Reviewerly/exHarmony huggingface dataset:
Super-resolution reconstruction of magnetic resonance images based on multi-scale feature extraction Super-Resolution Convolution Neural Network

Background: Low-resolution magnetic resonance imaging (MRI) has high imaging speed, but the image details cannot meet the needs of clinical diagnosis. More and more researchers are interested in neural network-based reconstruction methods. How to effectively process the super-resolution reconstruction of the low-resolution images has become highly valuable in clinical applications. Methods: We introduced Super-Resolution Convolution Neural Network (SRCNN) into the reconstruction of magnetic resonance images. The SRCNN consists of three layers, the image feature extraction layer, the nonlinear mapping layer, and the reconstruction layer. For the feature extraction layer, a multi-scale feature extraction (MFE) method was used to extract the features in different scales by involving three different levels of views, which is superior to the original feature extraction in views with fixed size. Compared with the original feature extraction only in fixed size views, we used three different levels of views to extract the features of different scales. This MFE could also be combined with residual learning to improve the performance of MRI super-resolution reconstruction. The proposed network is an end-to-end architecture. Therefore, no manual intervention or multi-stage calculation is required in practical applications. The structure of the network is extremely simple by omitting the fully connected layers and the pooling layers from traditional Convolution Neural Network. Results and Conclusions: After comparative experiments, the effectiveness of the MFE SRCNN-based network in super-resolution reconstruction of MR images has been greatly improved. The performance is significantly improved in terms of evaluation indexes peak signal-to-noise ratio and structural similarity index measure, and the detail recovery of images is also improved.
Day-Ahead Photovoltaic Power Production Forecasting Following Traditional and Hierarchical Approach

Forecasting power production in photovoltaic plants is becoming one of the leading research areas, owing to its potential for electricity production stability, as precisely estimated predictions are crucial for power systems operations and planning. In this work, three machine learning models are used (MLP, SVR, ANN) to predict the power production of a self-consumption PV plant following the traditional and hierarchical approach. The data used correspond to a PV plant located in the Moroccan city of Settat collected from December 2019 to October 2020. The performance of the models was measured using different evaluation matrices, RMSE (root mean squared error), MSE (mean squared error), and MAE (mean absolute error). Based on the MAPE error indices, the suggested (hierarchical) approach showed an improvement of 7% using the ANN model. This work represents the benefits of using outputs of individual inverters for forecasts in PV plants where more than one inverter is installed.
Introduction

Deep reinforcement learning studies how we learn to solve complex problems, problems that require us to find a solution to a sequence of decisions in high-dimensional states. To make bread, we must use the right flour, add some salt, yeast and sugar, prepare the dough (not too dry and not too wet), pre-heat the oven to the right temperature, and bake the bread (but not too long); to win a ballroom dancing contest we must find the right partner, learn to dance, practice, and beat the competition; to win in chess we must study, practice, and make all the right moves.
A Hadamard Matrix-Based Algorithm to Evaluate the Strength of Binary Sequences

Nowadays, a wide range of critical services relies on Internet of Things (IoT) devices. Nevertheless, they often lack proper security, becoming the gateway to attack the whole system. IoT security protocols are often based on stream ciphers, where pseudo-random number generators (PRNGs) are an essential part of them. In this work, we introduce a novel algorithm based on Hadamard matrices to evaluate the strength (unpredictability) of binary sequences, a key part of the IoT security stack. A comparative study with other algorithms that compute the same parameter is also presented.
Bmsmlet: Boosting Multi-Scale Information on Multi-Level Aggregated Features for Salient Object Detection

Nowadays, salient object detection methods based on deep learning have become a research focus. Therefore, how to reveal the representation mechanism and association rules of features at different levels and scales in order to improve the accuracy of salient object detection is a key issue to be solved. This paper proposes a salient object detection method to boost multi-scale information on multi-level aggregated features, which can accurately and flexibly aggregate multi-scale feature information via effective multi-level feature utilization. First, a scalable feature pyramid module is proposed, which can aggregate deep feature information in shallow features, thus obtaining aggregated features between different levels. Then, the global information enhancement module is built in the bottom-up network path to make up for the lost or weakened feature information in the process of multi-scale integration feature transmission. Next, internally convolve each level of aggregated feature via self-interactive module to enrich multi-scale information and improve the multi-scale representation ability of aggregated features. Finally, the global associativity loss function is designed to solve the noise caused by multi-scale variation so as to optimize the network training process, which effectively compensates for the deficiency of cross-entropy in the salient object detection task. The experimental results on four public datasets show that the performance of the proposed method has improved in contrast to the state-of-the-art methods.
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems

Localizing root causes for multi-dimensional data is critical to ensure online service systems’ reliability. When a fault occurs, only the measure values within specific attribute combinations (e.g., Province=Beijing) are abnormal. Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multi-dimensional data. This paper proposes a generic and robust root cause localization approach for multi-dimensional data, PSqueeze. We propose a generic property of root cause for multi-dimensional data, generalized ripple effect (GRE). Based on it, we propose a novel probabilistic cluster method and a robust heuristic search method. Moreover, we identify the importance of determining external root causes and propose an effective method for the first time in literature. Our experiments on two real-world datasets with 5400 faults show that the F1-score of PSqueeze outperforms baselines by 32.89%, while the localization time is around 10 seconds across all cases. The F1-score in determining external root causes of PSqueeze achieves 0.90. Furthermore, case studies in several production systems demonstrate that PSqueeze is helpful to fault diagnosis in the real world.
Design of Cricket Control System Based on STM32

At that time, the 7670 plus warship with atom was used. It proved that it was difficult for warships to complete this topic. On the one hand, the 72 m dominant frequency was still slow for image processing. I tested to read and output a frame of QQVGA image, and it took nearly 30 ms after simple binary processing. In addition, the image read out from the FIFO of the 7670 is the image taken at the last time, which determines that the 7670 plus warship of STM32 is not suitable for cricket system.
Knowledge Graph Construction and its Application in Automatic Radiology Report Generation from Radiologist's Dictation

Conventionally, the radiologist prepares the diagnosis notes and shares them with the transcriptionist. Then the transcriptionist prepares a preliminary formatted report referring to the notes, and finally, the radiologist reviews the report, corrects the errors, and signs off. This workflow causes significant delays and errors in the report. In this paper, we focus on applications of NLP techniques like Information Extraction (IE) and domain-specific Knowledge Graph (KG) to automatically generate radiology reports from radiologistsâ€™ dictation. This paper focuses on KG construction for each organ by extracting information from an existing large corpus of free-text radiology reports. We develop an information extraction pipeline that combines rule-based, pattern-based, and dictionary-based techniques with lexical-semantic features to extract entities and relations. Missing information in short dictation can be accessed from the KGs to generate pathological descriptions and hence the radiology report. Generated pathological descriptions evaluated using semantic similarity metrics, which shows 97% similarity with gold standard pathological descriptions. Also, our analysis shows that our IE module is performing better than the OpenIE tool for the radiology domain. Furthermore, we include a manual qualitative analysis from radiologists, which shows that 80-85% of the generated reports are correctly written, and the remaining are partially correct.
Hard Negative Sample Mining for Contrastive Representation in Reinforcement Learning

In recent years, contrastive learning has become an important technology of self-supervised representation learning and achieved SOTA performances in many fields, which has also gained increasing attention in the reinforcement learning (RL) literature. For example, by simply regarding samples augmented from the same image as positive examples and those from different images as negative examples, instance contrastive learning combined with RL has achieved considerable improvements in terms of sample efficiency. However, in the contrastive learning-related RL literature, the source images used for contrastive learning are sampled in a completely random manner, and the feedback of downstream RL task is not considered, which may severely limit the sample efficiency of the RL agent and lead to sample bias. To leverage the reward feedback of RL and alleviate sample bias, by using gaussian random projection to compress high-dimensional image into a low-dimensional space and the Q value as a guidance for sampling the hard negative pairs, i.e. samples with similar representation but diverse semantics that can be used to learn a better contrastive representation, we propose a new negative sample method, namely Q value-based Hard Mining (QHM). We conduct experiments on the DeepMind Control Suite and show that compared to the random sample manner in vanilla instance-based contrastive method, our method can effectively utilize the reward feedback in RL and improve the performance of the agent in terms of both sample efficiency and final scores, on 5 of 7 tasks.
TG: Accurate and Efficient RGB-D Feature With Texture and Geometric Information

Feature extraction and matching are the basis of many computer vision problems, such as image retrieval, object recognition, and visual odometry. In this article, we present a novel RGB-D feature with texture and geometric information (TG). It consists of a keypoint detector and a feature descriptor, which is accurate, efficient, and robust to scene variance. In the keypoint detection, we build a simplified Gaussian image pyramid to extract the texture feature. Meanwhile, the gradient of the point cloud is superimposed as the geometric feature. In the feature description, the texture information and spatial information are encoded in relative order to build a discriminative descriptor. We also construct a novel RGB-D benchmark dataset for RGB-D detector and descriptor evaluation under single variation. Comprehensive experiments are carried out to prove the superior performance of the proposed feature compared with state-of-the-art algorithms. The experimental results also demonstrate that our TG can achieve better performance especially on accuracy and the computational efficiency, making it more suitable for the real-time applications, e.g., visual odometry.
Geochemical features of the rocks of the South Permian Cuprous area

A study of 11 samples of sandstones, mudstones, limestones from copper occurrences in the villages of Yug and N. Lyady, Perm region, was carried out using the ICP-MS method. It was found that the instrumental rocks are relatively barren, enriched in Cu, Ag, Au, Cd, V, Os, Ru, Rh. An analysis of the results obtained made it possible to show that the Ufa deposits of the Middle Urals are quite comparable with the Tatar deposits of the Orenburg Cis-Urals, differing from them in a larger share in the petrofund of the area of demolition of basic and felsic rocks. It was suggested that some of the Permian copper was transported in the form of vanadates, and the Orenburg copper - in the composition of chromates under strongly oxidizing conditions. The low contents of light REEs in the accompanying mudstones, as well as the low contents of titanium in all sedimentary rocks, suggest that the areas of drift of clastic material were covered by significant weathering changes.
A New Hybrid Chaotic Map-Based Image Steganography Using Spectral Graph Wavelet

This paper proposes a new hybrid chaotic map-based image steganography using spectral graph wavelet (SGWT). A new hybrid chaotic map method is proposed using the composition of two existing chaotic maps: Hénon map and Duffing map. Using the proposed hybrid chaotic map, the secret image is scrambled. The SGWT transform using graph signal processing (GSP) is performed on cover and secret image, where each image is generated as a 2D grid graph and each pixel is displayed as a vertex of a graph. To hide the secret image in a cover image, the alpha blending operation is performed. Furthermore, the inverse graph wavelet is applied to bring forth the stego image. The experimental analysis and outcomes show that the suggested technique gives improved PSNR and NCC.
A Blockchain-Based Approach for COVID-19 Vaccine Lifecycle

Vaccination is a cost-effective health practice that prevents or reduces patients’ hospitalization, illness, and death. In case of a pandemic as the COVID-19 outbreak, vaccination is a more challenging situation due to the urgency in controlling the spread of a high infecting disease. Public acceptance of vaccines is critical to the success of immunization campaigns, being affected by the poor knowledge about vaccination and the existence of fake news. People need to know who needs to be vaccinated, which vaccines exist, when they should get vaccinated, and where to get vaccinated. Governments and health agencies need to approve, acquire, distribute, and administer the vaccines in a short period. Information related to the vaccine lifecycle is important for decision-makers to deliver vaccines effectively and equitably, through the development of immunization programs and their continuous improvement. Blockchain is a technology that stores data in blocks chained together in chronological order and replicated over a network. Blockchain data is decentralized, encrypted, and cross-checked, which in turn ensures security, immutability, transparency and trust. Such characteristics make Blockchain a reliable platform to address COVID-19 vaccine lifecycle. We investigate how the lifecycle of the COVID-19 vaccine can benefit from an approach based on Blockchain. We discuss the scenarios of vaccine research, production, distribution, and administration, as well as monitoring of disease cases. The main goal of the proposed Blockchain-based approach is to offer a transparent and trustable platform for all stakeholders that take part in the scenarios related to COVID-19 vaccines, including government entities and citizens.
Do Mathematical Typesetting Applications Affect User Manipulation of Fractions? Evidence from an Eye-Tracking Experiment

What-you-see-is-what-you-get word processing editors that support the typesetting of mathematical content allow for direct manipulation of 2-dimensional mathematical notation within a 1-dimensional environment. The models used to represent mathematical structure generally fall into two categories: (1) free-form, in which symbols are placed and moved freely in the workspace and (2) structure-based, where 2-dimensional structures are inserted and then populated with other structures or symbols. To understand the consequences of these models for writing mathematical expressions we examined the sequential order of symbol insertion when transcribing fractions, and compare this to the natural order employed when handwritten. Markov transition matrices were created from recordings of fraction transcriptions to quantify the probability of inserting a symbol immediately following insertion of other symbols. The three writing techniques yielded different dominant transition patterns and also differed in their ability to tolerate differences from the dominant pattern. This study likely represents the first empirical assessment of symbol insertion order probabilities when handwriting fractions, while also identifying differences in insertion order during use of different types of digital editors for typesetting mathematical content. These results have important implications for the usability of mathematical typesetting software applications and should be considered in the future design of such programs.
Proof Mate: An Interactive Proof Helper for PVS (Tool Paper)

This paper presents Proof Mate, an interactive proof helper for the PVS verification system. The helper is integrated in VSCode-PVS, the Visual Studio Code extension for PVS. It extends the capabilities of VSCode-PVS by introducing new functionalities for suggesting proof commands, sketching proof attempts, and repairing broken proofs during interactive proof sessions. This work further aligns VSCode-PVS to the functionalities provided by modern development tools, with the ultimate aim to facilitate the adoption of formal methods in engineering practices and education. Tool available at: https://github.com/nasa/vscode-pvs
The Lifecycle of Technical Debt that Manifest in Both Source Code and Issue Trackers

Context:Although Technical Debt (TD) has increasingly gained attention in recent years, most studies exploring TD are based on a single source (e.g., source code, code comments or issue trackers). Objective:Investigating information combined from different sources may yield insight that is more than the sum of its parts. In particular, we argue that exploring how TD items are managed in both issue trackers and software repositories (including source code and commit messages) can shed some light on what happens between the commits that incur TD and those that pay it back. Method:To this end, we randomly selected 3,000 issues from the trackers of five projects, manually analyzed 200 issues that contained TD information, and identified and investigated the lifecycle of 219 TD items. Results:The results indicate that most of the TD items marked as resolved in issue trackers are also paid back in source code, although many are not discussed after being identified in the issue tracker. Design Debt items are more likely to be paid back in source code, while Test Debt items are the least likely. We also learned that although TD items may be resolved a few days after being identified, it often takes a long time to be identified (around one year). In general, time is reduced if the same developer is involved in consecutive moments (i.e., introduction, identification, repayment decision-making and remediation), but the number of people involved in discussing the TD item does not seem to affect how quickly it is resolved.Conclusions:Investigating how developers manage TD across both source code repositories and issue trackers can lead to a more comprehensive oversight of this activity and support efforts to shorten the lifecycle of undesirable debt.
Heteroskedastic Proxy Vector Autoregressions: Testing for Time-Varying Impulse Responses in the Presence of Multiple Proxies

We propose a test for time-varying impulse responses in heteroskedastic structural vector autoregressions that can be used when the shocks are identified by external proxy variables as a group. The test can be used even if the shocks are not identified individually. The asymptotic analysis is supported by small sample simulations which show good properties of the test. An investigation of the impact of productivity shocks in a small macroeconomic model for the U.S. illustrates the importance of the issue for empirical work.
Large Electrode Movements: Realistic Electrical Crater Models

The operation of silicon submerged-arc furnaces is sometimes vexed by large, comparatively rapid electrode movements on the order of one meter in about one hour. The physical origin of such extreme electrode movements is still unclear, as is the question of how the current or resistance can stay more or less constant despite such a large change of the position of the electrode tip. Finite-element models of the furnaces’ electrical conditions usually either assign a constant conductivity to the crater gas or employ predefined high-conductivity regions that simulate the electrical arcs. As such, present models are ill equipped to handle large electrode movements and the continuous rearrangement of the arc positions. This paper presents a new approach to crater modeling, in which the crater-gas conductivity depends on the distance between the electrode surface and the crater wall. Combined with more realistic crater geometries, this enables pragmatic estimates to be made for the accessible current paths, and further makes it possible to winnow the candidate mechanisms behind large electrode movements from the long list of suggestions available in the literature and in the industry.
UNICON: Uncertainty-Conditioned Policy for Robust Behavior in Unfamiliar Scenarios

Deep reinforcement learning has been used to solve complex tasks in various fields, particularly in robotics control. However, agents trained using deep reinforcement learning have a problem of taking overconfident actions, even when the input state is far from the learned state distribution. This restricts deep reinforcement learning from being applied to real-world environments as overconfident actions in unlearned situations can result in catastrophic events; such as the collision of an autonomous vehicle. To address this, the agents should know “what they do not know” and choose an action by considering not only the state but also its uncertainty. In this study, we propose a novel uncertainty-conditioned policy (UNICON) inspired by the human behavior of changing policies according to uncertainty, e.g., slowing a car on a narrow road that has never been visited before. Our experimental results demonstrate that the proposed method is robust to unfamiliar scenarios that are not seen during training.
Anchored Belief Updating from Recommendations

Abstract. We study a belief updating behavior in a framework where information is presented as a recommendation from a menu of actions. We introduce a property on belief updating called order independence of recommendations , which is analogous to the Bayesâ€™ Rule property of path independence of signals. We show that order independence and the properties that characterize the contraction rule of Ke et al. (2021) lead to an impossibility result on the general domain of recommendations. We then show that such a rule exists if and only if the domain is substantially restricted. Lastly, we propose the anchored contraction rule , which satisfies order independence on the general domain.