Reviewerly/exHarmony huggingface dataset:
Gold-related Alteration Zones Detection at the Um Balad Area, Egyptian Eastern Desert, using Remote Sensing, Geophysical, and GIS Data Analysis

The Um Balad area is a part of the Arabian Nubian Shield (ANS), which hosts various gold and base metal deposits. Gold can be prospected in the alterations and sheared zones in the widely exposed metagabbro-diorite rocks in the Um Balad area. In this regard, the image processing and analysis of the ASTER data is a rapid and efficient technique for such studies, where the structural pattern and associated lineaments are probable indicators of hydrothermal flow and activities developing in mineral-bearing alteration zones as targets for gold exploration. The Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Mixture Tuned Matched Filtering (MTMF) techniques have identified the alteration zones and the characterized altered minerals of each alteration. The automatic lineament extraction method was used by ASTER's visible and near-infrared bands to produce a lineament density map. Using gamma-ray spectrometry data, the potassium percent (K %) and hydrothermal alteration parameters (F-parameter and Th-normalization) were calculated. As well as ratio maps (eTh/K and eU/K) were used to determine the potassic and phyllic hydrothermal alteration. The model of exploration for gold-related alterations was developed in the Um Balad area, using multiple layers derived from remote sensing and gamma-ray spectrometry data through a GIS (Weighted Sum Overlay) approach. The probability of gold-related alterations was graded as low, medium, good, and very good. A final map has been generated showing the gold-potential alteration zones in four localities (Area-1, Area-2, Area-3, and Area-4). In these altered zones, valuable minerals such as sulphides and gold may be precipitated, making them ideal gold exploration sites. The field observations and spectral signature analyses of the collected samples from the various identified alterations supported the results of the remote sensing and aero-spectrometry data analysis. Therefore, these techniques have been suggested for similar investigations in other areas.
Learn to adapt: Robust drift detection in security domain

Deploying robust machine learning models has to account for concept drifts arising due to the dynamically changing and non-stationary nature of data. Addressing drifts is particularly imperative in the security domain due to the ever-evolving threat landscape and lack of sufficiently labeled training data at the deployment time leading to performance degradation. Recently proposed concept drift detection methods in literature tackle this problem by identifying the changes in feature/data distributions and periodically retraining the models to learn new concepts. While these types of strategies should absolutely be conducted when possible, they are not robust towards attacker-induced drifts and suffer from a delay in detecting new attacks. We aim to address these shortcomings in this work. We propose a robust drift detector that not only identifies drifted samples but also discovers new classes as they arrive in an on-line fashion. We evaluate the proposed method with two security-relevant data sets—network intrusion data set released in 2018 and APT Command and Control dataset combined with web categorization data. Our evaluation shows that our drifting detection method is not only highly accurate but also robust towards adversarial drifts and discovers new classes from drifted samples.
Utilizing <scp>3D</scp> head and neck specimen scanning for intraoperative margin discussions: Proof of concept of our novel approach

The current standard of documenting and communicating frozen section margin results is inefficient. We present a novel method of generating 3D digital models of gross tumor specimens to more clearly visualize histopathological margin results.Fifty-five head and neck specimens were scanned and virtually "inked" using 3D software. These 3D specimen maps were displayed in the operating room to provide the surgeon with a real-time specimen-to-defect relationship by which further resections could be guided.Margin results were reported within an average of 34 min using the proposed workflow. The scanner rendered accurate models of specimens that exceeded 3.0 × 3.0 × 3.0 cm. Critical specimen features to consider were size, color, textural complexity, and the presence of discernible anatomic landmarks.Optical 3D scanning technology can improve the quality of head and neck margin documentation and the efficiency with which results are communicated between the pathologist and surgeon.
Binary Representation via Jointly Personalized Sparse Hashing

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g., color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely, Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, first, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our proposed JPSH, we incorporate the proposed PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. We apply the proposed representation learning algorithm JPSH to the similarity search task. Extensive experiments on four benchmark datasets verify that the proposed JPSH outperforms several state-of-the-art unsupervised hashing algorithms.
How to Make chatbots productive – A user-oriented implementation framework

Many organizations are pursuing the implementation of chatbots to enable automation of service processes. However, previous research has highlighted the existence of practical setbacks in the implementation of chatbots in corporate environments. To gain practical insights on the issues related to the implementation processes from several perspectives and stages of deployment, we conducted semi-structured interviews with developers and experts of chatbot development. Using qualitative content analysis and based on a review of literature on human computer interaction (HCI), information systems (IS), and chatbots, we present an implementation framework that supports the successful deployment of chatbots and discuss the implementation of chatbots through a user-oriented lens. The proposed framework contains 101 guiding questions to support chatbot implementation in an eight-step process. The questions are structured according to the people, activity, context, and technology (PACT) framework. The adapted PACT framework is evaluated through expert interviews and a focus group discussion (FGD) and is further applied in a case study. The framework can be seen as a bridge between science and practice that serves as a notional structure for practitioners to introduce a chatbot in a structured and user-oriented manner.
Dynamic Evolutionary Game-based Modeling, Analysis and Performance Enhancement of Blockchain Channels

The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations. When transactions are transmitted through the channels created by nodes, the nodes need to cooperate with each other. If one party refuses to do so, the channel is unstable. A stable channel is thus required. Because nodes may show uncooperative behavior, they may have a negative impact on the stability of such channels. In order to address this issue, this work proposes a dynamic evolutionary game model based on node behavior. This model considers various defense strategies' cost and attack success ratio under them. Nodes can dynamically adjust their strategies according to the behavior of attackers to achieve their effective defense. The equilibrium stability of the proposed model can be achieved. The proposed model can be applied to general channel networks. It is compared with two state-of-the-art blockchain channels: Lightning network and Spirit channels. The experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable state. Thus its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.
Blockchain Meets Covert Communication: A Survey

Covert communication enables covert information transmission in an undetectable way to prevent the exposure of communication behaviors. Blockchain-based covert communication breaks through the limitations on concealment, reliability and anti-traceability, and has shown promising application prospects in both sensitive data transmission and botnets. Although there are studies on blockchain-based covert communication, it still lacks a systematic investigation. In this paper, we conduct a comprehensive study on channel building and survey its core technologies by information embedding, transaction filtering, and transaction obfuscation. We also summarize evaluation metrics to better analyze blockchain-based covert channels. Privacy aspects are also discussed. Finally, we suggest seven future directions to stir research efforts into this area.
Converted state equation Kalman filter for nonlinear maneuvering target tracking

Handling the nonlinearity between the measurement and kinematic states is the core issue in target tracking based on radar or sonar. The main novelty of this paper is the proposal of a new filter with a linear structure to achieve nonlinear tracking by integrating information in the polar coordinate system. The state vectors composed of the range, bearing and their differentials are constructed to make the measurement equations linear. After discretizing the ordinary differential dynamic equations in the polar coordinate system, linear time-varying state transition matrixes are established for two common Cartesian motions: constant velocity (CV) and constant acceleration (CA). For the process noises converted from the Cartesian coordinate system to the polar coordinate system, the first and second moments are derived with a closed form. Consequently, the coordinate system of the conventional state equations is converted so that the tracking can be conducted by a standard Kalman filter. Several simulation scenarios show that such a new filter effectively improves the tracking accuracy. The reasons for the superior performance of the proposed method are analyzed and exemplified. In addition, different posterior Cramer–Rao lower bounds (PCRLBs) for fusion estimation in Cartesian coordinates and polar coordinates are given and compared.
Dream Painter

This paper describes an interactive robotic art installation Dream Painter by the artistic duo Varvara &amp; Mar that deploys artificial intelligence (AI), a KUKA industrial robot and interaction technology in order to offer the audience an artistic interpretation of their past dreams, which are then turned into a collective painting. The installation is composed of four larger parts: audience interaction design, AI-driven multicoloured drawing software, communication with an arm robot, and a kinetic part that is the automatic paper progression following each completed dream drawing. All these interconnected parts are orchestrated into an interactive and autonomous system in the form of an art installation that occupies two floors of a cultural centre. In the article, we document the technical and conceptual frameworks of the project, and the experience gained through the creation and exhibition of the interactive robotic art installation. In addition, the paper explores the creative potential of speech-to-AI-drawing transformation, which is a translation of different semiotic spaces performed by a robot as a method for audience interaction in the art exhibition context.
Bootstrapping Human Activity Recognition Systems for Smart Homes from Scratch

Smart Homes have come a long way: From research laboratories in the early days, through (almost) neglect, to their recent revival in real-world environments enabled through the existence of commodity devices and robust, standardized software frameworks. With such availability, human activity recognition (HAR) in smart homes has become attractive for many real-world applications, especially in the domain of Ambient Assisted Living. Yet, getting started with an activity recognition system in specific smart homes, which are highly specialized spaces inhabited by individuals with idiosyncratic behaviors and habits, is a non-trivial endeavor. We present an approach for bootstrapping HAR systems for individual smart homes from scratch. At the beginning of the life cycle of a smart home, our system passively observes activities and derives rich representations for sensor data-action units-which are then aggregated into activity models through motif learning with minimal supervision. The resulting HAR system is then capable of recognizing relevant, most frequent activities in a smart home. We demonstrate the effectiveness of our bootstrapping procedure through experimental evaluations on CASAS datasets that show the practical value of our approach.
AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation

The whole world is suffering from a novel coronavirus, which has become an epidemic. According to a World Health Organization report, this is a communicable disease, i.e., it transfers from an infected person to a healthy person. Therefore, wearing a mask is the most important precaution to protect from COVID-19. This paper presented a deep learning-based approach to design a Face Mask Detection framework to predict whether a person is wearing a mask or not. The proposed method uses a Single Shot Multibox detector as a face detector model and a deep Inception V3 architecture (SSDIV3) to extract the pertinent features of images and discriminate them in mask and without masks labels. Optimizing the SSDIV3 approach using different modeling parameters is a genuine contribution of this work. In addition to this, the system is tested and analyzed on VGG16, VGG19, Xception, Mobilenet V2 models at different modeling parameters. Furthermore, two synthesized novel Face Mask Datasets are introduced containing diversified masks (2d_printed, 3d_printed, handkerchief, transparent, natural-looking mask appearance masks) and unmask images of humans collected in outdoor and indoor environments such as parks, homes, laboratories. The experiment outcomes demonstrate that the proposed system has achieved an accuracy of 98% on the synthesized benchmark datasets, which comparatively outperforms other state-of-art methods and datasets in a real-time environment.
Generative Moment Matching Networks for Genotype Simulation

The generation of synthetic genomic sequences using neural networks has potential to ameliorate privacy and data sharing concerns and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks. In this paper, we explore the use of Generative Moment Matching Networks (GMMNs) for SNP simulation, we present some architectural and procedural changes to properly train the networks, and we introduce an evaluation scheme to qualitatively and quantitatively assess the quality of the simulated sequences.
Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges caused by various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architectures have been proposed and widely used, but their performance remains unsatisfactory. To further address these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations. (1) Since the discrimination of the features learned via square convolutional kernels needs to be further improved, we propose utilizing nonsquare vertical and horizontal convolutional kernels in a double-branch encoder so that the features learned by both branches can be expected to complement each other. (2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation of small-sized targets. With the above two schemes, we develop a novel double-branch encoder-based segmentation framework for medical image segmentation, namely, Crosslink-Net, and validate its effectiveness on five datasets with experiments. The code is released at https://github.com/Qianyu1226/Crosslink-Net.
One Digital Health for more FAIRness

One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.
On the Differential Spectrum and the APcN Property of a Class of Power Functions Over Finite Fields

In this paper, we investigate the power function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F(x)=x^{d}$ </tex-math></inline-formula> over the finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{2^{4n}}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is a positive integer and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d=2^{3n}+2^{2n}+2^{n}-1$ </tex-math></inline-formula> . We prove that this power function is AP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c\text{N}$ </tex-math></inline-formula> with respect to all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c\in \mathbb {F}_{2^{4n}}\setminus \{1\}$ </tex-math></inline-formula> satisfying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c^{2^{2n}+1}=1$ </tex-math></inline-formula> , and we determine its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -differential spectrum. To the best of our knowledge, this is the second class of AP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c\text{N}$ </tex-math></inline-formula> power functions over finite fields of even characteristic. By the same proof ideas, we completely determine the differential spectrum of this function, and give an affirmative answer to a recent conjecture proposed by Budaghyan, Calderini, Carlet, Davidova and Kaleyski.