Reviewerly/exHarmony huggingface dataset:
Monitoring the Microseismicity through a Dense Seismic Array and a Similarity Search Detection Technique: Application to the Seismic Monitoring of Collalto Gas-Storage, North Italy

Seismic monitoring in areas where induced earthquakes could occur is a challenging topic for seismologists due to the generally very low signal to noise ratio. Therefore, the seismological community is devoting several efforts to the development of high-quality networks around the areas where fluid injection and storage and geothermal activities take place, also following the national induced seismicity monitoring guidelines. The use of advanced data mining strategies, such as template matching filters, auto-similarity search, and deep-learning approaches, has recently further fostered such monitoring, enhancing the seismic catalogs and lowering the magnitude of completeness of these areas. In this framework, we carried out an experiment where a small-aperture seismic array was installed within the dense seismic network used for monitoring the gas reservoir of Collalto, in North Italy. The continuous velocimetric data, acquired for 25 days, were analysed through the application of the optimized auto-similarity search technique FAST. The array was conceived as a cost-effective network, aimed at integrating, right above the gas storage site, the permanent high-resolution Collalto Seismic Network. The analysis allowed to detect micro-events down to magnitude Ml = −0.4 within a distance of ~15 km from the array. Our results confirmed that the system based on the array installation and the FAST data analysis might contribute to lowering the magnitude of completeness around the site of about 0.7 units.
A Hybrid Path Planning and Formation Control Strategy of Multi-Robots in a Dynamic Environment

This paper proposes a hybrid path planning and formation control strategy for multi-robots in a dynamic environment. Under a leader-follower formation structure, the followers can track the motion of one leader after the leader’s path is determined. First, a hybrid path planning strategy that contains global path planning and local path planning of the leader is investigated, in which an improved hybrid grey wolf optimizer with whale optimizer algorithm (GWO-WOA) is designed for the global path planning in a given map, meanwhile, a dynamic window approach (DWA) is fused for the local path planning to avoid dynamic obstacles. Then, a leader-follower formation control algorithm is proposed for multiple mobile robots. The followers are controlled to track their corresponding virtual robots which are generated according to the leader’s position and the formation. Finally, simulation experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithm in different environments.
Implementation of Digital Literacy in Indonesia Early Childhood Education

Digital literacy is a person's interests, attitudes, abilities, and skills in using digital technology-based devices. These skills are used to access, manage, integrate, analyze and evaluate the information needed and obtained, build new knowledge and create relationships or communication with others to participate effectively in society. This study aims to explore and explain the application of digital literacy in Early Childhood Education (ECE) institutions. The research method is descriptive quantitative with a survey technique. The instrument used is a questionnaire in the form of a Google-Form (G-Form) which is distributed online via Instagram, WhatsApp, and email. The population is ECE teachers, and a sample of 318 ECE teachers from various provinces was collected randomly. The data collected were analyzed descriptively with simple quantification. The results of the study indicate that the application of digital literacy in ECE can be implemented in a limited way and the implementation process must involve teachers and parents/guardians. The recommendation conveyed was the need for strict supervision for the implementation of digital literacy in early childhood.
Development of a Vision-Guided Shared-Control System for Assistive Robotic Manipulators

Assistive robotic manipulators (ARMs) provide a potential solution to mitigating the difficulties and lost independence associated with manipulation deficits in individuals with upper-limb impairments. However, achieving efficient control of an ARM can be a challenge due to the multiple degrees of freedom (DoFs) of an ARM that need to be controlled. This study describes the development of a vision-guided shared-control (VGS) system and how it is applied to a multi-step drinking task. The VGS control allows the user to control the gross motion of the ARM via teleoperation and commands the ARM to autonomously perform fine manipulation. A bench-top test of the autonomous actions showed that success rates for different subtasks ranged from 80% to 100%. An evaluation with three test pilots showed that the overall task performance, in terms of success rate, task completion time, and joystick mode-switch frequency, was better with VGS than with teleoperation. Similar trends were observed with a case participant with a spinal cord injury. While his performance was better and he perceived a smaller workload with VGS, his perceived usability for VGS and teleoperation was similar. More work is needed to further improve and test VGS on participants with disabilities.
In Rust We Trust – A Transpiler from Unsafe C to Safer Rust

Rust is a type-safe system programming language with a compiler checking memory and concurrency safety. For a smooth transition from existing C projects, a source-to-source transpiler can auto-transform C programs into Rust using program transformation. However, existing C-to-Rust transformation tools (e.g. the open-source C2Rust transpiler <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> project) have the drawback of preserving the unsafe semantics of C, while rewriting them in Rust’s syntax. The work by Emre et el. [2] acknowledged these drawbacks, and used rustc compiler feedback to refactor one certain type of raw pointers to Rust references to improve overall safety and idiomaticness of C2Rust output. Focusing on improving API-safeness (i.e. lowering unsafe keyword usage in function signatures), we apply source-to-source transformation technique to auto-refactor C2Rust output using code structure pattern matching and transformation, which does not rely on rustc compiler feedback. And by relaxing the semantics-preserving constraints of transformations, we present CRustS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> a fully-automated source-to-source transformation approach that increases the ratio of the transformed code passing the safety checks of the rustc compiler. Our method uses 220 new TXL [1] source-to-source transformation rules, of which 198 are strictly semantics-preserving and 22 are semantics-approximating, thus reducing the scope of unsafe expressions and exposing more opportunities for safe Rust refactoring. Our method has been evaluated on both open-source and commercial C projects, and demonstrates significantly higher safe code ratios after the transformations, with function-level safe code ratios comparable to the average level of idiomatic Rust projects.
The role of blockchain for food safety and market efficiency

It is well known that the introduction of Blockchain in the agri-food sector represents a digital innovation aimed at increasing business income through the reduction of production inputs (and therefore of production costs expressed at constant prices) and/or the increase of output (increase in the quantity produced and therefore in revenues expressed at constant prices). According to Schumpeter, innovation and entrepreneurship mainly depend on innovative people, their skills and knowledge. In fact, digital innovation is always aimed at increasing the competitiveness of the company and can concern an improvement in technical and economic efficiency. On an existing company structure, efficiency concerns an optimization of the variable production factors to be used in the production process (reduction of variable costs: example quantity of water used; quantity of fertilizers to be used according to seasonal trends; quantity of pesticides to be used) which have repercussions on the structure of the cost of production and therefore positive effects on the net income of the entrepreneur. In the present study after examining the economic theory of innovation, through the theory of value examined why agri-food companies should adopt innovations such as the Blockchain. The study highlights that digital innovations can be implemented by entrepreneurs according to company size and with a view to increasing the value of production and that the affirmation of innovation requires long periods of time.
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks

Meta-learning has become a practical approach towards few-shot image classification, where &#x201C;a strategy to learn a classifier&#x201D; is meta-learned on labeled base classes and can be applied to novel classes. We remove the requirement of base class labels and learn generalizable embeddings via Unsupervised Meta-Learning (UML). Specifically, episodes of tasks are constructed with data augmentations from unlabeled base classes during meta-training, and we apply embedding-based classifiers to novel tasks with labeled few-shot examples during meta-test. We observe two key elements matter in UML, i.e., how to sample tasks and measure the similarity between instances. Thus we construct a strong baseline with two simple modifications - a sufficient sampling strategy constructing multiple tasks per episode efficiently together with a semi-normalized similarity. We then take advantage of the characteristics of tasks from two directions to get further improvements. First, confusing instances generated by mixed embeddings are incorporated so that difficult synthesized tasks lead to more discriminative embeddings. Second, we utilize an additional task-specific embedding transformation as an auxiliary component during meta-training to promote the generalization ability of the pre-adapted embeddings. Experiments on few-shot learning benchmarks verify that our approaches outperform previous UML methods and achieve even better performance than its supervised variants.
Distortion spot correction and center location base on deep neural network and MBAS in measuring large curvature aspheric optical element

Large curvature aspheric optical elements are widely used in visual system. But its morphological detection is very difficult because its accuracy requirement is very high. When we use the self-developed multi-beam angle sensor (MBAS) to detect large curvature aspheric optical elements, the accuracy will be reduced due to spot distortion. Therefore, we propose a scheme combining distorted spot correction neural network (DSCNet) and gaussian fitting method to improve the detection accuracy of distorted spot center. We develop a spot discrimination method to determine spot region in multi-spot images. The spot discrimination threshold is obtained by the quantitative distribution of pixels in the connected domain. We design a DSCNet, which corrects the distorted spot to Gaussian spot, to extract the central information of distorted spot images by multiple pooling. The experimental results demonstrate that the DSCNet can effectively correct the distorted spot, and the spot center can be extracted to sub-pixel level, which improves the measurement accuracy of the MBAS. The standard deviations of plano-convex lenses with curvature radii of 500 mm, 700 mm and 1000 mm measured with the proposed method are respectively 0.0112 um, 0.0086 um and 0.0074 um.
Retina-based quality assessment of tile-coded 360-degree videos

Nowadays, omnidirectional content, which delivers 360-degree views of scenes, is a significant aspect of Virtual Reality systems. While 360 video requires a lot of bandwidth, users only see visible tiles, therefore a large amount of bitrate can be saved without affecting the user’s experience on the service. The fact leads to current video adaptation solutions to filter out superfluous parts and extraneous bandwidth. To form a good basis for these adaptations, it is necessary to understand human’s video quality perception. In our research, we contribute to building an effective omnidirectional video database that can be applied to study the effects of the five zones of the human retina. We also design a new video quality assessment method to analyze the impacts of those zones of a 360 video according to the human retina. The proposed scheme is found to outperform 22 current objective quality measures by 11 to 31% in terms of the PCC parameter.
GATSum: Graph-Based Topic-Aware Abstract Text Summarization

The purpose of text summarization is to compress a text document into a summary containing key information. abstract approaches are challenging tasks, it is necessary to design a mechanism to effectively extract salient information from the source text, and then generate a summary. However, most of the existing abstract approaches are difficult to capture global semantics, ignoring the impact of global information on obtaining important content. To solve this problem, this paper proposes a Graph-Based Topic Aware abstract Text Summarization (GTASum) framework. Specifically, GTASum seamlessly incorporates a neural topic model to discover potential topic information, which can provide document-level features for generating summaries. In addition, the model integrates the graph neural network which can effectively capture the relationship between sentences through the document representation of graph structure, and simultaneously update the local and global information. The further discussion showed that latent topics can help the model capture salient content. We conducted experiments on two datasets, and the result shows that GTASum is superior to many extractive and abstract approaches in terms of ROUGE measurement. The result of the ablation study proves that the model has the ability to capture the original subject and the correct information and improve the factual accuracy of the summarization.
Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training

Recently, masked prediction pre-training has seen remarkable progress in self-supervised learning (SSL) for speech recognition. It usually requires a codebook obtained in an unsupervised way, making it less accurate and difficult to interpret. We propose two supervision-guided codebook generation approaches to improve automatic speech recognition (ASR) performance and also the pre-training efficiency, either through decoding with a hybrid ASR system to generate phoneme-level alignments (named PBERT), or performing clustering on the supervised speech features extracted from an end-to-end CTC model (named CTC clustering). Both the hybrid and CTC models are trained on the same small amount of labeled speech as used in fine-tuning. Experiments demonstrate significant superiority of our methods to various SSL and self-training baselines, with up to 17.0% relative WER reduction. Our pre-trained models also show good transferability in a non-ASR speech task.
Immunohistochemistry scoring of breast tumor tissue microarrays: A comparison study across three software applications

Digital pathology can efficiently assess immunohistochemistry (IHC) data on tissue microarrays (TMAs). Yet, it remains important to evaluate the comparability of the data acquired by different software applications and validate it against pathologist manual interpretation. In this study, we compared the IHC quantification of 5 clinical breast cancer biomarkers—estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and cytokeratin 5/6 (CK5/6)—across 3 software applications (Definiens Tissue Studio, inForm, and QuPath) and benchmarked the results to pathologist manual scores. IHC expression for each marker was evaluated across 4 TMAs consisting of 935 breast tumor tissue cores from 367 women within the Nurses’ Health Studies; each women contributing three 0.6-mm cores. The correlation and agreement between manual and software-derived results were primarily assessed using Spearman’s ρ, percentage agreement, and area under the curve (AUC). At the TMA core-level, the correlations between manual and software-derived scores were the highest for HER2 (ρ ranging from 0.75 to 0.79), followed by ER (0.69–0.71), PR (0.67–0.72), CK5/6 (0.43–0.47), and EGFR (0.38–0.45). At the case-level, there were good correlations between manual and software-derived scores for all 5 markers (ρ ranging from 0.43 to 0.82), where QuPath had the highest correlations. Software-derived scores were highly comparable to each other (ρ ranging from 0.80 to 0.99). The average percentage agreements between manual and software-derived scores were excellent for ER (90.8%–94.5%) and PR (78.2%–85.2%), moderate for HER2 (65.4%–77.0%), highly variable for EGFR (48.2%–82.8%), and poor for CK5/6 (22.4%–45.0%). All AUCs across markers and software applications were ≥0.83. The 3 software applications were highly comparable to each other and to manual scores in quantifying these 5 markers. QuPath consistently produced the best performance, indicating this open-source software is an excellent alternative for future use.
Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection

Objective This study aimed to evaluate a convolution neural network algorithm for breast lesion detection with multi-center ABUS image data developed based on ABUS image and Yolo v5. Methods A total of 741 cases with 2,538 volume data of ABUS examinations were analyzed, which were recruited from 7 hospitals between October 2016 and December 2020. A total of 452 volume data of 413 cases were used as internal validation data, and 2,086 volume data from 328 cases were used as external validation data. There were 1,178 breast lesions in 413 patients (161 malignant and 1,017 benign) and 1,936 lesions in 328 patients (57 malignant and 1,879 benign). The efficiency and accuracy of the algorithm were analyzed in detecting lesions with different allowable false positive values and lesion sizes, and the differences were compared and analyzed, which included the various indicators in internal validation and external validation data. Results The study found that the algorithm had high sensitivity for all categories of lesions, even when using internal or external validation data. The overall detection rate of the algorithm was as high as 78.1 and 71.2% in the internal and external validation sets, respectively. The algorithm could detect more lesions with increasing nodule size (87.4% in ≥10 mm lesions but less than 50% in &amp;lt;10 mm). The detection rate of BI-RADS 4/5 lesions was higher than that of BI-RADS 3 or 2 (96.5% vs 79.7% vs 74.7% internal, 95.8% vs 74.7% vs 88.4% external). Furthermore, the detection performance was better for malignant nodules than benign (98.1% vs 74.9% internal, 98.2% vs 70.4% external). Conclusions This algorithm showed good detection efficiency in the internal and external validation sets, especially for category 4/5 lesions and malignant lesions. However, there are still some deficiencies in detecting category 2 and 3 lesions and lesions smaller than 10 mm.
Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives

This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contrast to the standard InfoNCE loss, which requires a strict binary separation of the training pairs into similar and dissimilar samples, RINCE can exploit information about a similarity ranking for learning a corresponding embedding space. We show that the proposed loss function learns favorable embeddings compared to the standard InfoNCE whenever at least noisy ranking information can be obtained or when the definition of positives and negatives is blurry. We demonstrate this for a supervised classification task with additional superclass labels and noisy similarity scores. Furthermore, we show that RINCE can also be applied to unsupervised training with experiments on unsupervised representation learning from videos. In particular, the embedding yields higher classification accuracy, retrieval rates and performs better on out-of-distribution detection than the standard InfoNCE loss.
Semantic image fuzzing of AI perception systems

Perception systems enable autonomous systems to interpret raw sensor readings of the physical world. Testing of perception systems aims to reveal misinterpretations that could cause system failures. Current testing methods, however, are inadequate. The cost of human interpretation and annotation of real-world input data is high, so manual test suites tend to be small. The simulation-reality gap reduces the validity of test results based on simulated worlds. And methods for synthesizing test inputs do not provide corresponding expected interpretations. To address these limitations, we developed semSensFuzz, a new approach to fuzz testing of perception systems based on semantic mutation of test cases that pair real-world sensor readings with their ground-truth interpretations. We implemented our approach to assess its feasibility and potential to improve software testing for perception systems. We used it to generate 150,000 semantically mutated image inputs for five state-of-the-art perception systems. We found that it synthesized tests with novel and subjectively realistic image inputs, and that it discovered inputs that revealed significant inconsistencies between the specified and computed interpretations. We also found that it produced such test cases at a cost that was very low compared to that of manual semantic annotation of real-world images.
JAN: Joint Attention Networks for Automatic ICD Coding

The International Classification of Diseases (ICD) code is a disease classification method formulated by the World Health Organization(WHO). ICD coding usually requires clinicians to manually allocate ICD codes to clinical documents, which is labor-intensive, expensive, and error-prone. Therefore, many methods have been introduced for automatic ICD coding. However, most of the methods have ignored or cannot combine two essential features well: long-tailed label distribution and label correlation. In this paper, we propose a novel end-to-end Joint Attention Network (JAN) to solve these two problems. JAN includes Document-based attention and Label-based attention to capture semantic information from clinical document text and label description, respectively, which helps solve the classification of dense and sparse data in long-tailed label distribution. Besides, an Adaptive fusion layer and CorNet block are presented to adaptively adjust the weight of these two attentions and exploit label co-occurrence relations, respectively. Experiments on the MIMIC-III and MIMIC-II datasets demonstrate that our proposed JAN outperformed previous state-of-art methods achieving Micro-F1 of 0.553, Micro-AUC of 0.989 and precision at top 8(P@8) of 0.735. Finally, we also provide attention and label correlation visualization to verify the effectiveness of our model and improve the interpretation of our deep learning-based method.