Reviewerly/exHarmony huggingface dataset:
Explainable and Interpretable Anomaly Detection Models for Production Data

Summary Trusting a machine-learning model is a critical factor that will speed the spread of the fourth industrial revolution. Trust can be achieved by understanding how a model is making decisions. For white-box models, it is easy to “see” the model and examine its prediction. For black-box models, the explanation of the decision process is not straightforward. In this work, we compare the performance of several white- and black-box models on two production data sets in an anomaly detection task. The presence of anomalies in production data can significantly influence business decisions and misrepresent the results of the analysis, if not identified. Therefore, identifying anomalies is a crucial and necessary step to maintain safety and ensure that the wells perform at full capacity. To achieve this, we compare the performance of K-nearest neighbor (KNN), logistic regression (Logit), support vector machines (SVMs), decision tree (DT), random forest (RF), and rule fit classifier (RFC). F1 and complexity are the two main metrics used to compare the prediction performance and interpretability of these models. In one data set, RFC outperformed the remaining models in both F1 and complexity, where F1 = 0.92, and complexity = 0.5. In the second data set, RF outperformed the rest in prediction performance with F1 = 0.84, yet it had the lowest complexity metric (0.04). We further analyzed the best performing models by explaining their predictions using local interpretable model-agnostic explanations, which provide justification for decisions made for each instance. Additionally, we evaluated the global rules learned from white-box models. Local and global analysis enable decision makers to understand how and why models are making certain decisions, which in turn allows trusting the models.
Genesis and mineralization style of gold occurrences of the Lower Lom Belt, Bétaré Oya district, eastern Cameroon

The Bétaré Oya district has a substrate composed of the Neoproterozoic metavolcanic-metasedimentary rocks of the Lom Belt in eastern Cameroon. The district is well-known for alluvial gold mining activities, however, the primary gold mineralization has received little attention. In the current study, we newly report geological, ore mineralogy, fluid inclusion microthermometric, laser Raman spectroscopy, and stable isotope data from auriferous veins of the Lom Belt to characterize the style of gold mineralization and to constrain the origin of the ore-forming fluids. The auriferous quartz veins are laminated, fractured, N- to NE-trending, and spatially associated with the Bétaré Oya Shear Zone. Fieldwork coupled with microscopic examination and the textural relationships of ore minerals revealed two stages of mineralization. The first stage is characterized by the presence of pyrite, sphalerite, galena, chalcopyrite, pyrrhotite, hematite, petzite, hessite, wolframite, electrum, and gold, while the second stage is characterized by the presence of a later deposition of galena and pyrite, as well as minor greenockite. The gangue minerals are quartz, sericite, muscovite, chlorite, calcite, ankerite, and barite, whereas the supergene assemblage (stage 3) includes goethite, hematite, covellite, and enargite. Two fluid inclusion assemblages containing three types of inclusions are identified in mineralized quartz veins. Assemblage 1 consists of gold-related types 1 and 2 with daughter minerals consisting of nahcolite, magnesite, and arsenolamprite (black native arsenic), whereas assemblage 2 consists of post-gold type 3 fluid inclusions. Type 1 has H2O-NaCl-CO2 ± N2 ± CH4 primary fluid inclusions (FI), with a TmCO2 ranging from −59.8 to −56.6 °C, salinities from 0.5 to 10.8 wt% NaCl eq., densities from 0.87 to 1.00 g.cm−3, and total homogenization temperatures between 280 and 360 °C. Type 2 contains CO2 (±H2O-NaCl) ± N2 ± CH4 FI that exhibit TmCO2 ranging between −60.0 and −56.7 °C, ThCO2 from 13 to 25 °C, and densities between 0.73 and 0.85 g.cm−3. Type 3 shows H2O-NaCl FI with salinities between 0.2 and 10.1 wt% NaCl eq., densities between 0.82 and 0.98 g.cm−3, and total homogenization temperatures from 160 to 235 °C. Measured δ18O for gold-bearing quartz (+11.5 to +16.0‰), δD from FI (−50.6 to −21.8‰), δ13C from FI (−5.8 to −5.5‰), and δ34S from galena and pyrite grains (+5.3‰ and +8.2‰, respectively) suggest a metamorphic source as most likely for the ore-forming fluids and sulfur, although a mantle CO2 contribution cannot be ruled out. The gold deposition probably took place by fluid-rock interaction and fluid unmixing at ∼310 °C and at a depth of about 6–9 km. The ore-forming fluid was a low salinity (∼6.2 wt% NaCl eq.) H2O-NaCl-CO2 ± N2 ± CH4 solution. These data revealed that the Lom Belt gold mineralization is best classified as a mesozonal orogenic gold deposit.
Hierarchical clustering and compositional data analysis for interpreting groundwater hydrogeochemistry: The application to Campi Flegrei volcanic aquifer (south Italy)

Comprehensive hydrogeochemical studies have been conducted in the Campi Flegrei volcanic aquifer since late 20th century due to the volcanic unrest. In the last decade, groundwater samples were grouped based on the dominant anion species (i.e. bicarbonate, sulfate and chloride) to explain the general hydrogeochemical processes. In this article, 44 groundwater samples are collected from Campi Flegrei aquifer to geochemically and spatially capture the main characteristics of the groundwater body. The hierarchical clustering algorithm is then performed on proportion of bicarbonate, sulfate and chloride, and the optimum number of clusters are determined regarding the results of deep hydrogeochemical investigations published in the past. The collected samples are categorized in the following groups: (1) bicarbonate-rich groundwater; (2) chlorine-rich groundwater; (3) sulfate-rich groundwater; and (4) mixed groundwater. The first group (As = 158.2 ± 169 μg/l, electric conductivity = 1,732.1 ± 1,086 μS/cm and temperature = 25.6 ± 8 °C) is mainly derived from poor arsenic meteoric water, but there is significant thermal/seawater contribution in the second one (As = 1,457.8 ± 2,210 μg/l, electric conductivity = 20,118.3 ± 11,139 μS/cm and temperature = 37.1 ± 20 °C). Interaction of the bicarbonate-rich groundwater and hydrothermal vapors gives rise to the sulfate-rich groundwater (As = 847.2 ± 679 μg/l, electric conductivity = 3,940.0 ± 540 μS/cm and temperature = 82.8 ± 3 °C) around Solfatara volcano. The mixed groundwater (As = 451.4 ± 388 μg/l, electric conductivity = 4,482.9 ± 4,027 μS/cm and temperature = 37.1 ± 16 °C) is observed where the three main groundwater groups undergo a mixing process, depending on the hydrogeology of the volcanic aquifer. Contrary to the bicarbonate- and sulfate-rich groundwater, the chlorine-rich and mixed groundwater generally occurs at low piezometric levels (approximately <1 m above sea level) near the coastline. The hierarchical cluster analysis provides more information about the volcanic aquifer, particularly when compositional data analysis is applied to study hydrogeochemistry of the homogeneous groundwater groups and to uncover the relationships between variables. Addressing compositional nature of data is recommended in the future studies for developing new tools that help deeper understanding of groundwater evolution in volcanic aquifers and identifying promising precursors of volcanic eruption.
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding

The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output is under input perturbations. While most previous works focused on the local robustness property around an input sample, the studies of the global robustness property, which bounds the maximum output change under perturbations over the entire input space, are still lacking. In this work, we formulate the global robustness certification for neural networks with ReLU activation functions as a mixed-integer linear programming (MILP) problem, and present an efficient approach to address it. Our approach includes a novel interleaving twin-network encoding scheme, where two copies of the neural network are encoded side-by-side with extra interleaving dependencies added between them, and an over-approximation algorithm leveraging relaxation and refinement techniques to reduce complexity. Experiments demonstrate the timing efficiency of our work when compared with previous global robustness certification methods and the tightness of our over-approximation. A case study of closed-loop control safety verification is conducted, and demonstrates the importance and practicality of our approach for certifying the global robustness of neural networks in safety-critical systems.
In-Car Violence Detection Based on the Audio Signal

When it is intended to detect violence in the car, audio, speech processing, music, and ambient sound are some of the main points of this problem since it is necessary to find the similarities and differences between these domains. The recent increase in interest in deep learning has allowed practical applications in many areas of signal processing, often surpassing traditional signal processing on a large scale. This paper presents a comparative study of state-of-the-art deep learning architectures applied for inside car violence detection based only on the audio signal. The methodology proposed for audio signal representation was Mel-spectrogram, after an in-depth review of the literature. We build an In-Car video dataset in the experiments and apply four different deep learning architectures to solve the classification problem. The results have shown that the ResNet-18 model presents the best accuracy results on the test set.
A Variational Approach to Additive Image Decomposition into Structure, Harmonic, and Oscillatory Components

We propose a non-convex variational decomposition model which separates a given image into piecewise-constant, smooth and oscillatory components.This decomposition is motivated not only by image denoising and structure separation, but also by shadow and spot light removal.The proposed model clearly separates the piecewise constant structure and smoothly varying harmonic part, thanks to having a separated oscillatory component.The piecewise-constant part is captured by TV-like non-convex regularization, harmonic term via second-order regularization, and oscillatory (noise and texture) term via a H -1 -norm penalty.There are interesting interactions between these three regularization terms.We explore effects of each regularization and the choice of parameters carefully.We propose an efficient alternating direction method of multipliers based minimization for fast numerical computation of the optimization problem.Various experiments are presented to show the robustness against high level of noise, applications to soft spotlight and shadow removal, and the comparisons with other methods.
Lightweight Texture Correlation Network for Pose Guided Person Image Generation

Pose Guided Person Image Generation (PGPIG) is a popular task in deepfake, which aims at generating a person image with the given pose based on the source image. However, existing methods cannot comprehensively model the correlation between the source and the target domain. Most of them only focus on the correlation of the keypoints but ignore detail textures. In this paper, we propose a novel Texture Correlation Network (TCN) to simultaneously build pose and texture correlations. Specifically, our TCN adopts a two-stage design, including two networks: Pose Guided Person Alignment Network (PGPAN) and Texture Correlation Attention Network (TCAN). The PGPAN generates a coarse person image aligned with the target pose, while the TCAN produces a target generated image with the guidance of multiple correlations. The key component of TCAN is our new module, Texture Correlation Attention Module (TCAM), which explicitly builds geometry and texture correlation between the source image and the coarse target image. Those kinds of correlations facilitate to transfer real textures from the source to the target. Extensive experiments on the DeepFashion and Market1501 benchmarks demonstrate the superior performance of the proposed method. In addition, our model only uses 8.5 million parameters, which is significantly smaller than other methods.
Federated Learning and Proactive Computation Reuse at the Edge of Smart Homes

Edge-based technologies have emerged as a key enabler to empower low-latency services and incorporate machine learning techniques for learning/inference. However, transferring user data to the edge server to conduct learning could violate data privacy and overburden the network. In addition, the server could receive multiple redundant tasks for inference which leads to redundant computations. In this article, we study both communication and computation issues in edge networks by emphasizing data privacy in a smart home scenario. We design an architecture that incorporates federated edge learning to promote data privacy and a node weighting and dropping scheme to select the appropriate participating devices with quality data and therefore improve the training and reduce communication cost. We further apply Long Short-Term Memory to predict future tasks and proactively store them locally at the edge device. We adopt the computation reuse concept to satisfy incoming tasks with less-to-no computation and thus eliminating redundant computation and further decreasing the computation cost. Simulation results based on real-world dataset show the effectiveness and efficiency of the proposed architecture. The training phase is reached with few iterations, while computation and communication are reduced by up to 80% and 70%, respectively, compared with existing schemes while data privacy is promoted.
dcSR

With the next generation immersive video applications, network capacity is becoming a growing bottleneck to deliver a high quality video to end-users. Recent advances to tackle this challenge introduced super-resolution (SR) for video quality enhancement through neural computations by leveraging client-side compute capacity. However, the existing SR models are bulky, compute-, and memory-expensive, which makes it difficult to deploy them in practice. In this work, we present dcSR, a lightweight data-centric SR approach that enables a practical neural quality enhancement for videos. On the server-side, dcSR constructs micro SR models trained on a few selected frames from each video through a data-centric paradigm by employing a long term video scene understanding mechanism. On the client-side, dcSR integrates the micro SR models into the regular video decoder and enhances the video quality in real-time without compromising on quality enhancement. We evaluate dcSR and show its benefits by comparing it with previous methods.
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization

Domain adaptation is crucial to adapt a learned model to new scenarios, such as domain shifts or changing data distributions. Current approaches usually require a large amount of labeled or unlabeled data from the shifted domain. This can be a hurdle in fields which require continuous dynamic adaptation or suffer from scarcity of data, e.g. autonomous driving in challenging weather conditions. To address this problem of continuous adaptation to distribution shifts, we propose Dynamic Unsupervised Adaptation (DUA). By continuously adapting the statistics of the batch normalization layers we modify the feature representations of the model. We show that by sequentially adapting a model with only a fraction of unlabeled data, a strong performance gain can be achieved. With even less than 1% of unlabeled data from the target domain, DUA already achieves competitive results to strong baselines. In addition, the computational overhead is minimal in contrast to previous approaches. Our approach is simple, yet effective and can be applied to any architecture which uses batch normalization as one of its components. We show the utility of DUA by evaluating it on a variety of domain adaptation datasets and tasks including object recognition, digit recognition and object detection.
Influence of visual background complexity and task difficulty on action video game players’ performance

The purpose of this study was to investigate the influence of visual background complexity and task difficulty on players' performance in a typical video game task, as a function of their expertise. Challenge in a video game is usually defined by the difficulty level of the main task. The use of low-level visual features of game interfaces to modulate the challenge has only been evaluated by a few studies. Two experiments were designed in which action video game players (AVGPs) and non-players of action video games (NVGPs) were engaged in a target-shooting task. The difficulty level of task as well as the complexity of visual background were manipulated. The enhanced attentional abilities of AVGPs allowed them to perform the task better than NVGPs. Both task difficulty and background complexity impaired task performance. However, this impairment was not modulated by player expertise. Recommendations are proposed to game designers to design the challenge around the low-level visual features of game interfaces.
ServiceBERT: A Pre-trained Model for Web Service Tagging and Recommendation

Pre-trained models have shown their significant values on a number of natural language processing (NLP) tasks. However, there is still a lack of corresponding work in the field of service computing to effectively utilize the rich knowledge accumulated in the Web service ecosystem. In this paper, we propose ServiceBERT, which learns domain knowledge of Web service ecosystem aiming to support service intelligence tasks, such as Web API tagging and Mashup-oriented API recommendation. The ServiceBERT is developed with the Transformer-based neural architecture. In addition to using the objective of masked language modeling (MLM), we also introduce the replaced token detection (RTD) objective for efficiently learning pre-trained model. Finally, we also implement the contrastive learning to learn noise-invariant representations at the sentence level in pre-training stage. Comprehensive experiments on two service-related tasks successfully demonstrate the better performance of ServiceBERT through the comparison with a variety of representative methods.
An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.
A study on path planning optimization of mobile robots based on hybrid algorithm

Abstract The purpose of this study is to develop a novel hybrid meta‐heuristic algorithm for optimal path planning of the mobile robot. A novel hybrid algorithm based on particle swarm optimization (PSO), firefly algorithm (FA), and cuckoo search (CS) is proposed in order to improve the efficiency of algorithms and minimize the cost performance criterion in path planning. First, A MATLAB based interface was designed to easily perform all activities such as generating maps by processing the images taken from the camera, finding paths with algorithms, communicating with robots, and navigating according to path information that the robot determines with algorithms. Second, for path planning in mobile robots, the developed CS‐PSO‐FA hybrid algorithm and CS, FA, PSO algorithms were carried out both simulation and experimentally without hitting obstacles in similar working environments. In addition, various applications are performed in the various simulation environments to verify the developed CS‐PSO‐FA hybrid algorithm and the obtained results are compared. In conclusion, it is demonstrated that the path obtained with the novel hybrid CS‐PSO‐FA algorithm is shorter than CS, PSO, and FA algorithms and thus has a higher performance and the feasibility.
Quantum Spatial Search in Two-Dimensional Waveguide Arrays

Continuous-time quantum walks (CTQW) have shown the capability to perform efficiently the spatial search of a marked site on many kinds of graphs. However, most of such graphs are hard to realize in an experimental setting. Here we study CTQW spatial search on a planar triangular lattice by means of both numerical simulations and experiments. The experiments are performed using three-dimensional waveguide arrays fabricated by femtosecond laser pulses, illuminated by coherent light. We show that the retrieval of the marked site by the quantum walker is accomplished with higher probability than the classical counterpart, in a convenient time window placed early in the evolution.
PRPN: Progressive region prediction network for natural scene text detection

With the development of deep learning, scene text detection methods have made great progress in recent years. Most text detection methods are based on bounding box prediction with a 0-1 discrete distribution; thus, separating adjacent text instances is difficult. Direct prediction of the bounding box also renders difficult the detection various shapes of text, such as quadrangular text and curved text. In this work, we design a 2D progressive kernel for describing the progressive variety of text regions. It transforms the original ground truth (GT) of bounding boxes into the GT of a 0-1 progressive probability distribution. We also propose a novel progressive region prediction network (PRPN) with directional pooling for predicting the probability distributions of text regions. Then, a postprocessing algorithm is used to transform the probability distributions of the text regions into bounding box output for text detection. Experiments on standard datasets, including ICDAR 2013, ICDAR 2015, MSRA-TD500, and SCUT-CTW1500, demonstrate that the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness. The method obtains an F-measure of 86.0% on ICDAR 2015 and 81.4% on SCUT-CTW1500. The code is available at https://github.com/xinyu-ch/ProgressiveTextDetection.