Reviewerly/exHarmony huggingface dataset:
ResumeTailor: Improving Resume Quality Through Co-Creative Tools

Clear and well-written resumes can help jobseekers find better and better-suited jobs. However, many people struggle with writing their resumes, especially if they just entered the job market. Although many tools have been created to help write resumes, an analysis we conducted showed us that these tools focus mainly on layout and only give very limited content-related support. This paper presents a co-creative resume building tool that provides tailored advice to jobseekers. It is based on a comprehensive computational analysis of 444k resumes and the development of a Dutch language model, ResumeRobBERT, to provide contextual suggestions. Through the analysis of the resumes, we found that some expected sections, such as language proficiency, are often missing entirely, while conversely some resumes contain unexpected content, such as negative personality traits. This implies that jobseekers could benefit from more guidance when writing resumes. We aim to support them in the resume-writing process through our tool ResumeTailor, a co-creative resume building tool that gives textual suggestions and provides a template for important resume sections.
The Automated Model of Comprehension Version 3.0: Paying Attention to Context

Reading comprehension is essential for both knowledge acquisition and memory reinforcement. Automated modeling of the comprehension process provides insights into the efficacy of specific texts as learning tools. This paper introduces an improved version of the Automated Model of Comprehension, version 3.0 (AMoC v3.0). AMoC v3.0 is based on two theoretical models of the comprehension process, namely the Construction-Integration and the Landscape models. In addition to the lessons learned from the previous versions, AMoC v3.0 uses Transformer-based contextualized embeddings to build and update the concept graph as a simulation of reading. Besides taking into account generative language models and presenting a visual walkthrough of how the model works, AMoC v3.0 surpasses the previous version in terms of the Spearman correlations between our activation scores and the values reported in the original Landscape Model for the presented use case. Moreover, features derived from AMoC significantly differentiate between high-low cohesion texts, thus arguing for the model’s capabilities to simulate different reading conditions.
Designing a 1D extended logistic map for a secure image encryption

Abstract The performance of the keystream is an important index for determining the security of a cryptosystem. However, the parameter range of the classical one-dimensional chaotic map is limited, and the distribution of the generated chaotic sequence is aggregated. Using the keystream generated by such chaotic systems can lead to a small secret key space and low security of the cryptosystem. To address this, this paper proposes a 1D Extended Logistic Map (1D-ELM), which combines the Sin and Logistic maps. The 1D-ELM has the same control parameters as the Logistic map, but the parameter range in which 1D-ELM exhibits chaotic behavior is larger than that of the Logistic map. The performance of 1D-ELM is evaluated using the Lyapunov exponent, bifurcation diagram, and 0-1 test. Additionally, 1D-ELM is applied in image encryption. In the secret key generation stage, the designed cryptosystem employs a combination of public and private keys, ensuring the algorithm’s security. An attacker needs to find two secret keys to crack the algorithm. Furthermore, in the diffusion stage, most ciphertext values are related to the three adjacent ciphertext values, reducing the correlation of adjacent ciphertext pixels. The experiment verifies that 1D-ELM has good application in image encryption and plays an important role. The designed cryptographic system has high security.
A little 😀 goes a long way: examining the limits of immediacy cues on students’ perceptions of instructor credibility, immediacy, liking, and clarity

This paper examines how textisms, defined as cues that facilitate interaction in text-dominant communication (e.g., emojis), as a type of immediacy cue, may increase perceived immediacy in instructor–student mediated out-of-class communication (e.g., a welcome email). Using an experiment manipulating cue number and placement and instructor gender, we examined how emojis impacted participants’ (N = 443) impressions of instructors’ closeness, credibility, liking, and clarity. Results suggest that using emojis in instructor–student communication is best done sparingly and strategically, but if done this way, emojis can be potentially useful. These findings have implications for current instructors; limitations and future directions are also discussed.
Detecting Fine-Grained Emotions in Literature

Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model’s performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis.
Optimal Scheduling in the Multiserver-job Model under Heavy Traffic

Multiserver-job systems, where jobs require concurrent service at many servers, occur widely in practice. Essentially all of the theoretical work on multiserver-job systems focuses on maximizing utilization, with almost nothing known about mean response time. Our goal in this paper is to minimize mean response time in a multiserver-job setting. Minimizing mean response time requires prioritizing small jobs while simultaneously maximizing utilization. Our question is how to achieve these joint objectives. We devise the ServerFilling-SRPT scheduling policy, which is the first policy to minimize mean response time in the multiserver-job model in the heavy traffic limit. In addition to proving this heavy-traffic result, we present empirical evidence that ServerFilling-SRPT outperforms all existing scheduling policies for all loads, with orders of magnitude improvements at high load. Because ServerFilling-SRPT requires knowing job sizes, we also define the ServerFilling-Gittins policy, which is optimal when sizes are unknown or partially known. For more detail, see the full paper https://doi.org/10.1145/3570612
Do Foreign Grants and Capital Formation Indeed Impact Economic Growth? An Empirical Evidence from Bangladesh

The paper examined how the growth of the foreign grants and the growth of gross fixed capital formation impact the growth of GDP in Bangladesh. We studied the relationships between those variables for a period spanning between 1983 and 2017. The Johansen cointegration test indicated that there is a long-run relationship between the variables. Therefore, by employing a vector error correction model (VECM), we found that previous year’s divergence between the variables adjusted by 69% in the following year. The study also found that both foreign grants, and the gross fixed capital formation had a positive impact on GDP growth in Bangladesh.
Uncertainty-Aware Self-Training for Low-Resource Neural Sequence Labeling

Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by traditional supervised-based approaches heavily depend on the large amounts of human annotation data, which may not be feasible in real-world scenarios due to data privacy and computation efficiency issues. This paper presents SeqUST, a novel uncertain-aware self-training framework for NSL to address the labeled data scarcity issue and to effectively utilize unlabeled data. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data based on the model confidence and certainty. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels. In addition, we develop a Gaussian-based consistency regularization technique to further improve the model robustness on Gaussian-distributed perturbed representations. This effectively alleviates the over-fitting dilemma originating from pseudo-labeled augmented data. Extensive experiments over six benchmarks demonstrate that our SeqUST framework effectively improves the performance of self-training, and consistently outperforms strong baselines by a large margin in low-resource scenarios.
Data Imputation with Iterative Graph Reconstruction

Effective data imputation demands rich latent ``structure" discovery capabilities from ``plain" tabular data. Recent advances in graph neural networks-based data imputation solutions show their structure learning potentials by translating tabular data as bipartite graphs. However, due to a lack of relations between samples, they treat all samples equally which is against one important observation: ``similar sample should give more information about missing values." This paper presents a novel Iterative graph Generation and Reconstruction framework for Missing data imputation(IGRM). Instead of treating all samples equally, we introduce the concept: ``friend networks" to represent different relations among samples. To generate an accurate friend network with missing data, an end-to-end friend network reconstruction solution is designed to allow for continuous friend network optimization during imputation learning. The representation of the optimized friend network, in turn, is used to further optimize the data imputation process with differentiated message passing. Experiment results on eight benchmark datasets show that IGRM yields 39.13% lower mean absolute error compared with nine baselines and 9.04% lower than the second-best. Our code is available at https://github.com/G-AILab/IGRM.
Solving Large-Scale Pursuit-Evasion Games Using Pre-trained Strategies

Pursuit-evasion games on graphs model the coordination of police forces chasing a fleeing felon in real-world urban settings, using the standard framework of imperfect-information extensive-form games (EFGs). In recent years, solving EFGs has been largely dominated by the Policy-Space Response Oracle (PSRO) methods due to their modularity, scalability, and favorable convergence properties. However, even these methods quickly reach their limits when facing large combinatorial strategy spaces of the pursuit-evasion games. To improve their efficiency, we integrate the pre-training and fine-tuning paradigm into the core module of PSRO -- the repeated computation of the best response. First, we pre-train the pursuer's policy base model against many different strategies of the evader. Then we proceed with the PSRO loop and fine-tune the pre-trained policy to attain the pursuer's best responses. The empirical evaluation shows that our approach significantly outperforms the baselines in terms of speed and scalability, and can solve even games on street maps of megalopolises with tens of thousands of crossroads -- a scale beyond the effective reach of previous methods.
ConTextual Masked Auto-Encoder for Dense Passage Retrieval

Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages. Recent studies have explored improving pre-trained language models to boost dense retrieval performance. This paper proposes CoT-MAE (ConTextual Masked Auto-Encoder), a simple yet effective generative pre-training method for dense passage retrieval. CoT-MAE employs an asymmetric encoder-decoder architecture that learns to compress the sentence semantics into a dense vector through self-supervised and context-supervised masked auto-encoding. Precisely, self-supervised masked auto-encoding learns to model the semantics of the tokens inside a text span, and context-supervised masked auto-encoding learns to model the semantical correlation between the text spans. We conduct experiments on large-scale passage retrieval benchmarks and show considerable improvements over strong baselines, demonstrating the high efficiency of CoT-MAE. Our code is available at https://github.com/caskcsg/ir/tree/main/cotmae.
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning

Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation from a multilingual source code dataset has been shown to be more effective than learning from single-language datasets separately, since more training data from multilingual dataset improves the model's ability to extract language-agnostic information from source code. However, existing multilingual training overlooks the language-specific information which is crucial for modeling source code across different programming languages, while only focusing on learning a unified model with shared parameters among different languages for language-agnostic information modeling. To address this problem, we propose MetaTPTrans, a meta learning approach for multilingual code representation learning. MetaTPTrans generates different parameters for the feature extractor according to the specific programming language type of the input code snippet, enabling the model to learn both language-agnostic and language-specific information with dynamic parameters in the feature extractor. We conduct experiments on the code summarization and code completion tasks to verify the effectiveness of our approach. The results demonstrate the superiority of our approach with significant improvements on state-of-the-art baselines.
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual abnormal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications.
Australia's AI future

How can Artificial Intelligence (AI) improve the economic, societal and environmental well-being of Australia?I explore why AI is now able to take on a range of cognitive tasks.I discuss the technologi¬ cal challenges remaining to build intelligent machines.In addition, I identify some of the ethical and societal obstacles that this is, and will be, creating.
Analyzing the Performance of Searchable Symmetric Encryption over Huawei Cloud

Cloud computing environments store data from companies and end users, bringing reliability and scalability to this remote access. Additionally, encryption techniques have been applied to protect this data in the cloud, where Searchable Symmetric Encryption (SSE) enables a fast search of keywords in encrypted databases. However, the configuration of the cloud environment directly impacts the processing time of SSE and, consequently, affects Service Level Agreement (SLA) requirements. Within this context, this paper analyzes the impact of Huawei Cloud configuration over the SSE task, encompassing the possible existing options regarding computing and architectures available. The experiments performed using a real database over the Huawei Cloud suggest the most suitable configuration according to the requirements needed to meet.
Continual Learning with Scaled Gradient Projection

In neural networks, continual learning results in gradient interference among sequential tasks, leading to catastrophic forgetting of old tasks while learning new ones. This issue is addressed in recent methods by storing the important gradient spaces for old tasks and updating the model orthogonally during new tasks. However, such restrictive orthogonal gradient updates hamper the learning capability of the new tasks resulting in sub-optimal performance. To improve new learning while minimizing forgetting, in this paper we propose a Scaled Gradient Projection (SGP) method, where we combine the orthogonal gradient projections with scaled gradient steps along the important gradient spaces for the past tasks. The degree of gradient scaling along these spaces depends on the importance of the bases spanning them. We propose an efficient method for computing and accumulating importance of these bases using the singular value decomposition of the input representations for each task. We conduct extensive experiments ranging from continual image classification to reinforcement learning tasks and report better performance with less training overhead than the state-of-the-art approaches.
A Model-Agnostic Heuristics for Selective Classification

Selective classification (also known as classification with reject option) conservatively extends a classifier with a selection function to determine whether or not a prediction should be accepted (i.e., trusted, used, deployed). This is a highly relevant issue in socially sensitive tasks, such as credit scoring. State-of-the-art approaches rely on Deep Neural Networks (DNNs) that train at the same time both the classifier and the selection function. These approaches are model-specific and computationally expensive. We propose a model-agnostic approach, as it can work with any base probabilistic binary classification algorithm, and it can be scalable to large tabular datasets if the base classifier is so. The proposed algorithm, called SCROSS, exploits a cross-fitting strategy and theoretical results for quantile estimation to build the selection function. Experiments on real-world data show that SCROSS improves over existing methods.
Do Invariances in Deep Neural Networks Align with Human Perception?

An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural networks (DNNs) are shared with humans. We identify challenges in measuring these invariances. Prior works used gradient-based methods to generate identically represented inputs (IRIs), ie, inputs which have identical representations (on a given layer) of a neural network, and thus capture invariances of a given network. One necessary criterion for a network's invariances to align with human perception is for its IRIs look 'similar' to humans. Prior works, however, have mixed takeaways; some argue that later layers of DNNs do not learn human-like invariances yet others seem to indicate otherwise. We argue that the loss function used to generate IRIs can heavily affect takeaways about invariances of the network and is the primary reason for these conflicting findings. We propose an adversarial regularizer on the IRI generation loss that finds IRIs that make any model appear to have very little shared invariance with humans. Based on this evidence, we argue that there is scope for improving models to have human-like invariances, and further, to have meaningful comparisons between models one should use IRIs generated using the regularizer-free loss. We then conduct an in-depth investigation of how different components (eg architectures, training losses, data augmentations) of the deep learning pipeline contribute to learning models that have good alignment with humans. We find that architectures with residual connections trained using a (self-supervised) contrastive loss with l_p ball adversarial data augmentation tend to learn invariances that are most aligned with humans. Code: github.com/nvedant07/Human-NN-Alignment
Deep Convolutional Network Aided by Non-Local Method for Hyperspectral Image Denoising

This paper introduces a new hyperspectral image denoising method called Non-local Convolutional Neural Network Denoiser (NL-CNND). The technique exploits data in four bands adjacent to the target one as additional information for the restoring process, and it uses a pre-denoising step based on BM4D. All the bands paired with their pre-denoised versions in a second step feed a Convolutional Neural Network. To network generalization, one of the inputs is the noise level of the input image, allowing a single model to work with different noise levels. This restoration technique overcomes quality when compared to current eight classical and neural methods. The results show higher peak signal to noise ratio, structural similarity index, and spectral angle mapper metrics than all the other restoration methods, surpassing those achieved using Block Matching and 4D Filtering alone. Besides, the results show a higher level of detail visually while at the same time reducing over-smoothing on the input images’ features. The paper also includes an algorithm for complete image restoration, allowing for denoising full-sized hyperspectral images independent of their shape. The dataset creation used for network training is detailed, based on a small set of available hyperspectral images, encompassing data normalization, conversion, and storage.