Reviewerly/exHarmony huggingface dataset:
Invasion depth estimation of carcinoma cells using adaptive stain normalization to improve epidermis segmentation accuracy

Submucosal invasion depth is a significant prognostic factor when assessing lymph node metastasis and cancer itself to plan proper treatment of the patient. Conventionally, oncologists measure the invasion depth by hand which is a laborious, subjective, and time-consuming process. The manual pathological examination by measuring accurate carcinoma cell invasion with considerable inter and intra-observer variations is still challenging. The increasing use of medical imaging and artificial intelligence reveals a significant role in clinical medicine and pathology. In this paper, we propose an approach to study invasive behavior and measure the invasion depth of carcinoma from stained histopathology images. Specifically, our model includes adaptive stain normalization, color decomposition, and morphological reconstruction with adaptive thresholding to separate the epithelium areas with blue ratio image. Our method splits the image into multiple non-overlapping meaningful segments and successfully finds the homogeneous segments to measure accurate invasion depth. The invasion depths are measured from the inner epithelium edge to outermost pixels of the deepest part of particles in image. We conduct our experiments on skin melanoma tissue samples as well as on organotypic invasion model utilizing myoma tissue and oral squamous cell carcinoma. The performance is experimentally compared to three closely related reference methods and our method provides a superior result in measuring invasion depth. This computational technique will be beneficial for the segmentation of epithelium areas and other particles for the development of novel computer-aided diagnostic tools in biobank applications.
Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering

Knowledge underpins reasoning.Recent research demonstrates that when relevant knowledge is provided as additional context to commonsense question answering (QA), it can substantially enhance the performance even on top of state-of-the-art.The fundamental challenge is where and how to find such knowledge that is high quality and on point with respect to the question; knowledge retrieved from knowledge bases are incomplete and knowledge generated from language models are inconsistent.We present RAINIER 1 , or Reinforced Knowledge Introspector, that learns to generate contextually relevant knowledge in response to given questions.Our approach starts by imitating knowledge generated by GPT-3, then learns to generate its own knowledge via reinforcement learning where rewards are shaped based on the increased performance on the resulting question answering.RAINIER demonstrates substantial and consistent performance gains when tested over 9 different commonsense benchmarks: including 5 datasets that are seen during model training, as well as 4 datasets that are kept unseen.Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3.
DISCO: Distilling Counterfactuals with Large Language Models

Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization.However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale.When crowdsourced, such data is typically limited in scale and diversity; when generated using supervised methods, it is computationally expensive to extend to new counterfactual dimensions.In this work, we introduce DISCO (DIStilled COunterfactual Data), a new method for automatically generating highquality counterfactual data at scale.DISCO engineers prompts to generate phrasal perturbations with a large general language model.Then, a task-specific teacher model filters these generations to distill high-quality counterfactual data.While task-agnostic, we apply our pipeline to the task of natural language inference (NLI) and find that on challenging evaluations such as the NLI stress test, comparatively smaller student models trained with DISCOgenerated counterfactuals are more robust (6% absolute) and generalize better across distributions (2%) compared to models trained without data augmentation.Furthermore, DISCOaugmented models are 10% more consistent between counterfactual pairs on three evaluation sets, demonstrating that DISCO augmentation enables models to more reliably learn causal representations.
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?

Recent research on dialogue state tracking (DST) focuses on methods that allow few-and zero-shot transfer to new domains or schemas.However, performance gains heavily depend on aggressive data augmentation and fine-tuning of ever larger language model based architectures.In contrast, general purpose language models, trained on large amounts of diverse data, hold the promise of solving any kind of task without task-specific training.We present preliminary experimental results on the Chat-GPT research preview, showing that ChatGPT achieves state-of-the-art performance in zeroshot DST.Despite our findings, we argue that properties inherent to general purpose models limit their ability to replace specialized systems.We further theorize that the in-context learning capabilities of such models will likely become powerful tools to support the development of dedicated and dynamic dialogue state trackers.
How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech

When acquiring syntax, children consistently choose hierarchical rules over competing nonhierarchical possibilities.Is this preference due to a learning bias for hierarchical structure, or due to more general biases that interact with hierarchical cues in children's linguistic input?We explore these possibilities by training LSTMs and Transformers-two types of neural networks without a hierarchical biason data similar in quantity and content to children's linguistic input: text from the CHILDES corpus.We then evaluate what these models have learned about English yes/no questions, a phenomenon for which hierarchical structure is crucial.We find that, though they perform well at capturing the surface statistics of childdirected speech (as measured by perplexity), both model types generalize in a way more consistent with an incorrect linear rule than the correct hierarchical rule.These results suggest that human-like generalization from text alone requires stronger biases than the general sequence-processing biases of standard neural network architectures.
NatiQ: An End-to-end Text-to-Speech System for Arabic

NatiQ is end-to-end text-to-speech system for Arabic.Our speech synthesizer uses an encoder-decoder architecture with attention.We used both tacotron-based models (tacotron-1 and tacotron-2) and the faster transformer model for generating mel-spectrograms from characters.We concatenated Tacotron1 with the WaveRNN vocoder, Tacotron2 with the WaveGlow vocoder and ESPnet transformer with the parallel wavegan vocoder to synthesize waveforms from the spectrograms.We used in-house speech data for two voices: 1) neutral male "Hamza"-narrating general content and news, and 2) expressive female "Amina"narrating children story books to train our models.Our best systems achieve an average Mean Opinion Score (MOS) of 4.21 and 4.40 for Amina and Hamza respectively.The objective evaluation of the systems using word and character error rate (WER and CER) as well as the response time measured by real-time factor favored the end-to-end architecture ESPnet.
PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation

Logical table-to-text generation is a task that involves generating logically faithful sentences from tables, which requires models to derive logical-level facts from table records via logical inference.It raises a new challenge on the logical-level content planning of table-to-text models.However, directly learning the logical inference knowledge from table-text pairs is very difficult for neural models because of the ambiguity of natural language and the scarcity of parallel data.Hence even large-scale pretrained language models present low logical fidelity on logical table-to-text.In this work, we propose a Pretrained Logical Form Generator (PLOG) framework to improve generation fidelity.Specifically, PLOG is first pretrained on a table-to-logical-form generation (table-to-logic) task, then finetuned on downstream table-to-text tasks.The logical forms are formally defined with unambiguous semantics.Hence we can collect a large amount of accurate logical forms from tables without human annotation.In addition, PLOG can learn logical inference from table-logic pairs much more reliably than from table-text pairs.To evaluate our model, we further collect a controlled logical table-to-text dataset CONTLOG based on an existing dataset.On two benchmarks, LOGICNLG and CONTLOG, PLOG outperforms strong baselines by a large margin on logical fidelity, demonstrating the effectiveness of table-to-logic pretraining.
Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer

Pre-trained Transformers currently dominate most NLP tasks.They impose, however, limits on the maximum input length (512 subwords in BERT), which are too restrictive in the legal domain.Even sparse-attention models, such as Longformer and BigBird, which increase the maximum input length to 4,096 sub-words, severely truncate texts in three of the six datasets of LexGLUE.Simpler linear classifiers with TF-IDF features can handle texts of any length, require far less resources to train and deploy, but are usually outperformed by pre-trained Transformers.We explore two directions to cope with long legal texts: (i) modifying a Longformer warm-started from LegalBERT to handle even longer texts (up to 8,192 sub-words), and (ii) modifying Legal-BERT to use TF-IDF representations.The first approach is the best in terms of performance, surpassing a hierarchical version of LegalBERT, which was the previous state of the art in LexGLUE.The second approach leads to computationally more efficient models at the expense of lower performance, but the resulting models still outperform overall a linear SVM with TF-IDF features in long legal document classification.
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem

The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for this algorithm, however only for synthetic benchmark problems. In this work, we give the first proven performance guarantees for a classic optimization problem, the NP-complete bi-objective minimum spanning tree problem. More specifically, we show that the NSGA-II with population size N &gt;= 4((n-1) wmax + 1) computes all extremal points of the Pareto front in an expected number of O(m^2 n wmax log(n wmax)) iterations, where n is the number of vertices, m the number of edges, and wmax is the maximum edge weight in the problem instance. This result confirms, via mathematical means, the good performance of the NSGA-II observed empirically. It also shows that mathematical analyses of this algorithm are not only possible for synthetic benchmark problems, but also for more complex combinatorial optimization problems. As a side result, we also obtain a new analysis of the performance of the global SEMO algorithm on the bi-objective minimum spanning tree problem, which improves the previous best result by a factor of |F|, the number of extremal points of the Pareto front, a set that can be as large as n wmax. The main reason for this improvement is our observation that both multi-objective evolutionary algorithms find the different extremal points in parallel rather than sequentially, as assumed in the previous proofs.
ALL-E: Aesthetics-guided Low-light Image Enhancement

Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions, i.e., its corresponding adjustment curve is estimated sequentially. Extensive experiments show that integrating aesthetic assessment improves both subjective experience and objective evaluation. Our results on various benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods. Source code: https://dongl-group.github.io/project pages/ALLE.html
Divide Rows and Conquer Cells: Towards Structure Recognition for Large Tables

Recent advanced Table Structure Recognition (TSR) models adopt image-to-text solutions to parse table structure. These methods can be formulated as image caption problem, i.e., input a single-table image and output table structure description in a specific text format, e.g., HTML. With the impressive success of Transformer in text generation tasks, these methods use Transformer architecture to predict HTML table text in an autoregressive manner. However, tables always emerge with a large variety of shapes and sizes. Autoregressive models usually suffer from the error accumulation problem as the length of predicted text increases, which results in unsatisfactory performance for large tables. In this paper, we propose a novel image-to-text based TSR method that relieves error accumulation problems and improves performance noticeably. At the core of our method is a cascaded two-step decoder architecture with the former decoder predicting HTML table row tags non-autoregressively and the latter predicting HTML table cell tags of each row in a semi-autoregressive manner. Compared with existing methods that predict HTML text autoregressively, the superiority of our row-to-cell progressive table parsing is twofold: (1) it generates an HTML tag sequence with a vertical-and-horizontal two-step `scanning', which better fits the inherent 2D structure of image data, (2) it performs substantially better for large tables (long sequence prediction) since it alleviates error accumulation problem specific to autoregressive models. Extensive experiments demonstrate that our method achieves competitive performance on three public benchmarks.
Learning Depth-Density Priors for Fourier-based Unpaired Image Restoration

Deep learning-based image restoration methods trained on synthetic datasets have witnessed notable progress, but suffer from significant performance drops on real-world images due to huge domain shifts. To alleviate this issue, some recent methods strive to improve the generalization ability of models with unpaired training. However, these solutions typically handle each problem individually and ignore the shared physical properties of different harsh scenarios, i.e., heavy rain, hazy and low-light images degrade more densely with increasing scene depth. Such limitations make them generalize poorly to real-world images. In this paper, we propose a novel Physically Oriented Generative Adversarial Network (POGAN) for unpaired image restoration with depth-density priors. Specifically, our POGAN consists of two core designs: Physical Restoration Network (PRNet) and Degradation Rendering Network (DRNet). The former focuses on estimating the physical components related to the depth and density distribution for restoration, while the latter re-renders degradation effects guided by the estimated depth information. To further facilitate learning the above physical prior, we design a Spatial-Frequency Interaction Residual block (SFIR), which efficiently learns global frequency information and local spatial features in an interactive manner. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our method in heavy rain, haze, and low-light scenarios.
Efficient Certificateless Online/Offline Signcryption Scheme Without Bilinear Pairing for Smart Home Consumer Electronics

Smart home refers to the automation and intelligent management of family life using information technology and Internet of Things technology, including various smart household appliances and low-power sensors. Low-power sensors can monitor the home environment and share data with household appliances, and users can remotely control household appliances through smartphones and other devices. With the popularity of consumer electronic devices, smart home has become an indispensable part of our life. Smart home data security and privacy leakage accidents occur frequently, how to protect data confidentiality and unforgeability for low-power smart home consumer electronics is a key problem. To solve this problem, scholars have proposed many cryptographic schemes. However, the existing schemes have low computational efficiency, high communication overhead, and poor adaptation to application scenarios. Therefore, to realize secure lightweight data sharing in smart home systems, we propose an efficient certificateless online/offline signcryption (CLOOSC) scheme without bilinear pairing. The performance analysis demonstrates that our scheme has an order of magnitude advantage in computation and communication overhead compared with bilinear pairing-based CLOOSC. Compared with the latest pairing-free CLOOSC scheme, our proposed approach significantly reduces the computational cost by one-third during the offline signcryption phase while maintaining the same communication overhead. The proposed cryptographic algorithm has made a substantial contribution to the data security sharing in smart homes.
High Dynamic Range Structured Light 3-D Measurement Based on Region Adaptive Fringe Brightness

Fringe projection profilometry is one of the main methods for active 3-D measurement, but practical problems, such as how to obtain a complete point cloud by overcoming the effects of high surface reflectivity of objects, remain to be solved. So, in this article, we propose a set of hardware devices and region adaptive structured light algorithms. First, we introduce the surface structured light measurement system adopted in this article. Second, based on the accurate optical modeling of the measurement scene, the principles for generating the adaptive optimal projection brightness are derived in detail and the process is accelerated by exploiting the speed of neural network. Meanwhile, we creatively use the chain code combined with the M-estimator sample consensus method to find the homography from the saturated region of the camera plane to the corresponding region of the projector plane for generating fringe images with adaptive brightness. Furthermore, we chose the more robust line-shifting code and binarization method for subpixel 3-D reconstruction. Experiments have shown that our work is efficient, superior, and accurate in measuring objects with high reflective surface properties.
Collection development policy: A panacea for collection development challenges in university libraries in Africa

Collection development is faced with various challenges such as decreasing budget allocation, increasing cost of materials, increasing demand for information, the complexity of electronic resources, and legal issues arising from copyright and censorship. To meet these challenges, university libraries in developed countries have adopted different strategies such as using a written collection development policy (CDP) to guide their collection development activities. However, university libraries in Africa are lagging in this regard. It is either they have not formulated the policy or used it to guide their collection development activities. Therefore, this study argues that a written CDP, if well prepared and used, can address collection development challenges facing university libraries in Africa and provide standard collections necessary for teaching and research activities of the university communities. Specifically, the paper discusses major collection development challenges facing university libraries in Africa and establishes ways in which a written CDP could be used to overcome them. Methodologically, the paper is based on a review of related literature. The paper contains a literature review of papers discussing the major collection development challenges facing university libraries in Africa and ways a written CDP could be used to overcome them.
Country-level institutional quality and financial system efficiency: An international evidence

The optimum economic outcome of financial system development depends on its level of efficiency. The purpose of this study is to investigate the effect of institutional quality on financial system efficiency. For empirical analysis, we have used a panel dataset of 108 countries from 1996–2020 and employed fixed effect regression and two stages least squares (2SLS) regression methods. The empirical results show that institutional quality has a significant positive effect on financial system efficiency. Particularly all the constituting elements—voice and accountability, political stability and absence of violence, regulatory quality, government effectiveness, rule of law, and control of corruption—of institutional quality are found to have a significant positive impact on financial system efficiency. Moreover, we found that the effect of institutional quality is more pronounced in countries with low-income levels and strong institutional quality. These findings are robust across several robustness tests conducted using additional controls, alternative methodologies, an alternative measure of institutional quality, and financial system efficiency. The results of the study suggest that policy makers should prioritize both enhancing and sustaining institutional quality to promote the efficiency of the financial system, which is crucial for sustainable growth and development.
AI-based Spam Detection Techniques for Online Social Networks: Challenges and Opportunities

In recent years, online social networks (OSNs) have become a huge used platform for sharing activities, opinions, and advertisements. Spam content is considered one of the biggest threats in social networks. Spammers exploit OSNs for falsifying content as part of phishing, such as sharing forged advertisements, selling forged products, or sharing sexual words. Therefore, machine learning (ML) and deep learning (DL) techniques are the best methods for detecting phishing attacks and minimize their risk. This paper provides an overview of prior studies of OSNs spam detection modeling based on ML and DL techniques. The research papers are classified into three categories: the features used for prediction, the dataset size corresponding language used, real-time based applications, and machine learning or deep learning techniques. Challenges and opportunities in phishing attacks prediction using ML and DL techniques are also concluded in our study.