Reviewerly/exHarmony huggingface dataset:
The Significance of Erosion Channels on Gold Metallogeny in the Witwatersrand Basin (South Africa): Evidence from the Carbon Leader Reef in the Carletonville Gold Field

Abstract Within the eastern portion of the Carletonville gold field, the gold- and uranium-rich Carbon Leader reef of the Central Rand Group (Witwatersrand Supergroup) is truncated by an erosion channel. This channel is asymmetrical and lenticular in shape, measuring 150 to 180 m in width and up to 100 m in depth. High-resolution seismic data show that the erosion channel cuts from the Carbon Leader reef into all older units of the Central Rand Group down to the Roodepoort Formation of the underlying West Rand Group. A total of seven boreholes were drilled into the channel, revealing that it is composed of quartzite at its base (9 m thick), overlain by deformed (lower) and laminated (upper) chloritoid-bearing shale (21 m thick) and quartzite (18 m thick). The Carbon Leader reef is highly enriched in gold (5–40 g/t Au), whereas the gold tenor of the erosion channel fill is in general much lower (&amp;lt;1 g/t Au), although locally grades of as much as 3.8 g/t Au are reached. Detailed seismic, sedimentological, and petrographic analyses revealed that the channel was filled with locally sourced sediments from the Main Formation. A closed-system mass balance further demonstrates that gold in the erosion channel could have been entirely sourced from the Carbon Leader reef. Sediment load played a crucial role in the distribution of gold in the channel, thus supporting a stratigraphically controlled modified placer model for the origin of gold in the Carbon Leader reef.
KP-YOLO: A Modification of YOLO Algorithm for the Keypoint-Based Detection of QR Codes

The YOLO (v1/v2/v3) algorithm is a well-known, extensively used, and much studied detection algorithm. YOLO detects objects in an image by predicting their bounding boxes and class distributions. This paper studies a modified version of the YOLO model applied to a specific problem: the detection of a set of feature points in a one-class setting. We apply this approach to the particular case of detecting QR Codes. Instead of detecting rectangular, axis-aligned bounding boxes, our objective is to detect a set of keypoints—the QR Code’s finder patterns—and hence the model’s name KP-YOLO. Although QR Code detection was chosen to showcase this experiment, the KP-YOLO algorithm can be applied to other keypoint-based detection problems (under certain constraints).
Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embeddings
The application of virtual reality in empathy establishment: Foresee the future

The establishment of empathy is the premise and foundation for diverse innovative proposals and problem solutions. Virtual reality has provided a full range of depth and breadth for the establishment of empathy in many different types of fields due to its immersive, interactive, and imaginative characteristics. In this study, bibliometric analysis and VOSviewer software are used to cluster and visualize relevant 190 articles from the Web of Science core collection. The essay proposes a positioning of how to apply virtual reality on empathy based on two dimensions, from internal world to external world, and from business innovation to social innovation, by integrating each two of them, four application methods are summarized, which are meaning shaping, value creation, individual satisfaction, and self-realization. What's more, using the bibliometric analysis result as a basis, the application landscape of virtual reality technology for establishing empathy has been constructed, including individual level, society level, and nature level, which reveals the existing and coming possibilities of using VR technology on building empathy in different fields. Last but not least, the paper has discussed the impact of virtual reality for empathy-building from five aspects, economy, politics, culture, society, and ecology. The efforts of this study reveal the VR tendency and have important reference significance for promoting the application of virtual reality technology in creating empathy and innovation in different fields.
Bayesian generalizations of the integer-valued autoregressive model

We develop two Bayesian generalizations of the Poisson integer-valued autoregressive model. The AdINAR(1) model accounts for overdispersed data by means of an innovation process whose marginal distributions are finite mixtures, while the DP-INAR(1) model is a hierarchical extension involving a Dirichlet process, which is capable of modeling a latent pattern of heterogeneity in the distribution of the innovations rates. The probabilistic forecasting capabilities of both models are put to test in the analysis of crime data in Pittsburgh, with favorable results.
Automatic Translation for Multiple NLP tasks: a Multi-task Approach to Machine-oriented NMT Adaptation

Although machine translation (MT) traditionally pursues “human-oriented” objectives, humans are not the only possible consumers of MT output. For instance, when automatic translations are used to feed downstream Natural Language Processing (NLP) components in cross-lingual settings, they should ideally pursue “machine-oriented” objectives that maximize the performance of these components. Tebbifakhr et al. (2019) recently proposed a reinforcement learning approach to adapt a generic neural MT(NMT) system by exploiting the reward from a downstream sentiment classifier. But what if the downstream NLP tasks to serve are more than one? How to avoid the costs of adapting and maintaining one dedicated NMT system for each task? We address this problem by proposing a multi-task approach to machine-oriented NMT adaptation, which is capable to serve multiple downstream tasks with a single system. Through experiments with Spanish and Italian data covering three different tasks, we show that our approach can outperform a generic NMT system, and compete with single-task models in most of the settings.
ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation

Existing works address the problem of generating high frame-rate sharp videos by separately learning the frame deblurring and frame interpolation modules. Most of these approaches have a strong prior assumption that all the input frames are blurry whereas in a real-world setting, the quality of frames varies. Moreover, such approaches are trained to perform either of the two tasks - deblurring or interpolation - in isolation, while many practical situations call for both. Different from these works, we address a more realistic problem of high frame-rate sharp video synthesis with no prior assumption that input is always blurry. We introduce a novel architecture, Adaptive Latent Attention Network (ALANET), which synthesizes sharp high frame-rate videos with no prior knowledge of input frames being blurry or not, thereby performing the task of both deblurring and interpolation. We hypothesize that information from the latent representation of the consecutive frames can be utilized to generate optimized representations for both frame deblurring and frame interpolation. Specifically, we employ combination of self-attention and cross-attention module between consecutive frames in the latent space to generate optimized representation for each frame. The optimized representation learnt using these attention modules help the model to generate and interpolate sharp frames. Extensive experiments on standard datasets demonstrate that our method performs favorably against various state-of-the-art approaches, even though we tackle a much more difficult problem.
On Privacy and Accuracy in Data Releases (Invited Paper)

In this paper we study the relationship between privacy and accuracy in the context of correlated datasets. We use a model of quantitative information flow to describe the the trade-off between privacy of individuals' data and and the utility of queries to that data by modelling the effectiveness of adversaries attempting to make inferences after a data release.We show that, where correlations exist in datasets, it is not possible to implement optimal noise-adding mechanisms that give the best possible accuracy or the best possible privacy in all situations. Finally we illustrate the trade-off between accuracy and privacy for local and oblivious differentially private mechanisms in terms of inference attacks on medium-scale datasets.
Noisy, Greedy and Not so Greedy k-Means++

The k-means++ algorithm due to Arthur and Vassilvitskii [David Arthur and Sergei Vassilvitskii, 2007] has become the most popular seeding method for Lloyd’s algorithm. It samples the first center uniformly at random from the data set and the other k-1 centers iteratively according to D²-sampling, i.e., the probability that a data point becomes the next center is proportional to its squared distance to the closest center chosen so far. k-means++ is known to achieve an approximation factor of 𝒪(log k) in expectation.Already in the original paper on k-means++, Arthur and Vassilvitskii suggested a variation called greedy k-means++ algorithm in which in each iteration multiple possible centers are sampled according to D²-sampling and only the one that decreases the objective the most is chosen as a center for that iteration. It is stated as an open question whether this also leads to an 𝒪(log k)-approximation (or even better). We show that this is not the case by presenting a family of instances on which greedy k-means++ yields only an Ω(𝓁⋅log k)-approximation in expectation where 𝓁 is the number of possible centers that are sampled in each iteration.Inspired by the negative results, we study a variation of greedy k-means++ which we call noisy k-means++ algorithm. In this variation only one center is sampled in every iteration but not exactly by D²-sampling. Instead in each iteration an adversary is allowed to change the probabilities arising from D²-sampling individually for each point by a factor between 1-e₁ and 1+e₂ for parameters e₁ ∈ [0,1) and e₂ ≥ 0. We prove that noisy k-means++ computes an 𝒪(log² k)-approximation in expectation. We use the analysis of noisy k-means++ to design a moderately greedy k-means++ algorithm.
Non-Hierarchical Clustering

Differently from hierarchical clustering procedures, non-hierarchical clustering methods need the user to specify in advance the number of clusters; therefore, in this case, a single partition is obtained. The two most famous non-hierarchical clustering algorithms are the k-Means and the k-Medoids one. They differ in the definition of the cluster prototypes. In particular, the k-Means prototypes, called centroids, are defined to be the average values of units assigned to the clusters, while the k-Medoids prototypes, called medoids, identify the most representative observed units for each cluster. In this chapter, non-hierarchical clustering methods will be briefly introduced from a theoretical point of view and their implementation will be presented in detail by means of some real-life case studies.
Revisiting Round-trip Translation for Quality Estimation.

Quality estimation (QE), a task of evaluating the quality of translation automatically without human-translated reference, is one of the important challenges for machine translation (MT). As the QE methods, BLEU score for round-trip translation (RTT) had been considered. However, it was found to be a poor predictor of translation quality since BLEU was not an adequate metric to detect semantic similarity between input and RTT. Recently, the pre-trained language models have made breakthroughs in many NLP tasks by providing semantically meaningful word and sentence embeddings. In this paper, we employ the semantic embeddings to RTT-based QE metric. Our method achieves the highest correlations with human judgments compared to WMT 2019 quality estimation metric task submissions. Additionally, we observe that with semantic-level metrics, RTT-based QE is robust to the choice of a backward translation system and shows consistent performance on both SMT and NMT forward translation systems.
Sample-Efficient Automated Deep Reinforcement Learning

Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters. This sensitivity can partly be attributed to the non-stationarity of the RL problem, potentially requiring different hyperparameter settings at various stages of the learning process. Additionally, in the RL setting, hyperparameter optimization (HPO) requires a large number of environment interactions, hindering the transfer of the successes in RL to real-world applications. In this work, we tackle the issues of sample-efficient and dynamic HPO in RL. We propose a population-based automated RL (AutoRL) framework to meta-optimize arbitrary off-policy RL algorithms. In this framework, we optimize the hyperparameters and also the neural architecture while simultaneously training the agent. By sharing the collected experience across the population, we substantially increase the sample efficiency of the meta-optimization. We demonstrate the capabilities of our sample-efficient AutoRL approach in a case study with the popular TD3 algorithm in the MuJoCo benchmark suite, where we reduce the number of environment interactions needed for meta-optimization by up to an order of magnitude compared to population-based training.
English Letter Recognition Based on TensorFlow Deep Learning

Abstract Image recognition has always been a hot research direction. With the continuous progress of technology theory and application, deep learning has a very significant role in the field of image recognition. Today’s image international competitions and enterprise applications are mainly based on deep learning technology, compared with traditional technologies, deep learning is significantly more effective in the application of feature extraction and algorithm models. This paper based on the TensorFlow framework and use deep learning transfer learning fine-tuning to recognize handwritten English characters. In the course of the experiment, the data enhancement method is used to pre-process the collected data, which can increase the amount of training data and improve the generalization ability of the model. At the same time, the parameters and optimizer are continuously optimized to accelerate the convergence speed and finally reach the convergence loss value. Experimental results show that the application of deep learning algorithms has achieved good results in training model feature extraction and recognition accuracy.
Authentication of Electronic Legal Statements by a Trust Service Provider Using Two-Factor Dynamic Handwritten Signature Verification

It is a necessary premise of electronic administration that legal statements of natural persons could be authenticated. The use of electronic signatures is an appropriate and sound approach for guaranteeing authenticity, but its use is not prevalent among natural persons. It is proposed that a trust service provider could create an electronic signature on behalf of a natural person, where the client is authenticated by two-factor dynamic handwritten signature verification. This procedure, supplemented with further controls, would provide a secure and reliable basis for authenticating of electronic legal statements.
Three New Imaging Technologies That Are Worth a Look: Aided by Signal Processing, Advanced Imaging Research Projects Are Opening Doors to New Vistas [Special Reports]

Whether being developed for entertainment, environmental monitoring, exploration, or any of a wide range of other applications, advanced imaging technologies are literally changing the way people see things. In virtually all areas of imaging innovation, signal processing is an essential tool that helps researchers craft technologies that once existed only in the realms of fantasy and science fiction.
Further Comparison of 4 Display Modes for a Multi-Resolution Foveated Laparoscope

Background. To overcome field of view and ergonomic limitations of standard laparoscopes, we are developing a multi-resolution foveated laparoscope (MRFL), which can simultaneously obtain both wide- and zoomed-in-view images. To facilitate the effectiveness of our MRFL, we have been investigating various ways of organizing and visualizing dual-view multi-resolution images acquired by the MRFL. In our prior study, we implemented and compared 6 display modes for the MRFL, assuming a typical clinical environment where a standard (but limited) resolution monitor is available. To take full advantage of our MRFL, displays having sufficient screen resolutions might be advantageous. The present study aims to further understand the effects of view configurations through displays with a standard high-definition (HD) resolution and a 4K resolution. In this study, we compare 3 display modes for limited-resolution displays against a new mode for sufficient-resolution displays. Methods. Twenty subjects performed 3 evaluation trials of a touching task with each display mode in an emulated MRFL environment. Various objective measurements including task completion time and the number of collisions, and subjective preference were recorded. Results. The new mode showed a better task completion time than the other modes, while it maintained a low number of collisions similar to the others. Moreover, the majority of participants selected the new mode as their most preferred one. Conclusions. With a sufficient display resolution, the co-registration between the unblocked and unwarped wide context view and the high-resolution zoomed-in view offered by the new mode was highly effective on both task performance and user preference.
Improved YOLO v3 network-based object detection for blind zones of heavy trucks

Object detection for blind zones is critical to ensuring the driving safety of heavy trucks. We propose a scheme to realize object detection in the blind zones of heavy trucks based on the improved you-only-look-once (YOLO) v3 network. First, according to the actual detection requirements, the targets are determined to establish a new data set of persons, cars, and fallen pedestrians, with a focus on small and medium objects. Subsequently, the network structure is optimized, and the features are enhanced by combining the shallow and deep convolution information of the Darknet platform. In this way, the feature propagation can be effectively enhanced, feature reuse can be promoted, and the network performance for small object detection can be improved. Furthermore, new anchors are obtained by clustering the data set using the K-means technique to improve the accuracy of the detection frame positioning. In the test stage, detection is performed using the trained model. The test results demonstrate that the proposed improved YOLO v3 network is superior to the original YOLO v3 model in terms of the blind zone detection and can satisfy the accuracy and real-time requirements with an accuracy of 94% and runtime of 13.792 ms / frame. Moreover, the mean average precision value for the improved model is 87.82%, which is 2.79% higher than that of the original YOLO v3 network.
An Efficient Text Classification Using fastText for Bahasa Indonesia Documents Classification

Text classification using a simple word representation with a linear classifier often considered as strong baselines to gain the best performances. However, a simple word representation like Bag of Word (BOW) has a deficiency of curse dimensionality, so it is only suitable for small datasets. BOW also needs some dependent pre-processing steps like stopwords-removal and stemming. Therefore, the BOW model cannot be implemented automatically because of the dependency in a specific language. On the other hand, deep neural network classifiers can eliminate the pre-processing prerequisite, but this model not efficient in time processing and need a large dataset for the learning process. It becomes a challenge for language that has limitation resources like Bahasa Indonesia. Another novel approach of text classifier is using the fastText model for text classification. This model can minimize pre-processing dependencies and more efficient in training time processing. However, there hasn't been much observation whether the fastText model outperformed the BOW model for small datasets. This paper aims to compare text classification using the TFIDF model as one of the BOW models with a fastText model for 500 news articles in Bahasa Indonesia. The result of this study showed both models gain an outstanding performance, which is 0.97 F-Score. The TFIDF model needs longer pre-processing stages and requiring more training time. Meanwhile, the fastText model only needs to tune some hyperparameters and get similar performance results to the TFIDF model. Based on this study, we can conclude that the fastText model is efficient text classification.
An efficient classification of secure and non-secure bug report material using machine learning method for cyber security

In the field of software development, the main problem is to recognize the security-oriented issues within the reported bugs due to its inacceptable rate to provide the satisfied reliability on customer and software dataset. The objective is to propose a novel machine learning approach for multiclass supervised classification named Bug Severity classification to overcome these challenges with the use of supervised Artificial Neural Network and stacking based Navies Bayes classifier. This proposed approach directly examines the latent and highly descriptive features. Primarily, using the natural language programming approaches bug report text is preprocessed. After then, N gram is employed for extracting features by overcoming data sparsity problems. Further, the supervised Artificial Neural Network extracts the salient feature patterns of the corresponding severity classes. Finally, the stacking-based Navies Bayes classifier is employed for classifying multiple bug severity classes.