Reviewerly/exHarmony huggingface dataset:
Self-organized Operational Neural Networks with Generative Neurons

Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as network homogeneity with the sole linear neuron model. ONNs are heterogeneous networks with a generalized neuron model. However the operator search method in ONNs is not only computationally demanding, but the network heterogeneity is also limited since the same set of operators will then be used for all neurons in each layer. Moreover, the performance of ONNs directly depends on the operator set library used, which introduces a certain risk of performance degradation especially when the optimal operator set required for a particular task is missing from the library. In order to address these issues and achieve an ultimate heterogeneity level to boost the network diversity along with computational efficiency, in this study we propose Self-organized ONNs (Self-ONNs) with generative neurons that can adapt (optimize) the nodal operator of each connection during the training process. Moreover, this ability voids the need of having a fixed operator set library and the prior operator search within the library in order to find the best possible set of operators. We further formulate the training method to back-propagate the error through the operational layers of Self-ONNs. Experimental results over four challenging problems demonstrate the superior learning capability and computational efficiency of Self-ONNs over conventional ONNs and CNNs.
TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition With Short Range Radars

This work proposes a low-power high-accuracy embedded hand-gesture recognition algorithm targeting battery-operated wearable devices using low-power short-range RADAR sensors. A 2-D convolutional neural network (CNN) using range-frequency Doppler features is combined with a temporal convolutional neural network (TCN) for time sequence prediction. The final algorithm has a model size of only 46 thousand parameters, yielding a memory footprint of only 92 KB. Two data sets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20'210 gesture instances. On the 11 hand gesture data set, accuracies of 86.6% (26 users) and 92.4% (single user) have been achieved, which are comparable to the state of the art, which achieves 87% (10 users) and 94% (single user), while using a TCN-based network that is 7500× smaller than the state of the art. Furthermore, the gesture recognition classifier has been implemented on a parallel ultralow power processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 120 mW is achieved. We provide open-source access to example code and all data collected and used in this work on tinyradar.ethz.ch.
Application of Gradient Boosting Algorithms for Anti-money Laundering in Cryptocurrencies

The recent emergence of cryptocurrencies has added another layer of complexity in the fight towards financial crime. Cryptocurrencies require no central authority and offer pseudo-anonymity to its users, allowing criminals to disguise themselves among legitimate users. On the other hand, the openness of data fuels the investigator’s toolkit to conduct forensic examinations. This study focuses on the detection of illicit activities (e.g., scams, financing terrorism, and Ponzi schemes) on cryptocurrency infrastructures, both at an account and transaction level. Previous work has identified that class imbalance and the dynamic environment created by the evolving techniques deployed by criminals to avoid detection are widespread in this domain. In our study, we propose Adaptive Stacked eXtreme Gradient Boosting (ASXGB), an adaptation of eXtreme Gradient Boosting (XGBoost), to better handle dynamic environments and present a comparative analysis of various offline decision tree-based ensembles and heuristic-based data-sampling techniques. Our results show that: (i) offline decision tree-based gradient boosting algorithms outperform state-of-the-art Random Forest (RF) results at both an account and transaction level, (ii) the data-sampling approach NCL-SMOTE further improves recall at a transaction level, and (iii) our proposed ASXGB successfully reduced the impact of concept drift while further improving recall at a transaction level.
Federated Edge Learning: Design Issues and Challenges

Federated Learning (FL) is a distributed machine learning technique, where each device contributes to the learning model by independently computing the gradient based on its local training data. It has recently become a hot research topic, as it promises several benefits related to data privacy and scalability. However, implementing FL at the network edge is challenging due to system and data heterogeneity and resource constraints. In this article, we examine the existing challenges and trade-offs in Federated Edge Learning (FEEL). The design of FEEL algorithms for resources-efficient learning raises several challenges. These challenges are essentially related to the multidisciplinary nature of the problem. As the data is the key component of the learning, this article advocates a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. Hence, we propose a general framework for data-aware scheduling as a guideline for future research directions. We also discuss the main axes and requirements for data evaluation and some exploitable techniques and metrics.
HF-TPE: High-Fidelity Thumbnail- Preserving Encryption

With the popularity of cloud storage services, people are increasingly accustomed to storing images in the cloud. However, cloud storage services raise privacy concerns, e.g., leakage of images to unauthorized third parties and service providers may exploit image detection technologies to portrait users without permission. Although privacy concerns can be solved by encrypting images before they are uploaded to the cloud, traditional encryption methods significantly affect the usability and user experience, for example, users cannot preview images in the cloud. Recently, Marohn <i>et al.</i> proposed two approximate thumbnail-preserving encryption schemes, called DRPE and TPE-LSB, to balance the privacy and usability of images in the cloud. However, both schemes have defects that either the decryption may fail or the ciphertext images have poor performance in perceived quality and too many noise points after decryption. To this end, we pertinently propose a high-fidelity thumbnail-preserving encryption scheme (HF-TPE). Compared with the previous works, on the one hand, the HF-TPE scheme not only ensures the correct decryption of ciphertext images, but also makes the ciphertext thumbnails more close to the plaintext images perceptually. On the other hand, the decrypted thumbnails have lower noise intensity and upper limit of the number of noises. In addition, simulation experiments further show that the HF-TPE scheme can guarantee users&#x2019; usability.
CausaLM: Causal Model Explanation Through Counterfactual Language Models

Abstract Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all machine learning–based methods, they are as good as their training data, and can also capture unwanted biases. While there are tools that can help understand whether such biases exist, they do not distinguish between correlation and causation, and might be ill-suited for text-based models and for reasoning about high level language concepts. A key problem of estimating the causal effect of a concept of interest on a given model is that this estimation requires the generation of counterfactual examples, which is challenging with existing generation technology. To bridge that gap, we propose CausaLM, a framework for producing causal model explanations using counterfactual language representation models. Our approach is based on fine-tuning of deep contextualized embedding models with auxiliary adversarial tasks derived from the causal graph of the problem. Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance. A byproduct of our method is a language representation model that is unaffected by the tested concept, which can be useful in mitigating unwanted bias ingrained in the data.
Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.
Attention aware cost volume pyramid based multi-view stereo network for 3D reconstruction

We present an efficient multi-view stereo (MVS) network for 3D reconstruction from multi-view images. While previous learning based reconstruction approaches performed quite well, most of them estimate depth maps at a fixed resolution using plane sweep volumes with a fixed depth hypothesis at each plane, which requires densely sampled planes for desired accuracy and therefore is difficult to achieve high resolution depth maps. In this paper we introduce a coarse-to-fine depth inference strategy to achieve high resolution depth. This strategy first estimates the depth map at coarsest level, and the depth maps at finer levels are considered as the upsampled depth map from previous level with pixel-wise depth residual. Thus, we narrow the depth searching range with the priori information from previous level and construct new cost volumes from the pixel-wise depth residual to perform depth map refinement. Then the final depth map can be achieved iteratively since all the parameters are shared among different levels. At each level, the self-attention layer is introduced to the feature extraction block for capturing the important information in depth inference task, and the cost volume is generated using similarity measurement instead of the variance based methods used in previous work. Experiments were conducted on three diverse datasets including the DTU benchmark dataset, BlendedMVS dataset and the Tanks and Temples dataset. The results demonstrated that our proposed approach could outperform most state-of-the-arts (SOTA) methods.2
SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution

Learning a classifier from class-imbalance data is an important challenge. Among existing solutions, SMOTE is one of the most successful methods and has an extensive range of practical applications. The performance of SMOTE and its extensions usually degrades owing to noisy and borderline examples. Filtering-based methods have been developed to address this problem but still have the following technical defects: (a) Error detection techniques heavily rely on parameter settings; (b) Examples detected by error detection techniques are directly eliminated, leading to deviation of obtained decision boundary and class imbalance again. To advance the state of the art, a novel filtering-based oversampling method called SMOTE-NaN-DE is proposed in this paper. In SMOTE-NaN-DE, a SMOTE-based method is first used to generate synthetic samples and improve original class-imbalance data. Secondly, an error detection technique based on natural neighbors is used to detect noisy and borderline examples. Thirdly, the differential evolution (DE) is used to optimize and change iteratively the position (attributes) of found examples instead of eliminating them. The main advantages of SMOTE-NaN-DE are that (a) It can improve almost all of SMOTE-based methods in terms of the noise problem; (b) Error detection technique is parameter-free; (c) Examples found by error detection technique are optimized by the differential evolution rather than removed, which keeps imbalance ratio and improve the boundary; (d) It is more suitable for data sets with more noise (especially class noise). The effectiveness of the proposed SMOTE-NaN-DE is validated by intensive comparison experiments on artificial and real data sets.
Interpretable heartbeat classification using local model-agnostic explanations on ECGs

Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG interpretation is subjective and prone to errors. Machine learning models are often developed and used to support doctors; however, their lack of interpretability stands as one of the main drawbacks of their widespread operation. This paper focuses on an Explainable Artificial Intelligence (XAI) solution to make heartbeat classification more explainable using several state-of-the-art model-agnostic methods. We introduce a high-level conceptual framework for explainable time series and propose an original method that adds temporal dependency between time samples using the time series' derivative. The results were validated in the MIT-BIH arrhythmia dataset: we performed a performance's analysis to evaluate whether the explanations fit the model's behaviour; and employed the 1-D Jaccard's index to compare the subsequences extracted from an interpretable model and the XAI methods used. Our results show that the use of the raw signal and its derivative includes temporal dependency between samples to promote classification explanation. A small but informative user study concludes this study to evaluate the potential of the visual explanations produced by our original method for being adopted in real-world clinical settings, either as diagnostic aids or training resource.
Knowledge Embedding Based Graph Convolutional Network

Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN). How to effectively leverage the rich structural information in complex graphs, such as knowledge graphs with heterogeneous types of entities and relations, is a primary open challenge in the field. Most GCN methods are either restricted to graphs with a homogeneous type of edges (e.g., citation links only), or focusing on representation learning for nodes only instead of jointly propagating and updating the embeddings of both nodes and edges for target-driven objectives. This paper addresses these limitations by proposing a novel framework, namely the Knowledge Embedding based Graph Convolutional Network (KE-GCN), which combines the power of GCNs in graph-based belief propagation and the strengths of advanced knowledge embedding (a.k.a. knowledge graph embedding) methods, and goes beyond. Our theoretical analysis shows that KE-GCN offers an elegant unification of several well-known GCN methods as specific cases, with a new perspective of graph convolution. Experimental results on benchmark datasets show the advantageous performance of KE-GCN over strong baseline methods in the tasks of knowledge graph alignment and entity classification1.
An Empirical Study of Real-World WebAssembly Binaries

WebAssembly has emerged as a low-level language for the web and beyond. Despite its popularity in different domains, little is known about WebAssembly binaries that occur in the wild. This paper presents a comprehensive empirical study of 8,461 unique WebAssembly binaries gathered from a wide range of sources, including source code repositories, package managers, and live websites. We study the security properties, source languages, and use cases of the binaries and how they influence the security of the WebAssembly ecosystem. Our findings update some previously held assumptions about real-world WebAssembly and highlight problems that call for future research. For example, we show that vulnerabilities that propagate from insecure source languages potentially affect a wide range of binaries (e.g., two thirds of the binaries are compiled from memory unsafe languages, such as C and C++) and that 21% of all binaries import potentially dangerous APIs from their host environment. We also show that cryptomining, which once accounted for the majority of all WebAssembly code, has been marginalized (less than 1% of all binaries found on the web) and gives way to a diverse set of use cases. Finally, 29% of all binaries on the web are minified, calling for techniques to decompile and reverse engineer WebAssembly. Overall, our results show that WebAssembly has left its infancy and is growing up into a language that powers a diverse ecosystem, with new challenges and opportunities for security researchers and practitioners. Besides these insights, we also share the dataset underlying our study, which is 58 times larger than the largest previously reported benchmark.
Tac-Miner: Visual Tactic Mining for Multiple Table Tennis Matches

In table tennis, tactics specified by three consecutive strokes represent the high-level competition strategies in matches. Effective detection and analysis of tactics can reveal the playing styles of players, as well as their strengths and weaknesses. However, tactical analysis in table tennis is challenging as the analysts can often be overwhelmed by the large quantity and high dimension of the data. Statistical charts have been extensively used by researchers to explore and visualize table tennis data. However, these charts cannot support efficient comparative and correlation analysis of complicated tactic attributes. Besides, existing studies are limited to the analysis of one match. However, one player's strategy can change along with his/her opponents in different matches. Therefore, the data of multiple matches can support a more comprehensive tactical analysis. To address these issues, we introduced a visual analytics system called Tac-Miner to allow analysts to effectively analyze, explore, and compare tactics of multiple matches based on the advanced embedding and dimension reduction algorithms along with an interactive glyph. We evaluate our glyph's usability through a user study and demonstrate the system's usefulness through a case study with insights approved by coaches and domain experts.
VAE-based Deep SVDD for anomaly detection

Anomaly detection is an essential task for different fields in the real world. The imbalanced data and lack of labels make the task challenging. Deep learning models based on autoencoder (AE) have been applied to address the above difficulties successfully. However, in these AE-based deep methods, the AE-based model’s optimization and the anomaly detector design are separated. Therefore, the latent representations in AE are less relevant for the anomaly detection task, which reduces the accuracy of anomaly detection. A deep support vector data description based on variational autoencoder (Deep SVDD-VAE) is proposed in this paper to solve this problem. In the proposed model, VAE is used to reconstruct the input instances, while a spherical discriminative boundary is learned with the latent representations simultaneously based on SVDD. Unlike existing AE-based methods, we seek the model parameters via the joint optimization of VAE and SVDD, which ensures the separability of the latent representations. Experimental results on MNIST, CIFAR-10, and GTSRB datasets show the effectiveness of Deep SVDD-VAE.
Baseline maps of potentially toxic elements in the soils of Garhwal Himalayas, India: Assessment of their eco‐environmental and human health risks

Abstract Geochemical mapping is frequently used to identify the regions of the Planet with critical chemical elements and their natural or anthropogenic sources. In order to understand elements' geochemistry and to identify the area's potentially harmful associations, high‐resolution maps of chemical elements, physico‐chemical parameters, a noncarcinogenic hazard index, and a potential ecological risk index were modelled with soil samples collected in the Himalaya region. It is the first environmental and geochemical atlas for this region. It was found that natural processes are determining the distribution of the variable in this area. The granulometric features seem to be strongly influenced by land use, with coarser grain sizes in forest soils, contrasting with the finer grain sizes in agricultural soils. The map distribution of the noncarcinogenic hazard index showed that there is an expected hazard for some variables for both children and adults. Potential ecological risk is revealed to be low to moderate. Remediation of contaminated soils is necessary to reduce the associated risks, make the land resource available for agricultural production, enhance food security, and scale down land tenure problems arising from changes in the land use pattern. This study is subsequent implementation of natural‐based approaches to system stability in largely unspoilt area.
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning

Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models. Gradient quantization is an effective way of reducing the number of bits required to communicate each model update, albeit at the cost of having a higher error floor due to the higher variance of the stochastic gradients. In this work, we propose an adaptive quantization strategy called AdaQuantFL that aims to achieve communication efficiency as well as a low error floor by changing the number of quantization levels during the course of training. Experiments on training deep neural networks show that our method can converge in much fewer communicated bits as compared to fixed quantization level setups, with little or no impact on training and test accuracy.