Reviewerly/exHarmony huggingface dataset:
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders

Abstract Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which identifies the most relevant and informative attributes of a dataset, has been introduced as a solution to this problem. Most of the existing feature selection methods are computationally inefficient; inefficient algorithms lead to high energy consumption, which is not desirable for devices with limited computational and energy resources. In this paper, a novel and flexible method for unsupervised feature selection is proposed. This method, named QuickSelection (The code is available at: https://github.com/zahraatashgahi/QuickSelection), introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance. This criterion, blended with sparsely connected denoising autoencoders trained with the sparse evolutionary training procedure, derives the importance of all input features simultaneously. We implement QuickSelection in a purely sparse manner as opposed to the typical approach of using a binary mask over connections to simulate sparsity. It results in a considerable speed increase and memory reduction. When tested on several benchmark datasets, including five low-dimensional and three high-dimensional datasets, the proposed method is able to achieve the best trade-off of classification and clustering accuracy, running time, and maximum memory usage, among widely used approaches for feature selection. Besides, our proposed method requires the least amount of energy among the state-of-the-art autoencoder-based feature selection methods.
REFORM

Knowledge graphs (KGs) are of great importance in various artificial intelligence systems, such as question answering, relation extraction, and recommendation. Nevertheless, most real-world KGs are highly incomplete, with many missing relations between entities. To discover new triples (i.e., head entity, relation, tail entity), many KG completion algorithms have been proposed in recent years. However, a vast majority of existing studies often require a large number of training triples for each relation, which contradicts the fact that the frequency distribution of relations in KGs often follows a long tail distribution, meaning a majority of relations have only very few triples. Meanwhile, since most existing large-scale KGs are constructed automatically by extracting information from crowd-sourcing data using heuristic algorithms, plenty of errors could be inevitably incorporated due to the lack of human verification, which greatly reduces the performance for KG completion. To tackle the aforementioned issues, in this paper, we study a novel problem of error-aware few-shot KG completion and present a principled KG completion framework REFORM. Specifically, we formulate the problem under the few-shot learning framework, and our goal is to accumulate meta-knowledge across different meta-tasks and generalize the accumulated knowledge to the meta-test task for error-aware few-shot KG completion. To address the associated challenges resulting from insufficient training samples and inevitable errors, we propose three essential modules neighbor encoder, cross-relation aggregation, and error mitigation in each meta-task. Extensive experiments on three widely used KG datasets demonstrate the superiority of the proposed framework REFORM over competitive baseline methods.
Cases for Explainable Software Systems: Characteristics and Examples

The need for systems to explain behavior to users has become more evident with the rise of complex technology like machine learning or self-adaptation. In general, the need for an explanation arises when the behavior of a system does not match the user’s expectations. However, there may be several reasons for a mismatch including errors, goal conflicts, or multi-agent interference. Given the various situations, we need precise and agreed descriptions of explanation needs as well as benchmarks to align research on explainable systems. In this paper, we present a taxonomy that structures needs for an explanation according to different reasons. We focus on explanations to improve the user interaction with the system. For each leaf node in the taxonomy, we provide a scenario that describes a concrete situation in which a software system should provide an explanation. These scenarios, called explanation cases, illustrate the different demands for explanations. Our taxonomy can guide the requirements elicitation for explanation capabilities of interactive intelligent systems and our explanation cases build the basis for a common benchmark. We are convinced that both, the taxonomy and the explanation cases, help the community to align future research on explainable systems.
The Entanglement-Assisted Communication Capacity Over Quantum Trajectories

The unique and often-weird properties of quantum mechanics allow an information carrier to propagate through multiple trajectories of quantum channels simultaneously.This ultimately leads us to quantum trajectories with an indefinite causal order of quantum channels.It has been shown that indefinite causal order enables the violation of bottleneck capacity, which bounds the amount of the transferable classical and quantum information through a classical trajectory with a well-defined causal order of quantum channels.In this treatise, we investigate this beneficial property in the realm of both entanglementassisted classical and quantum communications.To this aim, we derive closed-form capacity expressions of entanglement-assisted classical and quantum communication for arbitrary quantum Pauli channels over classical and quantum trajectories.We show that by exploiting the indefinite causal order of quantum channels, we obtain capacity gains over classical trajectory as well as the violation of bottleneck capacity for various practical scenarios.Furthermore, we determine the operating region where entanglement-assisted communication over quantum trajectory obtains capacity gain against classical trajectory and where the entanglement-assisted communication over quantum trajectory violates the bottleneck capacity.
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement

Image-to-image translation is an ill-posed problem as unique one-to-one mapping may not exist between the source and target images. Learning-based methods proposed in this context often evaluate the performance on test data that is similar to the training data, which may be impractical. This demands robust methods that can quantify uncertainty in the prediction for making informed decisions, especially for critical areas such as medical imaging. Recent works that employ conditional generative adversarial networks (GANs) have shown improved performance in learning photo-realistic image-to-image mappings between the source and the target images. However, these methods do not focus on (i) robustness of the models to out-of-distribution (OOD)-noisy data and (ii) uncertainty quantification. This paper proposes a GAN-based framework that (i) models an adaptive loss function for robustness to OOD-noisy data that automatically tunes the spatially varying norm for penalizing the residuals and (ii) estimates the per-voxel uncertainty in the predictions. We demonstrate our method on two key applications in medical imaging: (i) undersampled magnetic resonance imaging (MRI) reconstruction (ii) MRI modality propagation. Our experiments with two different real-world datasets show that the proposed method (i) is robust to OOD-noisy test data and provides improved accuracy and (ii) quantifies voxel-level uncertainty in the predictions.
Verifying Low-Dimensional Input Neural Networks via Input Quantization

Deep neural networks are an attractive tool for compressing the control policy lookup tables in systems such as the Airborne Collision Avoidance System (ACAS). It is vital to ensure the safety of such neural controllers via verification techniques. The problem of analyzing ACAS Xu networks has motivated many successful neural network verifiers. These verifiers typically analyze the internal computation of neural networks to decide whether a property regarding the input/output holds. The intrinsic complexity of neural network computation renders such verifiers slow to run and vulnerable to floating-point error.
Study of Blockchain Forensics and Analytics tools

Cryptocurrencies have elicited tremendous interest in the recent past due to their ability to enable financial transactions without the need for a central authority. The most appealing aspect of cryptocurrencies that garnered significant attention is its potential to enable (pseudo) anonymous transactions on the blockchain ledger. Unsurprisingly, this has led to rapid adoption of cryptocurrencies for unlawful activities by malicious actors in several ways. Hence, to detect these unlawful activities, it is essential to analyze the blockchain ledgers to derive insights by investigating anonymous transactions and activities, which is where blockchain forensics comes in. In this paper, we present a taxonomy mapping the identified high-level forensics features with the supporting forensics tools that we surveyed. We provide a comparison of the surveyed tools using three practical parameters (number of cryptocurrencies supported, number of features provided and ease of accessing services) and give an overview of their theoretical effectiveness in general with some open challenges identified.
SV-sim

High-performance quantum circuit simulation in a classic HPC is still imperative in the NISQ era. Observing that the major obstacle of scalable state-vector quantum simulation arises from the massively fine-grained irregular data-exchange with remote nodes, in this paper we present SV-Sim to apply the PGAS-based communication models (i.e., direct peer access for intra-node CPUs/GPUs and SHMEM for inter-node CPU/GPU clusters) for efficient generalpurpose quantum circuit simulation. Through an orchestrated design based on device functional pointer, SV-Sim is able to abstract various quantum gates across multiple heterogeneous backends, including IBM/Intel/AMD CPUs, NVIDIA/AMD GPUs, and Intel Xeon Phi, in a unified framework, but still asserting outstanding performance and tractable interface to higher-level quantum programming environments, such as IBM Qiskit, Microsoft Q# and Google Cirq. Circumventing the obstacle from the lack of polymorphism in GPUs and leveraging the device-initiated one-sided communication, SV-Sim can process circuit that are dynamically generated in Python using a single GPU/CPU kernel without the need of expensive JIT or runtime parsing, significantly simplifying the programming complexity and improving performance for QC simulation. This is especially appealing for the variational quantum algorithms given the circuits are synthesized online per iteration. Evaluations on the latest NVIDIA DGX-A100, V100-DGX-2, ALCF Theta, OLCF Spock, and OLCF Summit HPCs show that SV-Sim can deliver scalable performance on various state-of-the-art HPC platforms, offering a useful tool for quantum algorithm validation and verification. SV-Sim has been released at http://github.com/pnnl/sv-sim. A version specially tweaked for Q#/QDK is also provided.
Machine Learning for Automated Industrial IoT Attack Detection: An Efficiency-Complexity Trade-off

Critical city infrastructures that depend on smart Industrial Internet of Things (IoT) devices have been increasingly becoming a target of cyberterrorist or hacker attacks. Although this has led to multiple studies in the recent past, there exists a paucity of literature concerning real-time Industrial IoT attack detection. The goal of this article is to build a machine-learning approach using Industrial IoT sensor readings for accurately tracking down Industrial IoT attacks in real time. We analyze IoT system behavior under a lab-controlled series of attacks on a Secure Water Treatment (SWaT) system. The system is analytically challenging in that it results in sensor readings that resemble waveforms. To that end, we develop a novel early detection method using functional shape analysis (FSA) to extract features from the data that can capture the profile of the waveform. Our results show an efficiency-complexity trade-off between functional and non-functional methods in predicting IoT attacks.
Energy-efficient collaborative optimization for VM scheduling in cloud computing

Energy-efficient resource scheduling has become a hot issue in the field of cloud computing. However, there is an inevitable conflict between energy-saving and QoS optimization. In real-world scenarios, the volatility of cloud task arrival will cause the optimization problem to become more difficult. To achieve a better trade-off between these two goals, a novel resource scheduling framework based on collaborative optimization is proposed for the cloud computing environment. Based on the Lyapunov optimization method, the optimization problem can be solved explicitly in each time slice. We build a multi virtual machine queuing model and analyze the relationships between the task queues’ backlog and the system energy consumption. We also introduce a method of using stacked denoising auto-encoder for extracting the QoS features to improve the constraints of the collaborative optimization objective function. Finally, we propose an efficient resource scheduling strategy to give full play to the processing capabilities of the virtual machine. Experimental results show that, compared with other advanced energy-saving strategies, our scheduling strategy can effectively reduce the energy consumption of the cloud data center while guaranteeing QoS, and reduce the total scheduling time cost of data center by more than 20%.
Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)

Abstract Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables—derived from the different bands of the Sentinel 2 images—and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (&gt; 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.
Real-time Sleeping Posture Recognition For Smart Hospital Beds

Unsuitable sleeping positions are the important contributors that result in bad sleep quality and even serious long-term consequences. Many studies emphasize that pressure sensor-based solutions are effective on the in-bed postures assessment in both home and hospital environments. Surprisingly, none of the studies considers Edge computing-based solution for body pose recognition on smart hospital beds. In this paper, we propose the development of a real-time sleeping posture recognition algorithm which is a combination of a preprocessing technique and an EfficientNet B0 based classifier with an AM-Softmax loss function. Experimental results confirm that our proposed method can gain the accuracy of over 99 % in 5-fold as well as 10-fold cross-validation and 95.32% in the Leave-One-Subject-Out (LOSO) validation for 17 sleeping postures, which greatly surpasses the previous method in the same task. Furthermore, our solution can satisfy the real-time requirement for various data sampling rates when deploying on the Edge computing-based smart hospital bed.
BiAttnNet: Bilateral Attention for Improving Real-Time Semantic Segmentation

Semantic segmentation requires both speed and accuracy. This paper presents a two-branch network BiAttnNet with a unique Bilateral Attention structure that separates all attention modules into the Detail Branch to contribute semantic detail selections for specialized detail exploring. Specifically, the Detail Branch comprises AttnTrans entirely, which provides a better alternate for regular convolution. AttnTrans is a computationally efficient filtration entirely composed of concurrent spatial and channel attention. Meanwhile, a Context Branch is implemented with FCN-ResNet for rough segmentation. By combining two branches’ outputs, BiAttnNet achieves a good balance between speed and accuracy. Evaluations on the Cityscapes testing set conclude that BiAttnNet achieves 74.7% mIoU at 89.2 FPS at a quarter (512 × 1024) resolution with only 2.2 million parameters, running on a single GTX 2080 Ti card.
Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure

While there is increasing interest in crypto assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we considered a unique dataset of 144 exchanges, active from the first quarter of 2018 to the first quarter of 2021. We analyzed the determinants surrounding the decision to close an exchange using credit scoring and machine learning techniques. Cybersecurity grades, having a public developer team, the age of the exchange, and the number of available traded cryptocurrencies are the main significant covariates across different model specifications. Both in-sample and out-of-sample analyzes confirm these findings. These results are robust in regard to the inclusion of additional variables, considering the country of registration of these exchanges and whether they are centralized or decentralized.
3D face reconstruction and dense alignment with a new generated dataset

Face alignment and reconstruction are classical problems in the computer vision field, one of the greatest difficulties of which is the limited number of facial images with landmark points. The 300 W-LP dataset is the most commonly used for the existing methods of single-view 3D Morphable Model (3DMM)-based reconstruction; however, the model performance is limited by the small variety of facial images in this dataset. In this work, a 3D facial image dataset with landmark points generated by the rotate-and-render method is proposed. The key innovation of the proposed method is that the back-and-forth rotation of faces in 3D space and then re-rendering them to the 2D plane can provide strong self-supervision. The recent advances in 3D face modeling and high-resolution generative adversarial networks (GANs) are leveraged to constitute the blocks. To obtain more precise facial landmark points, the 3D dense face alignment (3DDFA) model is used to label the generated images and filter the landmark points. Finally, the 3DDFA model is retrained using the proposed dataset, and an improved result is achieved.
LT-OCF

Collaborative filtering (CF) is a long-standing problem of recommender systems. Many novel methods have been proposed, ranging from classical matrix factorization to recent graph convolutional network-based approaches. After recent fierce debates, researchers started to focus on linear graph convolutional networks (GCNs) with a layer combination, which show state-of-the-art accuracy in many datasets. In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF). The main novelty in our method is that after redesigning linear GCNs on top of the NODE regime, i) we learn the optimal architecture rather than relying on manually designed ones, ii) we learn smooth ODE solutions that are considered suitable for CF, and iii) we test with various ODE solvers that internally build a diverse set of neural network connections. We also present a novel training method specialized to our method. In our experiments with three benchmark datasets, our method consistently outperforms existing methods in terms of various evaluation metrics. One more important discovery is that our best accuracy was achieved by dense connections.
Trajectory Risk Modelling and Planning for Unmanned Cargo Aircraft

A key challenge for low-altitude unmanned air transportation is to minimize operational risks by all means. Besides many other measures to be considered, the aircraft’s trajectory must be planned carefully and optimized as there are inevitable remaining risks which should be minimized when flying over sparsely populated areas. The risk may be mitigated by a safe termination of the flight if circumstances permit. Also, the probability of violating any operational constraints that would lead to a flight termination should be reduced as much as possible. Adequate risk models and efficient algorithmic risk assessment techniques are required to perform such optimizations. Furthermore, the aircraft may have to react to certain events such as high-priority traffic by changing its trajectory online during flight. As command and control (C2) links may have limited reliability, it must be possible to perform trajectory re-planning onboard with limited computational resources. This poses high demands on the runtime efficiency of the planning algorithms. In this work, we present conceptual approaches to risk modeling and assessment based on geospatial datasets and aircraft dynamic models. We further present the design and experimental results of a software framework for onboard and online trajectory planning. Our results demonstrate that risk-based motion planning for unmanned aircraft can be performed with limited onboard computational resources allowing for safe autonomous flight.