Reviewerly/exHarmony huggingface dataset:
<i>q</i>-Analogs Over Finite Fields: Definition, Algebraic Properties, and Application in Pseudo-Random Number Generators

In this brief we define the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -analog, also known as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -deformation, of the finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{p}$ </tex-math></inline-formula> , denoted as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[\mathbb {Z}_{p}]_{q}$ </tex-math></inline-formula> . We derive some algebraic properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[\mathbb {Z}_{p}]_{q}$ </tex-math></inline-formula> and establish the necessary conditions for which the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -analogs define a bijection between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{p}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[\mathbb {Z}_{p}]_{q}$ </tex-math></inline-formula> , or a permutation in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{p}$ </tex-math></inline-formula> . We employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -analogs over finite fields to design a pseudo-random number generator (PRNG) and its statistical properties are analyzed using the NIST test suite. The proposed PRNG is implemented in a field-programmable gate array (FPGA) and its hardware requirements are analyzed. The FPGA implementation shows that the pseudo-random number generator based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -analogs exhibits a superior throughput while requiring less hardware resources in comparison with recent proposals in the literature.
A Stochastic Grammar Approach to Mass Classification in Mammograms

Breast cancer is responsible for approximately 15% of all cancer-related deaths among women worldwide, and early and accurate diagnosis increases the chances of survival. Over the last decades, several machine learning approaches have been used to improve the diagnosis of this disease, but most of them require a large set of samples for training. Syntactic approaches were barely used in this context, although it can present good results even if the training set has few samples. This article presents a syntactic approach to classify masses as benign or malignant. There were used features extracted from a polygonal representation of masses combined with a stochastic grammar approach to discriminate the masses found in mammograms. The results were compared with other machine learning techniques, and the grammar-based classifiers showed superior performance in the classification task. The best accuracies achieved were from 96% to 100%, indicating that grammatical approaches are robust and able to discriminate the masses even when trained with small samples of images. Syntactic approaches could be more frequently employed in the classification of masses, since they can learn the pattern of benign and malignant masses from a small sample of images achieving similar results when compared to the state of art.
Unconventional Arithmetic Circuits

While silicon-based electronic computing systems have been successful in exploiting binary arithmetic, the positional nature of binary representation itself imposes the need to handle both carrying generation and carrying propagation. The current computers are thus limited in terms of the extent of parallelism possible and require excessive power. This article explores the possibilities of using two specific unconventional number representations Sousa, (2021), namely, logarithmic number systems (LNS) and residue number system (RNS), which offer parallelism while increasing energy efficiency also. Both LNS and RNS arithmetic take all dimensions of the system into account, which include not only computer arithmetic theory but also technological advances and emergent applications. Both have their own individual strong suits as well as limitations. LNS provides for extremely fast multiplication and division; while for addition and subtraction, it requires the use of look-up tables. RNS, in comparison, is used across a variety of domains including nanophotonic computers, DNA-based computing, integrated photonics, and cryptography. Different technologies have different uses and operate differently internally, which is where unconventional number systems can come in and specialize each piece of technology.
Beyond Individuals: Modeling Mutual and Multiple Interactions for Inductive Link Prediction between Groups

Link prediction is a core task in graph machine learning with wide applications. However, little attention has been paid to link prediction between two group entities. This limits the application of the current approaches to many real-life problems, such as predicting collaborations between academic groups or recommending bundles of items to group users. Moreover, groups are often ephemeral or emergent, forcing the predicting model to deal with challenging inductive scenes. To fill this gap, we develop a framework composed of a GNN-based encoder and neural-based aggregating networks, namely the Mutual Multi-view Attention Networks (MMAN). First, we adopt GNN-based encoders to model multiple interactions among members and groups through propagating. Then, we develop MMAN to aggregate members' node representations into multi-view group representations and compute the final results by pooling pairwise scores between views. Specifically, several view-guided attention modules are adopted when learning multi-view group representations, thus capturing diversified member weights and multifaceted group characteristics. In this way, MMAN can further mimic the mutual and multiple interactions between groups. We conduct experiments on three datasets, including two academic group link prediction datasets and one bundle-to-group recommendation dataset. The results demonstrate that the proposed approach can achieve superior performance on both tasks compared with plain GNN-based methods and other aggregating methods.
A Framework for Detecting Frauds from Extremely Few Labels

In this paper, we present a framework to deal with the fraud detection task with extremely few labeled frauds. We involve human intelligence in the loop in a labor-saving manner and introduce several ingenious designs to the model construction process. Namely, a rule mining module is introduced, and the learned rules will be refined with expert knowledge. The refined rules will be used to relabel the unlabeled samples and get the potential frauds. We further present a model to learn with the reliable frauds, the potential frauds, and the rest normal samples. Note that the label noise problem, class imbalance problem, and confirmation bias problem are all addressed with specific strategies when building the model. Experimental results are reported to demonstrate the effectiveness of the framework.
DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms

User engagement prediction plays a critical role in designing interaction strategies to grow user engagement and increase revenue in online social platforms. Through the in-depth analysis of the real-world data from the world's largest professional social platforms, i.e., LinkedIn, we find that users expose diverse engagement patterns, and a major reason for the differences in user engagement patterns is that users have different intents. That is, people have different intents when using LinkedIn, e.g., applying for jobs, building connections, or checking notifications, which shows quite different engagement patterns. Meanwhile, user intents and the corresponding engagement patterns may change over time. Although such pattern differences and dynamics are essential for user engagement prediction, differentiating user engagement patterns based on user dynamic intents for better user engagement forecasting has not received enough attention in previous works. In this paper, we proposed a Dynamic Intent Guided Meta Network (DIGMN), which can explicitly model user intent varying with time and perform differentiated user engagement forecasting. Specifically, we derive some interpretable basic user intents as prior knowledge from data mining and introduce prior intents to explicitly model dynamic user intent. Furthermore, based on the dynamic user intent representations, we propose a meta-predictor to perform differentiated user engagement forecasting. Through a comprehensive evaluation of LinkedIn anonymous user data, our method outperforms state-of-the-art baselines significantly, i.e., 2.96% and 3.48% absolute error reduction, on coarse-grained and fine-grained user engagement prediction tasks, respectively, demonstrating the effectiveness of our method.
Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation

This paper is concerned with TV recommendation, where one major challenge is the coupling behavior issue that the behaviors of multiple users are coupled together and not directly distinguishable because the users share the same account. Unable to identify the current watching user and use the coupling behaviors directly could lead to sub-optimal recommendation results due to the noise introduced by the behaviors of other users. Most existing methods deal with this issue either by unsupervised clustering algorithms or depending on latent user representation learning with strong assumptions. However, they neglect to sophisticatedly model the current session behaviors, which carry the information of user identification. Another critical limitation of the existing models is the lack of supervision signal on distinguishing behaviors because they solely depend on the final click label, which is insufficient to provide effective supervision. To address the above problems, we propose the Coupling Sequence Model (COSMO) for TV recommendation. In COSMO, we design a session-aware co-attention mechanism that uses both the candidate item and session behaviors as the query to attend to the historical behaviors in a fine-grained manner. Furthermore, we propose to use the data of accounts with multiple devices (e.g., families with various TV sets), which means the behaviors of one account are generated on different devices. We regard the device information as weak supervision and propose a novel pair-wise attention loss for learning to distinguish the coupling behaviors. Extensive offline experiments and online A/B tests over a commercial TV service provider demonstrate the efficacy of COSMO compared to the existing models.
MMBench: The Match Making Benchmark

Video gaming has gained huge popularity over the last few decades. As reported, there are about 2.9 billion gamers globally. Among all genres, competitive games are one of the most popular ones.
Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface

In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.
Quantitative evaluation of myometrial infiltration depth ratio for early endometrial cancer based on deep learning

The diagnosis of Endometrial Cancer (EC) was usually made empirically by radiologists, which introduces subjectivity and error. Computer-aided diagnosis methods, rely excessively on deep learning, which makes people difficult to understand the diagnostic process intuitively due to its black-box nature. This study combines deep learning and radiologist experience to address these issues. Firstly, we adopt the U-net to segment the tumor and uterus from the EC magnetic resonance imaging (MRI) images. Secondly, three myometrial infiltration (MI) depth assessment algorithms (fast thinning, fit-ellipse and area ratio) have been employed to deal with the segmentation images. Finally, A convolutional neural network (CNN) classifier has been trained to choose the best depth assessment algorithms according to the different shapes of uterus and tumor. The fit-ellipse MI depth assessment algorithm outperforms the other two algorithms (fast thinning and area ratio) at detecting the sub-stage of the EC, within stage I. Compared to using a single algorithm, the CNN classifier is able to select the optimum algorithm based on the shapes of the uterus and tumor, with an accuracy of 93.34%, for the test samples. In addition, the CNN classifier demonstrated a better performance in evaluating the trend of tumor spreading in early EC, which may help specialists to better evaluate the tumor spreading trend of early EC and make better decisions. It also could be a part of the EC monitoring process, for patients on medication or undergoing fractional curettage.
DOCoR: Document-level OpenIE with Coreference Resolution

Open Information Extraction (OpenIE) extracts relational fact tuples in the form of <subject, relation, object> from text. Most existing OpenIE solutions operate at sentence level and extract relational tuples solely from a sentence. However, many sentences exist as a part of paragraph or a document, where coreferencing is common. In this demonstration, we present a system which refines the semantic tuples generated by OpenIE with the aid of a coreference resolution tool. Specifically, all coreferential mentions across the entire document are identified and grouped into coreferential clusters. Objects and subjects in the extracted tuples from OpenIE which match any coreferential mentions are then resolved with a suitable representative term. In this way, our system is able to resolve both anaphoric and cataphoric references, to achieve Document-level OpenIE with Coreference Resolution (DOCoR). The demonstration video can be viewed at https://youtu.be/o9ZSWCBvlDs
Reducing Negative Effects of the Biases of Language Models in Zero-Shot Setting

Pre-trained language models (PLMs) such as GPTs have been revealed to be biased towards certain target classes because of the prompt and the model's intrinsic biases. In contrast to the fully supervised scenario where there are a large number of costly labeled samples that can be used to fine-tune model parameters to correct for biases, there are no labeled samples available for the zero-shot setting. We argue that a key to calibrating the biases of a PLM on a target task in zero-shot setting lies in detecting and estimating the biases, which remains a challenge. In this paper, we first construct probing samples with the randomly generated token sequences, which are simple but effective in detecting inputs for stimulating GPTs to show the biases; and we pursue an in-depth research on the plausibility of utilizing class scores for the probing samples to reflect and estimate the biases of GPTs on a downstream target task. Furtherly, in order to effectively utilize the probing samples and thus reduce negative effects of the biases of GPTs, we propose a lightweight model Calibration Adapter (CA) along with a self-guided training strategy that carries out distribution-level optimization, which enables us to take advantage of the probing samples to fine-tune and select only the proposed CA, respectively, while keeping the PLM encoder frozen. To demonstrate the effectiveness of our study, we have conducted extensive experiments, where the results indicate that the calibration ability acquired by CA on the probing samples can be successfully transferred to reduce negative effects of the biases of GPTs on a downstream target task, and our approach can yield better performance than state-of-the-art (SOTA) models in zero-shot settings.
Separating Examination and Trust Bias from Click Predictions for Unbiased Relevance Ranking

Alleviating the examination and trust bias in ranking systems is an important research line in unbiased learning-to-rank (ULTR). Current methods typically use the propensity to correct the biased user clicks and then learn ranking models based on the corrected clicks. Though successes have been achieved, directly modifying the clicks suffers from the inherent high variance because the propensities are usually involved in the denominators of corrected clicks. The problem gets even worse in the situation of mixed examination and trust bias. To address the issue, this paper proposes a novel ULTR method called Decomposed Ranking Debiasing (DRD). DRD is tailored for learning unbiased relevance models with low variance in the existence of examination and trust bias. Unlike existing methods that directly modify the original user clicks, DRD proposes to decompose each click prediction as the combination of a relevance term outputted by the ranking model and other bias terms. The unbiased relevance model, therefore, can be learned by fitting the overall click predictions to the biased user clicks. A joint learning algorithm is developed to learn the relevance and bias models' parameters alternatively. Theoretical analysis showed that, compared with existing methods, DRD has lower variance while retains unbiasedness. Empirical studies indicated that DRD can effectively reduce the variance and outperform the state-of-the-art ULTR baselines.
FineSum: Target-Oriented, Fine-Grained Opinion Summarization

Target-oriented opinion summarization is to profile a target by extracting user opinions from multiple related documents. Instead of simply mining opinion ratings on a target (e.g., a restaurant) or on multiple aspects (e.g., food, service) of a target, it is desirable to go deeper, to mine opinion on fine-grained sub-aspects (e.g., fish). However, it is expensive to obtain high-quality annotations at such fine-grained scale. This leads to our proposal of a new framework, FineSum, which advances the frontier of opinion analysis in three aspects: (1) minimal supervision, where no document-summary pairs are provided, only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis drills down to a specific subject or characteristic within each general aspect; and (3) phrase-based summarization, where short phrases are taken as basic units for summarization, and semantically coherent phrases are gathered to improve the consistency and comprehensiveness of summary. Given a large corpus with no annotation, FineSum first automatically identifies potential spans of opinion phrases, and further reduces the noise in identification results using aspect and sentiment classifiers. It then constructs multiple fine-grained opinion clusters under each aspect and sentiment. Each cluster expresses uniform opinions towards certain sub-aspects (e.g., "fish" in "food" aspect) or characteristics (e.g., "Mexican" in "food" aspect). To accomplish this, we train a spherical word embedding space to explicitly represent different aspects and sentiments. We then distill the knowledge from embedding to a contextualized phrase classifier, and perform clustering using the contextualized opinion-aware phrase embedding. Both automatic evaluations on the benchmark and quantitative human evaluation validate the effectiveness of our approach.
Graph Neural Networks with Interlayer Feature Representation for Image Super-Resolution

Although deep learning has been extensively studied and achieved remarkable performance on single image super-resolution (SISR), existing convolutional neural networks (CNN) mainly focus on broader and deeper architecture design, ignoring the detailed information of the image itself and the potential relationship between the features. Recently, several attempts have been made to address the SISR with graph representation learning. However, existing GNN-based methods learning to deal with the SISR problem are limited to the information processing of the entire image or the relationship processing between different feature images of the same layer, ignoring the interdependence between the extracted features of different layers, which is not conducive to extracting deeper hierarchical features. In this paper, we propose an interlayer feature representation based graph neural network for image super-resolution (LSGNN), which consists of a layer feature graph representation learning module and a channel spatial attention module. The layer feature graph representation learning module mainly captures the interdependence between the features of different layers, which can learn more fine-grained image detail features. In addition, we also unified a channel attention module and a spatial attention module into our model, which takes into account the channel dimension information and spatial scale information, to improve the expressive ability, and achieve high quality image details. Extensive experiments and ablation studies demonstrate the superiority of the proposed model.
A Tutorial on Domain Generalization

With the availability of massive labeled training data, powerful machine learning models can be trained. However, the traditional I.I.D. assumption that the training and testing data should follow the same distribution is often violated in reality. While existing domain adaptation approaches can tackle domain shift, it relies on the target samples for training. Domain generalization is a promising technology that aims to train models with good generalization ability to unseen distributions. In this tutorial, we will present the recent advance of domain generalization. Specifically, we introduce the background, formulation, and theory behind this topic. Our primary focus is on the methodology, evaluation, and applications. We hope this tutorial can draw interest of the community and provide a thorough review of this area. Eventually, more robust systems can be built for responsible AI. All tutorial materials and updates can be found online at https://dgresearch.github.io/.