Reviewerly/exHarmony huggingface dataset:
A Data-driven Approach for the Identification of Features for Automated Feedback on Academic Essays

For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological and semantic features) can be used to provide formative feedback to the students in higher education. In this study, the goal was to identify a sufficient number of features that exhibit a fair proxy of the scores given by the human raters via a data-driven approach. Using an existing corpus and a text analysis tool for the Dutch language, a large number of features were extracted. Artificial neural networks, Levenberg Marquardt algorithm and backward elimination were used to reduce the number of features automatically. Irrelevant features were eliminated based on the inter-rater agreement between predicted and human scores calculated using Cohen's Kappa ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> ). The number of features in this study was reduced from 457 to 28 and grouped into different categories. The results reported in this paper are an improvement over a similar previous study. Firstly, the inter-rater reliability between the predicted scores and human raters was increased by tweaking the corpus for overfitting for average scores. The resulting maximum value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> showed substantial agreement compared to moderate inter-rater reliability in the prior study. Secondly, instead of using a dedicated training and test set, the training and testing phases in the new experiments were performed using k-fold cross validation on the corpus of texts. The approach presented in this research paper is the first step towards our ultimate goal of providing meaningful formative feedback to the students for enhancing their writing skills and capabilities.
Optimalisasi Teknologi Informasi, Budaya Organisasi dan Kompetensi dalam Meningkatkan Kinerja Dosen (Tinjauan Pustaka)

This research is a literature review that aims to examine the relationship between information technology optimization, organizational culture, and competence with improving lecturer performance. In today's digital era, information technology plays an increasingly important role in higher education. Information technology optimization includes the utilization of technology to improve efficiency, productivity, and quality in academic and administrative activities. Organizational culture reflects the values, norms and practices prevailing within educational institutions. Lecturer competencies refer to the skills, knowledge and attitudes relevant to teaching and research tasks. Through the literature review, the relationship between information technology optimization, organizational culture, and competencies with lecturer performance improvement will be identified. The results of the literature review show that information technology optimization can improve the efficiency and productivity of lecturers in carrying out teaching, research, and administrative tasks. An organizational culture that supports innovation, collaboration, and technology utilization also contributes to improving lecturer performance. The technological knowledge and skills mastered by lecturers contribute significantly to their performance. Improving lecturers' competencies in technology use, understanding of digital curriculum, and technology-based teaching skills can also improve teaching and research effectiveness.
Experiments and Simulations to Investigate How Air Flows in Speech Can Transport a Virus: Research and Teaching Experiences during the COVID-19 Pandemic

Early in the COVID-19 pandemic, there were reports that the spread of the SARS-CoV-2 virus occurred via an airborne route created by asymptomatic individuals. This transport pathway inspired us to better understand air flows in everyday activities such as speech, which, surprisingly, had been little studied. We undertook this challenge using 1) laboratory experiments designed to visualize exhalations accompanying simple phrases and 2) numerical solutions of the Navier-Stokes equations for conditions representative of breathing and speaking; both research directions were new to all of the co-authors. The results suggested that in spite of the complexity of speech, the vortical nature of plosive sounds produce significant transport in front of a speaker, so much so that even after a few seconds of speech in a poorly ventilated space the mitigation recommended by the “six-foot rule” is questionable. In addition, the studies led us to investigate how such flows occur for professional musicians performing in opera, the effects of masks on the air flow, and the interplay between contact time and separation distance when thinking about strategies to minimize contact with an airborne virus. As a result of their expertise, the authors were involved in other pandemic-related projects and some of these educational and research connections are documented also, e.g., modeling of crowds and student projects such as developing a suite of CO2 sensors to monitor the effectiveness of ventilation. Finally, these activities occurred during a time of rapid evolution of institutional policies in response to the pandemic and we record the evolution of policies at Princeton University, including times of restricted access to the lab followed by the stepwise re-start of different laboratory and university activities.
Cloud security divides industry and US policymakers

Headline US: Cloud security divides industry and policymakers
Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-Based NLG Method

Counterfactual Explanations (cf-XAI) describe the smallest changes in feature values necessary to change an outcome from one class to another. However, many cf-XAI methods neglect the feasibility of those changes. In this paper, we introduce a novel approach for presenting cf-XAI in natural language (Natural-XAI), giving careful consideration to actionable and comprehensible aspects while remaining cognizant of immutability and ethical concerns. We present three contributions to this endeavor. Firstly, through a user study, we identify two types of themes present in cf-XAI composed by humans: content-related, focusing on how features and their values are included from both the counterfactual and the query perspectives; and structure-related, focusing on the structure and terminology used for describing necessary value changes. Secondly, we introduce a feature actionability taxonomy with four clearly defined categories, to streamline the explanation presentation process. Using insights from the user study and our taxonomy, we created a generalisable template-based natural language generation (NLG) method compatible with existing explainers like DICE, NICE, and DisCERN, to produce counterfactuals that address the aforementioned limitations of existing approaches. Finally, we conducted a second user study to assess the performance of our taxonomy-guided NLG templates on three domains. Our findings show that the taxonomy-guided Natural-XAI approach (n-XAI^T) received higher user ratings across all dimensions, with significantly improved results in the majority of the domains assessed for articulation, acceptability, feasibility, and sensitivity dimensions.
A Logic-Based Framework for Explainable Agent Scheduling Problems

Agent Scheduling Problems (ASPs) are common in various real-world situations, requiring explainable decision-making processes to effectively allocate resources to multiple agents while fostering understanding and trust. To address this need, this paper presents a logic-based framework for providing explainable decisions in ASPs. Specifically, the framework addresses two types of queries: reason-seeking queries, which explain the reasoning behind scheduling decisions, and modification-seeking queries, which offer guidance on making infeasible decisions feasible. Acknowledging the importance of privacy in multi-agent scheduling, we introduce a privacy-loss function that measures the disclosure of private information in explanations, enabling a privacy-preserving aspect in our framework. By using this function, we introduce the notion of privacy-aware explanations and present an algorithm for computing them. Empirical evaluations demonstrate the effectiveness and versatility of our approach.
Enhancing Link Prediction with Self-Discriminating Augmentation for Structure-Aware Contrastive Learning

Link prediction is a crucial research area for both data mining and machine learning. Despite the success of contrastive learning in node classification tasks, applying it directly to link prediction tasks has revealed two major weaknesses, i.e., single positive sample contrasting and random augmentation, resulting in inferior performance. To overcome these issues, we propose a new contrastive learning approach for link prediction, called Structure-aware Contrastive Representation Learning with Self-discriminating Augmentation (SECRET). Our approach includes a novel data augmentation scheme based on the prediction model itself and takes into account both the contrastive objective and the reconstruction loss, which jointly improve the performance of link prediction. Our experiments on 11 benchmark datasets demonstrate that SECRET significantly outperforms the other state-of-the-art baselines.
Cartesian Abstractions and Saturated Cost Partitioning in Probabilistic Planning

Stochastic shortest path problems (SSPs) capture probabilistic planning tasks with the objective of minimizing expected cost until reaching the goal. One of the strongest methods to solve SSPs optimally is heuristic search guided by an admissible (lower-bounding) heuristic function. Recently, probability-aware pattern database (PDB) abstractions have been highlighted as an efficient way of generating such lower bounds, with significant advantages over traditional determinization-based approaches. Here, we follow this work, yet consider a more general type, Cartesian abstractions, which have been used successfully in the classical setting. We show how to construct probability-aware Cartesian abstractions via a counterexample-guided abstraction refinement (CEGAR) loop akin to classical planning. This method is complete, meaning it guarantees convergence to the optimal expected cost if not terminated prematurely. Furthermore, we investigate the admissible combination of multiple such heuristics using saturated cost partitioning (SCP), marking its first application in the probabilistic setting. In our experiments, we show that probability-aware Cartesian abstractions yield much more informative heuristics than their determinization-based counterparts. Finally, we show that SCP yields probability-aware abstraction heuristics that are superior to the previous state of the art.
Neural Architecture Search for Explainable Networks

One of the main challenges in machine learning is providing understandable explanations for complex models. Despite outperforming humans in many tasks, machine learning models are often treated as black boxes that are difficult to interpret. Post-hoc explanation methods have been developed to create interpretable surrogate models that explain the behavior of black-box models. However, these methods have been shown to perpetuate bad practices and lack stability. Recently, inherent explainable approaches have been proposed to provide built-in explainability to models. However, most of these methods sacrifice performance. This paper proposes the Neural Architecture Search for Explainable Networks (NASXNet) approach to address the trade-off between performance and interpretability. Our method applies architecture search to generate high-performance and explainable neural networks for image classification tasks. We conduct experiments on four datasets: CUB-200-2011, Stanford Cars, CIFAR 10, and CIFAR 100. The results demonstrate that our models provide a high-level interpretation of prediction results, achieving state-of-the-art performance that is on par with non-explainable models. This paper contributes by solving the trade-off problem between performance and interpretability. It is the first to apply neural architecture search to develop explainable deep learning models, generating state-of-the-art explainable models that outperform existing approaches. Additionally, a new training process is proposed that enables faster convergence during model training.
PARIS: Planning Algorithms for Reconfiguring Independent Sets

Combinatorial reconfiguration is the problem of transforming one solution of a combinatorial problem into another, where each transformation may only apply small changes to a solution and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another by a sequence of transformations that can replace a vertex in the current subset such that the new subset is still an independent set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team successfully participated with two solvers that model the ISR problem as a planning task and employ different planning techniques for solving it. In this work, we describe these models and solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.
Mad or mad-mad: conveying subtle emotion with face emoji

Introduction: To compensate for the lack of pragmatic information available when communicating via text message, texters make frequent use of texting-specific cues, or textisms, to convey meaning that would otherwise be apparent in spoken conversation. Here, we explore how one such cue, face emoji, can impact the interpretation of text messages.In Experiment 1, we paired neutral text messages with valenced face emoji to determine whether the emoji can alter the meaning of the text. In Experiment 2, we paired valenced text messages with valenced face emoji to determine whether the emoji can modulate the valence of the text.In Experiment 1, we found that texts paired with positive emoji were rated more positively than texts paired with negative emoji. Furthermore, texts paired with stronger-valenced emoji were rated as less neutral compared to texts paired with milder-valenced emoji. In Experiment 2, we found that slightly positive texts paired with strong positive emoji were rated somewhat similarly to the same texts paired with mild positive emoji; however, slightly negative texts paired with strong negative emoji were rated much more negatively than the same texts paired with mild negative emoji.These results indicate that the presence of face emoji, particularly negative face emoji, can alter the interpretation of text messages, allowing texters to communicate nuanced meaning and subtle emotion.
A design approach for building a digital platform to augment human abilities based on a more-than-human perspective

Abstract To acquire information from the real world and respond appropriately to life's circumstances, vision is indispensable for humans. However, due to its ubiquitous nature, we often perceive the world unconsciously, thereby overlooking the opportunity to contemplate the significance of sight. Seeing goes beyond being a mere method of gathering information; it is an act of uncovering new perspectives and engaging in profound exploration. Theories on creative problem-solving strongly advocate for the advantages of adopting multiple viewpoints. By generating a multitude of alternatives through information gleaned from diverse perspectives, we enhance our ability to expand the range of choices available to us, thus facilitating more effective problem-solving. In this paper, we present Posthuman CollectiveEyes, a digital platform that enriches the human act of visual perception by integrating diverse viewpoints such as collective human, augmented human, and nonhuman viewpoints, and constructs posthuman viewpoints from the diverse viewpoints. In the design of Posthuman CollectiveEyes, we adopt the more-than-human perspective, widely employed in the social sciences to analyze the impact of technology on human actions and decision-making in organizations and societies. This perspective enables us to uncover knowledge that conventional human-centered approaches cannot capture, as the objective of Posthuman CollectiveEyes is to expand human cognitive capabilities through enhanced visual perception. The novel contribution of our approach lies in demonstrating that the design of innovative digital platforms aimed at enhancing human abilities necessitates a fresh design approach that incorporates the more-than-human perspective.
Banach Space Valued Neural Network Approximation Based on a Parametrized Arctangent Sigmoid Function

Here we present the univariate quantitative approximation of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. We perform also the related Banach space valued fractional approximation.
LTLf Best-Effort Synthesis in Nondeterministic Planning Domains

We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The notion of best-effort strategy has been introduced to also deal with the scenario when no agent strategy exists that fulfills the goal against every possible nondeterministic environment reaction. Such strategies fulfill the goal if possible, and do their best to do so otherwise. We present a game-theoretic technique for synthesizing best-effort strategies that exploit the specificity of nondeterministic planning domains. We formally show its correctness and demonstrate its effectiveness experimentally, exhibiting a much greater scalability with respect to a direct best-effort synthesis approach based on re-expressing the planning domain as generic environment specifications.
Using Self-Supervised Dual Constraint Contrastive Learning for Cross-Modal Retrieval

In this work, we present a self-supervised dual constraint contrastive method for efficiently fine-tuning the vision-language pre-trained (VLP) models that have achieved great success on various cross-modal tasks, since full fine-tune these pre-trained models is computationally expensive and tend to result in catastrophic forgetting restricted by the size and quality of labeled datasets. Our approach freezes the pre-trained VLP models as the fundamental, generalized, and transferable multimodal representation and incorporates lightweight parameters to learn domain and task-specific features without labeled data. We demonstrated that our self-supervised dual contrastive model performs better than previous fine-tuning methods on MS COCO and Flickr 30K datasets on the cross-modal retrieval task, with an even more pronounced improvement in zero-shot performance. Furthermore, experiments on the MOTIF dataset prove that our self-supervised approach remains effective when trained on a small, out-of-domain dataset without overfitting. As a plug-and-play method, our proposed method is agnostic to the underlying models and can be easily integrated with different VLP models, allowing for the potential incorporation of future advancements in VLP models.
XGBD: Explanation-Guided Graph Backdoor Detection

Backdoor attacks pose a significant security risk to graph learning models. Backdoors can be embedded into the target model by inserting backdoor triggers into the training dataset, causing the model to make incorrect predictions when the trigger is present. To counter backdoor attacks, backdoor detection has been proposed. An emerging detection strategy in the vision and NLP domains is based on an intriguing phenomenon: when training models on a mixture of backdoor and clean samples, the loss on backdoor samples drops significantly faster than on clean samples, allowing backdoor samples to be easily detected by selecting samples with the lowest loss values. However, the ignorance of topological feature information on graph data limits its detection effectiveness when applied directly to the graph domain. To this end, we propose an explanation-guided backdoor detection method to take advantage of the topological information. Specifically, we train a helper model on the graph dataset, feed graph samples into the model, and then adopt explanation methods to attribute model prediction to an important subgraph. We observe that backdoor samples have distinct attribution distribution than clean samples, so the explanatory subgraph could serve as more discriminative features for detecting backdoor samples. Comprehensive experiments on multiple popular datasets and attack methods demonstrate the effectiveness and explainability of our method. Our code is available: https://github.com/GuanZihan/GNN_backdoor_detection.
A Factorial Importance Feature Recognition Algorithm Based on KPCA- LightGBM Coupling Algorithm

Based on Kernel Principal Component Analysis (KPCA) and Light Gradient Boosting Machine (LightGBM) algorithm, this paper proposes a new method for machine learning important feature analysis. We first use KPCA to transform all sample feature data, and then use LightGBM to estimate the representation data of the sample. In this paper, 1974 sets of case data are used for empirical analysis. The experimental results show that the mean square error (MSE), coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and mean absolute percentage error (MAPE) of the proposed method in prediction are 1.162, 12.308, and 0.432, respectively, which can accurately and efficiently identify important data features. In summary, the KPCA-LightGBM proposed in this paper can solve the problem of model instability and inaccurate recognition of important features caused by the direct use of data containing high-dimensional features as input.
Human Activity Recognition Based on a Cascade Network Architecture Composed of DFSMN and ACGAN

Currently, human activity recognition (HAR) has been applied in many fields, including healthcare, smart homes, and sports tracking. The commonly-used LSTM-based methods have the disadvantage of having a relatively large number of parameters. This article proposes a HAR method based on a cascade architecture of deep feedforward sequential memory networks (DFSMN) and auxiliary classifier generative adversarial networks (ACGAN). In this research, a self-collected dataset was constructed through sampling data from inertial sensors worn by humans. DFSMN was used to extract temporal features from sensor data, and then in ACGAN, the extracted features were used to enhance the generator's ability to generate temporal data while using a discriminator for classification of activities. The model combining DFSMN with ACGAN can effectively improve the model's generalization ability and classification accuracy The experimental results indicate that this method can accurately recognize a wide range of human activities, such as walking, running, sitting, standing, going upstairs, going downstairs, and so on. This shows that the proposed method has a considerable application potential in practical scenarios.