Reviewerly/exHarmony huggingface dataset:
Augmentation leak-prevention scheme using an auxiliary classifier in GAN-based image generation

Although a generative adversarial network (GAN) can generate realistic and distinct images, it requires numerous training data. Data augmentation is a popular method of incrementing data using various augmentation operations. However, data augmentation for training GANs can lead to augmentation leaks where unintended distortion effects in augmented images appear in images generated by the GAN. This augmentation leaks occur because the GAN is trained to generate images similar to the training dataset containing augmented images. Although recent studies have revealed that maintaining the invertibility of data augmentation can prevent augmentation leaks, maintaining it for various data augmentation configurations and datasets is challenging. This paper proposes a novel auxiliary neural network called an auxiliary augmentation operation classifier (AAOC) to prevent augmentation leaks. The AAOC learns the classification of augmentation operations during GAN training, guiding the GAN to avoid generating images with unintended distortions. Due to its simple and lightweight architecture, the AAOC does not increase the training time of GANs. Extensive experiments on the three public datasets revealed that the AAOC reduced the occurrence of augmentation leaks by a factor of 30 compared to the basic method and improved image quality by 50% for leak-free images.
The Effect of Video Analysis of Inquiry School Practices on Pre-Service Teachers’ Scientific Skills Knowledge

ABSTRACTThis study provides evidence of the effect of video analysis of real classroom situations as a support for promoting the professional vision among primary education pre-service teachers. Specifically, it focuses on the Science Process Skills involved in scientific inquiry when teaching through this method. This quasi-experimental study (n = 90) analyzes a didactic strategy based on the noticing and coding of inquiry teaching practices in school contexts. It aims to encourage pre-service teachers’ knowledge of the Science Process Skills. The quantitative results show a significant improvement in all these skills in the experimental group, mainly with regard to the proficiency of Planning and Experimentation and Interpretation skills. These data are congruent with the results obtained from the qualitative analysis of the codification engaged in by this group. The results of this study contribute to responding to the need for resources in the scientific inquiry education of pre-service teachers in primary education. Video analysis was observed to help mobilize a wide range of concepts involving knowledge of the Science Process Skills that are studied in the initial stages of teacher education.KEYWORDS: Inquiry-based learningpre-service teachersscience process skillsvideo analysis Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has received the help of the Agency for Research and University Grants (AGAUR) under the Department of Economy and Knowledge Society [2017 ARMIF 00028 and 2020 ARMIF 00019], as well as the help of the University of Lleida in the Program of Research Promotion 2019. This research article has received a grant for its linguistic revision from the Language Institute of the University of Lleida (2022 call).
Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots

Robots are active agents that operate in dynamic scenarios with noisy sensors. Predictions based on these noisy sensor measurements often lead to errors and can be unreliable. To this end, roboticists have used fusion methods using multiple observations. Lately, neural networks have dominated the accuracy charts for perception-driven predictions for robotic decision-making and often lack uncertainty metrics associated with the predictions. Here, we present a mathematical formulation to obtain the heteroscedastic aleatoric uncertainty of any arbitrary distribution without prior knowledge about the data. The approach has no prior assumptions about the prediction labels and is agnostic to network architecture. Furthermore, our class of networks, Ajna, adds minimal computation and requires only a small change to the loss function while training neural networks to obtain uncertainty of predictions, enabling real-time operation even on resource-constrained robots. In addition, we study the informational cues present in the uncertainties of predicted values and their utility in the unification of common robotics problems. In particular, we present an approach to dodge dynamic obstacles, navigate through a cluttered scene, fly through unknown gaps, and segment an object pile, without computing depth but rather using the uncertainties of optical flow obtained from a monocular camera with onboard sensing and computation. We successfully evaluate and demonstrate the proposed Ajna network on four aforementioned common robotics and computer vision tasks and show comparable results to methods directly using depth. Our work demonstrates a generalized deep uncertainty method and demonstrates its utilization in robotics applications.
Research on Blockchain Anonymous Communication Based on Key Derivation

Abstract With the continuous development of the Internet and communication technologies, network communication provides convenience but also brings security problems such as exposure of users' personal privacy information and theft, private tampering, and forgery of false information. Modern cryptography technology is an important safeguard against message eavesdropping and tampering, while the rapidly developing anonymous communication technology in this century makes it difficult for attackers to infer user's personal information and communication relationships. In response to the potential threats of traditional centralized systems such as central nodes being vulnerable to attacks and data storage being tampered with, this paper proposes a blockchain anonymous communication algorithm KDAC based on key derivation, which takes advantage of the decentralization, data immutability, consensus mechanism and anonymity of blockchain and combines the ECC cryptographic derivation algorithm and anonymous communication technology to realize the key-at-a-time, one-address-at-a-time The key derivation scheme ensures the message integrity and tamper-evident while effectively hiding the identity information of both communication parties. In addition, this paper also optimizes the blockchain anonymous communication system with key derivation. Users only need the initial key of blockchain nodes to join the network for communication, and the information transmission is difficult to trace based on the blockchain network, which can effectively guarantee communication security and anonymity. The experimental results show that the efficiency of the derived key algorithm is roughly in the same order of magnitude as that of the 256-bit AES symmetric encryption algorithm, which can play a better role in practical applications. On the other hand, the derived key generated based on the algorithm has complete randomness in association verification, and it is impossible to reverse the initial parameters, which can well guarantee the anonymity of user identity.
Research on Intelligentization of Cloud Computing Programs Based on Self-awareness

Abstract Through the research of MapReduce programming framework of cloud computing, the current MapReduce program only solves specific problems, and there is no design experience or design feature summary of MapReduce program, let alone formal description and experience inheritance and application of knowledge base. In order to solve the problem of intelligent cloud computing program, a general MapReduce program generation method is designed. This paper proposes the architecture of intelligent cloud computing by studying AORBCO model and combining cloud computing technology. According to the behavior control mechanism in AORBCO model, a program generation method of MapReduce in intelligent cloud computing is proposed. This method will extract entity information in input data set and entity information in knowledge base in intelligent cloud computing for similarity calculation, and extract the entity in the top order as key key-value pair information in intelligent cloud computing judgment data set. The data processing types are divided, and then aligned with each specific MapReduce capability, and the MapReduce program generation experiment is verified in the AORBCO model development platform. The experiment shows that the complexity of big data MapReduce program code is simplified, and the generated code execution efficiency is good.
Exploring the Potential of A-ResNet in Person-Independent Face Recognition and Classification

Abstract This study offers a novel face recognition and classification method based on classifiers that use statistical local features. The use of ResNet has generated growing interest in a variety of areas of image processing and computer vision in recent years and demonstrated its usefulness in several applications, especially for facial image analysis, which includes tasks as varied as face detection, face recognition, facial expression analysis, demographic classification, etc. This paper is divided into two steps i.e. face recognition and classification. The first step in face recognition is automatic data cleansing which is done with the help of Multi-Task Cascaded Convolutional Neural Networks (MTCNNs) and face.evoLVe, followed by parameter changes in MTCNN to prevent dirty data. The authors next trained two models: Inception-ResNetV1, which had pre-trained weights, and Altered-ResNet (A-ResNet), which used Conv2d layers in ResNet for feature extraction and pooling and softmax layers for classifications. The authors use the best optimizer after comparing a number of them during the training phase, along with various combinations of batch and epoch. A-ResNet, the top model overall, detects 86/104 Labelled Faces in the Wild (LFW) dataset images in 0.50 seconds. The proposed approach was evaluated and received an accuracy of 91.7%. Along with this, the system achieved a training accuracy of 98.53% and a testing accuracy of 99.15% for masked face recognition. The proposed method exhibits competitive outcomes when measured against other cutting-edge algorithms and models. Finally, when it comes to why the suggested model is superior to ResNet, it may be because the A-ResNet is simpler thus it can perform at its best with little data, whereas deeper networks require higher data size.
Semi-automated Video Archiving Using Object Detection, Tracking and Ontological Annotation

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries.
Cross-functional Analysis of Generalization in Behavioral Learning

Abstract In behavioral testing, system functionalities underrepresented in the standard evaluation setting (with a held-out test set) are validated through controlled input-output pairs. Optimizing performance on the behavioral tests during training (behavioral learning) would improve coverage of phenomena not sufficiently represented in the i.i.d. data and could lead to seemingly more robust models. However, there is the risk that the model narrowly captures spurious correlations from the behavioral test suite, leading to overestimation and misrepresentation of model performance—one of the original pitfalls of traditional evaluation. In this work, we introduce BeLUGA, an analysis method for evaluating behavioral learning considering generalization across dimensions of different granularity levels. We optimize behavior-specific loss functions and evaluate models on several partitions of the behavioral test suite controlled to leave out specific phenomena. An aggregate score measures generalization to unseen functionalities (or overfitting). We use BeLUGA to examine three representative NLP tasks (sentiment analysis, paraphrase identification, and reading comprehension) and compare the impact of a diverse set of regularization and domain generalization methods on generalization performance.1
Time-and-Space-Efficient Weighted Deduction

Abstract Many NLP algorithms have been described in terms of deduction systems. Unweighted deduction allows a generic forward-chaining execution strategy. For weighted deduction, however, efficient execution should propagate the weight of each item only after it has converged. This means visiting the items in topologically sorted order (as in dynamic programming). Toposorting is fast on a materialized graph; unfortunately, materializing the graph would take extra space. Is there a generic weighted deduction strategy which, for every acyclic deduction system and every input, uses only a constant factor more time and space than generic unweighted deduction? After reviewing past strategies, we answer this question in the affirmative by combining ideas of Goodman (1999) and Kahn (1962). We also give an extension to cyclic deduction systems, based on Tarjan (1972).
The Incorporation of Learning Theories in VR-Based Safety Training Programs Within the Construction Industry

Virtual reality (VR) technology has gained wide momentum over the past few years. Accordingly, it is rapidly becoming a solid foundation for existing and future educational programs in medical and engineering fields. VR aids in the provision of a student-centered learning approach; creating learning experiences that were not otherwise possible in real life. In the construction industry, the use of VR showed significant potential in enabling the development of a preventative culture in safety training programs through enhanced hazard identification, assessment, response, and mitigation skills. However, none of the few existing attempts—albeit beneficial—has considered major learning theories in the design of their VR-based training. In fact, the understanding of adult learning processes is a matter of serious concern for scholars and practitioners advocating any educational reforms. This concern has been exacerbated with the increasing advocacy for incorporating learning theory foundations in game-based learning. In line with this, this research aims to develop a conceptual framework for the integration of social learning theories in the design and development of VR-based safety training programs within the construction industry. Accordingly, six main theories were analyzed to identify the main principles that are most beneficial to VR-based safety training models. In doing so, major adult learning attributes, such as self-direction, problem/life-centeredness, learning orientations, reflective learning, and others, were taken into account. The results revealed that combining behaviorism, constructivism, and andragogy would tap into all the learning needs of adult learners, thereby aiding in the development of self-actualized individuals who are capable of acquiring declarative and procedural knowledge while adopting new problem-solving skills. They also reveal the importance of stimulating emotions and linking them to hazards. This research will help in significantly improving future VR training models and amplifying their desired outcomes, which in turn will enhance safety in construction sites.
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions

Abstract Contrast consistency, the ability of a model to make consistently correct predictions in the presence of perturbations, is an essential aspect in NLP. While studied in tasks such as sentiment analysis and reading comprehension, it remains unexplored in open-domain question answering (OpenQA) due to the difficulty of collecting perturbed questions that satisfy factuality requirements. In this work, we collect minimally edited questions as challenging contrast sets to evaluate OpenQA models. Our collection approach combines both human annotation and large language model generation. We find that the widely used dense passage retriever (DPR) performs poorly on our contrast sets, despite fitting the training set well and performing competitively on standard test sets. To address this issue, we introduce a simple and effective query-side contrastive loss with the aid of data augmentation to improve DPR training. Our experiments on the contrast sets demonstrate that DPR’s contrast consistency is improved without sacrificing its accuracy on the standard test sets.1
Collective Human Opinions in Semantic Textual Similarity

Abstract Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as gold standard. Averaging masks the true distribution of human opinions on examples of low agreement, and prevents models from capturing the semantic vagueness that the individual ratings represent. In this work, we introduce USTS, the first Uncertainty-aware STS dataset with ∼15,000 Chinese sentence pairs and 150,000 labels, to study collective human opinions in STS. Analysis reveals that neither a scalar nor a single Gaussian fits a set of observed judgments adequately. We further show that current STS models cannot capture the variance caused by human disagreement on individual instances, but rather reflect the predictive confidence over the aggregate dataset.
Conditional Generation with a Question-Answering Blueprint

Abstract The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details. In this work, we advocate planning as a useful intermediate representation for rendering conditional generation less opaque and more grounded. We propose a new conceptualization of text plans as a sequence of question-answer (QA) pairs and enhance existing datasets (e.g., for summarization) with a QA blueprint operating as a proxy for content selection (i.e., what to say) and planning (i.e., in what order). We obtain blueprints automatically by exploiting state-of-the-art question generation technology and convert input-output pairs into input-blueprint-output tuples. We develop Transformer-based models, each varying in how they incorporate the blueprint in the generated output (e.g., as a global plan or iteratively). Evaluation across metrics and datasets demonstrates that blueprint models are more factual than alternatives which do not resort to planning and allow tighter control of the generation output.
A Cross-Linguistic Pressure for Uniform Information Density in Word Order

Abstract While natural languages differ widely in both canonical word order and word order flexibility, their word orders still follow shared cross-linguistic statistical patterns, often attributed to functional pressures. In the effort to identify these pressures, prior work has compared real and counterfactual word orders. Yet one functional pressure has been overlooked in such investigations: The uniform information density (UID) hypothesis, which holds that information should be spread evenly throughout an utterance. Here, we ask whether a pressure for UID may have influenced word order patterns cross-linguistically. To this end, we use computational models to test whether real orders lead to greater information uniformity than counterfactual orders. In our empirical study of 10 typologically diverse languages, we find that: (i) among SVO languages, real word orders consistently have greater uniformity than reverse word orders, and (ii) only linguistically implausible counterfactual orders consistently exceed the uniformity of real orders. These findings are compatible with a pressure for information uniformity in the development and usage of natural languages.1
Research on deformation prediction of deep foundation pit excavation based on GWO-ELM model

&lt;abstract&gt; &lt;p&gt;Given the complex nonlinear problem between the control and prediction of the surrounding surface settlement deformation caused (GWO), the GWO-ELM deep foundation pit excavation deformation prediction model was proposed. Extreme learning machine and Grey Wolf optimization algorithm combining training and predicting land subsidence. Based on MIDAS GTS NX software, we established a finite element simplified model for deep foundation pit construction, conducted structural calculations, and utilized the Grey Wolf optimization algorithm to optimize the deep foundation pit excavation and its influencing factors, input weights, and hidden layer thresholds in the ELM neural network. Taking the deep foundation pit project of Baoding Automobile Science and Technology Industrial Park as an example, the actual monitoring value is compared with the simulated value, verifying the model's accuracy. The number of soil nails in the finite element model, the excavation depth, the settlement of surrounding buildings and other factors are taken as the input factors of the prediction model. The DB-2 surface settlement of the monitoring point in the finite element model is taken as the output factor of the prediction model. The predicted value of the GWO-ELM model was compared with that of the ELM model. We draw three main conclusions from the results. First, the surface settlement of a bottomless foundation pit can be predicted in advance by using finite element software and the distribution law of surface settlement and horizontal displacement is consistent with the measured values. Second, the Grey Wolf optimization algorithm optimizes the input weights and thresholds in the extreme learning machine neural network. The GWO-ELM prediction model has good generalization ability, can effectively reduce human errors and can improve the accuracy of the prediction model. Third, through practical engineering verification, the average absolute error of the GWO-ELM model is 0.26145, the mean square error is 0.31258 and the R&lt;sup&gt;2&lt;/sup&gt; is 0.98725, all of which are superior to the ELM model and are an effective method for predicting deformation and settlement of deep foundation pit excavation.&lt;/p&gt; &lt;/abstract&gt;
Program equivalence in a typed probabilistic call-by-need functional language

We extend a call-by-need variant of PCF with a binary probabilistic fair choice operator, which makes a lazy and typed variant of probabilistic functional programming. We define a contextual equivalence that respects the expected convergence of expressions and prove a corresponding context lemma. This enables us to show correctness of several program transformations with respect to contextual equivalence. Distribution-equivalence of expressions of numeric type is introduced. While the notion of contextual equivalence stems from program semantics, the notion of distribution equivalence is a direct description of the stochastic model. Our main result is that both notions are compatible: We show that for closed expressions of numeric type contextual equivalence and distribution-equivalence coincide. This provides a strong and often operationally feasible criterion for contextual equivalence of expressions and programs.