Reviewerly/exHarmony huggingface dataset:
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models

Recently, NLP models have achieved remarkable progress across a variety of tasks; however, they have also been criticized for being not robust.Many robustness problems can be attributed to models exploiting spurious correlations, or shortcuts between the training data and the task labels.Most existing work identifies a limited set of task-specific shortcuts via human priors or error analyses, which requires extensive expertise and efforts.In this paper, we aim to automatically identify such spurious correlations in NLP models at scale.We first leverage existing interpretability methods to extract tokens that significantly affect model's decision process from the input text.We then distinguish "genuine" tokens and "spurious" tokens by analyzing model predictions across multiple corpora and further verify them through knowledge-aware perturbations.We show that our proposed method can effectively and efficiently identify a scalable set of "shortcuts", and mitigating these leads to more robust models in multiple applications.
Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm

To improve the path planning efficiency of a robotic arm in three-dimensional space and improve the obstacle avoidance ability, this paper proposes an improved artificial potential field and rapid expansion random tree (APF-RRT) hybrid algorithm for the mechanical arm path planning method. The improved APF algorithm (I-APF) introduces a heuristic method based on the number of adjacent obstacles to escape from local minima, which solves the local minimum problem of the APF method and improves the search speed. The improved RRT algorithm (I-RRT) changes the selection method of the nearest neighbor node by introducing a triangular nearest neighbor node selection method, adopts an adaptive step and generates a virtual new node strategy to explore the path, and removes redundant path nodes generated by the RRT algorithm, which effectively improves the obstacle avoidance ability and efficiency of the algorithm. Bezier curves are used to fit the final generated path. Finally, an experimental analysis based on Python shows that the search time of the hybrid algorithm in a multi-obstacle environment is reduced to 2.8 s from 37.8 s (classic RRT algorithm), 10.1 s (RRT* algorithm), and 7.4 s (P_RRT* algorithm), and the success rate and efficiency of the search are both significantly improved. Furthermore, the hybrid algorithm is simulated in a robot operating system (ROS) using the UR5 mechanical arm, and the results prove the effectiveness and reliability of the hybrid algorithm.
Automatic Identification of Harmful, Aggressive, Abusive, and Offensive Language on the Web: A Survey of Technical Biases Informed by Psychology Literature

The automatic detection of conflictual languages (harmful, aggressive, abusive, and offensive languages) is essential to provide a healthy conversation environment on the Web. To design and develop detection systems that are capable of achieving satisfactory performance, a thorough understanding of the nature and properties of the targeted type of conflictual language is of great importance. The scientific communities investigating human psychology and social behavior have studied these languages in details, but their insights have only partially reached the computer science community. In this survey, we aim both at systematically characterizing the conceptual properties of online conflictual languages, and at investigating the extent to which they are reflected in state-of-the-art automatic detection systems. Through an analysis of psychology literature, we provide a reconciled taxonomy that denotes the ensemble of conflictual languages typically studied in computer science. We then characterize the conceptual mismatches that can be observed in the main semantic and contextual properties of these languages and their treatment in computer science works; and systematically uncover resulting technical biases in the design of machine learning classification models and the dataset created for their training. Finally, we discuss diverse research opportunities for the computer science community and reflect on broader technical and structural issues.
Deep learning approaches to inverse problems in imaging: Past, present and future

In recent years, deep learning-based models have gained momentum in imaging problems such as image and video super-resolution, image restoration or inpainting. The analytical approaches that have traditionally been used to solve image inverse problems have started to be replaced by deep learning ones, being outperformed in terms of efficacy and efficiency in many applications. However, deep learning-based models lack the adaptability of analytical models, thus making them unsuitable for dealing simultaneously with different forward image formation models. In contrast to analytical methods, deep learning models typically do not use domain knowledge and rely on learning the solution to the inverse problem from large data sets. This is making them susceptible to errors caused by the presence of degradations not seen during training. Hybrid models combining analytical and deep learning approaches have been introduced to solve such generalization issues while retaining the efficacy of deep learning models. In this work, we review deep learning and hybrid methods for solving imaging inverse problems, focusing on image and video super-resolution and image restoration. Furthermore, we discuss open problems in this area that would be of critical importance in the future, the challenges of applying deep learning models to solve them, and how future research could address them.
Data Access Control Based on Blockchain in Medical Cyber Physical Systems

The current medical cyber physical systems involve a wide range of institutions and a large number of participants. Data sharing among distributed medical institutions is already a development trend. However, the security is worrying; e.g., the access to medical data lacks uniformity and standardization. What is more, data is easy to be tampered with and leaked. This has a very negative impact on the medical industry. Therefore, a strict and reliable access control mechanism for data in the medical cyber physical systems is a prerequisite for ensuring the implementation of modern medical functions. We deal with how to design effective access control in medical cyber physical systems. Combined with blockchain technology, we design the medical cyber physical systems based on blockchain data access control mechanism and unite data in the chain of union Fabric network resources access control. We qualitatively classify medical data, define the weight level of different data, design a medical data access framework based on blockchain, build an applicable model, formulate access control strategy, and specify the role assignment and access task matching of users, so as to achieve secure and effective data access control. The Hyperledger Fabric network is established as the alliance chain for managing access control rights distribution through smart contracts so as to achieve case-based medical data access control under the blockchain.
Composition and Optimization of Higher Education Management System Based on Data Mining Technology

In order to improve the low efficiency of higher education management and the unequal distribution of curriculum resources, facing the actual situation of higher education management, a higher education management system based on data mining technology is constructed and optimized. Using the correlation between MVC components of higher education management platform and the support of data mining technology, this paper constructs the MVC model 2 framework of higher education management platform which is a powerful framework used for developing large-scale projects with ease. We collect the data structure in the database, extract the evaluation indicators of higher education management system, separate the display results and business logic, and improve code reusability. We adopt data input, curriculum management, curriculum scheduling, teacher scheduling, and system maintenance and help to form and optimize the higher education management system. The experimental results show that the constructed and optimized higher education management system can maintain good scheduling performance and provide better teaching services for teachers and students.
EU Artificial Intelligence Act: The European Approach to AI

On 21 April 2021, the European Commission presented the Artificial Intelligence Act. This Stanford Law School contribution lists the main points of the proposed regulatory framework for AI.The draft regulation seeks to codify the high standards of the EU trustworthy AI paradigm. It sets out core horizontal rules for the development, trade and use of AI-driven products, services and systems within the territory of the EU, that apply to all industries. The EU AI Act introduces a sophisticated ‘product safety regime’ constructed around a set of 4 risk categories. It imposes requirements for market entrance and certification of High-Risk AI Systems through a mandatory CE-marking procedure. This pre-market conformity regime also applies to machine learning training, testing and validation datasets. The AI Act draft combines a risk-based approach based on the pyramid of criticality, with a modern, layered enforcement mechanism. This means that as risk increases, stricter rules apply. Applications with an unacceptable risk are banned. Fines for violation of the rules can be up to 6% of global turnover for companies.The EC aims to prevent the rules from stifling innovation and hindering the creation of a flourishing AI ecosystem in Europe, by introducing legal sandboxes that afford breathing room to AI developers.The new European rules will forever change the way AI is formed. Pursuing trustworthy AI by design seems like a sensible strategy, wherever you are in the world.
Data Hiding and Its Applications: Digital Watermarking and Steganography

Data hiding techniques [...]
Smartphone-based Handwritten Signature Verification using Acoustic Signals

Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature patterns for verification. Particularly, SonarSign exploits the built-in speakers and microphones of smartphones to transmit a specially designed training sequence and record the corresponding echo for channel impulse response (CIR) estimation, respectively. Based on the sensitivity of CIR to the tiny surrounding environment changes including handwritten signature actions, SonarSign designs an attentional multi-modal Siamese network for end-to-end signatures verification. First, multi-modal CIR streams are fused to extract representative signature pattern features from spatio-temporal dimensions. Then an attentional Siamese network is elaborated to verify whether the given two signatures are from the same signatory. Extensive experiments in real-world scenarios show that SonarSign can achieve accurate and robust signatures verification with an AUC (Area Under ROC (Receiver Operating Characteristic) Curve) of 98.02% and an EER (Equal Error Rate) of 5.79% for unseen users.
SD-QA: Spoken Dialectal Question Answering for the Real World

Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces.However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users.To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers.We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance.Last, we study the fairness of the ASR and QA models with respect to the underlying user populations. 1
Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies.
An Image Preprocessing Model of Coal and Gangue in High Dust and Low Light Conditions Based on the Joint Enhancement Algorithm

The lighting facilities are affected due to conditions of coal mine in high dust pollution, which bring problems of dim, shadow, or reflection to coal and gangue images, and make it difficult to identify coal and gangue from background. To solve these problems, a preprocessing model for low-quality images of coal and gangue is proposed based on a joint enhancement algorithm in this paper. Firstly, the characteristics of coal and gangue images are analyzed in detail, and the improvement ways are put forward. Secondly, the image preprocessing flow of coal and gangue is established based on local features. Finally, a joint image enhancement algorithm is proposed based on bilateral filtering. In experimental, K-means clustering segmentation is used to compare the segmentation results of different preprocessing methods with information entropy and structural similarity. Through the simulation experiments for six scenes, the results show that the proposed preprocessing model can effectively reduce noise, improve overall brightness and contrast, and enhance image details. At the same time, it has a better segmentation effect. All of these can provide a better basis for target recognition.
On type-cases, union elimination, and occurrence typing

We extend classic union and intersection type systems with a type-case construction and show that the combination of the union elimination rule of the former and the typing rules for type-cases of our extension encompasses occurrence typing . To apply this system in practice, we define a canonical form for the expressions of our extension, called MSC-form. We show that an expression of the extension is typable if and only if its MSC-form is, and reduce the problem of typing the latter to the one of reconstructing annotations for that term. We provide a sound algorithm that performs this reconstruction and a proof-of-concept implementation.
Adaptive Initialization Method for K-Means Algorithm

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.
A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory

In this paper, a novel bio-inspired trajectory planning method is proposed for robotic systems based on an improved bacteria foraging optimization algorithm (IBFOA) and an improved intrinsic Tau jerk (named Tau-J*) guidance strategy. Besides, the adaptive factor and elite-preservation strategy are employed to facilitate the IBFOA, and an improved Tau-J* with higher-order of intrinsic guidance movement is used to avoid the nonzero initial and final jerk, so as to overcome the computational burden and unsmooth trajectory problems existing in the optimization algorithm and traditional interpolation algorithm. The IBFOA is utilized to determine a small set of optimal control points, and Tau-J* is then invoked to generate smooth trajectories between the control points. Finally, the results of simulation tests demonstrate the eminent stability, optimality, and rapidity capability of the proposed bio-inspired trajectory planning method.
Towards Task-Generic Image Compression: A Study of Semantics-Oriented Metrics

Instead of being observed by human, multimedia data are now more and more fed into machines to perform different kinds of semantic analysis. One image may be analyzed multiple times by different machine vision algorithms for different purposes. While machine vision-oriented image compression has been studied, the existing methods are usually driven by a specific machine vision task, and may not be applicable for the other tasks. We address the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task-generic</i> image compression, in the hope that an image is compressed once but used multiple times for different tasks, all with satisfactory performance. Our study is based on the end-to-end learned image compression. We focus ourselves on the distortion metric, i.e., finding out a task-agnostic metric to estimate the quality of reconstructed images. On the one hand, we study deep feature distance as the metric, which transforms images into a latent space by a pretrained convolutional network—the latent space is believed to be more aligned to semantics—and calculates distance in the latent space. On the other hand, inspired by the saliency mechanism, we study an importance-weighted pixel distance as the metric, where the weights are generated to reflect the importance of the pixels to semantics. Moreover, we combine the two distances into one metric to investigate their complementary nature. An extensive set of experiments are performed to evaluate these metrics. Experimental results show that using the combined metric performs the best, and leads to 20.79% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 42.69% bits saving under the same semantic analysis performance, compared to using signal fidelity metrics. Interestingly, we observe that using the combined metric also improves the visual quality of the reconstructed images.
Volcanic Gas Hazard Assessment in the Baia di Levante Area (Vulcano Island, Italy) Inferred by Geochemical Investigation of Passive Fluid Degassing

In a volcanic area, the composition of air is influenced by the interaction between fluids generated from many different environments (magmatic, hydrothermal, meteoric, and marine). Any physical and chemical variation in one of these subsystems is able to modify the outgassing dynamic. The increase of natural gas hazard, related to the presence of unhealthy components in air, may depend on temporary changes both in the pressure and chemical gradients that generate transient fluxes of gases and can have many different causes. Sometimes, the content of unhealthy gases approaches unexpected limits, without clear warning. In this case, an altered composition of the air can be only revealed after accurate sampling procedures and laboratory analysis. The investigations presented here are a starting point to response to the demand for a new monitoring program in the touristic area of Baia di Levante at Vulcano Island (Aeolian archipelago, Italy). Three multiparametric geochemical surveys were carried in the touristic area of Baia di Levante at Vulcano Island (Aeolian archipelago, Italy) in 2011, 2014, and 2015. Carbon dioxide (CO2) and hydrogen sulfide (H2S) are the main undesired components, usually present at the local scale. Anomalous CO2 and H2S outputs from soil and submarine bubbling vents were identified; the thermal anomaly of the ground was mapped; atmospheric concentrations of CO2 and H2S were measured in the air 30 cm above the ground surface. Atmospheric concentrations above the suggested limits for the wellbeing of human health were retrieved in open areas where tourists stay and where CO2 can accumulate under absence of wind.