Reviewerly/exHarmony huggingface dataset:
New technology for mineral deposits prediction to identify prospective areas in the Zhezkazgan ore region

Purpose. Exploration and predicting the prospective areas in the Zhezkazgan ore region to set up detailed prospecting and evaluation works using new integrated technologies of prediction constructions in the mineral deposits geology. Methods. An integrated methodological approach is used, including methods for deciphering the Earth’s remote sensing (ERS) data, the use of geophysical data and methods of analogy and actualism. All constructions are made in accordance with the principles of shock-explosive tectonics (SET). Prediction constructions are started with the selection of remote sensing data for the studied region and interpretation based on the processing of radar satellite images obtained from the Radarsat-1 satellite. The radar satellite images are processed in the Erdas Imagine software package. Findings. New local prospective areas have been identified, within which it is expected to discover the deposits. Their reserves are to replenish the depleted ore base in the Zhezkazgan region. Area of the gravity maximum 1 (the Near), considered to be the most promising, is located in close proximity to the city of Zhezkazgan; area of the gravity maximum 2 (the Middle); area of the gravity maximum 3 (the Distant-Tabylga); area of the gravity maximum 6 (the Central). A prospective area has been also revealed, overlaid by a loose sediment cover and located inside the Terekty ring structure, as well as the area of a thick stratum of pyritized grey sandstones, which is adjacent to the Sh-2 well drilled to the south of the Zhezkazgan field. Originality. The use of a new prediction technology, in contrast to the known ones, is conditioned by the widespread use of the latest remote information from satellite images, which increases the accuracy of identifying the prospective areas of fields. Practical implications. The new technology for predicting mineral deposits makes it possible to significantly reduce the areas exposed to priority prospecting, which provides significant cost savings.
The Investigation of the Success of Different Machine Learning Methods in Breast Cancer Diagnosis

Objective: The aim of this study is to identify cancer earlier in life using machine learning methods.&#x0D; &#x0D; Methods: For this purpose, the Wisconsin Diagnostic Breast Cancer dataset was classified using Naive Bayes, decision trees, artificial neural networks algorithms and comparison of these machine learning methods was made. KNIME Analytics Platform was used for applications. Before the classification process, the dataset was preprocessed. After the pre-processing stage, three different classifier methods were applied to the dataset. Accuracy, sensitivity, specificity and confusion matrices were used to measure the success of the methods.&#x0D; &#x0D; Results: The results show that Naive Bayes and artificial neural network methods classify tumors with 96.5% accuracy. The success of the decision tree method in classification was 92.6%.&#x0D; &#x0D; Conclusion: The machine learning algorithms can be used successfully in breast cancer diagnosis to determine whether the tumors are malign or benign.
Correction to: Entanglement-assisted quantum error-correcting codes over arbitrary finite fields

A Correction to this paper has been published: https://doi.org/10.1007/s11128-019-2234-5
Integration of a Human-aware Risk-based Braking System into an Open-Field Mobile Robot

Safety integration components for robotic applications are a mandatory feature for any autonomous mobile application, including human avoidance behaviors. This paper proposes a novel parametrizable scene risk evaluator for open-field applications that use humans motion predictions and pre-defined hazard zones to estimate a braking factor. Parameters optimization uses simulated data. The evaluation is carried out by simulated and real-time scenarios, showing the impact of human predictions in favor of risk reductions on agricultural applications.
Multi-target Tracking Based on Deep Sort in Traffic Scene

Abstract In this paper, we use the deep sort multi-target tracking algorithm to achieve multi-target tracking of pedestrians and vehicles in traffic scenes. In this paper, firstly, yolov4 is used to train the pedestrian and vehicle detection model in traffic scenes. Then, according to the detection frame predicted by yolov4, multi-target tracking is carried out for specific targets. The multi-target tracking algorithm uses deep Sort, which can be combined with yolov4, can achieve less ID switching in real-time reasoning and deal with the loss of occlusion, so as to achieve more stable tracking effect.
DEVELOPMENT OF PANORAMIC VIRTUAL TOURS SYSTEM BASED ON LOW COST DEVICES

Abstract. The virtual visit is a simulation of reality that allows virtually changing time or placing. Unlike this, virtual reality offers the possibility to associate the real world with the virtual world, while ensuring interactivity and an improvement in prospecting the reality. One of the most used form of virtual reality is the 360° panoramic imagery and video, currently in the market we can find many providers of panoramic cameras, going from costly professional cameras to the low cost consumer grade cameras. In addition, many software providers offers the virtual tours creation and visualisation, to our knowledge, only google street view offers full mapping functionalities, but the data capture and collection is an opaque operation. Generally, smartphones are equipped with positioning capabilities, based on sensors like gyroscopes, accelerometers, magnetometer used as compass and GPS, the integration of this data allow the estimation of the pose.This paper discusses the creation of an interactive virtual reality with 360° panoramic images with the possibility of automating its acquisition and integration in mapping environment by using smartphone positioning methods, while the panoramic images are taken using costumer grade camera, which composes a low cost system for acquiring and diffusing panoramic virtual tours map. The developed solution is a dynamic web interface using several open source libraries and programming tools.
CoDesc: A Large Code–Description Parallel Dataset

Translation between natural language and source code can help software development by enabling developers to comprehend, ideate, search, and write computer programs in natural language.Despite growing interest from the industry and the research community, this task is often difficult due to the lack of large standard datasets suitable for training deep neural models, standard noise removal methods, and evaluation benchmarks.This leaves researchers to collect new small-scale datasets, resulting in inconsistencies across published works.In this study, we present CoDesc -a large parallel dataset composed of 4.2 million Java methods and natural language descriptions.With extensive analysis, we identify and remove prevailing noise patterns from the dataset.We demonstrate the proficiency of CoDesc in two complementary tasks for code-description pairs: code summarization and code search.We show that the dataset helps improve code search by up to 22% and achieves the new state-of-the-art in code summarization.Furthermore, we show CoDesc's effectiveness in pre-training-finetuning setup, opening possibilities in building pretrained language models for Java.To facilitate future research, we release the dataset, a data processing tool, and a benchmark at https://github.com/csebuetnlp/CoDesc.
Deep Metric Learning with Graph Consistency

Deep Metric Learning (DML) has been more attractive and widely applied in many computer vision tasks, in which a discriminative embedding is requested such that the image features belonging to the same class are gathered together and the ones belonging to different classes are pushed apart. Most existing works insist to learn this discriminative embedding by either devising powerful pair-based loss functions or hard-sample mining strategies. However, in this paper, we start from an another perspective and propose Deep Consistent Graph Metric Learning (CGML) framework to enhance the discrimination of the learned embedding. It is mainly achieved by rethinking the conventional distance constraints as a graph regularization and then introducing a Graph Consistency regularization term, which intends to optimize the feature distribution from a global graph perspective. Inspired by the characteristic of our defined ’Discriminative Graph’, which regards DML from another novel perspective, the Graph Consistency regularization term encourages the sub-graphs randomly sampled from the training set to be consistent. We show that our CGML indeed serves as an efficient technique for learning towards discriminative embedding and is applicable to various popular metric objectives, e.g. Triplet, N-Pair and Binomial losses. This paper empirically and experimentally demonstrates the effectiveness of our graph regularization idea, achieving competitive results on the popular CUB, CARS, Stanford Online Products and In-Shop datasets.
Improved POMDP Tree Search Planning with Prioritized Action Branching

Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. This paper proposes a method called PA-POMCPOW to sample a subset of the action space that provides varying mixtures of exploitation and exploration for inclusion in a search tree. The proposed method first evaluates the action space according to a score function that is a linear combination of expected reward and expected information gain. The actions with the highest score are then added to the search tree during tree expansion. Experiments show that PA-POMCPOW is able to outperform existing state-of-the-art solvers on problems with large discrete action spaces.
A Comparison between Pre-training and Large-scale Back-translation for Neural Machine Translation

BERT has been studied as a promising technique to improve NMT.Given that BERT is based on the similar Transformer architecture to NMT and the current datasets for most MT tasks are rather large, how pre-training has managed to outperform standard Transformer NMT models is underestimated.We compare MT engines trained with pre-trained BERT and back-translation with incrementally larger amounts of data, implementing the two most widely-used monolingual paradigms.We analyze their strengths and weaknesses based on both standard automatic metrics and intrinsic test suites that comprise a large range of linguistic phenomena.Primarily, we find that 1) BERT has limited advantages compared with large-scale back-translation in accuracy and consistency on morphology and syntax; 2) BERT can boost the Transformer baseline in semantic and pragmatic tasks which involve intensive understanding; 3) pre-training on huge datasets may introduce inductive social bias thus affects translation fairness.
Relay/replay attacks on GNSS signals

Global Navigation Satellite Systems (GNSS) are ubiquitously relied upon for positioning and timing. Detection and prevention of attacks against GNSS have been researched over the last decades, but many of these attacks and countermeasures were evaluated based on simulation. This work contributes to the experimental investigation of GNSS vulnerabilities, implementing a relay/replay attack with off-the-shelf hardware. Operating at the signal level, this attack type is not hindered by cryptographically protected transmissions, such as Galileo's Open Signals Navigation Message Authentication (OS-NMA). The attack we investigate involves two colluding adversaries, relaying signals over large distances, to effectively spoof a GNSS receiver. We demonstrate the attack using off-the-shelf hardware, we investigate the requirements for such successful colluding attacks, and how they can be enhanced, e.g., allowing for finer adversarial control over the victim receiver.
Contrastive Self-supervised Learning for Graph Classification

Graph classification is a widely studied problem and has broad applications. In many real-world problems, the number of labeled graphs available for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose two approaches based on contrastive self-supervised learning (CSSL) to alleviate overfitting. In the first approach, we use CSSL to pretrain graph encoders on widely-available unlabeled graphs without relying on human-provided labels, then finetune the pretrained encoders on labeled graphs. In the second approach, we develop a regularizer based on CSSL, and solve the supervised classification task and the unsupervised CSSL task simultaneously. To perform CSSL on graphs, given a collection of original graphs, we perform data augmentation to create augmented graphs out of the original graphs. An augmented graph is created by consecutively applying a sequence of graph alteration operations. A contrastive loss is defined to learn graph encoders by judging whether two augmented graphs are from the same original graph. Experiments on various graph classification datasets demonstrate the effectiveness of our proposed methods.
White-Box Watermarking Scheme for Fully-Connected Layers in Fine-Tuning Model

For the protection of trained deep neural network(DNN) models, embedding watermarks into the weights of the DNN model have been considered. However, the amount of change in the weights is large in the conventional methods, and it is reported that the existence of hidden watermark can be detected from the analysis of weight variance. This helps attackers to modify the watermark by effectively adding noise to the weight. In this paper, we focus on the fully-connected layers of fine-tuning models and apply a quantization-based watermarking method to the weights sampled from the layers. The advantage of the proposed method is that the change caused by watermark embedding is much smaller and the distortion converges gradually without using any loss function. The validity of the proposed method was evaluated by varying the conditions during the training of DNN model. The results shows the impact of training for DNN model, effectiveness of the embedding method, and high robustness against pruning attacks.
Novel Authentication and Secure Trust based RPL Routing in Mobile sink supported Internet of Things

In the modern era, prevalence of the Internet of Things (IoT) devices that have de facto protocol as IPv6 routing protocol for low power and lossy networks (RPL). Yet, RPL protocol is vulnerable to many attacks such as rank attack, password spoofing and more. To this end, most of the works have focused their research on securing the RPL-based IoT network. However, still there exist downsides such as high energy consumption, lack of effective authentication and high packet losses. Motivated by these preceding defects, this paper proposes the Novel Authentication and Secure Trust-based RPL Routing in Mobile sink-supported Internet of Things (SecRPL-MS). At first, SecRPL-MS performs a registration process where all IoT nodes in the network register themselves in the security entity. In this work, the frequent death of IoT nodes is alleviated through deploying mobile sink in the network. If any grid member (GM) node wants to transmit their data to the grid head (GH) node, then it must undergo authentication process. Secure routing is adopted in RPL by utilising the sail fish optimisation algorithm. Each GM node encrypts its sensed data using the prince algorithm before transmitting it to the GH node. The moving points are selected for the mobile sink using the Quantum Inspired Neural Network (QINN) algorithm. This proposed SecRPL-MS performance is evaluated using the Network Simulator 3 (NS3) in terms of the Packet Delivery Ratio (%), Delay (ms), Energy Consumption (mJ), Key Generation Time (ms) and Malicious Node Detection Accuracy (%). The proposed SecRPL-Ms outperforms 23% of malicious node detection accuracy when compared to existing systems, which represent the proposed SecRPL-MS system providing high security by mitigating the following attacks such as rank attack, Sybil attack, blackhole attack and man in the middle attack.
Putting the argument back into argument structure constructions

Abstract This paper shows that low-level generalisations in argument structure constructions are crucial to understanding the concept of alternation: low-level generalisations inform and constrain more schematic generalisations and thus constructional meaning. On the basis of an analysis of the causative alternation in English, and more specifically of the theme (i.e., the entity undergoing the event denoted by the verb), I show that each construction has its own schematic meaning. This analysis is conducted on a dataset composed of 11,554 instances of the intransitive non-causative construction and the transitive causative construction. The identification of lower-level generalisations feeds into the idea that language acquisition is organic and abstractions are formed only gradually (if at all) from exposure to input. So far, most of the literature on argument structure constructions has focused on the verb itself, and thus fails to capture these generalisations. I make up for this deficit through an in-depth analysis of the causative alternation.
Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention

Abusive language is a massive problem in online social platforms. Existing abusive language detection techniques are particularly ill-suited to comments containing heterogeneous abusive language patterns, i.e., both abusive and non-abusive parts. This is due in part to the lack of datasets that explicitly annotate heterogeneity in abusive language. We tackle this challenge by providing an annotated dataset of abusive language in over 11,000 comments from YouTube. We account for heterogeneity in this dataset by separately annotating both the comment as a whole and the individual sentences that comprise each comment. We then propose an algorithm that uses a supervised attention mechanism to detect and categorize abusive content using multi-task learning. We empirically demonstrate the challenges of using traditional techniques on heterogeneous content and the comparative gains in performance of the proposed approach over state-of-the-art methods.
Learning to Augment for Data-scarce Domain BERT Knowledge Distillation

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of Natural Language Processing (NLP) tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment data for BERT Knowledge Distillation in target domains with scarce labeled data, by learning a cross-domain manipulation scheme that automatically augments the target domain with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced controller to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on different NLP tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ~13.3%) when only a few labeled examples available.
Quantum speed limit for the maximum coherent state under the squeezed environment*

The quantum speed limit time for quantum system under squeezed environment is studied. We consider two typical models, the damped Jaynes-Cummings model and the dephasing model. For the damped Jaynes-Cummings model under squeezed environment, we find that the quantum speed limit time becomes larger with the squeezed parameter $r$ increasing and indicates symmetry about the phase parameter value $\theta=\pi$. Meanwhile, the quantum speed limit time can also be influenced by the coupling strength between the system and environment. However, the quantum speed limit time for the dephasing model is determined by the dephasing rate and the boundary of acceleration region that interacting with vacuum reservoir can be broken when the squeezed environment parameters are appropriately chosen.
Compactness of Hashing Modes and Efficiency Beyond Merkle Tree

We revisit the classical problem of designing optimally efficient cryptographically secure hash functions. Hash functions are traditionally designed via applying modes of operation on primitives with smaller domains. The results of Shrimpton and Stam (ICALP 2008), Rogaway and Steinberger (CRYPTO 2008), and Mennink and Preneel (CRYPTO 2012) show how to achieve optimally efficient designs of 2n-to-n-bit compression functions from non-compressing primitives with asymptotically optimal $$2^{n/2-\epsilon }$$ -query collision resistance. Designing optimally efficient and secure hash functions for larger domains ( $${>}{2n}$$ bits) is still an open problem. To enable efficiency analysis and comparison across hash functions built from primitives of different domain sizes, in this work we propose the new compactness efficiency notion. It allows us to focus on asymptotically optimally collision resistant hash function and normalize their parameters based on Stam's bound from CRYPTO 2008 to obtain maximal efficiency. We then present two tree-based modes of operation as a design principle for compact, large domain, fixed-input-length hash functions. Both of our designs are closely related to the ubiquitous Merkle Trees and have the potential for real-world applicability where the speed of hashing is of primary interest.
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions

Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with.To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data.Our proposed augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes.Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and augmented examples.Through extensive experimentation, we show that RAD, unlike classical accuracy measures, can quantify when stateof-the-art systems are not robust to counterfactuals.We find substantial failure cases which reveal that current VQA systems are still brittle.Finally, we connect between robustness and generalization, demonstrating the predictive power of RAD for performance on unseen augmentations. 1
Image Captioning Algorithm Based on Multi-Branch CNN and Bi-LSTM

The development of deep learning and neural networks has brought broad prospects to computer vision and natural language processing. The image captioning task combines cutting-edge methods in two fields. By building an end-to-end encoder-decoder model, its description performance can be greatly improved. In this paper, the multi-branch deep convolutional neural network is used as the encoder to extract image features, and the recurrent neural network is used to generate descriptive text that matches the input image. We conducted experiments on Flickr8k, Flickr30k and MSCOCO datasets. According to the analysis of the experimental results on evaluation metrics, the model proposed in this paper can effectively achieve image caption, and its performance is better than classic image captioning models such as neural image annotation models.