Reviewerly/exHarmony huggingface dataset:
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning

Abstract As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability.
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages

We present the MASSIVE dataset-Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.MASSIVE contains 1M realistic, parallel, labeled virtual assistant utterances spanning 51 languages, 18 domains, 60 intents, and 55 slots.MASSIVE was created by tasking professional translators to localize the English-only SLURP dataset into 50 typologically diverse languages from 29 genera.We also present modeling results on XLM-R and mT5, including exact match accuracy, intent classification accuracy, and slot-filling F1 score.We have released our dataset, modeling code, and models publicly.
A Survey of Active Learning for Natural Language Processing

In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP).In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems.These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL.Finally, we conclude with a discussion of related topics and future directions.
Dimensionality reduction approach based on modified hunger games search: case study on Parkinson’s disease phonation

Abstract Hunger Games Search (HGS) is a newly developed swarm-based algorithm inspired by the cooperative behavior of animals and their hunting strategies to find prey. However, HGS has been observed to exhibit slow convergence and may struggle with unbalanced exploration and exploitation phases. To address these issues, this study proposes a modified version of HGS called mHGS, which incorporates five techniques: (1) modified production operator, (2) modified variation control, (3) modified local escaping operator, (4) modified transition factor, and (5) modified foraging behavior. To validate the effectiveness of the mHGS method, 18 different benchmark datasets for dimensionality reduction are utilized, covering a range of sizes (small, medium, and large). Additionally, two Parkinson’s disease phonation datasets are employed as real-world applications to demonstrate the superior capabilities of the proposed approach. Experimental and statistical results obtained through the mHGS method indicate its significant performance improvements in terms of Recall, selected attribute count, Precision, F-score, and accuracy when compared to the classical HGS and seven other well-established methods: Gradient-based optimizer (GBO), Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Harris Hawks Optimizer (HHO), and Ant Lion Optimizer (ALO).
YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities

Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges such as slow detection speeds and increased costs. To address these challenges, we introduce the YOLOv5-MS model, an YOLOv5-based solution for target detection. Initially, we optimize the multi-threaded acquisition of video streams within YOLOv5 to ensure image stability and real-time performance. Subsequently, leveraging reparameterization, we replace the original BackBone convolution with RepvggBlock, streamlining the model by reducing convolutional layer channels, thereby enhancing the inference speed. Additionally, the incorporation of a bioinspired “squeeze and excitation” module in the convolutional neural network significantly enhances the detection accuracy. This module improves target focusing and diminishes the influence of irrelevant elements. Furthermore, the integration of the K-means algorithm and bioinspired Retinex image augmentation during training effectively enhances the model’s detection efficacy. Finally, loss computation adopts the Focal-EIOU approach. The empirical findings from our internally developed smart city dataset unveil YOLOv5-MS’s impressive 96.5% mAP value, indicating a significant 2.0% advancement over YOLOv5s. Moreover, the average inference speed demonstrates a notable 21.3% increase. These data decisively substantiate the model’s superiority, showcasing its capacity to effectively perform pedestrian detection within an Intranet of over 50 video surveillance cameras, in harmony with our stringent requisites.
Asymptotically Faster Multi-Key Homomorphic Encryption from Homomorphic Gadget Decomposition

Homomorphic Encryption (HE) is a cryptosytem that allows us to perform an arbitrary computation on encrypted data. The standard HE, however, has a disadvantage in that the authority is concentrated in the secret key owner since computations can only be performed on ciphertexts encrypted under the same secret key. To resolve this issue, research is underway on Multi-Key Homomorphic Encryption (MKHE), which is a variant of HE supporting computations on ciphertexts possibly encrypted under different keys. Despite its ability to provide privacy for multiple parties, existing MKHE schemes suffer from poor performance due to the cost of multiplication which grows at least quadratically with the number of keys involved.
Midcircuit Measurements on a Single-Species Neutral Alkali Atom Quantum Processor

Shelving data qubits in protected hyperfine states while measuring an ancilla qubit allows for robust midcircuit measurements in a neutral atom array, a key step toward extending the lifetime of atomic quantum memories.
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process. Besides, these methods usually require a tedious multi-stage training pipeline and rely on convolutional neural networks, showing limitations in capturing long-range dependencies. In this paper, we formulate a simple yet principled One-stage Retinex-based Framework (ORF). ORF first estimates the illumination information to light up the low-light image and then restores the corruption to produce the enhanced image. We design an Illumination-Guided Transformer (IGT) that utilizes illumination representations to direct the modeling of non-local interactions of regions with different lighting conditions. By plugging IGT into ORF, we obtain our algorithm, Retinexformer. Comprehensive quantitative and qualitative experiments demonstrate that our Retinexformer significantly outperforms state-of-the-art methods on thirteen benchmarks. The user study and application on low-light object detection also reveal the latent practical values of our method. Code is available at https://github.com/caiyuanhao1998/Retinexformer
Virtual Reality As A Tool In The Education

The usage of VR is on the rise in the classroom because it gives students greater freedom to learn by doing.In this work, we provide an educational programme for use in either face-to-face or online classes on the topic of formal languages; it allows students to virtually investigate, analyse, and learn more about a given topic.This paper introduces an educational software that uses 3D object manipulation to make learning about automata, regular expressions, and automaton minimization much simpler for students.The software has a straightforward interface and is straightforward to use.The software incorporates the programming language PHP (Hypertext Pre-Processor) for web page publication and the 3D modelling tools Blender and VRML (Virtual Reality Modelling Language) for automaton creation.The results achieved using the created software demonstrate the characteristics that make the perfect Virtual Reality for scenarios of study and learning, transferring the discipline as a point of reference from the educational setting to the computer laboratories and making the subject matter more engaging for the student and the learning process simpler.
Making Use of Fuzzy Cognitive Maps in Agent-Based Modeling

One of the main challenges in Agent-Based Modeling (ABM) is to model agents’ preferences and behavioral rules such that the knowledge and decision-making processes of real-life stakeholders will be reflected. To tackle this challenge, we demonstrate the potential use of a participatory method, Fuzzy Cognitive Mapping (FCM), that aggregates agents’ qualitative knowledge (i.e., knowledge co-production). In our proposed approach, the outcome of FCM would be a basis for designing agents’ preferences and behavioral rules in ABM. We apply this method to a social-ecological system of a farming community facing water scarcity.
Facial expression recognition using the spectral graph wavelet

The authors propose a method to recognise facial expressions based on graph signal processing (GSP) techniques. Facial expressions are characterised by local patterns in the facial regions such as eyes, lips etc. and interrelationships among them. A facial expression recognition algorithm needs to capture these variations in the facial regions at the local level and the interrelationships of these regions at the global level. Hence, in the authors’ opinion, GSP seems to be an appropriate tool for the purpose. In this study, a novel method is presented which makes use of graph signals to represent the facial regions. They leverage spectral graph wavelet transform to extract information for creating the feature descriptor. Here, different types of two-channel and three-channel filter banks have been used by setting the weights of their channels for finding the optimum performance of the recognition rate. Through simulation studies, it is observed that the use of Abspline filter bank provides the best result. The experimental investigations on the extended Cohn–Kanade (CK+) and the JAFFE datasets have been carried out and the results confirm the effectiveness of the proposed method in recognition rate improvement.
Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

In the special issue on computational intelligence (CI) and complex systems a plethora of approaches is presented on how to apply computation intelligence in modeling, control, decision support, and optimization of complex problems.e three main components of CI are fuzzy systems, evolutionary and population based algorithms, and artificial neural networks.In addition, there are other related techniques, among others, chaos theory and subjective probability.O en, the combinations and hybrids of these methods, sometimes complemented by classic mathematical or modeling tools, turn out to be most efficient in the solution of real life problems.In the series of papers, illustrations for almost all of these methods may be found.What is the main goal and target in these research problems?Several years ago the Lead Guest Editor published a study on how to use fuzzy rule based systems as tools in solving what was called (maybe, in a somewhat exaggerating way) the "Key Problem of Engineering," even though "Key Problem of Engineering" is not a term accepted by consensus in the relevant literature.Moreover, the term Engineering has many different interpretations, the narrowest one referring to technological sciences and disciplines only, while in various broader interpretations it includes computer science, agricultural engineering, social engineering, and certain aspects of economic models.In the present special issue, Engineering may be replaced by the concepts "Applied Problems," "Real Life Applications," or similar.
Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation

We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of target examples. Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adaptation reduces to aligning source and target domain at class prediction uncertainty level. We tackle this problem by introducing a method based on adversarial learning which forces the label uncertainty predictions on the target domain to be indistinguishable from those on the source domain. Pre-trained deep neural networks are used to generate deep features having high transferability across related domains. We perform an extensive experimental analysis of the proposed method over a wide set of publicly available pre-trained deep neural networks. Results of our experiments on domain adaptation tasks for image classification show that class prediction uncertainty alignment with features extracted from pre-trained deep neural networks provides an efficient, robust and effective method for domain adaptation.
A system-level perspective to understand the vulnerability of deep learning systems

Deep neural network (DNN) is nowadays achieving the human-level performance on many machine learning applications like self-driving car, gaming and computer-aided diagnosis. However, recent studies show that such a promising technique has gradually become the major attack target, significantly threatening the safety of machine learning services. On one hand, the adversarial or poisoning attacks incurred by DNN algorithm vulnerabilities can cause the decision misleading with very high confidence. On the other hand, the system-level DNN attacks built upon models, training/inference algorithms and hardware and software in DNN execution, have also emerged for more diversified damages like denial of service, private data stealing. In this paper, we present an overview of such emerging system-level DNN attacks by systematically formulating their attack routines. Several representative cases are selected in our study to summarize the characteristics of system-level DNN attacks. Based on our formulation, we further discuss the challenges and several possible techniques to mitigate such emerging system-level DNN attacks.
Semi-Order Preserving Encryption Technique for Numeric Database

Order preserving encryption techniques are treated as some of the most efficient encryption schemes for securing numeric data in a database. Such schemes are popular because they resolve performance degradation issues, which are significant problems in database encryption. However, in some applications, the order itself is sensitive information, and should be hidden. Conventional order preserving encryption techniques published so far, did not consider this issue. Therefore, in this study, we consider three techniques that protect the order information and also show good performance. The three methods hide the data order such that comparison operators can be handled efficiently and performance degradation can be prevented. Our methods work on the top of an order preserving encryption scheme and enhance the security of data. Experimental results demonstrate the efficiency and effectiveness of the proposed three methods.
Visual Hindsight Experience Replay.

Reinforcement Learning (RL) algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (HER) was introduced as a technique to increase sample efficiency by reimagining unsuccessful trajectories as successful ones by altering the originally intended goals. However, it cannot be directly applied to visual environments where goal states are often characterized by the presence of distinct visual features. In this work, we show how visual trajectories can be hallucinated to appear successful by altering agent observations using a generative model trained on relatively few snapshots of the goal. We then use this model in combination with HER to train RL agents in visual settings. We validate our approach on 3D navigation tasks and a simulated robotics application and show marked improvement over baselines derived from previous work.
More Functions With Three-Valued Walsh Transform From Linear Combinations

Recently, we obtained three kinds of monomial functions, whose Walsh transforms have only three values. Based on the work, we present more such functions by considering linear combinations of some monomial functions. As applications, we also obtain three classes of low-valued Walsh transform Boolean functions.
Accurate Library Recommendation Using Combining Collaborative Filtering and Topic Model for Mobile Development

Background: The applying of third-party libraries is an integral part of many applications. But the libraries choosing is time-consuming even for experienced developers. The automated recommendation system for libraries recommendation is widely researched to help developers to choose libraries. Aim: from software engineering aspect, our research aims to give developers a reliable recommended list of third-party libraries at the early phase of software development lifecycle to help them build their development environment faster; and from technical aspect, our research aims to build a generalizable recommendation system framework which combines collaborative filtering and topic modeling techniques, in order to improve the performance of libraries recommendation significantly. Our works on this research: 1) we design a hybrid methodology to combine collaborative filtering and LDA text mining technology; 2) we build a recommendation system framework successfully based on the above hybrid methodology; 3) we make a well-designed experiment to validate the methodology and framework which use the data of 1,013 mobile application projects; 4) we do the evaluation for the result of the experiment. Conclusions: 1) hybrid methodology with collaborative filtering and LDA can improve the performance of libraries recommendation significantly; 2) based on the hybrid methodology, the framework works very well on the libraries recommendation for helping developers' libraries choosing. Further research is necessary to improve the performance of the libraries recommendation including: 1) use more accurate NLP technologies improve the correlation analysis; 2) try other similarity calculation methodology for collaborative filtering to rise the accuracy; 3) on this research, we just bring the time-series approach to the framework and make an experiment as comparative trial, the result shows that the performance improves continuously, so in further research we plan to use time-series data-mining as the basic methodology to update the framework.
Is the SAFE Approach Too Simple for App Feature Extraction? A Replication Study

[Context and motivation] Automatic extraction and analysis of app features from user reviews is helpful for software developers to better understand users perceptions of delivered app features. Recently, a rule-based approach called safe was proposed to automatically extract app features from user reviews. safe was reported to obtain superior performance in terms of precision and recall over previously proposed techniques. However, the procedure used to evaluate safe was in part subjective and not repeatable and thus the whole evaluation might not be reliable. [Question/problem] The goal of our study is to perform an external replication of the safe evaluation using an objective and repeatable approach. [Principal ideas/results] To this end, we first implemented safe and checked the correctness of our implementation on the set of app descriptions that were used and published by the authors of the original study. We applied our safe implementation to eight review datasets (six app review datasets, one laptop review dataset, one restaurant review dataset) and evaluated its performance against manually annotated feature terms. Our results suggest that the precision of the safe approach is strongly influenced by the density of the annotated app features in a review dataset. Overall, we obtained an average precision and recall of 0.120 and 0.539, respectively which is lower than the performance reported in the original safe study. [Contribution] We performed an unbiased and reproducible evaluation of the safe approach for user reviews. We make our implementation and all datasets used for the evaluation available for replication by others.
Research on Policy Text Clustering Algorithm Based on LDA-Gibbs Model

Policy text contains large amount of diversified data and strictly conforms to standards and specifications, but the traditional text clustering method cannot solve the problems of high dimensionality, sparse features, and similar meanings, so this paper proposes a weighted algorithm based on the LDA-Gibbs model to improve the accuracy of policy text clustering. Firstly, it provides realistic basis for the assumptions of the LDA-Gibbs topic model and the weighted algorithm; secondly, it pre-processes the existing policy text simulated data, establishes the LDA-Gibbs model, forms a weighted algorithm, and generates training data to determine the number of optimal topics in the LDA-Gibbs model and completes the final clustering of the policy text; finally, by summarizing, classifying and deducing the conclusions of the experimental data, this paper proves the objective validity and effects of this method. Hopefully the overall design of this method can be applied in the prospective study on the formulation of new policies in the future, the retrospective evaluation and testing of the existing policies and the formation of a two-way interactive mechanism.