Reviewerly/exHarmony huggingface dataset:
A Variational Bayesian Inference-Inspired Unrolled Deep Network for MIMO Detection

The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a model-driven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels.
Discriminative Region-based Multi-Label Zero-Shot Learning

Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image. However, the occurrence of multiple objects complicates the reasoning and requires region-specific processing of visual features to preserve their contextual cues. We note that the best existing multi-label ZSL method takes a shared approach towards attending to region features with a common set of attention maps for all the classes. Such shared maps lead to diffused attention, which does not discriminatively focus on relevant locations when the number of classes are large. Moreover, mapping spatially-pooled visual features to the class semantics leads to inter-class feature entanglement, thus hampering the classification. Here, we propose an alternate approach towards region-based discriminability-preserving multi-label zero-shot classification. Our approach maintains the spatial resolution to preserve region-level characteristics and utilizes a bi-level attention module (BiAM) to enrich the features by incorporating both region and scene context information. The enriched region-level features are then mapped to the class semantics and only their class predictions are spatially pooled to obtain image-level predictions, thereby keeping the multi-class features disentangled. Our approach sets a new state of the art on two large-scale multi-label zero-shot benchmarks: NUS-WIDE and Open Images. On NUS-WIDE, our approach achieves an absolute gain of 6.9% mAP for ZSL, compared to the best published results. Source code is available at https://github.com/akshitac8/BiAM.
Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index K T , day number, the length of the day, minimal temperature T min , maximal temperature T max , average temperature T average , difference temperature <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>Δ</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:math> , ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination ( R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R 2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.
Co-LDL: A Co-Training-Based Label Distribution Learning Method for Tackling Label Noise

Performances of deep neural networks are prone to be degraded by label noise due to their powerful capability in fitting training data. Deeming low-loss instances as clean data is one of the most promising strategies in tackling label noise and has been widely adopted by state-of-the-art methods. However, prior works tend to drop high-loss instances directly, neglecting their valuable information. To address this issue, we propose an end-to-end framework named Co-LDL, which incorporates the low-loss sample selection strategy with label distribution learning. Specifically, we simultaneously train two deep neural networks and let them communicate useful knowledge by selecting low-loss and high-loss samples for each other. Low-loss samples are leveraged conventionally for updating network parameters. On the contrary, high-loss samples are trained in a label distribution learning manner to update network parameters and label distributions concurrently. Moreover, we propose a self-supervised module to further boost the model performance by enhancing the learned representations. Comprehensive experiments on both synthetic and real-world noisy datasets are provided to demonstrate the superiority of our Co-LDL method over state-of-the-art approaches in learning with noisy labels. The source code and models have been made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUST-Machine-Intelligence-Laboratory/CoLDL</uri> .
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions

3D pose estimation has attracted increasing attention with the availability of high-quality benchmark datasets. However, prior works show that deep learning models tend to learn spurious correlations, which fail to generalize beyond the specific dataset they are trained on. In this work, we take a step towards training robust models for cross-domain pose estimation task, which brings together ideas from causal representation learning and generative adversarial networks. Specifically, this paper introduces a novel framework for causal representation learning which explicitly exploits the causal structure of the task. We consider changing domain as interventions on images under the data-generation process and steer the generative model to produce counterfactual features. This help the model learn transferable and causal relations across different domains. Our framework is able to learn with various types of unlabeled datasets. We demonstrate the efficacy of our proposed method on both human and hand pose estimation task. The experiment results show the proposed approach achieves state-of-the-art performance on most datasets for both domain adaptation and domain generalization settings.
Three-dimensional micro-displacement measurement method based on capacitance-grating sensor

In order to solve the problems that it‘s difficult in installing a sensor in a narrow space and identifying micro-displacement direction in micro-displacement measurement. A three-dimensional micro-displacement measurement method based on capacitance-grating sensor was proposed by highlighting the idea of combining several groups of capacitance grids and the theory of displacement coordinate decomposition. The sensor structure was optimized and a three-dimensional micro-displacement test system was also established. By taking into consideration the positional relationship of the sensors in measurement model, a micro-displacement measurement model of sphere eccentricity was established to deduce the relationship between the displacement in the x,y and z directions and capacitance change. The relationship between the displacement direction and the capacitance change trend was also analyzed, based on which, a three-dimensional spatial micro-displacement decoupling mathematical model was established to measure displacement and judge the specific direction. BP neural network was also used to realize the decoupling of three-dimensional spatial micro-displacement and verification of decoupling accuracy. Under the ideal simulation conditions, the decoupling errors in x,y and z directions are less than 4 μm. The experimental results show that decoupling errors are all less than 8 μm. According to the results, the three-dimensional micro-displacement measurement method based on capacitance-grating sensor could measure the three-dimensional micro-displacement and identify the direction. The accuracy could reach 1.6% over the full range of sensor.
Exploring Data and Model Poisoning Attacks to Deep Learning-Based NLP Systems

Natural Language Processing (NLP) is being recently explored also to its application in supporting malicious activities and objects detection. Furthermore, NLP and Deep Learning have become targets of malicious attacks too. Very recent researches evidenced that adversarial attacks are able to affect also NLP tasks, in addition to the more popular adversarial attacks on deep learning systems for image processing tasks. More precisely, while small perturbations applied to the data set adopted for training typical NLP tasks (e.g., Part-of-Speech Tagging, Named Entity Recognition, etc..) could be easily recognized, models poisoning, performed by the means of altered data models, typically provided in the transfer learning phase to a deep neural networks (e.g., poisoning attacks by word embeddings), are harder to be detected. In this work, we preliminary explore the effectiveness of a poisoned word embeddings attack aimed at a deep neural network trained to accomplish a Named Entity Recognition (NER) task. By adopting the NER case study, we aimed to analyze the severity of such a kind of attack to accuracy in recognizing the right classes for the given entities. Finally, this study represents a preliminary step to assess the impact and the vulnerabilities of some NLP systems we adopt in our research activities, and further investigating some potential mitigation strategies, in order to make these systems more resilient to data and models poisoning attacks.
Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities

Understanding how developers refactor their code is critical to support the design improvement process of software. This paper investigates to what extent code metrics are good indicators for predicting refactoring activity in the source code. In order to perform this, we formulated the prediction of refactoring operation types as a multi-class classification problem. Our solution relies on measuring metrics extracted from committed code changes in order to extract the corresponding features (i.e., metric variations) that better represent each class (i.e., refactoring type) in order to automatically predict, for a given commit, the method-level type of refactoring being applied, namely Move Method, Rename Method, Extract Method, Inline Method, Pull-up Method, and Push-down Method. We compared various classifiers, in terms of their prediction performance, using a dataset of 5004 commits and extracted 800 Java projects. Our main findings show that the random forest model trained with code metrics resulted in the best average accuracy of 75%. However, we detected a variation in the results per class, which means that some refactoring types are harder to detect than others.
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning

Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. It is therefore tricky to decide which learning strategies to use under different task conditions. Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs. The goal of our work is to search for good parameter adaptation policies that are applied to different stages in the network for few-shot classification. We present a search space that covers many popular few-shot learning algorithms in the literature, and develop a differentiable searching and decoding algorithm based on meta-learning that supports gradient-based optimization. We demonstrate the effectiveness of our searching-based method on multiple benchmark datasets. Extensive experiments show that our approach significantly outperforms baselines and demonstrates performance advantages over many state-of-the-art methods.
Fully Test-Time Adaptation for Image Segmentation

When adopting a model from the source domain to the target domain, its performance usually degrades due to the domain shift problem. In clinical practice, the source data usually cannot be accessed during adaptation for privacy policy and the label for the target domain is in shortage because of the high cost of professional labeling. Therefore, it is worth considering how to efficiently adopt a pretrained model with only unlabeled data from the target domain. In this paper, we propose a novel fully test-time unsupervised adaptation method for image segmentation based on Regional Nuclear-norm (RN) and Contour Regularization (CR). The RN loss is specially designed for segmentation tasks to efficiently improve discriminability and diversity of prediction. The CR loss constrains the continuity and connectivity to enhance the relevance between pixels and their neighbors. Instead of retraining all parameters, we modify only the parameters in batch normalization layers with only a few epochs. We demonstrate the effectiveness and efficiency of the proposed method in the pancreas and liver segmentation dataset from the Medical Segmentation Decathlon and CHAOS challenge.
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making

Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked to use ML predictions to make high-stakes decisions. Multiple ML usability challenges can appear as result, such as lack of user trust in the model, inability to reconcile human-ML disagreement, and ethical concerns about oversimplification of complex problems to a single algorithm output. In this paper, we investigate the ML usability challenges that present in the domain of child welfare screening through a series of collaborations with child welfare screeners. Following the iterative design process between the ML scientists, visualization researchers, and domain experts (child screeners), we first identified four key ML challenges and honed in on one promising explainable ML technique to address them (local factor contributions). Then we implemented and evaluated our visual analytics tool, Sibyl, to increase the interpretability and interactivity of local factor contributions. The effectiveness of our tool is demonstrated by two formal user studies with 12 non-expert participants and 13 expert participants respectively. Valuable feedback was collected, from which we composed a list of design implications as a useful guideline for researchers who aim to develop an interpretable and interactive visualization tool for ML prediction models deployed for child welfare screeners and other similar domain experts.
Analyzing sentiments in peer review reports: Evidence from two science funding agencies

Abstract Using a novel combination of methods and data sets from two national funding agency contexts, this study explores whether review sentiment can be used as a reliable proxy for understanding peer reviewer opinions. We measure reviewer opinions via their review sentiments on both specific review subjects and proposals’ overall funding worthiness with three different methods: manual content analysis and two dictionary-based sentiment analysis algorithms (TextBlob and VADER). The reliability of review sentiment to detect reviewer opinions is addressed by its correlation with review scores and proposals’ rankings and funding decisions. We find in our samples that review sentiments correlate with review scores or rankings positively, and the correlation is stronger for manually coded than for algorithmic results; manual and algorithmic results are overall correlated across different funding programs, review sections, languages, and agencies, but the correlations are not strong; and manually coded review sentiments can quite accurately predict whether proposals are funded, whereas the two algorithms predict funding success with moderate accuracy. The results suggest that manual analysis of review sentiments can provide a reliable proxy of grant reviewer opinions, whereas the two SA algorithms can be useful only in some specific situations.
Impact of information display on worker performance for wood frame wall assembly using AR HMD under different task conditions

Augmented Reality (AR) head-mounted displays (HMDs) provide users with an immersive virtual experience in the real world. The portability of this technology affords various information display options for construction workers that are not possible otherwise. However, the impact of these different information presentation options on human performance should be carefully evaluated before such technology is deployed in the jobsite. In this paper, we describe a research effort examining how different information displays presented via AR HMD influence task performance when assembling three sized wooden wall frame assembly tasks. We asked 18 construction engineering students with framing experience to finish three wood frame assembly tasks (large, medium, and small) using one of the three information displays (AR 3D conformal, AR 2D tag-along, and paper blueprints). The task performance was measured by time of completion and framing errors, which were analyzed and compared among each factor.
Machine Learning Techniques for Anomaly Detection in Smart Healthcare

Anomaly detection is a vital research problem among the different domains intrusion detection, fraud detection, device health monitoring, fault data detection, event detection in sensor networks. Anomalies mean an outlier, noise, novelties, exceptions which do not match the expected behavior of the system. Machine learning techniques work well in identifying these abnormal patterns. In this paper, the unsupervised clustering technique K-means, and its variation K-medoids partitioning are applied to detect anomalies. Sensor-embedded wearable devices are allowing smart healthcare services for people even in remote areas. These devices support continuous monitoring of people's health and allow the caregivers to provide better health assistance. Early-stage anomaly detection in such types of smart healthcare practices increases the efficiency of health services. In experimental discussion, K-means, and K-medoids partitioning clustering algorithms are assessed, and their performance is addressed.
Implementing Extreme Gradient Boosting (XGBoost) Algorithm in Predicting Solar Irradiance

Globally, the consistent clamor by environmentalists for the need to mitigate the effects of climate change has necessitated the adoption of renewable energy sources (RES) for use by many developed/developing nations. The approach is geared towards gradually replacing or reducing the use of fossil fuels for electric power production. Solar power is among the major renewable energy sources in use today. But the use of solar energy is, unfortunately, characterized by fluctuations in its power generation due to the unpredictability of solar irradiance. Despite many methods in use already, accurate forecasting of solar irradiance has continued to be a great need both in the field of physical simulations and artificial intelligence. In this paper, an extreme gradient boosting (XGBoost) regression algorithm was deployed to successfully predict solar power with minimal error. Eighty percent of one-year and five years of historical hourly solar irradiance data of Johannesburg city were separately used as the training dataset. The results obtained using this algorithm were compared with the ones from Support Vector Machine (SVM), to determine the model with the least forecast errors. The results showed that the XGBoost model with an nRMSE value of 6.63% performed better than the SVM model with 6.81%. It is hoped that the implementation of the XGB algorithm for solar irradiance forecasts could greatly improve the stability of solar electric power generated for optimum connection to the power grid.
Dial4JaCa – A Communication Interface Between Multi-agent Systems and Chatbots

Multi-agent techniques have become mature technologies with many tools, programming languages, and methodologies currently available. As a result, it is a practical approach for the development of complex AI systems. However, there are still challenges in developing an interface that enables a more natural communication interface between software agents and human agents, for example, using natural language. We believe chatbot technologies can provide the support needed to face this challenge. In this paper, we propose an approach to the integration of multi-agent systems and chatbot technologies named Dial4JaCa. The resulting integration makes it possible to apply both those technologies together to a variety of domains. In this particular work, we use JaCaMo as a multi-agent programming platform and Dialogflow as a chatbot platform. We evaluate the Dial4JaCa integration using two different multi-agent applications.