Reviewerly/exHarmony huggingface dataset:
Exploiting textual queries for dynamically visual disambiguation

Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits the performance of current webly supervised models is the problem of visual polysemy. In this work, we present a novel framework that resolves visual polysemy by dynamically matching candidate text queries with retrieved images. Specifically, our proposed framework includes three major steps: we first discover and then dynamically select the text queries according to the keyword-based image search results, we employ the proposed saliency-guided deep multi-instance learning (MIL) network to remove outliers and learn classification models for visual disambiguation. Compared to existing methods, our proposed approach can figure out the right visual senses, adapt to dynamic changes in the search results, remove outliers, and jointly learn the classification models. Extensive experiments and ablation studies on CMU-Poly-30 and MIT-ISD datasets demonstrate the effectiveness of our proposed approach.
Geochemical metallogenic potential based on cluster analysis: A new method to extract valuable information for mineral exploration from geochemical data

Geochemical data are widely regarded as valuable for mineral exploration because they can be used to recognize ore-related geochemical anomalies. Many techniques have been used to identify geochemical anomalies within geochemical datasets, but most of these techniques focus on the concentration or fractal characteristics of one or a combination of indicator element(s). Indicator elements for a particular type of deposit are selected based on expert knowledge, but this approach is subjective and valuable information associated with the elements that are not selected is lost. This study describes a new method that maximize the extraction of valuable information from all elements available in a geochemical dataset. The technique involves geochemical metallogenic potential mapping based on cluster analysis (GMPM-CA), which enables the identification of prospective areas suitable for further mineral exploration. The results show that GMPM-CA can produce a map of mineralization potential with excellent predictive capacity. Furthermore, GMPM-CA is a practical method that does not require expert geological or geochemical knowledge, and can be integrated with existing geochemical anomaly identification methods.
Analog Joint Source-Channel Coding for Gaussian Sources over AWGN Channels with Deep Learning

We consider the design of neural network based joint source channel coding (JSCC) schemes for transmitting an independent and identically distributed (i.i. d.) Gaussian source over additive white Gaussian noise (AWGN) channels with bandwidth mismatch when the source dimension is small. Unlike existing deep learning based works on this topic, we do not resort to domain expertise to constrain the model; rather, we propose to employ fine tuning techniques to optimize the model. We show that our proposed techniques can provide performance that is comparable to that of the state-of-the-art when the source dimension is small. Furthermore, the proposed model can spontaneously learn encoding functions that are similar to those designed by conventional schemes. Finally, we empirically show that the learned JSCC scheme is robust to mismatch between the assumed and actual channel signal to noise ratios.
Infrared and Raman Spectroscopy of Ammoniovoltaite, (NH4)2Fe2+5Fe3+3Al(SO4)12(H2O)18

Ammoniovoltaite, (NH4)2Fe2+5Fe3+3Al(SO4)12(H2O)18, is a complex hydrated sulphate of the voltaite group that has been recently discovered on the surface of the Severo-Kambalny geothermal field (Kamchatka, Russia). Vibrational spectroscopy has been applied for characterization of the mineral. Both infrared and Raman spectra of ammoniovoltaite are characterized by an abundance of bands, which corresponds to the diversity of structural fragments and variations of their local symmetry. The infrared spectrum of ammoniovoltaite is similar to that of other voltaite-related compounds. The specific feature related to the dominance of the NH4 group is its ν4 mode observed at 1432 cm−1 with a shoulder at 1510 cm−1 appearing due to NH4 disorder. The Raman spectrum of ammoniovoltaite is basically different from that of voltaite by the appearance of an intensive band centered at 3194 cm−1 and attributed to the ν3 mode of NH4. The latter can serve as a distinctive feature of ammonium in voltaite-group minerals in resemblance to recently reported results for another NH4-mineral—tschermigite, where ν3 of NH4 occurs at 3163 cm−1. The values calculated from wavenumbers of infrared bands at 3585 cm−1, 3467 cm−1 and 3400 cm−1 for hydrogen bond distances: d(O···H) and d(O···O) correspond to bonding involving H1 and H2 atoms of Fe2+X6 (X = O, OH) octahedra. The infrared bands observed at 3242 cm−1 and 2483 cm−1 are due to stronger hydrogen bonding, that may refer to non-localized H atoms of Al(H2O)6 or NH4.
Distance Computation Based on Coupled Spin-Torque Oscillators: Application to Image Processing

Recent research on nano-oscillators has shown the possibility of using a coupled-oscillator network as a core-computing primitive for non-Boolean computation. The spin-torque oscillator (STO) is an attractive candidate because it is CMOS compatible, highly integrable, scalable, and frequency and phase tunable. Based on these promising features, we propose an alternative coupled-oscillator-based architecture for hybrid spintronic and CMOS hardware that computes a multidimensional norm. The hybrid system, composed of an array of four injection-locked STOs and a CMOS detector, is experimentally demonstrated. The measured performance is then used as the input to simulations that demonstrate the hybrid system as both a distance metric and a convolution computational primitive for image-processing applications. Energy and scaling analysis shows that the STO-based coupled-oscillatory system has a higher efficiency than the CMOS-based system with an order of magnitude faster computation speed in distance computation for high-dimensional input vectors.
Multi-objective evolutionary clustering with complex networks

Evolutionary clustering (EC) refers to the applications of evolutionary optimization algorithms such as genetic algorithm to data clustering. Although multi-objective evolutionary clustering algorithms were proposed to simultaneously consider different cluster properties such as compactness and separation, these techniques usually suffer from a reasonable initial population and a pre-defined number of clusters. Besides, the effectiveness of evolutionary operators is decreased in dealing with the clustering problem. On the other side, complex networks play an essential role in different fields of machine learning. In a complex network, points are considered as nodes, and the dataset is shown as a connected weighted graph. Also, complex networks tend to present a modular structure. This paper applies two concepts of complex networks including node centrality and community modularity to introduce a novel multi-objective evolutionary clustering. The proposed centrality modularity-based multi-objective evolutionary clustering (CMMOEC) takes the advantage of nodes similarity to find the best initial population of clustering solutions and provide new structural-based modularity to determine the optimal number of clusters automatically. Moreover, the proposed modularity is used to design a new recombination and mutation operator so that it generates offspring solutions that satisfy more diversity. Experiments carried out on several artificial and real-world datasets with different structures. The performance of the proposed algorithm is evaluated by the Adjusted Rand Index (ARI). Simulation results indicate that the proposed algorithm satisfies better performance in comparison to traditional methods.
Structural and hydrothermal alteration mapping related to orogenic gold mineralization in part of Kushaka schist belt, North-central Nigeria, using airborne magnetic and gamma-ray spectrometry data

This paper presents an interpretation of airborne magnetic and gamma-ray spectrometry data over a part of the Kushaka schist belt of Nigeria with a view to assess the sub-surface structural framework and hydrothermal alteration haloes that are related to orogenic gold mineralization in the study area. The geophysical analyses performed to improve the quality of the aeromagnetic data for better understanding of the subsurface geology were reduction to the equator, upward continuation, total horizontal derivative, analytical signal, source edge detection, tilt-angle derivative of total horizontal derivative. The analyses performed on the airborne gamma-ray spectrometry data were made on K/eTh, F parameter and deviation of ideal K values (Kd). Deep-seated, brittle–ductile shear/fracture zones with characteristic depth of 428.0–607.6 m and area extent of 15.5 km by 6.5 km that were delineated in the area coincide with beehive of artisanal miners exploiting gold deposits. In this study, subsurface geologic structures, specifically dykes/faults, that trend in the ENE–WSW direction constitute a first-order control on orogenic gold mineralization in the study area. The hydrothermally altered zones mapped in the area indicated they are structurally controlled and closely associated with orogenic gold mineralization. Apart from geologic structural evidences that are controlling orogenic gold mineralization, this study has indicated that hydrothermal alteration mapping is equally important for a promising orogenic gold mineralization mapping.
Multi-Layer Extreme Learning Machine-Based Keystroke Dynamics Identification for Intelligent Keyboard

Information security plays critical roles in modern society. However, traditional security measures like passwords, tokens and personal ID numbers only provide limited protection. Inspired by the fast training speed of extreme learning machine (ELM) and the promising feature extraction capability of extreme learning machine auto-encoder (ELM-AE), this paper proposes a keystroke dynamics identification method for intelligent keyboard (IKB) based on multi-layer extreme learning machine (ML-ELM). The IKB, as first demonstrated by Wang's group, is a self-powered, non-mechanical perforated keyboard that converts mechanical stimuli applied to the keyboard into electrical signals without the needing an external power source. ML-ELM is a multilayer neural network stacking on top of ELM-AE. Our major contribution is to develop an accurate and efficient keystroke dynamics identification method based on ML-ELM. One significant advantage of the proposed method is that it does not rely on manual feature extraction and selection. In other words, the raw current signals obtained by IKB are directly the input to the network. The network structure is determined by grid search, which minimizes the human involvement. The other one is that the whole training process of the model does not require fine tuning, which makes the training process significantly faster than that of existing deep networks. Experimental results demonstrate that the proposed method has excellent timeliness whereas obtaining high identification accuracy. The proposed method has great potential in applications to computer or network access control, online payment, and cyber security.
Architectural Implication of Graph Neural Networks

Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many graph-related tasks. However, GNN is not as well understood in the system and architecture community as its counterparts such as multi-layer perceptrons and convolutional neural networks. This letter tries to introduce the GNN to our community. In contrast to prior work that only presents characterizations of GCNs, our work covers a large portion of the varieties for GNN workloads based on a general GNN description framework. By constructing the models on top of two widely-used libraries, we characterize the GNN computation at inference stage concerning general-purpose and application-specific architectures and hope our work can foster more system and architecture research for GNNs.
Embedded Real-Time Pedestrian Detection System Using YOLO Optimized by LNN

The main challenge for real-time pedestrian detection in an embedded system is to keep a balance between accuracy and computation cost. To solve this, the main idea is to use Lightweight Neural Network (LNN), a special design Neural Networks which can provide acceptable accuracy with faster speed. In this paper, we modify the YOLO written in Darknet with MobileNet, one of the popular LNN in the embedded system. And we realize deploying real-time pedestrian detection using the modified YOLO on the platform Jetson TX2.
Heat-Map Based Occupancy Estimation Using Adaptive Boosting

There is a growing demand for efficient and privacy-preserving intelligent solutions in a multi-occupancy environment. This paper proposes a non-contact scheme for occupancy estimation using an infrared thermal sensor array, which has the advantages of low-cost, low-power, and high-performance capabilities. The proposed scheme offers an accurate human heat segmentation technique that extracts human body temperature from a noisy environment. It is shown that the proposed system can detect the empty occupancy state after utilising the segmentation technique with an accuracy of 100%. By using adaptive boosting, it is shown that the system is capable of measuring the non-empty occupancy with an overall accuracy of 98.2%.
Multi-Size Convolution and Learning Deep Network for SAR Ship Detection From Scratch

Synthetic aperture radar (SAR) ship detection is a popular branch of SAR interpretation. A growing number of scholars are devoting themselves to applying convolutional neural network (CNN) to SAR ship detection. Currently, most CNN-based SAR ship detectors are variants of object detectors in optical images; however, the essential differences between SAR and optical images restrict their performance. To this end, by focusing on the attribute of SAR image's “point” property which is determined by its imaging mechanism, we design a novel SAR ship detector from scratch. The innovatively designed PCB-MSK (parallel convolutional block of multi-size kernels) consists of two groups of convolutions, each group is composed of four convolutional layers corresponding to kernel sizes of 3, 5, 7, and 9; the stride is 1 for one group and 2 for another. In the designed convolutional module with features reused (CMFR), the output and input feature maps of the previous block are concatenated for current layer to reduce information loss during forward propagation and to strengthen the supervision for shallow layers during parameter optimization. For each source prediction layer, the binary classification is first conducted to alleviate the positive/negative imbalance; deconvolution and feature fusion are utilized to enhance the feature representation. Then, we perform fine detection. Experiments on RDISD-SAR and SSDD, in which RDISD-SAR is meticulously constructed by us based on two open-access datasets, show that our method achieves a state-of-the-art accuracy and competitive speed, the average precision (AP) reaches 88.70% and 90.57% for RDISD-SAR and SSDD, respectively. These APs are 10.43% and 4.23% higher than DSOD, and 6.64% and 1.70% higher than ScratchDet. The detection speed is 58.2 FPS on a GTX 1080Ti GPU, the number of parameters is 18.19M and the amount of computations is 21.33G. In addition, experimental results show that the robustness of our detector is very excellent.
Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field

SUMMARY This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.
Hybrid Deep Learning Type-2 Fuzzy Logic Systems For Explainable AI

The recent years have witnessed a rapid rise in the use of Artificial Intelligence (AI) systems, in particular Machine Learning (ML) models. The vast majority of AI systems employ black box models that lack transparency in operation and decision making. This lack of transparency curtails the use of these AI systems in regulated applications (such as medical, financial applications, etc.) where it is important to understand the reasoning behind the predictions of the AI system. In these situations, interpretable models need to be used. However, interpretable models can turn into black-box models for high dimensional inputs. There are a variety of approaches that have been proposed to solve this problem. In this paper, we present a novel hybrid deep learning type-2 fuzzy logic system for explainable AI which addresses these challenges to provide a highly interpretable model that has reasonable performance when compared to the other black box models.
CRecSys: A Context-Based Recommender System Using Collaborative Filtering and LOD

Linked Open Data (LOD) is an emerging Web technology to store and publish structured data in the form of interlinked knowledgebases like DBpedia, Freebase, Wikidata, and Yago. It uses structured data from multiple domains, and it can be used to conceptualize a concept of interest. Recently, researchers have shown that incorporating contextual features in recommender systems improves rating prediction accuracy. However, identification of contextual features for building context-aware recommender systems is a major bottleneck. To this end, in this article, we present the development of a context-based recommender system, CRecSys, for item ratings prediction in movie domain. CRecSys extracts item-based contextual features from the underlying dataset and generates an RDF graph to model items and their contextual features for computing context-based items similarity using graph matching techniques and item-based collaborative filtering. It uses LOD and two well-known movie data sources - Rotten Tomatoes and IMDB for item profiling using a dataset of 1300 movies. CRecSys is experimentally evaluated over two movie datasets, one is generated by the authors and second is the MovieLens-1M benchmark dataset. CRecSys is also compared with ten baselines and two state-of-the-art recommendation methods, and performs significantly better. It is also empirically established that CRecSys is able to effectively deal with some of the open challenges like cold-start and limited content problems of the traditional recommender systems.
Approximate selection with guarantees using proxies

Due to the falling costs of data acquisition and storage, researchers and industry analysts often want to find all instances of rare events in large datasets. For instance, scientists can cheaply capture thousands of hours of video, but are limited by the need to manually inspect long videos to identify relevant objects and events. To reduce this cost, recent work proposes to use cheap proxy models, such as image classifiers, to identify an approximate set of data points satisfying a data selection filter. Unfortunately, this recent work does not provide the statistical accuracy guarantees necessary in scientific and production settings. In this work, we introduce novel algorithms for approximate selection queries with statistical accuracy guarantees. Namely, given a limited number of exact identifications from an oracle, often a human or an expensive machine learning model, our algorithms meet a minimum precision or recall target with high probability. In contrast, existing approaches can catastrophically fail in satisfying these recall and precision targets. We show that our algorithms can improve query result quality by up to 30 x for both the precision and recall targets in both real and synthetic datasets.
Learning efficient multi-task stereo matching network with richer feature information

Accurate depth estimation is a research hotspot in the field of stereo vision. The accuracy of stereo matching algorithm directly determines the quality of depth map. Recent researches have transformed stereo matching methods into a supervised learning task. However, the previous methods may have mismatches in the regions of non-textures, boundaries and tiny details. In this paper, we propose a multi-task attention stereo network (MASNet) to integrate the feature information from a stereo image pairs for disparity estimation. Firstly, a segmentation attention head module (SAH) is proposed, which adds semantic segmentation clues for disparity estimation, uses global receptive field to guide network feature extraction learning refined features, and alleviates the negative impact of depth addition of the network. Secondly, we construct a multiple cost volume (MCV) to make full use of the aggregation ability of 3D convolution and provide a better similarity measures for disparity estimation. Thirdly, we embed Top-k pooling layer into the 3D CNN module to obtain the reduced aggregation feature. The feature is fed into the proposed shallow merging network and fused with the intermediate feature to obtain richer low-level features and make up for the comprehensiveness of network neck feature. The results of experiment on Scene Flow, KITTI 2012, and KITTI 2015 datasets show that our proposed network has a significant superiority over state-of-art stereo matching methods.
Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.