Reviewerly/exHarmony huggingface dataset:
Identifying Imbalance Thresholds in Input Data to Achieve Desired Levels of Algorithmic Fairness

Software bias has emerged as a relevant issue in the latest years, in conjunction with the increasing adoption of software automation in a variety of organizational and production processes of our society, and especially in decision-making. Among the causes of software bias, data imbalance is one of the most significant issues. In this paper, we treat imbalance in datasets as a risk factor for software bias. Specifically, we define a methodology to identify thresholds for balance measures as meaningful risk indicators of unfair classification output. We apply the methodology to a large number of data mutations with different classification tasks and tested all possible combinations of balance-unfairness-algorithm.The results show that on average the thresholds can accurately identify the risk of unfair output. In certain cases they even tend to overestimate the risk: although such behavior could be instrumental to a prudential approach towards software discrimination, further work will be devoted to better assess the reliability of the thresholds.The proposed methodology is generic and it can be applied to different datasets, algorithms, and context-specific thresholds.
Face Expression Recognition based on Improved Rank Expansion Network

A face expression recognition method based on an improved rank expansion network is proposed to address the problem that when traditional convolutional neural networks recognize facial expressions, the network has difficulty in fully extracting the important expression features leading to low recognition accuracy. First, based on the idea of migration learning, the pre-training parameters of large-scale image sets are used to initialize the rank expansion network and accelerate the training of the model. Then, the face expression features are further enriched by fusing the underlying detailed features and the high-level semantic features at different scales. Finally, a stepped attention module is embedded before feature fusion to construct the contextual relationships of semantic features from three different dimensions to strengthen the expression of important features. The experimental results show that the improved network achieves 88.07% and 88.68% accuracy on the face expression datasets RAF-DB and FERPlus, which is a significant improvement over the original network.
Optimal Scheduling of Distributed Resources in Multi area and Multi-station Optical Storage System Based on Improved Genetic Algorithm

Photovoltaic power generation is the main force that promotes the diversification of the main body of the modern energy industry, and plays a key role in solving the problem of electricity demand. In this paper, by studying the optimal scheduling of the energy storage system, taking into account the cost of the power generation system and the impact on the environment, taking photovoltaic output and battery micro gas turbine output as the optimization strategy, the Pareto genetic algorithm based on niche technology is used to conduct multiple optimizations of the target. Combined with the distributed resource online aggregation method, the multi-objective optimization solution is carried out. The simulation study is carried out with specific multi-area and multi-station examples. The results show that this method can effectively optimize the energy management and realize the economic operation of optical storage system.
Choosing More Important Convolution Kernels

With the rapid development of Convolutional Neural Networks (CNNs), higher accuracy is often accompanied by a large number of parameters and computations. In embedded devices with limited computing resources, it is often difficult to implement large network models. Therefore, the lightweight network came into being. Recent studies have shown that there are a large number of redundant feature maps in convolutional neural networks, which is an important reason for the success of convolutional neural networks. GhostNet which proposes that redundant feature maps can be obtained by linear variation. Compared with standard convolution, the Ghost module uses linear variation to replace the partial convolution operation that outputs the same number of feature maps, saving a lot of parameters and computation, resulting in a lightweight network. In CondenseNet, a convolutional neural network, a method is proposed to drop unimportant convolution kernel channels in stages during the training process, making the network a lightweight network in the inference stage. Based on the above ideas, we propose the SeLective module to discard unimportant convolution kernels and use linear changes to replace the missing feature maps, thereby improving the neural network.
Multiple Statistics: Marking Significance

Big data is often used to generate reports consisting of computed statistics. When the statistics are displayed, they may be marked for significance based on p-values. For multiple statistics, we propose a system to mark for significance adjusted for the number of statistics, using a Bonferroni correction in general, and a slightly milder correction for independent statistics.
Analysis and Estimation of News Article Reading Time with Multimodal Machine Learning

This paper highlights the importance of reading time for news media and evaluates the implementation methodology. The display of estimated reading time allows users to select and view articles that are appropriate for their situation. The simplest hypothesis for the implementation is that reading time correlates with text length. We analyzed real-world users of Japanese financial news and revealed that reading time does not strongly correlate with text length. Experiments also showed that a multimodal machine learning approach leads to a more accurate estimation. Specifically, fine-tuning neural networks that incorporated LSTM to process user history and BERT and Swin Transformer to acquire embeddings from the articles achieved the best results.
Truth or Fiction: Multimodal Learning Applied to Earnings Calls

A significant amount of resources have been used in both academia and industry to study the impact of financial text on company perception and performance. In order to mitigate potential adverse outcomes, companies have begun to regulate word usage based on perceived sentiment, making conventional text-based analysis less reliable. To address this, we present a multimodal bidirectional Long Short-Term Memory (LSTM) framework augmented with a cross-attention fusion mechanism trained on audio and text data obtained from quarterly earnings conferences calls. The framework is applied to two tasks: financial restatement prediction and market movement prediction. We compare the proposed model against several baseline methods and find that while it does not achieve superior performance, our results show that utilizing multimodal data leads to a substantial increase in model accuracy for restatement prediction. Furthermore, we gain insight on the effectiveness of semantic-and emotion-related features towards these tasks.
Mining Railway Grievances on Twitter for Efficient E-Governance in India

Recently, Twitter has been used as a citizen-engaging platform by Indian Railway Ministry (IRM) for collecting civic issues. However, due to the manual inspection model, a large percentage of complaints are left unaddressed affecting the credibility of the citizen-sourcing mediums. The existing solutions are for English reports and unable to capture the diverse range of code-mixed languages used in the complaints. In this paper, we developed a multilingual cased version of BERT (mBERT) for automated identification of monolingual and multilingual (aka code- mixed) complaints. The proposed mBERT is a transformers- based model pre-trained on multilingual Wikipedia corpus. In addition to the grievances classification, we also employ a BERT multi-label classifier for labelling the tweets with the issues reported in the complaints. The proposed solution approach obtains an accuracy of 82% with the binary classifier and overall accuracy of 95% with the multi-label model. Additionally, a critical analysis is presented on the classified reports and a location-based analysis to visualise the velocity and veracity of complaints across India.
Efficient Data Selection Indicators for Updating Models under Data Drifted Environment

The long-term use of machine learning models can result in degraded performance due to data drift and other factors. We have previously proposed a data selection mechanism for time-series data of machine learning models. When data drift occurs, the models have to learn again using large-scale stream data. Thus, it is important for machine learning algorithms to introduce a mechanism avoiding useless data. This study examines the effect of data selection with an adversarial classifier using synthetic data.
A Simple Approach to Optimize S3 Object Gateways for Massive Numbers of Small File Writes

This paper shows an approach to optimizing S3 Object Gateways for massive number of small file writes by combining database and file system technology. The basic concept is to route small data to a Wide Column Store database and big files to a distributed file system. This approach helps to overcome the limitation of distributed file systems which are commonly used as the storage backend for S3 Object Gateways. We have shown the technical feasibility of this concept based on an experiment for write operations by modifying an open-source S3 Object Gateway with an integrated routing mechanism; files equal or greater than 500 KiB are written to the HPE Data Fabric Distributed File System and files smaller than 500 KiB as well as object metadata are written to the HPE Data Fabric Database using the HBase-API. Our experiment using the Wassabi S3 Benchmark tool shows that performance can be significantly improved compared to cloud-based S3 Object Storage systems and we measured approximately 30,000 PUTs per second for files of 10 KiB in our test bed.
Teachable Insights from Working with The Mary Eliza Project for Gastronomy Students Working with Data

This paper discusses the use of the Boston’s City Archives dataset for a Data Analysis Course Completion Certificate from General Assembly. It documents insights gained while doing computational archival science using visualization tools designed for business use to be shared as part of a textual analysis workshop in October 2022 for graduate students in Boston University’s Gastronomy Program. The workshop focuses less on computational tools, although students will be using Google Sheets, and focuses more on challenges and considerations when working on digital humanities projects.
Efficient Inference on Convolutional Neural Networks by Image Difficulty Prediction

This paper introduces a scheme that predicts the difficulty of classifying an image, reduces the image size according to the prediction, and speeds up the inference time. We observe that models such as ResNet-50 and EfficientNet can classify specific images correctly even after downsizing. We consider these correctly classified images as easy images and others as complex images. Then we collect images with different difficulties and train a difficulty model that classifies the difficulty of an image and determines whether we should downsize an image. In addition, we use an inference model that consists of multiple models for classifying images of different image sizes, and each model is trained with specific datasets to increase its accuracy for the particular image sizes. Finally, we concatenate the difficulty and inference models to get the hybrid model. Our experiments use MobileNetV3-small as the lightweight difficulty model, and ResNet- 50 and EfficientNet-B4 as the inference models. Experimental results indicate a trade-off between the inference time and the image classification accuracy, and the confidence threshold of the difficulty model affects this trade-off. If the confidence threshold of the difficulty model is high/low, the inference time and the image classification accuracy increase/decrease. As a result, the user can control the behavior of the hybrid model by adjusting the confidence threshold of the difficulty model and finding a customized balance between the inference time and the classification accuracy.
Open Science and Research Data Management: A FAIR European Postgraduate Programme

Open Science is widely regarded as a culture that is characterised by the transparency and broad accessibility of scholarly work, where researchers share openly artefacts almost immediately and with a very wide audience. The overarching aim of this paper is to document the systematic development of a European postgraduate programme on Open Science and Research Data Management developed by the TRAINRDM project. TRAINRDM is a 30-month European Union funded project, which aims to develop a training network around Open Science and Research Data Management. We have applied a comprehensive survey collecting 239 responses from researchers across Europe, representative of 2.58 million individuals i.e. the total number of researchers employed in the EU-27 region. We then mapped out existing skills and offerings at different TRAINRDM partner institutions to produce a fully-online postgraduate programme with micro-credentials, fully distributed delivery, and compliance to FAIR principles to address academic and industrial research needs. The main outputs of the project are a training programme for Early Career Researchers delivered in Summer 2022, and a the postgraduate programme (Master degree) to be fully validated under the European Qualifications Framework at Level 7 and delivered in 2023. The TRAINRDM curricula, teaching materials, data, and software are openly released under CC BY 4.0 and GPL licenses.
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.
Enhancing k-Means Algorithm with Tensor Processing Unit

Clustering in Data Mining is the process of discovering groups of similar objects in data. The k-Means clustering algorithm, is designed to partition data into k distinct groups or clusters. With recent growth in data production, the need to scale-up existing algorithms and computational ability has increased. Google introduced the Tensor Processing Unit (TPU), a powerful hardware, to meet the growing computational needs of modern technologies.In this paper, we aim to enhance the k-Means algorithm with the use of the Google TPU in terms of runtime while preserving the quality of the clustering results. We developed two versions that distribute training on the TPU in two different ways. The clustering results of the versions have advantages that complement each other in terms of runtime and accuracy.
BigFeat: Scalable and Interpretable Automated Feature Engineering Framework

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features’ quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat.
Node Context Selection in Transformer-Based Graph Representation Learning Models

Transformer models have great potential in Graph Representation Learning (GRL) for efficiently scaling the learning process on large datasets and solving many challenges presented in Graph Neural Networks, e.g., oversmoothing and suspended animation. To represent each node of a graph, Transformer models as input usually take a node together with the node context, i.e., a set of other nodes that serve as learning context for the target node. However, current GRL Transformer models mainly consider the graph topology when selecting the node context for each target node. In this work, we demonstrate the important role of node features in selecting the node context. Specifically, we propose a hybrid approach for selecting node context that considers both the graph topology and the semantic similarities between node features. Through the empirical evaluations, we show the advantages of our hybrid node context selection method for a downstream classification task on various datasets compared to selection methods that only consider graph topology or semantic similarities. The best classification accuracy improvements of our proposed hybrid methods over the baseline methods on each dataset range from 0.77% to 6.05%.
Temporal Adaptive Aggregation Network for Dynamic Graph Learning

Dynamic graphs are common in many applications, such as social networks with evolving nodes and edges over time. When handling such dynamics, existing approaches typically suffer from two limitations: (1) they primarily focus on network topology, without taking node class connections and temporal changes into consideration; and (2) the learning objective is primarily constrained by labeled nodes, which often result in over-smoothing and weak-generalization in representation learning, because labeled nodes are limited. In this paper, we propose a temporal adaptive aggregation network (TAAN) for dynamic graph learning. We consider a dynamic graph as a network with changing nodes and edges in temporal order. The temporal adaptive aggregation is to ensure that, for each node, the information aggregation is to consider neighbors from different classes, as well as their temporal order. For each snapshot of the dynamic network, data augmentation and consistency loss are combined to leverage labeled and unlabeled nodes to learn good node embedding. Meanwhile, in order to accommodate temporal changes of graphs, an incremental learning process is used to ensure that learning on each snapshot can inherit weights learned from previous time points, so graph learning can adapt to the dynamic graph environments. Experiments on real-world datasets validate the effectiveness of our approach.
PromptRGD: Prompt Learning with Relation-aware Gradient Denoising for Low-resource Relation Extraction

Relation extraction is a fundamental task to construct a knowledge graph, which aims to find the relation categories between two entities from a string of text input sequences. Considering the fact that high-quality labeled data is rare, a recent trend is low-resource setting. Existing works either utilize prompt-based learning method to achieve encouraging results for few-shot tasks by determining an appropriate prompt template, or leverage unlabeled data by generating pseudo labels. However, the manual design of the prompt template requires not only labor-intensive but also expert knowledge. Roughly utilizing a semi-supervised method to assign pseudo-label to unlabeled data will suffer from noise accumulation. To tackle these problems, we propose a novel semi-supervised prompt learning framework with relation-aware gradient denoising for low-resource relation extraction(PromptRGD). Firstly, using learnable template words and virtual labels for prompt learning, we introduce entity type and relation prior knowledge into prompt template construction. Secondly, given the relation-aware gradient similarity between labeled and unlabeled data, PromptRGD generates a pseudo label and then improves the quality of pseudo labels on unlabeled samples in a self-training fashion. The main experimental results and a series of analyses prove the effectiveness of PromptRGD.
Integration of Rule-Based Reasoning and Transfer Learning in Legal Document Review

Protecting privileged communications and data from disclosure is paramount for legal teams. Unrestricted legal advice, such as attorney-client communication or litigation strategy, is exempt from disclosure in litigation or regulatory events and is vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel must review vast amounts of documents to determine those that contain privileged material. This process is costly and time consuming. As data volumes increase, legal counsel employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is a popular method to target privileged information and reduce document review populations. Keyword terms are effective at casting a wide net but generally return overly inclusive results – most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams have started using supervised learning techniques to more precisely target privileged information. However, reviewing and labeling training documents is costly and time intensive and may cause counsel to forego the use of supervised learning in certain scenarios. In addition, supervised learning techniques may not find all the privileged documents in a document review and require companies to use keyword terms to identify critical privileged information. In this paper, the authors propose a novel method to automatically identify privileged documents without the need to label new training documents. This method integrates rule-based reasoning with transfer learning. Experimental results show that the proposed integrated method performs better than rule-based reasoning and transfer learning individually and can effectively identify privileged documents.