geochemical method to evaluate groundwater quality and health risks of shallow groundwater in Dongting Lake Plain. Eighty-seven samples were collected and the content of As and twelve other heavy metals (e.g., Al, Fe, Zn, Cu, Mo, Ni, Mn, Co, Ba, Pb, Cd, and Cr) in the samples were detected by inductively coupled plasma–mass spectrum (ICP-MS) technology. The water pollution situation was assessed using heavy metal contents and evaluation indices, and human health risks were evaluated on the basis of both carcinogenic and noncarcinogenic aspects. Results showed that the shallow groundwater quality is moderately to heavily contaminated and should be considered in some areas of the Li and Xiangjiang River coasts. Several regions have the potential of carcinogenic risks induced by As and the groundwater in some regions may have the risk of Cr carcinogenesis in the wet season. These findings suggested that the potential harm caused by Fe, Zn, Mn, Cr, and As pollution of groundwater, especially As and Cr in wet season, must be considered. The spatio-temporal study on the groundwater quality evaluation may be beneficial to the protection and sustainable development of groundwater resources in Dongting Lake Plain.Summary: Although the overall noncarcinogenic health risk by metals in shallow groundwater of Dongting Lake is low, noncarcinogenic health risks caused by Fe, Zn, Mn, and As exist in some areas.