Reviewerly/exHarmony huggingface dataset:
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section

Abstract Extreme learning machine is a neural network algorithm widely accepted in the scientific community due to the simplicity of the model and its good results in classification and regression problems; digital image processing, medical diagnosis, and signal recognition are some applications in the field of physics addressed with these neural networks. The algorithm must be executed with an adequate number of neurons in the hidden layer to obtain good results. Identifying the appropriate number of neurons in the hidden layer is an open problem in the extreme learning machine field. The search process has a high computational cost if carried out sequentially, given the complexity of the calculations as the number of neurons increases. In this work, we use the search of the golden section and simulated annealing as heuristic methods to calculate the appropriate number of neurons in the hidden layer of an Extreme Learning Machine; for the experiments, three real databases were used for the classification problem and a synthetic database for the regression problem. The results show that the search for the appropriate number of neurons is accelerated up to 4.5× times with simulated annealing and up to 95.7× times with the golden section search compared to a sequential method in the highest-dimensional database.
Multi-Node Motion Estimation Method Based on B-Spline of Array Position and Orientation System

The array position and orientation system (array POS), composed of one main POS and multiple sub-inertial measurement units (sub-IMUs), is key equipment in the aerial remote-sensing system, especially the multi-load system, which can provide motion compensation for the multi-load remote-sensing system to improve imaging quality. Nevertheless, the measurement information of each sub-IMU can only realize the motion information of the corresponding remote-sensing load. Ideally, each remote-sensing load should be equipped with a sub-IMU for motion compensation, which is impossible in actual engineering considering the volume, weight and cost. To solve this problem, a multi-node motion estimation method based on the B-spline of the array POS is proposed to realize the motion compensation of remote-sensing loads without sub-IMUs. Firstly, the transfer alignment method based on fiber-grating multi-dimensional deformation measurement was adopted. Motion parameters of the remote-sensing payload equipped with sub-IMUs at different times can be obtained by observing and correcting the errors between the main POS and sub-IMUs. In this way, the space-time characteristics of each interpolation point are fully utilized. Additionally, the motion information of the main POS and all sub-IMUs is fitted through the estimation method based on the B-spline, during which wing deformation is considered to obtain the motion parameters of the remote-sensing payload equipped without a sub-IMU. In this way, the spatial correlation between the information of each node is fully utilized. Due to the full utilization of the spatiotemporal correlation of the motion information of each sub node, high-precision and highly reliable motion information of the remote-sensing loads not equipped with sub-IMUs is obtained. Furthermore, the proposed method can be modified locally without affecting other nodes, and has the advantages of a simple algorithm and easy engineering implementation. Finally, a semi-physical simulation based on ground-loading test was conducted. The results show that the baseline in the X-axis, Y-axis and Z-axis direction is improved by 0.484 mm, 0.137 mm and 1.225 mm, respectively, and that the measurement accuracy of roll angle is improved by 0.011°.
The ChildPoeDE Corpus: 1082 German Children’s Poems for Computational and Experimental Studies on Poetry Reception

We introduce childPoeDE: the first corpus of German poetry for children comprising poems which are still read today and cover a wide range of topics and authors. ChildPoeDE contains poem texts and both poem-level and token-level metadata. Poem-level metadata includes information about the anthologies and authors, quantitative text features, rhyme and lexical richness. Token-level metadata covers word length, position and frequency, parts-of-speech, onomatopoeia and sonority. This corpus can be used for computational text analysis, but also as a source for stimulus material in experimental studies. The corpus metadata is freely accessible via Zenodo. The poem texts are protected by copyright.
Lessons Learned from Modeling the Interaction with Conversational Agents

Intelligent conversational agents have become widespread. Inspired by conversations in natural language, they present different degrees of intelligence and autonomy, bringing challenges for Human-Computer Interaction (HCI). One such challenge concerns design languages for modeling user-agent interaction. We focus here on MoLIC, a design-phase dialogue model based on Semiotic Engineering theory devised to represent user-system interactions as conversations. We performed two case studies with MoLIC interaction diagrams representing two conversational agents – the ANA chatbot and Samsung Bixby. We examined how the interactive aspects of these agents could be expressed in MoLIC. Although it was possible to express the general interaction, our results showed limitations related to the language expressiveness or its inadequacy to represent these systems. We identified limitations in the applicability of MoLIC in modeling and pondered on how to extend or adapt it; directing the HCI community to issues and initiatives that can help design and model these technologies.
A Study of the Application of Virtual Reality Technology as an Anthropometric Measurement Tool

Fundamental factors such as constantly changing space usage habits, evolving technology, and various physical and demographic characteristics of space users today call the accuracy of anthropometric assumptions into question. This paper aims to reveal whether VR could be used as an anthropometric measurement tool based on VR’s capacity to measure size perception. For this, the parameters that reveal the difference in size perception between VR and the real world were determined, and it was aimed to reveal their effect on the size perception process. Participants received instructions to estimate the sizes of various space components in experimental environments in both real and virtual reality. However, this approach was used to analyze the convergence and divergence between the assumptions rather than the accuracy of user-generated dimensional assumptions. The study’s findings are presented as comparative graphical narratives of user estimates in physical and virtual environments.
The IBEM dataset: A large printed scientific image dataset for indexing and searching mathematical expressions

Searching for information in printed scientific documents is a challenging problem that has recently received special attention from the Pattern Recognition research community. Mathematical expressions are complex elements that appear in scientific documents, and developing techniques for locating and recognizing them requires the preparation of datasets that can be used as benchmarks. Most current techniques for dealing with mathematical expressions are based on Machine Learning techniques which require a large amount of annotated data. These datasets must be prepared with ground-truth information for automatic training and testing. However, preparing large datasets with ground-truth is a very expensive and time-consuming task. This paper introduces the IBEM dataset, consisting of scientific documents that have been prepared for mathematical expression recognition and searching. This dataset consists of 600 documents, more than 8200 page images with more than 160000 mathematical expressions. It has been automatically generated from the version of the documents and can be enlarged easily. The ground-truth includes the position at the page level and the transcript for mathematical expressions both embedded in the text and displayed. This paper also reports a baseline classification experiment with mathematical symbols and a baseline experiment of Mathematical Expression Recognition performed on the IBEM dataset. These experiments aim to provide some benchmarks for comparison purposes so that future users of the IBEM dataset can have a baseline framework.
Multi-Modal Entity Alignment Method Based on Feature Enhancement

Multi-modal entity alignment refers to identifying equivalent entities between two different multi-modal knowledge graphs that consist of multi-modal information such as structural triples and descriptive images. Most previous multi-modal entity alignment methods have mainly used corresponding encoders of each modality to encode entity information and then perform feature fusion to obtain the multi-modal joint representation. However, this approach does not fully utilize the multi-modal information of aligned entities. To address this issue, we propose MEAFE, a multi-modal entity alignment method based on feature enhancement. The MEAFE adopts the multi-modal pre-trained model, OCR model, and GATv2 network to enhance the model’s ability to extract useful features in entity structure triplet information and image description, respectively, thereby generating more effective multi-modal representations. Secondly, it further adds modal distribution information of the entity to enhance the model’s understanding and modeling ability of the multi-modal information. Experiments on bilingual and cross-graph multi-modal datasets demonstrate that the proposed method outperforms models that use traditional feature extraction methods.
Improving Semantic Segmentation via Decoupled Body and Edge Information

In this paper, we propose a method that uses the idea of decoupling and unites edge information for semantic segmentation. We build a new dual-stream CNN architecture that fully considers the interaction between the body and the edge of the object, and our method significantly improves the segmentation performance of small objects and object boundaries. The dual-stream CNN architecture mainly consists of a body-stream module and an edge-stream module, which process the feature map of the segmented object into two parts with low coupling: body features and edge features. The body stream warps the image features by learning the flow-field offset, warps the body pixels toward object inner parts, completes the generation of the body features, and enhances the object's inner consistency. In the generation of edge features, the current state-of-the-art model processes information such as color, shape, and texture under a single network, which will ignore the recognition of important information. Our method separates the edge-processing branch in the network, i.e., the edge stream. The edge stream processes information in parallel with the body stream and effectively eliminates the noise of useless information by introducing a non-edge suppression layer to emphasize the importance of edge information. We validate our method on the large-scale public dataset Cityscapes, and our method greatly improves the segmentation performance of hard-to-segment objects and achieves state-of-the-art result. Notably, the method in this paper can achieve 82.6% mIoU on the Cityscapes with only fine-annotated data.
PCB Defect Images Super-Resolution Reconstruction Based on Improved SRGAN

Image super-resolution reconstruction technology can boost image resolution and aid in the discovery of PCB flaws. The traditional SRGAN algorithm produces reconstructed images with great realism, but it also has the disadvantages of insufficient feature information extraction ability, a large number of model parameters, as well as a lack of fine-grained image reconstruction impact. To that end, this paper proposes an SRGAN-based super-resolution reconstruction algorithm for PCB defect images that is the first to add a VIT network to the generation network to extend the perceptual field and improve the model’s ability to extract high-frequency information. The high-frequency feature extraction module is then used to enhance the generator’s extraction of high-frequency information from the feature map while reducing the complexity of the model network. Finally, the inverted residual module and VIT network are combined to form the discriminator’s backbone network, which extracts and summarizes shallow features while synthesizing global features for higher-level features, allowing the discriminator effect to be achieved with less spatial complexity. On the test set, the improved algorithm increases the PSNR by 0.82 and the SSIM by 0.03, and the SRVIT algorithm’s number of discriminator model parameters and model space size are decreased by 2.01 M and 7.5 MB, respectively, when compared to the SRGAN algorithm. Moreover, the improved PCB defect image super-resolution reconstruction algorithm not only enhances the image reconstruction effect but also lowers model space complexity.
A Fast Cold-Start Solution: Container Space Reuse Based on Resource Isolation

With the emergence of cloud-native computing, serverless computing has become a popular way to deploy intensive applications due to its scalability and flexibility, and it has been increasingly applied in the field of big data processing on service platforms. Currently, the development momentum of cloud-native computing is strong, and serverless computing has become more attractive to the growing number of Internet services. However, how to more effectively address the issues of container resource usage and service startup time for serverless computing remains a huge challenge when exploring its potential. Our research is based on the complete life cycle of serverless functions and improves the performance of serverless computing by changing the original method of exchanging space for time or time for space. We focus on how to shorten the cold-start time of serverless computing while maximizing the usage of container resources. The research innovation is the dynamic control of functions and container pools, which mainly includes three aspects. First, we create a container pool with the classification identification based on the usage rate of functions. Then, we use namespace technology to achieve container resource reuse in the security isolated state. Next, we adaptively match the correspondence between functions and reusable container resources through system resource monitoring. Finally, the test results prove that converting the remaining space resources of the container into a prewarm container for new functions can effectively reduce the resource waste caused by idle function-containers, and container resource reuse can further shorten the cold-start time while ensuring the safety and isolation of functions. Compared to other open-source serverless platforms, our solution can reduce the cold-start time of general function calls to less than 20 ms and improve the ability to alleviate cold starts by 90% without enabling container prewarming.
Byzantine-Resilient Secure Federated Learning on Low-Bandwidth Networks

Privacy-preserving and Byzantine-resilient machine learning has been an important research issue, and many centralized methods have been developed. However, it is difficult for these methods to achieve fast learning and high accuracy simultaneously. In contrast, federated learning based on local model masking like Byzantine-Resilient Secure Aggregation (BREA), is a promising approach to simultaneously achieve them. Despite the advantage of light computation of randomizing local models of users for privacy preservation, the verification of shares generated from local models in BREA, which mitigates Byzantine attacks, still incurs large complexity in communication. The paper designs a share verification method for BREA to offload some parts of the share verification process from users to a semi-honest server, which avoids broadcasting large-size commitments to shares. In addition, to mitigate the increase in computation time due to computations offloaded to the server, our method makes the verification algorithm running on the server efficient and executes the server and user computations in parallel. In our experiments, our method provides a speedup of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> on low-bandwidth networks like mobile networks. Our method also preserves BREA’s resilience against Byzantine attacks.
Evaluation of effectiveness in conversations between humans and chatbots using parallel convolutional neural networks with multiple temporal resolutions

Abstract Chatbots enable the automation of several components in customer service and allow the support of multiple users. Despite their multiple advantages, due to the large amount of conversations generated by a chatbot, it is difficult to determine whether customer requests are well-addressed. For practical reasons, chatbot’s effectiveness is evaluated manually based upon a small sample (randomly chosen) of conversations or through self-reported user satisfaction. This procedure does not guarantee the correct evaluation of the service because the sample is generally not large enough and self-reports might be influenced by different external factors not directly associated to the chatbot’s functioning. This study proposes a methodology for automatic evaluation of chatbot effectiveness in real production environments. The analysis considers convolutional neural networks adapted for natural language processing, using two parallel convolutional layers to evaluate questions and answers independently. The proposed model also incorporates filters to extract features with multiple temporal resolution. This methodology is tested upon real conversations of chatbots that provide service to two different companies. The results are compared to baseline models based on classical techniques with different pre-trained word embedding models. According to our results, the proposed approach provides accuracies between 78.95% and 80.18%, which outperforms the best result of the baseline models by 2.9%.
The effects of <scp>COVID</scp>‐19 on information management in remote and hybrid work environments

Abstract This empirical research examines the effects of the COVID‐19 pandemic on information management (IM) in remote or hybrid work. We present an in‐depth statistical analysis of 716 responses to questionnaires received from employees and managers of four Israeli government ministries. The participants were asked to report characteristics such as accessibility, retrieval speed, ease of locating, and relevance of information, in order to assess the quality of organizational IM before and during COVID‐19. The findings reveal that IM quality was maintained even when organizations were forced to quickly adapt to working remotely during the pandemic. Regardless of work location, differences in perception of IM were found among organizations of different sizes: large, medium, and small. The majority of respondents who reported not using IM systems (IMS) before COVID‐19 also stated that even after the pandemic's onset, they still did not use them. A lower frequency of IMS use has been associated with a decline in IM quality. Given the far‐reaching changes in IM induced by the pandemic, many of which have the potential to be long‐lasting, these findings serve as an opening for valuable future research.
From Counterfactual Conditionals to Temporal Conditionals

Abstract Although it receives less attention, (Lewis in Noûs 13:455–476, 1979. https://doi.org/10.2307/2215339 ) admitted that the branching-time(-like) model fits a wide range of counterfactuals, including (Nix) ‘If Nixon had pressed the button, there would have been a nuclear war’ , which was raised by (Fine in Mind 84:451–458, 1975). However, Lewis then claimed that similarity analysis is more general than temporality analysis. In this paper, we do not scrutinise his claim. Instead, we re-analyse (Nix) not only model-theoretically but also proof-theoretically from the ‘meaning-as-use’ and ‘inferentialist’ points of view. Then, we re-formalise (Nix) in a natural extension of hybrid tense logic, which we refer to as hybrid tense logic for temporal conditionals (HTL $$_{TC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mrow> <mml:mi>TC</mml:mi> </mml:mrow> </mml:msub> </mml:math> ). Consequently, we find that not only among counterfactuals, but also among indicatives, there is a wide range of conditionals whose formalisation in HTL $$_{TC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mrow> <mml:mi>TC</mml:mi> </mml:mrow> </mml:msub> </mml:math> is appropriate. We refer to these conditionals as temporal conditionals . This suggests a new logical generality that temporality analysis has but similarity analysis does not, from which emerges a new logical perspective on conditionals in general: temporal ones and others .
PRESERVATION OF THE ANCIENT CANAL OF INDRAPATRA FORT

This study aims to study the damage and preserving ancient canals of Indrapatra Fort, Aceh. This study uses archaeological research methods that are descriptive analysis, with data collection techniques using observation, interviews, and documentation studies. The results of the study show that Indrapatra Fort is a defensive building inherited from the Aceh sultanate that is estimated to have been built in the 16th century AD. Inside the Indrapatra Fortress Complex, there are canals or waterways that function to regulate the flow of water when a flood occurs. Archaeological technical studies found that the condition of the canal was mostly covered with earth. After excavations were carried out in August 2021, the condition of the canal was 70–80% damaged. The form of damage is broken, cracked, collapsed, crushed, and even shifted. Therefore, the authors hope that the local government and the community will protect and maintain the canal structure of Indrapatra Fort so that the structure can continue to exist in the community.
A PSYCHOLINGUISTIC ANALYSIS OF THE ENGLISH STUDENT’ LANGUAGE DISORDER ON LANGUAGE COMPREHENSION IN LITERATURE COURSE

This research just focuses on analyzing comprehension disorder from language disorder, psycholinguistics is the discipline that investigates and describes the psychological processes that make it possible for humans to master and use language. The expert conduct research on speech development and language development and how individuals of all ages comprehend and produce language. Students with this issue may also struggle with basic reading skills such as decoding words, but comprehension is the greater weakness. Some students with a learning disability in reading comprehension can read aloud with little or no difficulty pronouncing words, but they do not understand or remember what they've read. This research uses descriptive qualitative with describes language disorder and attempts to explore the application of sentence from as language disorder on language comprehension utterances which are spoken by English student’ Literature in ELT course. Researchers will collect the data from tasks’ students in the class and look for how they analyzed the task such as: short story, Prose, Poetry, movie, and drama. The data are analyzed by classifying and analyzing the data into kinds of comprehension disorder from language disorder and contexts by using Ginsberg theory. The research will be found that students during studying literature in ELT have troubles in his language, especially in the language comprehension. Problems that may be experienced can involve grammar (syntax and/or morphology), semantics (meaning), or other aspects of language.