Reviewerly/exHarmony huggingface dataset:
Efficient generation of valid test inputs for deep neural networks via gradient search

Abstract The safety and robustness of deep neural networks (DNNs) are currently of great concern. Adequate testing is commonly an effective technique to ensure the software's trustworthiness. However, existing DNN testing methods generate many invalid test inputs, which inevitably brings increased computational overhead and reduces the efficiency of DNN testing. In this paper, we focus on testing task‐specific DNN and investigating diverse, valid and natural test input generation based on data augmentation techniques. Specifically, we propose AugTest, a DNN testing method based on stochastic optimization with momentum, searching for optimal compositions of data augmentation parameters to efficiently generate diverse and valid test inputs. Experimental results show that our proposed method can effectively explore the data manifold space and find valid test inputs with high diversity and naturalness. Compared with the best‐performing baseline, AugTest can generate more test inputs with more average diversity and less average time. Furthermore, the generated test inputs have competitive generalizability to DNNs with different structures. The test error rates exceed 70% when testing other DNN models performing similar tasks using the test inputs generated by AugTest. This implies that our method can produce more valid and generalized data to unveil DNNs' errors.
Generating watermarked adversarial texts

Adversarial example generation (AEG) has been a hot spot in recent years because it can cause deep neural networks (DNNs) to misclassify the generated adversarial examples, which reveals the vulnerability of DNNs, motivating us to find good solutions to improve the robustness of DNN models. Due to the extensiveness and high liquidity of natural language over the social networks, various natural language-based adversarial attack algorithms have been proposed in the literature. These algorithms generate adversarial text examples with high semantic quality. However, the generated adversarial text examples and the corresponding attack models may be maliciously or illegally used. To tackle this problem, we present a general framework encapsulated in the cloud application programming interfaces (APIs) for generating watermarked adversarial text examples to protect adversarial text examples and corresponding adversarial text attack models. For each word in a given text, a set of candidate words are determined to ensure that all the words in the set can be used to carry secret bits or facilitate the construction of adversarial example. By applying a word-level adversarial text generation algorithm, the watermarked adversarial text example can be finally generated. Experiment results show that the adversarial text examples generated by the proposed method not only successfully fool advanced DNN models, but also carry watermarks that can effectively verify the ownership and trace the source of the adversarial examples and the corresponding attack models. Moreover, the watermark can still survive after attacked with AEG algorithms, which has shown the applicability and superiority.
Further Thoughts on Measuring Narrational Complexity in Fiction Film

Abstract Cutting (2021) argues that the narrational complexity of fiction film can be quantified similarly to computational measures of text complexity. Narrational complexity refers to the structure that arises from how a story is told. This article expands upon Cutting's proposal by taking inspiration from contemporary approaches for measuring text complexity. These approaches reject the notion that complexity can be measured via a limited set of indices as Cutting proposed for narrational complexity. Similarly, we argue that narrational complexity for fiction films should be multi-dimension and include indices that are associated with events, characters, and the rules that govern the fictional world. We discuss the viability of using computational approaches to analyze video and natural language processing to develop approaches to measuring narrational complexity.
Analysis of Plane Displacement of Pressure Vessels with Defects

Abstract Aiming at the problem that the internal defects of the pressure vessel are not easy to detect, the out-of-plane displacement method is used to study it. The out-of-plane displacement field of circular flat plate heads with defects is obtained by the method of elastic theory analysis and compared with the finite element method to verify the accuracy of the finite element method. Based on the finite element method, the effects of vessel material, pressure load, defect shape, and defect size on the first-order out-of-plane displacement derivative are studied. The results show that the material of the container has little effect on the out-of-plane displacement. The first derivative of out-of-plane displacement increases linearly with the increase of pressure, and this relationship will not change with the increase of pressure. With the increase of defect aspect ratio, the maximum value of the first derivative of out-of-plane displacement increases gradually. When δ ˃ 9, the defect is detectable. The research results lay a theoretical foundation for the internal defect detection of pressure vessels based on digital speckle shearing interferometry.
Research on Setting Standards of applied technology colleges and Universities

Based on the setting standards of ordinary undergraduate colleges and universities, this study combined with the training objectives, training specifications and characteristics of applied technical talents to establish the setting (admission) standards of applied technical colleges and universities. Referring to the mature application technology university construction standards in foreign countries and combining with the actual situation of our country, qualified evaluation standards are established, leading the transition universities to stride towards the qualified goals, testing and evaluating the progress and level of the transition universities, and promoting the scientific transformation of local universities.
Phase-insensitive amplifier gain estimation at Cramér-Rao bound for two-mode squeezed state of light

Phase-insensitive amplifiers (PIAs), as a class of important quantum devices, have found significant applications in the subtle manipulation of multiple quantum correlation and multipartite quantum entanglement. Gain is a very important parameter for quantifying the performance of a PIA. Its absolute value can be defined as the ratio of the output light beam power to the input light beam power, while its estimation precision has not been extensively investigated yet. Therefore, in this work, we theoretically study the estimation precision from the vacuum two-mode squeezed state (TMSS), the estimation precision of the coherent state, and the bright TMSS scenario, which has the following two advantages: it has more probe photons than the vacuum TMSS and higher estimation precision than the coherent state. The advantage in terms of estimation precision of the bright TMSS compared with the coherent state is researched. We first simulate the effect of noise from another PIA with gain M on the estimation precision of the bright TMSS, and we find that a scheme in which the PIA is placed in the auxiliary light beam path is more robust than two other schemes. Then, a fictitious beam splitter with transmission T is used to simulate the noise effects of propagation loss and imperfect detection, and the results show that a scheme in which the fictitious beam splitter is placed before the original PIA in the probe light beam path is the most robust. Finally, optimal intensity difference measurement is confirmed to be an accessible experimental technique to saturate estimation precision of the bright TMSS. Therefore, our present study opens a new avenue for quantum metrology based on PIAs.
Sequential Likelihood-Free Inference with Neural Proposal

Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulation models on real-world datasets. As the likelihood evaluation is inaccessible, previous papers train the amortized neural network to estimate the ground-truth posterior for the simulation of interest. Training the network and accumulating the dataset alternatively in a sequential manner could save the total simulation budget by orders of magnitude. In the data accumulation phase, the new simulation inputs are chosen within a portion of the total simulation budget to accumulate upon the collected dataset so far. This newly accumulated data degenerates because the set of simulation inputs is hardly mixed, and this degenerated data collection process ruins the posterior inference. This paper introduces a new sampling approach, called Neural Proposal (NP), of the simulation input that resolves the biased data collection as it guarantees the i.i.d. sampling. The experiments show the improved performance of our sampler, especially for the simulations with multi-modal posteriors.
Mathematical Language to Reveal the Geometry of Shapes

Abstract This research reflects on using mathematics for processing and interpreting data obtained from digital surveying. The experiments outlined in this study demonstrate how mathematical algorithms, which can be written in computational environments like Wolfram Mathematica, can reveal the geometric structure of an architectural artifact.
Data stories and dashboard development: a case study of an aviation schedule and delay causes

Abstract In this case study, five key processes in modelling a data story of aviation data patterns during COVID-19 have been executed. It started with the collection of secondary data from relevant sources. Data inspection, transformation, and preparation activities, including data cleaning, filtering, and sampling, are all included in this work. Iterative exploratory data analysis (EDA) has been conducted to determine the pattern of each independent attribute, followed by an assessment after the data story is modelled and integrated on a dashboard. The questionnaire has been distributed and the visuals were assessed by giving respondents a few tasks to interpret stories based on their comprehension. The result shows that the data stories have been interpreted in a similar narrative by all the respondents. The overall mean score is 4.71, and this significantly shows that the respondents agree and strongly agree that the visual objects help in communicating patterns and stories. The overall process gives researchers experience and guidelines for future work. Overall, the objectives of the study have been met. Nevertheless, it gives researchers a lot of experience in interpreting data, cleansing and transformation, analysis, modelling the visualisation by selecting suitable charts, and integrating the objects together into a dashboard.
The preliminary study on the historical governing law of the frozen estates in Kampong Bharu, Malaysia

Abstract Kampong Bharu is situated in Kuala Lumpur, Malaysia and is categorised as a high commercial land located in the ‘Golden Triangle.’ However, this place is still left behind in terms of development as it is reported that there are more than RM60 billion in frozen estates. The existing legal framework is still considered inadequate as the planning development has been pending until today. Hence, this paper aims to analyse the legal history and the constraints preventing the development of Kampong Bharu. The discussion adopts the content analysis based on the qualitative research methods through the statutory provisions and other legal and non-legal literature. The research found that the Kampong Bharu Development Corporation Act 2011 was passed, and the Kampong Bharu Development Master Plan 2040 has been launched to carry out the development. Hence, it is proposed that the Kampong Bharu Development Corporation (KBDC) may have to wield the Land Acquisition Act 1960 so that the productive elements in Kampong Bharu can be activated. The impact of this research is in line with the benefit addressed by the Sustainable Development Goals (SDG) Plan (2030) in terms of making cities and human settlements inclusive, safe, resilient and sustainable.
Design of automatic operation system of trowel based on stereo vision-inertial

Abstract The trowel is a construction machinery used for slurry lifting and trowelling cement concrete surfaces. The machine is noisy and has certain dangers during operation. This paper focuses on the automatic operation of the trowel and takes the driving hydraulically driven trowel as the research platform. According to the overall requirements of automatic operation, with the stereo visual-inertial navigation system as the core, a positioning system and a full coverage path planning scheme are designed. The real data was collected in the test site for the experiment, and the results show that the stereo visual-inertial navigation system can provide the required positioning accuracy for the trowel.
Weibull Tone Mapping (WTM) for the Enhancement of Underwater Imagery

Domain experts prefer interactive and targeted control-point tone mapping operations (TMOs) to enhance underwater image quality and feature visibility; though this comes at the expense of time and training. In this paper, we provide end-users with a simpler and faster interactive tone-mapping approach. This is built upon Weibull Tone Mapping (WTM) theory; introduced in previous work as a preferred tool to describe and improve domain expert TMOs. We allow end-users to easily shape brightness distributions according to the Weibull distribution, using two parameter sliders which modify the distribution peak and spread. Our experiments showed that 10 domain experts found the two-slider Weibull manipulation sufficed to make a desired adjustment in >80% of images in a large dataset. For the remaining ∼20%, observers opted for a control-point TMO which can, broadly, encompass many global tone mapping algorithms. Importantly, 91% of these control-point TMOs can actually be visually well-approximated by our Weibull slider manipulation, despite users not identifying slider parameters themselves. Our work stresses the benefit of the Weibull distribution and significance of image purpose in underwater image enhancement.
Unsupervised Domain Adaptation with Differentially Private Gradient Projection

Domain adaptation is a viable solution for deep learning with small data. However, domain adaptation models trained on data with sensitive information may be a violation of personal privacy. In this article, we proposed a solution for unsupervised domain adaptation, called DP-CUDA, which is based on differentially private gradient projection and contradistinguisher. Compared with the traditional domain adaptation process, DP-CUDA involves searching for domain-invariant features between the source domain and target domain first and then transferring knowledge. Specifically, the model is trained in the source domain by supervised learning from labeled data. During the training of the target model, feature learning is used to solve the classification task in an end-to-end manner using unlabeled data directly, and the differentially private noise is injected into the gradient. We conducted extensive experiments on a variety of benchmark datasets, including MNIST, USPS, SVHN, VisDA-2017, Office-31, and Amazon Review, to demonstrate our proposed method’s utility and privacy-preserving properties.
Adaptive Prompt Learning-Based Few-Shot Sentiment Analysis

In the field of natural language processing, sentiment analysis via deep learning has a excellent performance by using large labeled datasets. Meanwhile, labeled data are insufficient in many sentiment analysis tasks, and obtaining these data is time-consuming and laborious. Prompt learning devotes to resolving the data deficiency by reformulating downstream tasks with the help of prompt. The model performance of this method depends on the quality of the prompt. This paper proposes an adaptive prompting (AP) construction strategy using seq2seq-attention structure to acquire the semantic information of the input sequence. Our method of dynamically constructing adaptive prompts can not only improve the quality of prompt, but also can effectively generalize to other fields by constructing a pre-trained prompt with existing public labeled data. The experimental results on FewCLUE datasets demonstrate that the proposed method AP can effectively construct appropriate adaptive prompt regardless of the quality of hand-crafted prompt and outperform the state-of-the-art baselines.
Self-Supervised Skill Learning for Semi-Supervised Long-Horizon Instruction Following

Language as an abstraction for hierarchical agents is promising to solve compositional long-time horizon decision-making tasks. The learning of the agent poses significant challenges, as it typically requires plenty of trajectories annotated with languages. This paper addresses the challenge of learning such an agent under the scarcity of language annotations. One approach for leveraging unannotated data is to generate pseudo-labels for unannotated trajectories using sparse seed annotations. However, as the scenes of the environment and tasks assigned to the agent are diverse, the inference of language instructions is sometimes incorrect, causing the policy to learn to ground incorrect instructions to actions. In this work, we propose a self-supervised language-conditioned hierarchical skill policy (SLHSP) which utilizes unannotated data to learn reusable and general task-related skills to facilitate learning from sparse annotations. We demonstrate that the SLHSP that learned with less than 10% of annotated trajectories has a comparable performance to one that learned with 100% of annotated data. Our approach to the challenging ALFRED benchmark leads to a notable improvement in the success rate over a strong baseline also optimized for sparsely annotated data.
ARLO: A framework for Automated Reinforcement Learning

Automated Reinforcement Learning (AutoRL) is a relatively new area of research that is gaining increasing attention. The objective of AutoRL consists in easing the employment of Reinforcement Learning (RL) techniques for the broader public by alleviating some of its main challenges, including data collection, algorithm selection, and hyper-parameter tuning. In this work, we propose a general and flexible framework, namely ARLO: Automated Reinforcement Learning Optimizer, to construct automated pipelines for AutoRL. Based on this, we propose a pipeline for offline and one for online RL, discussing the components, interaction, and highlighting the difference between the two settings. Furthermore, we provide a Python implementation of such pipelines, released as an open-source library. Our implementation is tested on an illustrative LQG domain and on classic MuJoCo environments, showing the ability to reach competitive performances requiring limited human intervention. We also showcase the full pipeline on a realistic dam environment, automatically performing the feature selection and the model generation tasks.
Robust two-layer partition clustering of sparse multivariate functional data

A novel elastic time distance for sparse multivariate functional data is proposed and used to develop a robust distance-based two-layer partition clustering method. With this proposed distance, the new approach not only can detect correct clusters for sparse multivariate functional data under outlier settings but also can detect those outliers that do not belong to any clusters. Classical distance-based clustering methods such as density-based spatial clustering of applications with noise (DBSCAN), agglomerative hierarchical clustering, and K-medoids are extended to the sparse multivariate functional case based on the newly-proposed distance. Numerical experiments on simulated data highlight that the performance of the proposed algorithm is superior to the performances of existing model-based and extended distance-based methods. The effectiveness of the proposed approach is demonstrated using Northwest Pacific cyclone tracks data as an example.
A dual‐modal graph attention interaction network for person Re‐identification

Abstract Person Re‐identification (Re‐ID) is a task of matching target pedestrians under cross‐camera surveillance. Learning discriminative feature representations is the main issue for person Re‐ID. A few recent methods introduce text descriptions as auxiliary information to enhance feature representations, as it offers richer semantic information and perspective consistency. However, these works usually process text and images separately, which leads to the absence of cross‐modal interactions. In this article, a Dual‐modal Graph Attention Interaction Network (Dual‐GAIN) is proposed to integrate visual features and textual features into a heterogeneous graph to model the relationship between them, simultaneously. The proposed Dual‐GAIN mainly consists of two components: a dual‐stream feature extractor and a Graph Attention Interaction Network (GAIN). Specifically, the two‐stream feature extractor is utilised to extract visual features and textual features respectively. Then, visual local features and textual features are treated as nodes to construct a multi‐modal graph. Cosine similarity constrained attention weights are introduced in GAIN, which is designed for cross‐modal interaction and feature fusion on this heterogeneous multi‐modal graph. Experiments on public large‐scale datasets, that is, Market‐1501, CUHK03 labelled, and CUHK03 detected, demonstrate our method achieves the state‐of‐the‐art performance.
Scholarly Behavior: Bounded Rational Production of Rational Scientific Knowledge

People differ greatly in cognitive abilities, but all of them have limited abilities, ranging from the ability to perceive the surrounding reality and ending with the implementation of mathematical calculations and logical conclusions from the premises made. If a fully rational individual not only has complete information about the world, but also unlimited possibilities to make calculations and draw logical conclusions, then real people, including professional researchers, are only bounded rational. However, the scientific knowledge produced by scientists is close to completely rational. The article discusses the components of bounded rationality and those mechanisms within science that allow such a transition to be made. The leading role among these mechanisms belongs to scholarly communication, one of the functions of which is the correction of involuntary and unconscious errors made by bounded rational researchers. It is shown that the execution of this function faces certain difficulties, which are important to investigate in order to improve the error correction process.