Reviewerly/exHarmony huggingface dataset:
Unsupervised Adaptation for High-Dimensional with Limited-Sample Data Classification Using Variational Autoencoder

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis.
Quantum Continual Learning Overcoming Catastrophic Forgetting

Catastrophic forgetting describes the fact that machine learning models will likely forget the knowledge of previously learned tasks after the learning process of a new one. It is a vital problem in the continual learning scenario and recently has attracted tremendous concern across different communities. We explore the catastrophic forgetting phenomena in the context of quantum machine learning. It is found that, similar to those classical learning models based on neural networks, quantum learning systems likewise suffer from such forgetting problem in classification tasks emerging from various application scenes. We show that based on the local geometrical information in the loss function landscape of the trained model, a uniform strategy can be adapted to overcome the forgetting problem in the incremental learning setting. Our results uncover the catastrophic forgetting phenomena in quantum machine learning and offer a practical method to overcome this problem, which opens a new avenue for exploring potential quantum advantages towards continual learning.
GraphReach: Position-Aware Graph Neural Network using Reachability Estimations

Majority of the existing graph neural networks(GNN) learn node embeddings that encode their local neighborhoods but not their positions. Consequently, two nodes that are vastly distant but located in similar local neighborhoods map to similar embeddings in those networks. This limitation prevents accurate performance in predictive tasks that rely on position information. In this paper, we develop GRAPHREACH , a position-aware inductive GNN that captures the global positions of nodes through reachability estimations with respect to a set of anchor nodes. The anchors are strategically selected so that reachability estimations across all the nodes are maximized. We show that this combinatorial anchor selection problem is NP-hard and, consequently, develop a greedy (1−1/e) approximation heuristic. Empirical evaluation against state-of-the-art GNN architectures reveal that GRAPHREACH provides up to 40% relative improvement in accuracy. In addition, it is more robust to adversarial attacks.
MushroomRL: Simplifying Reinforcement Learning Research

MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementing and testing novel RL methodologies. Indeed, the architecture of MushroomRL is built in such a way that every component of an RL problem is already provided, and most of the time users can only focus on the implementation of their own algorithms and experiments. The result is a library from which RL researchers can significantly benefit in the critical phase of the empirical analysis of their works. MushroomRL stable code, tutorials and documentation can be found at this https URL.
An Improved LOF Outlier Detection Algorithm

LOF (Local Outliers Factor) algorithm is a very classic anomaly detection algorithm. In order to detect the outliers more accurately, avoid too much testing error, and ensure the detection can be implemented relatively accurately in the data set without professional knowledge, on the basis of traditional LOF algorithm, an improved detection algorithm LOF Outliers is proposed. According to the different distribution densities of logarithmic data points, all the data point sets A1 that are most likely to become outliers are found out. Then, the information entropy weighted LOF algorithm is used to detect the data set to get the result A2. The point set A1 is intersected with A2 to get the final point set A which is the final result. The experimental results show that the algorithm is feasible, and it is more accurate and contains fewer false detection points.
COVID-19 Pandemic in Sweden: Measures, Policy Approach and Legal and Ethical Debates

In this article we analyse the legal approach and measures implemented by Sweden’s public authorities as a response to the COVID-19 pandemic and their legal background. Further, we discuss general legal and ethical questions related to measures to contain public health threats such as the current COVID-19 pandemic. It is outside of the scope of this paper to compare and critically analyse the effectiveness of Sweden’s public health strategy.
Tracking based on scale-estimated deep networks with hierarchical correlation ensembling for cross-media understanding

• We propose a scale estimation network (SEN) to predict the scale of the target. • We incorporate SEN into [6] to predict the position and scale of target simultaneously. • Experimental results show that our tracker performs better than the competitive trackers. In different vision based cross-media applications, the interest objects inside the visual regions usually need to be accurately localized/tracked to achieve more effective understanding and generating image descriptions (UGID), such as audio-visual lip recognition. Unfortunately, a robust tracking in realistic scenarios is usually challenged by the dynamic appearance variations when object motion is on-the-fly. Recent studies on deep neuron networks for the classification/recognition tasks have inspired a great progress in visual tracking, but the intrinsic assumption of scale invariance during target modeling still limited tracking performance to be further improved. Motivated by learning the object appearance with a scale estimation, in this study, a scale-estimated deep networks (SEN) is proposed to predict more accurate object size during tracking. By incorporating the proposed SEN into a hierarchical correlation ensembling framework, a joint translation-scale tracking scheme is accomplished to estimate the position and scale of the target object simultaneously. Substantial experiments on the challenging benchmark datasets have demonstrated that the proposed tracker is able to achieve the competitive results. Additionally, the performance evaluation of tracking lips also shows that the proposed work is also capable to support an audio-visual recognition task in different type of cross-media application.
SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition

Unsupervised user adaptation aligns the feature distributions of the data from training users and the new user, so a well-trained wearable human activity recognition (WHAR) model can be well adapted to the new user. With the development of wearable sensors, multiple wearable sensors based WHAR is gaining more and more attention. In order to address the challenge that the transferabilities of different sensors are different, we propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model. It aligns the data of each sensor separately to achieve local alignment, while uniformly aligning the data of all sensors to ensure global alignment. In addition, an attention mechanism is proposed to focus the activity classifier of SALIENCE on the sensors with strong feature discrimination and well distribution alignment. Experiments are conducted on two public WHAR datasets, and the experimental results show that our model can yield a competitive performance.
How disabled tests manifest in test maintainability challenges?

Software testing is an essential software quality assurance practice. Testing helps expose faults earlier, allowing developers to repair the code and reduce future maintenance costs. However, repairing (i.e., making failing tests pass) may not always be done immediately. Bugs may require multiple rounds of repairs and even remain unfixed due to the difficulty of bug-fixing tasks. To help test maintenance, along with code comments, the majority of testing frameworks (e.g., JUnit and TestNG) have also introduced annotations such as @Ignore to disable failing tests temporarily. Although disabling tests may help alleviate maintenance difficulties, they may also introduce technical debt. With the faster release of applications in modern software development, disabling tests may become the salvation for many developers to meet project deliverables. In the end, disabled tests may become outdated and a source of technical debt, harming long-term maintenance. Despite its harmful implications, there is little empirical research evidence on the prevalence, evolution, and maintenance of disabling tests in practice. To fill this gap, we perform the first empirical study on test disabling practice. We develop a tool to mine 122K commits and detect 3,111 changes that disable tests from 15 open-source Java systems. Our main findings are: (1) Test disabling changes are 19% more common than regular test refactorings, such as renames and type changes. (2) Our life-cycle analysis shows that 41% of disabled tests are never brought back to evaluate software quality, and most disabled tests stay disabled for several years. (3)We unveil the motivations behind test disabling practice and the associated technical debt by manually studying evolutions of 349 unique disabled tests, achieving a 95% confidence level and a 5% confidence interval. Finally, we present some actionable implications for researchers and developers.
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

The progress in deep reinforcement learning (RL) is heavily driven by the availability of challenging benchmarks used for training agents. However, benchmarks that are widely adopted by the community are not explicitly designed for evaluating specific capabilities of RL methods. While there exist environments for assessing particular open problems in RL (such as exploration, transfer learning, unsupervised environment design, or even language-assisted RL), it is generally difficult to extend these to richer, more complex environments once research goes beyond proof-of-concept results. We present MiniHack, a powerful sandbox framework for easily designing novel RL environments. MiniHack is a one-stop shop for RL experiments with environments ranging from small rooms to complex, procedurally generated worlds. By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use. With this sandbox framework, novel environments can be designed easily, either using a human-readable description language or a simple Python interface. In addition to a variety of RL tasks and baselines, MiniHack can wrap existing RL benchmarks and provide ways to seamlessly add additional complexity.
From General Language Understanding to Noisy Text Comprehension

Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors.
Blockchain based cloud service security architecture with distributed machine learning for smart device traffic record transaction

Abstract In today's world, the transaction through the smart device has received greater attention and configures numerous applications which can efficiently process huge traffic records on a growing demand for service centers from the edge of the networks. Due to these immense growths, the concern raises in critical transaction records in terms of system security threats and efficiency issues in the smart devices. However, existing methods failed due to security attacks during the tenure of access transactions and aggregated services. Recently, blockchain technology enables service centers depends on various platforms to share transaction records. But, it is difficult to store the transaction record because of its size. To address these issues, we proposed a SECure LearningChain (SEC‐ LearningChain) design based on the integration of blockchain technology, machine learning (ML), and cloud computing primitives are applied together for a secure data transaction in a Peer to Peer network as well as efficient data sharing service. This approach consists of four design models: First, an attack detection model detects the attack using threshold‐based anomalous traffic detector in the transaction network. Second, a mold blockchain transaction network model is designed based on the cryptographic hash and encryption to deal with threats and validate the identity verification process for a secure transaction. Next, the large‐scale transaction record is optimized and trains the ML model for the output prediction. Finally, the cloud assessment model manages the stored transaction records and easily share the accessed services across different cloud platforms for each service center. Furthermore, we prove that the SEC‐ LearningChain design resists transmission control protocol flooding attack, denial of service attack, and falsify attack. Experimental results demonstrate that the performance of the SEC‐ LearningChain achieves more number of transactions in each blocks over existing schemes.
Reducing Exposure Bias in Training Recurrent Neural Network Transducers

When recurrent neural network transducers (RNNTs) are trained using the typical maximum likelihood criterion, the prediction network is trained only on ground truth label sequences. This leads to a mismatch during inference, known as exposure bias, when the model must deal with label sequences containing errors. In this paper we investigate approaches to reducing exposure bias in training to improve the generalization of RNNT models for automatic speech recognition (ASR). A label-preserving input perturbation to the prediction network is introduced. The input token sequences are perturbed using SwitchOut and scheduled sampling based on an additional token language model. Experiments conducted on the 300-hour Switchboard dataset demonstrate their effectiveness. By reducing the exposure bias, we show that we can further improve the accuracy of a high-performance RNNT ASR model and obtain state-of-the-art results on the 300-hour Switchboard dataset.
Ad Hoc Teamwork in the Presence of Non-stationary Teammates

In this paper we address the problem of ad hoc teamwork and contribute a novel approach, PPAS, that is able to handle non-stationary teammates. Current approaches to ad hoc teamwork assume that the (potentially unknown) teammates behave in a stationary way, which is a significant limitation in real world conditions, since humans and other intelligent systems do not necessarily follow strict policies. In our work we highlight the current limitations of state-of-the-art approaches to ad hoc teamwork problem in the presence of non-stationary teammate, and propose a novel solution that alleviates the stationarity assumption by combining ad hoc teamwork with adversarial online prediction. The proposed architecture is called PLASTIC Policy with Adversarial Selection, or PPAS. We showcase the effectiveness of our approach through an empirical evaluation in the half-field offense environment. Our results show that it is possible to cooperate in an ad hoc manner with non-stationary teammates in complex environments.
An Ensemble Approach for Annotating Source Code Identifiers With Part-of-Speech Tags

This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75\% accuracy at the identifier level and 84-86\% accuracy at the word level. This is an increase of +17\% points at the identifier level from the closest independent part-of-speech tagger.
Analysis and Forecasting Students’ Academic Performance Using a Digital Educational Environment

Analysis and Forecasting Students’ Academic Performance Using a Digital Educational Environment The article discusses technical solutions used at Orenburg State University to organize a digital educational environment. Also, the authors have studied the academic performance of technical and humanities students during the period of face-to-face education and during the lockdown period. The analysis of academic performance shows the absence of significant deviations in one direction or another. The key internal and external factors that influence the students’ academic performance are highlighted. It can be concluded that the use of both internal and external factors gives a high accuracy in predicting the final progress of students.
MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering

Deep neural network-based models have achieved great success in extractive question answering. Recently, many works have been proposed to model multistage matching for this task, which usually first retrieve relevant paragraphs or sentences and then extract an answer span from the retrieved results. However, such a pipeline-based approach suffers from the error propagation problem, especially for sentence-level retrieval that is usually difficult to achieve high accuracy due to the severe data imbalance problem. Furthermore, since the paragraph/sentence selector and the answer extractor are closely related, modeling them independently does not fully exploit the power of multistage matching. To solve these problems, we propose a novel end-to-end multigranularity reading comprehension model, which is a unified framework to explicitly model three matching granularities, including paragraph identification, sentence selection, and answer extraction. Our approach has two main advantages. First, the end-to-end approach alleviates the error propagation problem in both the training and inference phases. Second, the shared features in a unified model improve the learning of representations of different matching granularities. We conduct a comprehensive comparison on four large-scale datasets (SQuAD-open, NewsQA, SQuAD 2.0, and SQuAD Adversarial) and verify that the proposed approach outperforms both the vanilla BERT model and existing multistage matching approaches. We also conduct an ablation study and verify the effectiveness of the proposed components in our model structure.
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts

Federated learning (FL) is an emerging distributed machine learning paradigm that avoids data sharing among training nodes so as to protect data privacy. Under the coordination of the FL server, each client conducts model training using its own computing resource and private data set. The global model can be created by aggregating the training results of clients. To cope with highly non-IID data distributions, personalized federated learning (PFL) has been proposed to improve overall performance by allowing each client to learn a personalized model. However, one major drawback of a personalized model is the loss of generalization. To achieve model personalization while maintaining better generalization, in this paper, we propose a new approach, named PFL-MoE, which mixes outputs of the personalized model and global model via the MoE architecture. PFL-MoE is a generic approach and can be instantiated by integrating existing PFL algorithms. Particularly, we propose the PFL-MF algorithm which is an instance of PFL-MoE based on the freeze-base PFL algorithm. We further improve PFL-MF by enhancing the decision-making ability of MoE gating network and propose a variant algorithm PFL-MFE. We demonstrate the effectiveness of PFL-MoE by training the LeNet-5 and VGG-16 models on the Fashion-MNIST and CIFAR-10 datasets with non-IID partitions.
Restaurant Recommendation in Vehicle Context Based on Prediction of Traffic Conditions

Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.