Reviewerly/exHarmony huggingface dataset:
Building interpretable predictive models with context-aware evolutionary learning

Building prediction models with the right balance between performance and interpretability is currently a great challenge in machine learning. A large number of recent studies have focused on either building intrinsically interpretable models or developing general explainers for blackbox models. Although these methods have been widely adopted, their interpretability or explanations are not always useful because of the lack of contexts considered in training machine learning models and producing explanations. This paper aims to tackle this significant challenge by developing a context-aware evolutionary learning algorithm (CELA) for building interpretable prediction models. A new context extraction method based on unsupervised self-structuring learning algorithms is developed to treat data in contexts. The proposed algorithm overcomes the limitations of existing evolutionary learning methods in handling a large number of features and large datasets by training specialised interpretable models based on the automatically extracted contexts. The new algorithm has been tested on complex regression datasets and a real-world building energy prediction task. The results suggest CELA can outperform well-known interpretable machine learning (IML) algorithms, the state-of-the-art evolutionary algorithm, and can produce predictions much closer to the results of blackbox algorithms such as XGBoost and artificial neural networks than the compared IML methods. Further analyses also demonstrate that the CELA’s prediction models are smaller and easier to interpret than those obtained by the evolutionary learning algorithm without context awareness.
Feed-forward Neural Network Classifiers with Bithreshold-like Activations

The paper deals with the issues concerning the modifications of the bithreshold neuron whose activation functions provide better ability of the solving the classification problems. The model of smoothed local bithreshold neuron is proposed, which is capable to recognize compact finite set of patterns in n-dimensional space. We design a binary classifier on the base of the feed-forward neural network whose hidden layer consists of such neurons with modified activations, propose the synthesis algorithm and estimate its time complexity alongside with the networks size. The simulation results demonstrate that the application of modified activations improves the accuracy of classification.
On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems

Recent High-Performance Computing (HPC) systems are facing important challenges, such as massive power consumption, while at the same time significantly under-utilized system resources. Given the power consumption trends, future systems will be deployed in an over-provisioned manner where more resources are installed than they can afford to power simultaneously. In such a scenario, maximizing resource utilization and energy efficiency, while keeping a given power constraint, is pivotal. Driven by this observation, in this position paper we first highlight the recent trends of resource management techniques, with a particular focus on malleability support (i.e., dynamically scaling resource allocations/requirements for a job), co-scheduling (i.e., co-locating multiple jobs within a node), and power management. Second, we consider putting them together, assess their relationships/synergies, and discuss the functionality requirements in each software component for future over-provisioned and power-constrained HPC systems. Third, we briefly introduce our ongoing efforts on the integration of software tools, which will ultimately lead to the convergence of malleability and power management, as it is designed in the HPC PowerStack initiative.
Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis

Abstract Noise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full‐reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis , was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full‐reference and three no‐reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters ( p &lt; 0.001). The second‐best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion ( p &lt; 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre‐processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies.
Do women hold the key to financial sector development in Africa?

This paper complements the literature on the determinants of financial development by offering one of the first studies on the effect of women's political empowerment on financial development. Across a sample of 43 African countries over the period 1996–2018, the main results show the positive effect of women's political empowerment on financial development. Our disaggregated analysis shows that women's political representation promotes both components of financial development; however, with a stronger effect for financial institutions. This result remains valid when we consider the subcomponents of these two indicators. Furthermore, the analysis of transmission mechanisms shows that control of corruption, democracy and human capital are the channels through which women's political empowerment affects financial development.
Accurate visible light positioning technique using extreme learning machine and meta-heuristic algorithm

Since input weights and hidden biases affect the overall performance of extreme learning machine (ELM) based on a single-hidden layer, two meta-heuristic algorithms of grey wolf optimizer (GWO) and particle swarm optimization (PSO) are adopted to optimize the input weights and hidden biases of ELM respectively in order to obtain higher classification accuracy. Namely, ELM-GWO and ELM-PSO are two enhanced ELM algorithms. Compared with the other existing techniques such as the traditional ELM and adaptive boosting (AdaBoost), the positioning performance of two enhanced ELM algorithms is analyzed by simulation and experiment in visible light positioning (VLP) systems. The experimental results show that the probabilities of two-dimensional (2D) positioning error being less than 10 cm for ELM, ELM-GWO, ELM-PSO, and AdaBoost are 84.25%, 90.25%, 93.25%, and 19.75%, respectively. And the probabilities of three-dimensional (3D) positioning error being less than 10 cm for ELM, ELM-GWO, ELM-PSO, and AdaBoost are 45.17%, 84.67%, 80.33%, and 16%, respectively. Both simulation and experimental results show that two enhanced ELM algorithms have better positioning performance and robustness. In addition, although increasing the number of iterations and the number of hidden neurons can effectively reduce the positioning error, the computational complexity is relatively high in large-scale fingerprint positioning scenarios. Therefore, a hybrid positioning method combining enhanced ELM and region division is further proposed. Both simulation and experimental results show that the proposed hybrid method can significantly improve the positioning accuracy while reducing the computational complexity.
Twitter Sentiment Analysis Using Naive Bayes-Based Machine Learning Technique

"Computational" sentiment analysis can determine whether a sentiment is favorable, negative, or neutral. Another term for this approach is "opinion mining," or obtaining a speaker's sentiments. Businesses use it to develop strategies, learn what customers think about products or brands, how people react to campaigns or new product releases, and why they do not buy certain products. It is used in politics to keep track of political ideas and to check for contradictions between government claims and actions. It can even be used to predict election results! It is also used to track and analyze social phenomena like recognizing dangerous circumstances and evaluating blogging mood. In this paper, we look tackle the problem of sentiment categorization using the Twitter dataset. To analyze sentiment, preprocessing and Naive Bayes classifier approaches are utilized. As a result, we applied a text preprocessing classification accuracy classifying strategy and improved our classification accuracy score on the Kaggle public leaderboard. The aim of this paper is to classify the twitter sentiments using machine learning algorithm based on Naïve Bayes Classifier. The proposed model indicated better accuracy and precision based on performance parameters such as precision, recall and accuracy.
The whole slide breast histopathology image detection based on a fused model and heatmaps

The analysis of whole slide breast histopathology image is one of the most effective techniques in the current breast cancer diagnosis. However, the size of whole slide image(WSI) is too large to be processed directly by machine learning or deep learning based classifier. To solve the problem of WSI-based computer aided diagnosis, a novel classification framework with three stages is proposed in this paper to locate and classify tumor region in the whole slide images. This framework contains three parts: patch-based classification, tumor region segmentation and location, and WSI-based classification. In the period of data processing, the Cycle-GAN(Cycle Generative Adversarial Network) model is firstly adopted to normalize the colors of the images. Then an improved DPN68(Dual Path Networks) based classifier and a Swin-Transformer based classifier are combined to improve the accuracy of patch-based classification. With the malignant confidence probability output by the fused model, the whole slide heatmap is generated to segment, locate and visually display the tumor regions. Statistical features are computed and selected from the heatmap based on medical diagnosis indicators, and then an SVM(Support Vector Machines) based classifier is applied to implement WSI-based classification to further confirm the tumor. Experimental results on the Camelyon16 dataset show that our proposed framework can effectively classify the whole slide breast histopathology image with high-precision.
Improved Lightweight YOLOv5 Using Attention Mechanism for Satellite Components Recognition

Recognizing typical components of the satellite is a challenging task for on-orbit services. This article proposes a YOLOv5-based satellite components recognition model (YSCRM) on a computationally limited platform. In particular, feature fusion layers and selective kernel networks (SK-Nets) are introduced to handle the complex multimodal recognition problem of the components, improving the model’s feature selection and representation capability and significantly increasing the recognition accuracy. The transformer encoder modules are used at the end of the Yolov5 Neck to explore the prediction potential with the self-attention mechanism. The channel sparseness training method is implemented to reduce the model size to deploy the model on an embedded platform. Moreover, for the purpose of enlarging the training datasets and their diversity, a data generation approach based on the synthetic images generated by 3ds Max and a data augmentation method based on cycle-consistent adversarial network (Cycle-GAN) are presented in this research. The proposed hybrid dataset is used for training and testing, and extensive qualitative and quantitative experiments are performed. The results demonstrate that the YSCRM has significant recognition performance, and the five typical components, solar panel, body, tripod, nozzle, and docking ring, can be recognized with high accuracy, which indicates that the proposed model can be used for real-world scenarios.
An incipient fault diagnosis method based on Att-GCN for analogue circuits

Abstract Incipient faults for analogue circuits in modern electronic systems are difficult to diagnose due to poor fault features. To address this issue, a method based on the attention weighted graph convolution network (Att-GCN) is proposed in this paper. The structural and data features of samples are jointly extracted to mine the effective characteristics from incipient faults. First, a wavelet packet energy transform and a probabilistic principal component analysis (ProbPCA) are employed to enhance the sample fault information. Then, the distance clustering method is deployed to construct the sample set into a non-European structure sample graph, where the structural features of fault samples are preserved. Second, an Att-GCN, which combines the spatial-domain graph convolution network and improved self-attention mechanism, is constructed to extract the structural features and data features to obtain more effective fault information. Additionally, the multisample dropout method is introduced to reduce network overfitting in the training process. To assess the method’s actual performance for fault diagnosis, experiments are carried out in the Sallen–Key bandpass filter circuit, the four-op-amp biquadratic filter circuit and the amplifier board circuit. The outcomes indicate that this method improves the incipient fault diagnosis accuracy for analogue circuits.
Investigating Reasons for Disagreement in Natural Language Inference

Abstract We investigate how disagreement in natural language inference (NLI) annotation arises. We developed a taxonomy of disagreement sources with 10 categories spanning 3 high- level classes. We found that some disagreements are due to uncertainty in the sentence meaning, others to annotator biases and task artifacts, leading to different interpretations of the label distribution. We explore two modeling approaches for detecting items with potential disagreement: a 4-way classification with a “Complicated” label in addition to the three standard NLI labels, and a multilabel classification approach. We found that the multilabel classification is more expressive and gives better recall of the possible interpretations in the data.
DevOps in Practice

DevOps connects development, delivery, and operations and, thus, facilitates a fluid collaboration of these traditionally separated silos. As an agile method, DevOps is today used across industries and is not limited anymore to IT services and specific technologies. Lorin Hochstein and I present a brief overview on DevOps best practice. A Netflix case study emphasizes DevOps in practice. I look forward to hearing from you about this column and the technologies that matter most for your work.
Almost-Surely Terminating Asynchronous Byzantine Agreement Against General Adversaries with Optimal Resilience ✱

In this work, we present an almost-surely terminating asynchronous Byzantine agreement (ABA) protocol for n parties. Our protocol requires expected time and is secure against a computationally-unbounded malicious (Byzantine) adversary, characterized by a non-threshold adversary structure , which enumerates all possible subsets of potentially corrupt parties. Our protocol has optimal resilience where satisfies the condition; i.e. union of no three subsets from covers all the n parties. To the best of our knowledge, this is the first almost-surely terminating ABA protocol with condition. Previously, almost-surely terminating ABA protocol is known with non-optimal resilience where satisfies the condition; i.e. union of no four subsets from covers all the n parties. To design our protocol, we present a shunning asynchronous verifiable secret-sharing (SAVSS) scheme with condition, which is of independent interest.
Agile Development of Secure Software for Small and Medium-Sized Enterprises

Although agile methods gained popularity and became globally widespread, developing secure software with agile methods remains a challenge. Method elements (i.e., roles, activities, and artifacts) that aim to increase software security on one hand can reduce the characteristic agility of agile methods on the other. The overall aim of this paper is to provide small- and medium-sized enterprises (SMEs) with the means to improve the sustainability of their software development process in terms of software security despite their limitations, such as low capacity and/or financial resources. Although software engineering literature offers various security elements, there is one key research gap that hinders the ability to provide such means. It remains unclear not only how much individual security elements contribute to software security but also how they impact the agility and costs of software development. To address the gap, we identified security elements found in the literature and evaluated them for their impact on software security, agility, and costs in an international study among practitioners. Finally, we developed a novel lightweight approach for evaluating agile methods from a security perspective. The developed approach can help SMEs to adapt their software development to their needs.
Construction, Test and Application of a Tungsten Metallogene Named MGW11: Case Studies in China

Geochemical gene is a new promising concept proposed recently in the discrimination and traceability of geological materials and is also a useful tool to recognize geochemical anomalies in mineral exploration. Based on the lithogenes of LG01 and LG03, geological materials can be classified into nine types of LG_CR compositionally. With respect to geological materials with 11 types of LG_CR, in order to eliminate the lithological influence and to further narrow the prospecting target area, a tungsten metallogene named MGW11 is proposed for geochemical tungsten exploration after the tungsten metallogene MGW. Six weathering profiles of 11 types of LG_CR developed on granitic intrusions in different areas in China are selected to test the stable properties such as heredity and inheritance of MGW11 and MGW. The results indicate that MGW11 and MGW metallogenes illustrate stable properties during rock weathering regardless of weathering degrees, although gene variations of MGW11 and MGW are also observed during extreme weathering. Based on the regional geochemistry survey data in the Lianyang area in south China, where stream sediments are mostly 11 types of LG_CR compositionally, geochemical maps of mineralization similarities of MGW11 and MGW are contoured, and the anomaly areas are determined on the mineralization similarity value of ≥40%. Comparing the tungsten deposits and anomaly areas determined on MGW11 and MGW metallogenes spatially, a total of six polymetallic W deposits recognized in the study area are all located in the anomaly areas. Therefore, mineralization similarities of MGW11 and MGW can be viewed as useful integrated indices on geochemical tungsten exploration. In areas with 11 types of LG_CR compositionally, anomaly areas determined on the MGW11 are smaller than those on the MGW, which indicates that MGW11 is more efficient than MGW in targeting W deposits during tungsten prospecting because of the elimination of the lithological influence.
Integrating Chatbot and Augmented Reality Technology into Biology Learning during COVID-19

The novel coronavirus (COVID-19) pandemic is rampant around the world, and teachers and students are unable to attend physical classes in the midst of a serious outbreak. This study aims to design a user-friendly, educational chatbot application interface that can be used as an after-school self-learning tool for students to enhance their interest and comprehension and increase the effectiveness of their learning at home. The system adopts the Chatfuel platform as the core interface and incorporates augmented reality technology to build a chatbot that allows users to interact with it after they have logged in to Facebook. The content is based on the biology subject of the first year of junior high school and is integrated into the online teaching with augmented reality teaching materials. A user survey is conducted to understand students’ attitudes towards learning biology with the aid of the ARCS motivation model, with 102 valid questionnaires received. The results show that the AR-based chatbot system developed in this study significant influenced the indicators in the ARCS motivation model; therefore, the intention to use the system is presumed to result in a noticeable increase in student learning outcomes when using the system. Accordingly, this study proposes new online learning tools for students to use at home during the pandemic, and the system also provides references for the future development and modification of educational chatbots.
Extractive Explanations for Interpretable Text Ranking

Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of parameters these (typically transformer-based) models are often non-interpretable in that ranking decisions can not be clearly attributed to specific parts of the input documents. In this article, we propose ranking models that are inherently interpretable by generating explanations as a by-product of the prediction decision. We introduce the Select-And-Rank paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document. Thereafter, we solely use the explanation or selection to make the prediction, making explanations first-class citizens in the ranking process. Technically, we treat sentence selection as a latent variable trained jointly with the ranker from the final output. To that end, we propose an end-to-end training technique for Select-And-Rank models utilizing reparameterizable subset sampling using the Gumbel-max trick . We conduct extensive experiments to demonstrate that our approach is competitive to state-of-the-art methods. Our approach is broadly applicable to numerous ranking tasks and furthers the goal of building models that are interpretable by design . Finally, we present real-world applications that benefit from our sentence selection method.