Mining Architecture Research Articles

Litho-structural interpretation of aeromagnetic anomalies reveals potential for mineral exploration in Tizi n'Test Region, Western High Atlas, Morocco

This study interprets aeromagnetic data from the Tizi n'Test area in the High Atlas massif of Morocco, aiming to gain insights into its litho-structural architecture and implications for mineral exploration and mining. We employed six different analytical techniques to the residual magnetic field data, including reduction to the pole (RTP), upward continuation, total horizontal derivative, Tilt angle, Centre for Exploration Targeting (CET) analysis, and Euler deconvolution. Our analyses differentiated the study area into three magnetic domains: the eastern Ouzellarh block, characterized by positive anomalies, a central domain characterized by a negative magnetic signature demarcating the transitional zone between the Anti-Atlas and the High Atlas separated by the Ouchden fault: and the western domain, represented by the Tichka massif. The application of total horizontal derivative, tilt angle, and a combination of filters in ternary image formats (Tilt angle, upward continuation 1000 + Tilt angle and upward continuation 3000 + Tilt angle) revealed both known and previously unidentified geological lineaments, mapping structural complexity across various orientations (NE–SW, NNE–SSE, E–W, NW–SE, and N–S). The CET grid analysis method unveiled the structural complexity, highlighting the geodynamic evolution of the region. Particularly, the Ouchden fault delineates a magnetic domain divide between the ancient High Atlas and the Ouzellarh block (Anti-Atlas). Furthermore, Euler deconvolution indicated magnetic source depths ranging from 52 m in the western domain of the Tichka massif to 6560 m in the Ouzellarh block. A comprehensive structural scheme, classified by C-A fractal analysis, identified zones favourable for exploration and mining, particularly along the Ouchden fault, Tizi n'Test, NE-SW trending lineaments in the northwestern domain, as well as along the Tichka granite’s margin.

Dual-extraction modeling: A multi-modal deep-learning architecture for phenotypic prediction and functional gene mining of complex traits

Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits, the absence of a universal multi-modal computational tool with robust interpretability for accurate phenotype prediction and identification of trait-associated genes remains a challenge. This study introduces the dual-extraction modeling (DEM) approach, a multi-modal deep-learning architecture designed to extract representative features from heterogeneous omics datasets, enabling the prediction of complex trait phenotypes. Through comprehensive benchmarking experiments, we demonstrate the efficacy of DEM in classification and regression prediction of complex traits. DEM consistently exhibits superior accuracy, robustness, generalizability, and flexibility. Notably, we establish its effectiveness in predicting pleiotropic genes that influence both flowering time and rosette leaf number, underscoring its commendable interpretability. In addition, we have developed user-friendly software to facilitate seamless utilization of DEM’s functions. In summary, this study presents a state-of-the-art approach with the ability to effectively predict qualitative and quantitative traits and identify functional genes, confirming its potential as a valuable tool for exploring the genetic basis of complex traits.

Co-saliency detection with two-stage co-attention mining and individual calibration

Learning-based methods have become popular in co-salient object detection (CoSOD). However, existing methods suffer from two challenging issues, including mining the inter-image co-attention and calibrating the intra-image salient objects. Moreover, the training data is insufficient. To address these challenges, we propose an end-to-end network using Two-stage Co-attention mining and Individual Calibration (TCIC) to predict the co-salient objects. Firstly, a two-stage co-attention mining architecture (TCM), including a classified co-attention module (CCM) and a focal co-attention module (FCM), is designed to model inter-image relationships. In the first stage, a CCM is applied to capture the classification interactions of multiple images, tentatively extracting the co-attention. In the second stage, we propose an FCM to adaptively suppress and aggregate multiple salient features, aiming to recalibrate the co-attention in the first stage. Secondly, considering the shape features and location information offered by the boundary features, an edge guidance module (EGM) is embedded into the individual calibration architecture (ICA) to calibrate individuals. Besides, we also adopt a co-attention transfer strategy (CTS) to keep the consistency of the co-attention during feature transfer in the decoder. Finally, TCM and ICA are integrated into a unified end-to-end framework to predict fine-grained boundary-preserving results. Besides, an image fusion algorithm (IFA) is tailored without extra pixel-level annotations for automatic generation of the composite images, aiming to supplement the training dataset. Experimental results on three prevailing testing datasets show the superiority of the proposed method in terms of various evaluation metrics.

Developing scalable management information system with big financial data using data mart and mining architecture

The traditional Management Information System (MIS) with Big Financial Data (BFD) for corporate financial diagnosis has many limitations such as the data is not summarized thus these causing increases in query times, and also the complexity in analysis. The creation of a Data Mart (DM) leads to a great summarization of data, such that contains only essential business information. And by using data mining techniques we can be extracting unknown useful information from DM and apply it to make important decisions for the business. Thus, in this paper we are adopting an architecture of six layers; interface layer, analysis layer, extract transformation load layer, data mart layer, data mining layer, and evaluating layer, MIS with BFD using DM and Mining (MIS-BFD-DMM) is proposed, which is not only permits the use of DM and mining technologies in decision support, but also the full utilization of non-financial/financial info held by businesses. This paper offers the benefits of building and integrating DM with mining. Also determines the distinction between DM and a relational database for decision-makers to get information. The test and analysis are achieved in the terms of useful metrics (accuracy, balance accuracy, F-measure, precision, recall, and time). As a result, Data returned from arranged star schema is far faster than ERD. In conclusion, the SVM is best than other algorithms in terms of the parameters of the confusion matrix.

A new approach of integrating industry prior knowledge for HAZOP interaction

Accidents often occur in the petrochemical industry, which have a negative impact on society and the environment. Learning Process Safety Knowledge (PSK) from accident cases is essential to prevent accidents and improve safety level. Hazard and Operability Analysis (HAZOP) is a popular hazard risk analysis method. Its report contains large-scale PSK, which can provide safety analysis and decision support for the industry. Subject to the characteristics of PSK, existing researches mine them in the form of sequence labeling. However, there are two intractable problems that cause the PSK mined by the model to be inaccurate. (1) PSK in HAZOP is domain specific, which is rare or even absent in general-domain texts. (2) The entity boundaries are ambiguous. Most domain-specific entities for HAZOP lack boundary characters. Inaccurate security knowledge is not acceptable from the perspective of process safety engineering. To solve the problems, we present a PSK mining architecture with External Lexicon Prior knowledge called EDPMA, EDPMA is prior knowledge-based multi-task HAZOP knowledge mining model. Specifically, EDPMA consists of prior knowledge constructor and sequence labeling model. The prior knowledge constructor expresses prior knowledge in the form of word embedding by three steps. For the sequence annotation model, we improve its embedding and decoding layers. The former incorporated the word vectors generated by the prior knowledge constructor, and the latter added the task of entity boundary prediction. We conduct multiple evaluation experiments on HAZOP datasets. The experimental results show that the accuracy, recall and F1-score of the EDPMA model are 92.92%, 91.85% and 92.38% respectively, which is better than the existing research. Our study represents a meaningful attempt to introduce prior knowledge in HAZOP knowledge mining and makes an important contribution to intelligence the field of process safety.

Data Mining for Autism Spectrum Disorder detection among Adults

Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with demographic data. Second, to provide a quick, acceptable and easy way to support the ASD diagnosis. this can be performed by conducting a comparison study to determine the efficacy of four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, and instance-based technique based on k-neighbors (IBK). These classifiers have been performed with Waikato Environment for Knowledge Analysis (WEKA) tools to distinguish autistic adults from healthy, normal subjects. The results showed that, with 99.71%, SMO classification accuracy was 99.71, which exceeded the accuracy of other classifiers. The proposed architecture allows for early detection of ASD, distinguishing between ASD and healthy control subjects. This study could help doctors and clinicians by giving them a better idea of what the future holds for people with autism spectrum disorder (ASD) and by improving therapy programs, allowing people with ASD to live a long and happy life.

Industrial Museums as Documentations and Memories of Labor: The Case of Zollern Colliery in Dortmund, Germany

The Zollern colliery in Dortmund, Germany, constructed in the late 19th- early 20th centuries, is a symbol of the German Empire’s successful steel-mining industry, and a monument to its industrial architecture. In the late 1960s, some artists began a campaign against plans to demolish it, which lead to the conception and development of Industriekultur as the German counterpart to the concept of “industrial heritage.” Based on field research and related literature, this study examines it through four categories: mining architecture, social movement, museum, and exhibition concepts. De-industrialization requires a restructuring of the economy, urban spaces, and lifestyles. A social movement against such changes in urban spaces, caused by de-industrialization, began in the Ruhr area. It was aided by social and political upheavals in the 1960s and 1970s, resulting in the successful institutionalization of Industriekutlur. Th is led to the transformation of the Zollern colliery into a museum depicting the social and cultural history of miners. Despite criticisms for its de-politicization of industrial heritage, the Zollern remains a core institution (re)presenting labor in the historical context.

Suggestion mining from online reviews using temporal convolutional network

Business and brand owners are using social media networks to provide and deliver various services to their clients and collect information about their products from customers. Customers give their opinions as well as ideas for the improvement of the products on the review platforms and portals. Suggestion Mining is a technique of automatic extraction of these innovative ideas or suggestions from online source data. In this paper, we proposed TCN architecture for suggestion mining from online reviews. The TCN uses causal and dilated convolutional layers to process sequential or temporal data and captures long-term dependencies. TCN architecture on the dataset of SemEval-2019 subtask A is experimented. The dataset is highly imbalanced and to overcome this problem, the ensemble oversampling technique to balance the dataset is applied. TCN is also experimented with the attention mechanism. Our proposed model outperforms the existing works by achieving an F1 score of 82.0 %.

A Text Classification Algorithm for Power Equipment Defects Based on Random Forest

A short text mining architecture with a unique design is suggested to uncover the worth of short texts in the power text and management of power equipment. A Text Classification Algorithm for Power Equipment Defects (TCA-PED) is proposed in this paper. The brief text mining method is initially outlined, with each module’s operation explained in sequence. An adaptation of the short text mining architecture to practical implementation is then presented, based on the particular features of short texts found in electrical equipment power text and management. The samples of faulty texts are submitted to show the deployment of short text mining in designing and management, based on the architecture with the specifically built modules. This framework is well suited to electrical equipment power text and management activities, as demonstrated by the dataset. The particular design of each component also contributes to the enhancement of the system. Finally, the results show the effectiveness of the proposed model.

Performance Evaluation of SHA-256 and BLAKE2b in Proof of Work Architecture

The popularity of blockchain, which is a technology with a distributed architecture, is increasing day by day due to the advantages it provides. Along with blockchain, interest in high-performance and efficient hash algorithm applications is increasing rapidly. While considering the amount of power and environmental problems required for these applications, more efficient algorithms were needed. In this study, a comparison of SHA-256 and BLAKE2b, one of the most popular algorithms, is presented. In the experiments, a standard Intel i5 based computing system is used. The benchmarking approach focuses on computationally heavy processes such as Proof of Work and Merkle Tree. This article presents a comparison of these two algorithms in a Bitcoin-like mining architecture.

Student Behavior Data Analysis Based on Association Rule Mining

With the advancement of intelligent campus data acquisition technology, student behavioral data are growing in size, variety, and real-time throughput, posing challenges to the storage capacity and computing power of traditional behavioral data analysis methods. The study focuses on the application of association rule mining in student behavioral data analysis. Data collection, storage, computation, and analysis all comprise integral parts of a four-layer data association mining architecture, and the three-step mining process from “data preprocessing” to “finding association rules” to “acquiring relevant knowledge” is described. The existing mining algorithm is updated to address the issues of overscanning of the original dataset and excess iterations. The findings from the case study reveal that the number of iterations in the modified mining algorithm is greatly lessened, effectively improving the mining efficiency of the massive student behavioral dataset.

Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques

We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services representation. In the third layer, four distance measures, namely, Cosine, Euclidean, Minkowski, and Word Mover, are considered to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between web services documents. Finally, in the fifth layer, three clustering algorithms, namely, affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated for clustering of web services based on observed similarities in documents. We demonstrate how each component of the five layers is employed in web services clustering using randomly selected web services documents. We conduct experimental analysis to cluster web services using a collected dataset consisting of web services documents and evaluate their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better than those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different word representations models and syntactic and semantic similarity measures, we found that the affinity propagation clustering technique performed better in discovering similarities among Web services.

Secure verifiable aggregation for blockchain-based federated averaging

IoT devices’ storage and computation capacities are constantly increasing in recent years, which brings critical challenges in data privacy protection. Federated learning (FL) and blockchain technology are two popular techniques used in IoT data aggregation, where FL enables data training with privacy protection, and blockchain provides a decentralized architecture for data storage and mining. However, very few the state-of-the-art works consider the applicability of the combination of FL and blockchain. In this paper, we adopt the federated averaging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance. We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy. Finally, we propose and implement a non-interactive Public Verifiable Secret Sharing (PVSS) algorithm with Distributed Hash Table (DHT) that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users. At last, we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

What the Ground Says…

Ground, as a body incised by natural and human actions (European Landscape Convention), carries “stories”, going beyond quantitative values. As in a text, it holds the keys to understand what it covers or hides. In its thickness, it shelters “implicit projects”. Understanding its complexity requires a physical and perceptual commitment, challenging the body in space: dimensions gradually forgotten by Environmental Sciences. As a “threshold” between visible and invisible, Underground-Built-Heritage represents the reverse of the emerged world: hollow space, both generator and mirror of open space (cities, landscapes). The focus is on physical and mental relationships between these two worlds. Past and present relationships emerge, allowing hypotheses to reconstitute collective memories, practices, knowledge, and values, which serve territorial development. The “Three Countries Park” is a place for cross-border experimentation to test how UBH can rebuild common links for fragmented environments. The cavities of a geo-park (planned) and the tangles of underground mining architecture are the fragments of a vocabulary whose meaning communities have to relearn. Built undergrounds will, thus, emerge from common stories that revive the imagination of populations who have lost all notion of belonging to a place. UBH will become a vector of new territorial coherence linking the physical and mental perceptions of people.

Exploring PIM Architecture for High-Performance Graph Pattern Mining

Graph mining applications, such as subgraph pattern matching and mining, are widely used in real-world domains such as bioinformatics, social network analysis, and computer vision. Such applications are considered as a new class of data-intensive applications that generate massive irregular computation workloads and memory accesses, which are different from many well-studied graph applications such as BFS and page rank. In this letter, we use the emerging process-in-memory architecture to accelerate data-intensive operations in graph mining tasks. We first identify the code blocks that are best suitable for PIM execution. Then, we observe a significant load imbalance on PIM architecture and analyze the root cause for such imbalance in graph mining applications. Lastly, we evaluate several scheduling schemes that help reduce the load imbalance and discuss potential optimizations to enhance performance further.

An Optimized Parallel Hybrid Architecture for Cryptocurrency Mining

One of the requirements for mining cryptocurrency (Crypto) is that the secured ledger of the blockchain must be updated. However, updating the secured ledger requires that the miner develop and solve complex mathematical equations in higher orders hexadecimal 64-digit solution called a hash. In addition to this challenge, the mining processes of cryptocurrency are both resource and cost-intensive. The resources required include, but not limited to mining software, hardware, power (energy usage), CPU or compute cycles, and NP-hard problem. Apart from these numerous challenges that are associated with mining cryptocurrency, the amount of speed that is required to mine a single block is core. Cryptocurrency mining speed requirement is significantly important because only miners that can have the fastest mining device are most likely to get the reward (profit) from competing for a block. In this paper, we designed and implemented a model to speed up mining process which is capable of giving miners an advantage to arrive at a block earlier. The novelty of our architecture is that our design is based on high performance computing paradigm where we achieve processor speed up by parallelizing the number of processors p. We experimented by varying p = 4, 8, 16. Our experimental results where we used the MC6800 simulated on Easy68k emulator demonstrate feasibility of our proposed model and prove that speed was an essential key to cryptocurrency mining. Keywords: Cryptocurrency, Blockchain, Architecture, Mining, Speedup, Bitcoin. CISDI Journal Reference Format Allenotor, D. & Oyemade, D. A. (2021): An Optimized Parallel Hybrid Architecture for Cryptocurrency Mining. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 12 No 1, Pp 95-104 DOI - https://doi.org/ 10.22624/AIMS/CISDI/V12N1P10. Available online at www.isteams.net/cisdijournal

Lunar thermal mining: Phase change interface movement, production decline and implications for systems engineering

Thermal mining is a promising architecture, which may provide reliable and ‘dirt-simple’ means of production of space-sourced water, oxygen and rocket propellant in the future. It is especially tailored to water ice deposits that exist within lunar Permanently Shadowed Regions, where our quest for riches of the Outer Space might begin.Here, the thermal mining extraction system is simulated and analysed with combined heat and mass transfer FEM modelling. The results exhibited that water extraction on the Moon might follow specific production phases, which closely relate to changes in the sublimation interface movement over large timeframes. The production behaviour on the Moon might have many characteristics of relevant production systems on Earth. This may open door for many well-established terrestrial models and production projections to be refitted to extraterrestrial case.It was found that the required water yields of the thermal mining architecture, which make its case economically and commercially viable, are hard to reach without production optimization and new systems development. The production is projected to be significantly hindered by sublimation lag build-up, which would create thermal insulation for the icy deposits. Sublimation lag removal and other strategies might be the answer to that problem.

The Business Case for Lunar Ice Mining

The key to human expansion into space and space development, in general, is developing space activities that deliver value in an economic sense. In other words, the key to space development is making money in space, that is, profit. However, the search for money making space activities has proven elusive. We present a business case for a commercial company to mine lunar ice and process the ice into rocket propellant. We discuss the existing and future markets for propellant and an architecture for mining and processing propellant and the associated costs. We then examine 3 scenarios, 1 commercial stand-alone and 2 involving a public/private partnership (PPP) model with NASA. We provide a comparison with other similar analyses. Business returns are positive for all 3 scenarios, although the PPP models provide increased returns and share risk with the government. Once established, lunar-sourced propellant will dramatically reduce the cost of all beyond low Earth orbit space activities and potentially enable other profitable commercial ventures to emerge.

Rings for Privacy: An Architecture for Large Scale Privacy-Preserving Data Mining

This article proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing intermediate results. A large-scale scenario is examined and the possibility that data become inaccessible during the aggregation process is considered, a possibility that traditional schemes often neglect. Here, it is explicitly examined, as it might be provoked by intermittent network connectivity or sudden user departures. For increasing system reliability, data sources are organized in multiple sets, called rings, which independently work on the aggregation process. Two different protocol schemes are proposed and their failure probability, i.e., the probability that the data mining output cannot guarantee the desired level of accuracy, is analytically modeled. The privacy degree, the communication cost and the computational complexity that the schemes exhibit are also characterized. Finally, the new protocols are applied to some specific use cases, demonstrating their feasibility and attractiveness.

Distributed Deep Learning Optimized System over the Cloud and Smart Phone Devices

Deep learning has been becoming a promising focus in data mining research. With deep learning techniques, researchers can discover deep properties and features of events from quantitative mobile sensor data. However, many data sources are geographically separated and have strict privacy, security, and regulatory constraints. Upon releasing the privacy-sensitive data, these data sources generally no longer physically possess their data and cannot interfere with the way their personal data being used. Therefore, it is necessary to explore distributed data mining architecture which is able to conduct consensus learning based on needs. Accordingly, we propose a distributed deep learning optimized system which contains a cloud server and multiple smartphone devices with computation capabilities and each device is served as a personal mobile data hub for enabling mobile computing while preserving data privacy. The proposed system keeps the private data locally in smartphones, shares trained parameters, and builds a global consensus model. The feasibility and usability of the proposed system are evaluated by three experiments and related discussion. The experimental results show that the proposed distributed deep learning system can reconstruct the behavior of centralized training. We also measure the cumulative network traffic in different scenarios and show that the partial parameter sharing strategy does not only preserve the performance of the trained model but also can reduce network traffic. User data privacy is protected on two levels. First, local private training data do not need to be shared with other people and the user has full control of their personal training data all the time. Second, only a small fraction of trained gradients of the local model are selected for sharing, which further reduces the risk of information leaking.