Reviewerly/exHarmony huggingface dataset:
Adding Why to What? Analyses of an Everyday Explanation

Abstract In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, onan explain, for example, an XAI’s decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 explanations of games using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplement results from analyzing the explanation contents with results from a video recall to explore how Explainers (EX) justified their explanation. We found that EX were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. EX justified shifting between addressing the two sides by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.
Compare-xAI: Toward Unifying Functional Testing Methods for Post-hoc XAI Algorithms into a Multi-dimensional Benchmark

In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI algorithms enable humans to understand the underlying models and explain their behavior, leading to insights through which the models can be analyzed and improved beyond the accuracy metric by, e.g., debugging the learned pattern and reducing unwanted biases. However, the widespread use of xAI and the rapidly growing body of published research in xAI have brought new challenges. A large number of xAI algorithms can be overwhelming and make it difficult for practitioners to choose the correct xAI algorithm for their specific use case. This problem is further exacerbated by the different approaches used to assess novel xAI algorithms, making it difficult to compare them to existing methods. To address this problem, we introduce Compare-xAI, a benchmark that allows for a direct comparison of popular xAI algorithms with a variety of different use cases. We propose a scoring protocol employing a range of functional tests from the literature, each targeting a specific end-user requirement in explaining a model. To make the benchmark results easily accessible, we group the tests into four categories (fidelity, fragility, stability, and stress tests). We present results for 13 xAI algorithms based on 11 functional tests. After analyzing the findings, we derive potential solutions for data science practitioners as workarounds to the found practical limitations. Finally, Compare-xAI is a tentative to unify systematic evaluation and comparison methods for xAI algorithms with a focus on the end-user’s requirements. The code is made available at: https://karim-53.github.io/cxai/ .
Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media

Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to enable the monitoring and enforcement of such regulations at scale. Current research in this field primarily frames this problem as a machine learning task, focusing on developing models that achieve high classification performance in detecting ads. These machine learning tasks rely on human data annotation to provide ground truth information. However, agreement between annotators is often low, leading to inconsistent labels that hinder the reliability of models. To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations. Our experiments show that this approach consistently improves inter-annotator agreement and annotation accuracy. Additionally, our survey of user experience in the annotation task indicates that the explanations improve the annotators’ confidence and streamline the process. Our proposed methods can ultimately lead to more transparency and alignment with regulatory requirements in sponsored content detection.
Outcome-Guided Counterfactuals from a Jointly Trained Generative Latent Space

We present a novel generative method for producing higher-quality counterfactual examples for decision processes using a latent space that jointly encodes observations and associated behavioral outcome variables, such as classification decisions, actions taken, and estimated values. Our approach trains a variational autoencoder over behavior traces to both reconstruct observations and predict the outcome variables from the latent encoding. The resulting joint observation and outcome latent allows for unconditioned sampling of both observations and outcome variables from this space. This grants us the ability to generate counterfactuals using multiple methods, such as gradient-driven updates to move towards desired outcomes, interpolations against relevant cases drawn from a memory of examples, and combinations of these two. This also permits us to sample counterfactuals where constraints can be placed over some outcome variables, while others are allowed to vary. This flexibility also permits us to directly address the plausibility of generated counterfactuals by using gradient-driven updates to raise the data-likelihood of generated examples. We use this method to analyze the behavior of reinforcement learning (RL) agents against several outcome variables that characterize agent behavior. From experiments in three different RL environments, we show that these methods produce counterfactuals that score higher on standard counterfactual quality measures of proximity to the query and plausibility in contrast to observation-only gradient updates and case-based baselines. We also empirically demonstrate that counterfactuals sampled from a jointly trained space are of higher quality than those from the common practice of using latents from reconstruction-only autoencoders. We conclude with an analysis of counterfactuals produced over the joint latent using combinations of latent and case-based approaches for an agent trained to play a complex real-time strategy game, and discuss future directions of investigation for this approach.
Dynamic Navigation in Unconstrained Environments Using Reinforcement Learning Algorithms

The potential for the use of drones in logistics and transportation is continuously growing, with multiple applications both in urban and rural environments. The safe navigation of drones in such environments is a major challenge that requires sophisticated algorithms and systems, that can quickly and efficiently estimate the situation, find the shortest path to the target, and detect and avoid obstacles. Traditional path planning algorithms are unable to handle the dynamic and uncertain nature of real environments, while traditional machine learning models are insufficient due to the constantly changing conditions that affect the drone location and the location of obstacles. Reinforcement learning (RL) algorithms have been widely used for autonomous navigation problems, however, computational complexity and energy demands of such methods can become a bottleneck to the execution of UAV flights. In this paper, we propose the use of a minimum set of sensors and reinforcement learning (RL) algorithms for the safe and efficient navigation of drones in urban and rural environments. Our approach considers the complex and dynamic nature of such environments by incorporating real-time data from low-cost onboard sensors. After performing a thorough review of the existing solutions in drone path planning and navigation in 3-D environments, we experimentally evaluate our proposed approach in a simulated environment, and in various scenarios. The test results demonstrate the effectiveness of the proposed RL-based approach in the navigation of drones in complex and unconstrained environments. The implemented approach can serve as a basis for the development of advanced and robust navigation systems for drones, which can improve safety and efficiency in transportation applications in the near future.
A Precise Oriented Ship Detector in SAR Images Based on Dynamic Rotated Positive Sample Mining

The synthetic aperture radar (SAR) oriented ship detection is a more complex and finer task than the horizontal, and it provides ship orientation for military and civilian applications, such as trajectory prediction. However, many oriented target detection methods discarded high-resolution (low-level) feature map, which provides morphological features for predicting accurate bounding box for small ships, due to computational cost and lack of semantic information. Additionally, most methods follow the samples assignment in horizontal detection after padding oriented bounding box. The inaccurate geometric relationship causes that some low-quality positive samples without contributing accuracy improvement are introduced. To improve the precision of small oriented ship detection, we proposed the feature pyramid network contained high-resolution feature (FPNHRF) to fuse high-level semantics and highlight contours and textures of ships in the low-level feature map. To select high-quality positive rotated samples, dynamic rotated positive samples mining (DRPSM) is designed to mine hard positive samples for oriented ship detection specially. In one word, a two-stage and anchor-free detector trained by DRPSM and utilized high-resolution feature map is proposed for detecting oriented ships in SAR imagery. Subsequently, extensive experiments have been conducted on two oriented SAR datasets: the SSDD and the HRSID. As a result, our method attains Recall of 96.2% and 90.1%, and AP50 of 90.7% and 86.5% on SSDD and HRSID respectively. It shows that our detector achieves state-of-the-art results applied in capturing ships, when compared with other popular remote sensing oriented detectors.
A Waste Classification model in Low-illumination scenes based on ConvNeXt

Waste classification is an essential part of environmental pollution management in modern society. Object detection is an accurate and efficient way to classify waste, which is conducive to recycling resources. However, due to low object discriminability, existing waste classification models cannot classify waste in low-illumination scenes. A waste classification model, Dark-Waste, is designed to classify wastes in a low-illumination scenario. Firstly, to solve the scarcity of training data, an efficient and low-cost Illumination Conversion method is proposed to generate the low-light image. Secondly, the improved ConvNeXt network is combined with YOLOv5 to accurately and efficiently classify waste. Finally, we validated the model on a self-built dataset in real scenarios. The experimental results show that Dark-Waste achieves the best detection performance in low-illumination scenes. The Dark-Waste provides a new approach to waste management in complex environments and effectively contributes to the sustainable development of the urban ecological environment.
BrightMarker: 3D Printed Fluorescent Markers for Object Tracking

Existing invisible object tagging methods are prone to low resolution, which impedes tracking performance. We present BrightMarker, a fabrication method that uses fluorescent filaments to embed easily trackable markers in 3D printed color objects. By using an infrared-fluorescent filament that "shifts" the wavelength of the incident light, our optical detection setup filters out all the noise to only have the markers present in the infrared camera image. The high contrast of the markers allows us to track them robustly regardless of the moving objects’ surface color.
Generative models for protein sequence modeling: recent advances and future directions

The widespread adoption of high-throughput omics technologies has exponentially increased the amount of protein sequence data involved in many salient disease pathways and their respective therapeutics and diagnostics. Despite the availability of large-scale sequence data, the lack of experimental fitness annotations underpins the need for self-supervised and unsupervised machine learning (ML) methods. These techniques leverage the meaningful features encoded in abundant unlabeled sequences to accomplish complex protein engineering tasks. Proficiency in the rapidly evolving fields of protein engineering and generative AI is required to realize the full potential of ML models as a tool for protein fitness landscape navigation. Here, we support this work by (i) providing an overview of the architecture and mathematical details of the most successful ML models applicable to sequence data (e.g. variational autoencoders, autoregressive models, generative adversarial neural networks, and diffusion models), (ii) guiding how to effectively implement these models on protein sequence data to predict fitness or generate high-fitness sequences and (iii) highlighting several successful studies that implement these techniques in protein engineering (from paratope regions and subcellular localization prediction to high-fitness sequences and protein design rules generation). By providing a comprehensive survey of model details, novel architecture developments, comparisons of model applications, and current challenges, this study intends to provide structured guidance and robust framework for delivering a prospective outlook in the ML-driven protein engineering field.
Analysis of the solar radiation/atmosphere interaction at a Mediterranean site: The role of clouds

This study is the last of a series of three research papers analysing the solar radiation and its interaction with the atmospheric components spanning an eleven-year period (2008–2018) at a mid-latitude urban site in the Mediterranean basin. During the previous works a detailed characterization of the photosynthetically active radiation (PAR; 400–700 nm), as well as analysis the aerosol and clouds effects on PAR was carried out. This research work addresses an analysis of solar radiation on the total solar irradiance spectrum (TSI; 280–3000 nm) in terms of the effects of different atmospheric conditions on it, considered by the clearness index and the cloud cover, bringing very valuable findings from the long-term comparative analysis of radiative effects of clouds on TSI and PAR wavelength ranges. The average values in the entire period of the study for the global solar radiation in the total solar irradiance (TSIGlobal) found to be 450 ± 100 Wm−2 and 530 ± 110 Wm−2 under all and clear- sky conditions, respectively. Meanwhile, the average values for the diffuse component (TSIDiffuse) are 141 ± 21 Wm−2 and 130 ± 21 Wm−2 (all and clear skies, respectively), with a relatively low interannual variation up to 11% for both global and diffuse TSI, as well as for both sky conditions. Analysis on the total cloud cover (TCC) shows that the clearness index is not a good parameter to discriminate between all and clear sky conditions, since there is a marked overlap in the ranges of the kt values for the different categories of TCC. Additionally, the cloud radiative forcing (CRF) are computed as the difference in solar radiation measured under all and clear sky conditions. A high seasonal variability is found for CRF, where CRFTSI,Global ranges between −37.6 Wm−2 and -137.4 Wm−2, while CRFTSI,Diffuse from 4.4 Wm−2 to 22.6 Wm−2. The positive sign implies increase in solar radiation at the surface, while the negative one implies the opposite, i.e., less availability of solar radiation on the surface. Finally, the analysis of the annual evolution of CRF reveals a downward trend on CRFTSI and CRFPAR, for both global and diffuse This relevant finding implies that clouds are exerting less cooling effects over time at this Mediterranean site.
Image steganography based on smooth cycle-consistent adversarial learning

Steganography is the process of concealing a secret message in ordinary digital media by making small modifications that try to preserve cover statistics. In this paper, a novel unified steganographic framework is introduced inspired by image-to-image translation networks and adversarial learning. We consider image steganography as an image-to-image translation which is a transformation from cover distribution to stego distribution. The image-to-image translation models can exploit imperceptible perturbations to embed useful input information into the target image so that the input image can be retrieved more accurately. So, we adapt the CycleGAN as a popular translator to conceal the secret message in the form of an imperceptible, high-frequency signal in the container image called stego, while the cyclic consistency constraint helps to extract the secret message. As deep neural networks are sensitive to small adversarial perturbations, adversarial learning is exploited to embed the secret message more semantically to preserve the statistical characteristics of the cover as much as possible. According to the competitive relation of steganography and steganalysis, the steganographic methods act as an adversarial attack to mislead the steganalysis network toward determining the input images as the cover images. Therefore, in the steganographic concept, a CNN-based steganalyzer is considered as the discriminator such that the steganographer and the steganalyzer alternately adjust their weights according to the updated knowledge of one another. On the other hand, we append the smoothness constraint into the objective function to encourage the model to encode the secret message as smooth embedding modifications to improve the secrecy of generated image. This constraint limits the range of local differences and enforces a stronger correlation between the cover and the corresponding stego image. Smoothing modifications mitigate the statistical impact and enhance the robustness against steganalyzers. Experiment results show the efficiency of our proposed framework which achieves an appropriate trade-off between secrecy and capacity and outperforms the existing works.
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training. Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent.
On the question of the specifics of the formulation and use of the Turing test for the ChatGPT

The subject of the research in this article are the features and regularities of the functioning of systems based on ChatGPT technologies, the knowledge of which makes it possible to formulate appropriate modifications of the Turing test, as well as the features and regularities of the formulation and use of the Turing test for systems based on ChatGPT technologies. The purpose of the study is to identify the features and patterns of functioning of systems based on the technologies of ChatGPT, as well as the features and patterns of formulation and use of the Turing test for systems based on the technologies of Chat GPT. As research methods, the method of social experiment was used, when during the study of a system based on Chat GPT technologies, certain questions were asked, answers were received, the analysis of which allowed us to conclude about the features of the "thinking" of systems based on ChatGPT technologies. In the course of the study, the following was found. Unlike human thinking, which is based on certain facts, the "thinking" of systems based on ChatGPT technologies, in some cases is not based on facts that take place in reality, often the user is given deliberately false information about facts and circumstances that take place in reality. In contrast to human thinking, which is usually systemic in nature, the "thinking" of systems based on ChatGPT technologies is disorderly and fragmentary. Systems based on ChatGPT technologies cannot admit their mistakes, and attempts to force systems based on ChatGPT technologies to critically comprehend their answers lead to a malfunction of these systems. The article also provides a Turing test developed by the author for ChatGPT, which made it possible to identify the features of the "thinking" of systems based on ChatGPT technologies.
EAGLE: Enhance Target-Oriented Dialogs by Global Planning and Topic Flow Integration

In this study, we propose a novel model EAGLE for target-oriented dialogue generation. Without relying on any knowledge graphs, our method integrates the global planning strategy in both topic path generation and response generation given the initial and target topics. EAGLE comprises three components: a topic path sampling strategy, a topic flow generator, and a global planner. Our approach confers a number of advantages: EAGLE is robust to the target that has never appeared in the training data set and able to plan the topic flow globally. The topic path sampling strategy samples topic paths based on two predefined rules and use the sampled paths to train the topic path generator. The topic flow generator then applies a non-autoregressive method to generate intermediate topics that link the initial and target topics smoothly. In addition, the global planner is a response generator that generates a response based on the future topic sequence and conversation history, enabling it to plan how to transition to future topics smoothly. Our experimental results demonstrate that EAGLE produces more coherent responses and smoother transitions than state-of-the-art baselines, with an overall success rate improvement of approximately 25% and an average smoothness score improvement of 10% in both offline and human evaluations.