Reviewerly/exHarmony huggingface dataset:
A copper porphyry promising zones mapping based on the exploratory data, multivariate geochemical analysis and GIS integration

The aim of this paper is to generate the mineral prospectivity mapping (MPM) for a porphyry copper mineralization area. First, three information layers of geochemistry, geophysics, and geology were defined as the main criteria with a total of 13 sub-criteria, eight for geochemical criterion, two for geophysical criterion, and three for geology criteria. As the Cu element is the main variable in copper porphyry mineralization. The geochemical criteria included the concentration values of 36 elements (Cu, Au, Mo, Pb, Zn, Ag, Ni, Co, Mn, Fe, As, Th, Sr, Cd, Sb, Bi, V, Ca, P, La, Cr, Mg, Ba, Ti, B, Al, Na, K, W, Hg, Tl, S, Sc, Se, Ga, and Te) from 87 rock samples that were analyzed by the induced coupled plasma (ICP) method. Before assigning the class scores for the quantitative and qualitative classes, a multivariate geochemical analysis (MGA) was done to reduce the number of the measured elements to a smaller number most related to the Cu mineralization. It was achieved through the probability plot modeling, calculating the threshold values of the Cu variable along with background and anomaly separation. The final step was discriminant analysis (DA), PCA, and agglomerative hierarchical clustering (AHC) analysis that reduced the number of elements to 8 elements as the geochemical sub-criteria. Then, the qualitative information was quantitated for the two criteria of geophysics and geology which were in the form of geophysical maps and geological information respectively. Then, the Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) as one of the newest multiple attribute decision-making (MADM) methods was performed to obtain the net outranking flow for each criterion and for the integration of all criteria. Finally, the net outranking flow as the input data was used to generate the MPMs in Arc GIS 10.5 software which shows the promising areas for each criterion and for the integration of all criteria. The generated MPMs were then compared and the map obtained by integration of all criteria identified as a better result with the sharper boundary of the promising areas that can be used by decision-makers for future exploration programs. • 1-prospectivity map generation of the promising zones for a copper porphyry mineralization. • 2-Using geological, geophysical and geochemical information. • 3-A multivariate geochemical analysis using PCA, Clustering and Discriminant analysis. • 4-Implementation of the PROMETHEE as one of the MADM method and modeling in GIS.
On Parallel Mechanism of Consortium Blockchain: Take PoV as an example

Blockchain is an emerging distributed storage technology in recent years. It can hold all historical data, transaction records, and other related information from the past by using a self-referencing blockchain data storage structure. Consensus is the core technology of blockchain, which ensures consistency of data of different nodes and also enables the blockchain to have Byzantine fault tolerance. However, most consensus algorithms are serialized. With the increase in the number of nodes, throughput of system decreases rapidly. Proof of Vote (PoV) is an efficient voting-based consensus algorithm. It separates the voting and bookkeeping rights and has a great traffic complexity of . Based on PoV, we propose a more efficient and practical consensus algorithm called Parallel Proof of Vote (PPoV), which allows multiple nodes to generate blocks in parallel within a consensus cycle. Analysis and experiments show that the performance of PPoV is 2-5 times higher than typical BFT consensus as the number of nodes in range of 4-100.
Scoring of Resume and Job Description Using Word2vec and Matching Them Using Gale–Shapley Algorithm

Pudasaini, Shushanta Shakya, Subarna Lamichhane, Sagar Adhikari, Sajjan Tamang, Aakash Adhikari, SujanThe paper introduces a quick-witted system that assists employers to find the right candidate for a job and vice-versa. Multiple approaches have to be taken into account for parsing, analyzing, and scoring documents (CV, vacancy details). However, In this paper, we have devised an approach for ranking such documents using word2vec algorithm and matching them to their appropriate pair using Gale–Shapley algorithm. When ranking a CV, different cases are taken into consideration: skills, experience, education, and location. The ranks are then used to find an appropriate match of employers and employees with the use of Gale–Shapley algorithm which eases companies for higher best possible candidates. The methods experimented for the scoring, and matching is explained below on the paper.
Decentralised Internet Infrastructure: Securing Inter-Domain Routing (DEMO)

The Border Gateway Protocol (BGP) is the interdomain routing protocol that glues the Internet.BGP does not incorporate security and instead, it relies on careful configuration and manual filtering to offer some protection.As a consequence, the current inter-domain routing infrastructure is partially vulnerable to prefix and path hijacks as well as in misconfigurations that results in route leaks.There are many instances of these vulnerabilities being exploited by malicious actors on the Internet, resulting in disruption of services.To address this issue the IETF has designed RPKI, a centralised trust architecture that relies on Public Key Infrastructure.RPKI has slow adoption and its centralised nature is problematic: network administrators are required to trust CAs and do not have the ultimate control of their own critical Internet resources (e.g,.IP blocks, AS Numbers).In this context, we have built the Decentralised Internet Infrastructure (DII), a distributed ledger to securely store inter-domain routing information.The main advantages of DII are (i) it offers flexible trust models where the Internet community can define the rules of a consensus algorithm that properly reflects the power balance of its members and, (ii) offers protection against vulnerabilities (path hijack and route leaks) that goes well beyond what RPKI offers.We have deployed the prototype on the wild in a worldwide testbed including 7 ASes, we will use the testbed to demonstrate in a realistic scenario how allocation and delegation of Internet resources in DII work, and how this protects ASes against artificially produced path and prefix hijack as well as a route leak.
Enhancing Low-Light Color Image via <i>L</i> <sub>0</sub> Regularization and Reweighted Group Sparsity

Classic Retinex model based low-light image enhancement methods ignored the interference of noise, which causes annoying artifacts.In this paper, we propose to estimate the illumination, reflectance and suppress the noise in a whole framework.Instead of using the L 1 norm to constrain the piece-wise smoothness, we utilize the L 0 norm to preserve the structure of the illumination map and remove the intensive noise.The clean reflectance is obtained via a novel group sparsity regularization to preserve the small scale details.Instead of using a zero-mean model for all sparse coefficients, we propose to adaptively estimate the mean of each coefficient according to the statistical characteristics of the image content.A re-weighting scheme is introduced to adjust how close the estimated patch is to the mean value.In addition, based on the observation that the noise levels in different color channels are different, the noise variance in each channel is estimated and updated during the model optimization process.Experimental results show that the proposed method outperforms the compared schemes in terms of both objective quality and visual quality.
First Arrival Traveltime Picking Through 3-D U-Net

In seismic exploration, picking first arrival traveltimes is an important step toward the estimation of subsurface velocity model, which has the direct impact on the well placement. In this letter, we present a 3-D deep learning method for automatic picking. In specific, we employ a supervised 3-D U-shaped full-convolutional network (3-D U-Net) to classify each sample in the 3-D seismic data into two categories: samples before and after the first break. Subsequently, we delineate a surface to separate these two categories, and such surface is corresponding to the desired first arrival traveltimes. In our training phase, besides the original normalized waveform data, we include the energy semblance feature as a channel of the input data, which helps to involve human understanding. The trained 3-D U-Net is applied to both synthetic and real datasets. The synthetic test shows that even if the labels contain some outliers, the predictions are still stable and reasonable in the sense that 3-D U-Net is capable of correcting some incorrect pickings. As for the field dataset test, the predicted results of 3-D U-Net are satisfactory based on human visual verification, and the picked traveltime surface is more consistent compared with the one predicted by the 2-D U-Net.
“The Flow in the Funnel”: Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems

Nowadays, the current application of artificial intelligence (AI) to financial context is opening a new field of study, named financial intelligence, in which the implementation of AI-based solutions as “financial brain” aims at assisting in complex decision-making (DM) processes as wealth and risk management, financial security, financial consulting, and blockchain. For venture capitalist organizations (VCOs), this aspect becomes even more critical, since different actors (shareholders, bondholders, management, suppliers, customers) with different DM behaviors are involved. One last layer of complexity is the potential variation of behaviors performed by managers even in presence of fixed organizational goals. The aim of this study is twofold: a general analysis of the debate on implementing AI in DM processes is introduced, and a proposal for modeling financial AI-based services is presented. A set of qualitative methods based on the application of cultural psychology is presented for modeling financial DM processes of all actors involved in the process, machines as well as individuals and organizations. The integration of some design thinking techniques with strategic organizational counseling supports the modeling of a hierarchy of selective criteria of fund-seekers and the creation of an innovative value proposition accordingly with goals of VCOs to be represented and supported in AI-based systems. Implications suggest that human/AI integration in the field can be implemented by developing systems where AI can be conceived in two distinct functions: (a) automation: treating Big Data from the market defined by management of VCO; and (b) support: creating alert systems that are coherent with ordered weighted decisional criteria of VCO.
Privacy Protection in LTE and 5G Networks

Privacy needs to be secured in cellular networks. In the domain of telecommunications, privacy attributes to personal information and subscriber identity. In service providing organizations, privacy impact assessment is performed to identify possible risks to business operations of the organizations caused by collecting personally identifiable information and to ensure compliance with applicable legal, regulatory and policy requirements for privacy protection. However, privacy risks can be estimated not only from organizations' business but also from customers' perspectives. While there are many tools, techniques and templates available assisting organizations in performing privacy impact assessments, the subscribers,' in most cases subjective, perspective on privacy risks has not attracted too much attention by research communities. The paper intends to show the existence of the gap and to contribute towards the understanding of privacy aspects of LTE and 5G networks from subscribers' perspective. This paper first outlines the main vulnerabilities that can be exploited to violate subscriber privacy in LTE networks. Then, controls to mitigate privacy risks in 5G networks are evaluated. The paper also discusses privacy risks introduced by new technologies, including software defined networking (SDN), network function virtualization (NFV) and cloud computing in 5G networks. The privacy risk assessment in LTE and 5G networks is performed from the perspective of customers, not from the perspective of service providers. Protection of subscriber's privacy in the 6G networks is also briefly discussed.
Setting Diversity at the Core of HCI

The advancing awareness of diversity in HCI is touted as laudable in addressing inequality and the digital divide. This progress however has not been reflected in HCI research. Diversity in HCI has focused on giving underrepresented users voice and confronting homogeneity. By increasing representations, though a diversity affinity is immediately visible, the deconstruction of institutionalized prejudice fails. Additionally, underrepresented voices have the unsurmountable task of representing their group. These approaches to diversity become tokenistic fixes failing to tackle the systemic causes of inequality. In this paper, we argue for a systemic approach to diversity in HCI. We call for setting diversity at the core of HCI where human realities and experiences are embodied rather than defined by "what users must be" or "what users are not". The paper contributes the following: a detailed overview of diversity in HCI and presents a conceptual framework for diversity driven HCI with three correlated recommendations for setting diversity at the core of HCI.
Text to Image Generation using GAN

Text-to-image creation with generative adversarial networks (GAN) is a deep learning model that can produce images from text descriptions. For several years, due to low technological resources, police officials required a sketch artist to get the face of a criminal. So, to overcome this time-consuming process the proposed approach uses textual inputs of human facial traits and give the corresponding image of criminal as output. Artificial intelligence (AI) that links natural language processing and image processing have a challenge with converting information between text and image (NLP). As a result, we present a new retrieval system based on the generative adversarial network (GAN) that will improve performance by easy processes. Our work primarily focus on criminal face generation that extract facial traits from text descriptions and create a realistic human face.
Toward Real-Time Federated Evolutionary Neural Architecture Search

Neural architecture search (NAS) is an important research topic of automated machine learning, which aims to automatically search for neural network architectures that can efficiently learn for a given task. Furthermore, there is an increasing demand for deploying computationally efficient NAS systems that can be used on edge devices that have limited computational powers. In particular, federated learning is an online distributed machine learning scheme that requires online and federated search of neural architectures involving edge devices. However, most exiting NAS methods are naturally not well suited for distributed real-time systems, due to high computational and communication costs. This chapter provides a brief introduction to methods for reducing computational cost in NAS, followed by a presentation of two evolutionary frameworks we recently developed for real-time federated NAS.
A diagnostic framework for imbalanced classification in business process predictive monitoring

One of the use cases of business process predictive monitoring is predicting the next activity in a running case, which results in a multi-class classification problem. Approaches to this use case are usually evaluated considering average performance across all classes. This often masks poor performance on minority classes, particularly when classes to be predicted are imbalanced. This is the natural case in next activity prediction, where exceptions or optional activities occur, by design, less frequently than others. In this paper we propose a framework to diagnose poor predictive performance on the minority class in the next activity prediction use case that comprises two tools: an empirical comparison of different resampling techniques in the data preparation phase and a novel classification performance measure. The proposed performance measure aims at highlighting the poor recall on the minority class of a classifier, which is a particularly important performance in the context of next activity prediction, whereas the benchmark helps understanding which resampling technique would be the best at mitigating the poor recall. We also discuss how the two tools of the proposed framework can be combined from an AutoML perspective. The proposed framework has been evaluated on a set of publicly available event logs.
Practical Face Swapping Detection Based on Identity Spatial Constraints

The generalization of face swapping detectors against unseen face manipulation methods is important to practical applications. Most existing methods based on convolutional neural networks (CNN) simply map the facial images to real/fake binary labels and achieve high performance on the known forgeries, but they almost fail to detect new manipulation methods. In order to improve the generalization of face swapping detection, this work concentrates on a practical scenario to protect specific persons by proposing a novel face swapping detector requiring a reference image. To this end, we design a new detection framework based on identity spatial constraints (DISC), which consists of a backbone network and an identity semantic encoder (ISE). When inspecting an image of a particular person, the ISE utilizes a real facial image of that person as the reference to constrain the backbone to focus on the identity-related facial areas, so as to exploit the intrinsic discriminative clues to the forgery in the query image. Cross-dataset evaluations on five large-scale face forgery datasets show that DISC significantly improves the performance against unseen manipulation methods and is robust against the distortions. Compared to the existing detection methods, the AUC scores achieve 10%~40% performance improvements.
Empirical Investigation of the Impact of the Adapter Design Pattern on Software Maintainability

Design patterns have a key role in software development process. However, their impact on software quality is not well investigated. There is no common agreement on how design patterns impact software quality. This paper aims to empirically investigate whether the Adapter design pattern impacts software maintainability positively or negatively. To achieve this aim, refactoring techniques have been applied to generate a non-pattern version for four subject systems. Then, software metrics have been calculated and compared for both versions; the pattern version and non-pattern version. The correlation of software metrics to software maintainability have been used and adopted from previous research studies. The empirical results show that the Adapter pattern versions have a better software metrics than do the non-Adapter pattern versions i.e. a sign that the Adapter design pattern impacts software maintainability positively.
Haze Relevant Feature Attention Network for Single Image Dehazing

Single image dehazing methods based on deep learning technique have made great achievements in recent years. However, some methods recover haze-free images by estimating the so-called transmission map and global atmospheric light, which are strictly limited to the simplified atmospheric scattering model and do not give full play to the advantages of deep learning to fit complex functions. Other methods require pairs of training data, whereas in practice pairs of hazy and corresponding haze-free images are difficult to obtain. To address these problems, inspired by cycle generative adversarial model, we have developed an end-to-end haze relevant feature attention network for single image dehazing, which does not require paired training images. Specifically, we make explicit use of haze relevant feature by embedding an attention module into a novel dehazing generator that combines an encoder-decoder structure with dense blocks. The constructed network adopts a novel strategy which derives attention maps from several hand-designed priors, such as dark channel, color attenuation, maximum contrast and so on. Since haze is usually unevenly distributed across an image, the attention maps could serve as a guidance of the amount of haze at image pixels. Meanwhile, dense blocks can maximize information flow along features from different levels. Furthermore, color loss is proposed to avoid color distortion and generate visually better haze-free images. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.
Dual-Channel Reasoning Model for Complex Question Answering

Multihop question answering has attracted extensive studies in recent years because of the emergence of human annotated datasets and associated leaderboards. Recent studies have revealed that question answering systems learn to exploit annotation artifacts and other biases in current datasets. Therefore, a model with strong interpretability should not only predict the final answer, but more importantly find the supporting facts’ sentences necessary to answer complex questions, also known as evidence sentences. Most existing methods predict the final answer and evidence sentences in sequence or simultaneously, which inhibits the ability of models to predict the path of reasoning. In this paper, we propose a dual-channel reasoning architecture, where two reasoning channels predict the final answer and supporting facts’ sentences, respectively, while sharing the contextual embedding layer. The two reasoning channels can simply use the same reasoning structure without additional network designs. Through experimental analysis based on public question answering datasets, we demonstrate the effectiveness of our proposed method
BOFT: Exploitable Buffer Overflow Detection by Information Flow Tracking

Buffer overflow is one of the most critical software vulnerabilities with numerous functional and security impacts on memory boundaries and program calls. An exploitable buffer overflow, which can be directly or indirectly triggered through external user domain inputs, is of a greater concern because it can be misused during run-time for adversarial intention. Although some existing tools offer buffer overflow detection to certain extents, there are major limitations, such as, poor detection coverage and ad-hoc/manual verification efforts due to inadequate predefined executions for static analysis and substantially large input subspace for dynamic verification. In this paper, to provide program verification in static time with high detection coverage, we propose an automated framework for Exploitable Buffer Overflow Detection by Information Flow Tracking (BOFT). We achieve this goal following three steps - first, BOFT analyzes the usage of arrays, pointers, and vulnerable application programming interface (APIs) in the program code and automatically inserts assertions required for buffer overflow detection. Second, BOFT instruments the program with taints for direct and indirect information flow tracking using an extensive set of formal expressions. Finally, it symbolically analyzes the instrumented code for maximum coverage and provides the list of exploitable buffer overflow vulnerabilities. BOFT is evaluated on standard benchmarks from SAMATE Juliet Test Suite (NIST) with a successful detection of ~94.87% (minimum) of exploitable buffer overflows with zero false positives.