Reviewerly/exHarmony huggingface dataset:
Suitability of surficial media for Ni–Cu–PGE exploration in an established mining camp: a case study from the South Range of the Sudbury Igneous Complex, Canada

A geochemical study over the southwestern part of the South Range of the Sudbury Igneous Complex (SIC) was completed to assess the suitability of surficial media (humus, B-horizon soil and C-horizon soil) for delineating geochemical anomalies associated with Ni-Cu-PGE mineralization. Another objective was to test whether Na pyrophosphate can eliminate the effects of anthropogenic contamination in humus. Results of this study suggest that the natural geochemical signature of humus is strongly overprinted by anthropogenic contamination. Despite no indication of underlying or nearby mineralization, metal concentrations in humus samples by aqua regia collected downwind from smelting operations are higher compared to background, including up to 13 times higher for Pt, 12 times higher for Cu and 9 times higher for Ni. The high anthropogenic background masks the geogenic signal such that it is only apparent in humus samples collected in the vicinity of known Ni-Cu-PGE deposits. Results of this study also demonstrate that anthropogenically-derived atmospheric fallout also influences the upper B-horizon soil; however, lower B-horizon soil (at &gt; 20 cm depth) and C-horizon soil (both developed in till) are not affected. Glacial dispersal from Ni-Cu-PGE mineralization is apparent in C-horizon till samples analyzed in this study. Compared to the background concentrations, the unaffected C-horizon till samples collected immediately down-ice of the low-sulfide, high precious metal (LSHPM) Vermilion Cu-Ni-PGE deposit are enriched over 20 times in Pt (203 ppb), Au (81 ppm) and Cu (963 ppm), and over 30 times in Ni (1283 ppm). Supplementary material: https://doi.org/10.6084/m9.figshare.c.5691080
Comparative Analysis of Human Face Recognition Using SURF and Neural Network Methods

In computer vision, facial recognition technology is used to recognize every person. This approach is a revolution that is perfect for analyzing a graphic image or a video frame specially or differently. There are, however, systemic methods where facial appreciation schemes initiative, typically, and the effort by comparing chosen facial features from a specific image with faces in a database. It's also known as a Biometric Artificial Intelligence-based app that can see a person in extraordinary detail by dissecting structures related to their facial exteriors and figures. The professional employs a variety of techniques in order to complete the mission. The SURF and Neural Network methods are two of these methods. The writers of this paper address the methods mentioned above and how they operate. The emphasis of the debate is on the methods' accuracy rates and determining which approach produces the most reliable outcome based on facial image results.
Exploring Blockchain-enabled Intelligent IoT Architecture

Internet of Things (IoT) architecture, despite its strong functionality and compatibility with numerous smart devices, is limited by its vulnerability to security threats. To overcome this limitation, attempts to introduce blockchain and Artificial Intelligence (AI) to improve IoT architecture have been gaining traction in the past few years. While a significant number of iterations have been made in this regard, the complexity of the integration process has made it difficult to identify best practices that are suitable across different applications. This study analyses the issues and limitations of integrating Blockchain and AI in an IoT architecture, by looking at different iterations and implementations to arrive at a clear picture of existing trends involving research limitations and challenges. The overall results seem to indicate a positive trajectory, as the integration of IoT, blockchain, and AI has been successful across various implementation. While the extent of blockchain integration of different components depend upon the purpose of the system, the caveat is that there are possible issues involving increased complexity, compatibility, and efficiency. The use of AI algorithms has been instrumental in filling in the gaps and improving the overall efficiency of such systems.
Production Allocation Using Combined Geochemical Fingerprinting and Multivariate Curve Resolution

Summary Production Allocation by geochemical fingerprinting is widely used to quantify relative proportions of liquid hydrocarbons produced from distinct reservoir layers, wells or fields. In some Middle East gas/condensate fields liquid phases are often characterised by only limited compositional differences, and hence, endmember identification can be challenging. This study proposes the use of Multivariate Curve Resolution Alternating Least Square (MCR-ALS) to identify unknown endmembers and to allocate the relative endmembers proportions in commingled fluids. A benchmarking of five distinct analytical techniques (NIR, SimDist, GC2D-MS, GC-FID, LC-UV) interpreted by MCR was performed to assess the MCR accuracy on condensate mixtures artificially prepared. The results show that GC2D-MS and GC-FID coupled with MCR were able to correctly identify unknown endmembers within the tested condensate samples. The quantification of the endmember proportions in artificial mixtures (commingled liquid phase) by MCR without prior endmember selection showed an accuracy of ±3%.
Research on the Large-scale Application of 5G in Smart Distribution Network

Based on the demand of large-scale application of smart distribution network, this paper studies 5G technology support system for smart distribution network infrastructure and designs edge computing deployment planning scheme. In view of the actual business scenarios in the smart distribution network and considering the current network conditions, a multi-service-oriented 5G edge computing technology support and resource allocation planning scheme is proposed. At the same time, the dedicated 5G public network platform involves multiple information interaction between a large number of terminals, and further proposes multiple information sharing and multi-terminal mutual trust security guarantee mechanism for 5G extension service in smart distribution network, so as to improve the reasonable allocation and utilization of resources.
Adaptive Quality-of-Service Allocation Scheme for Improving Video Quality over a Wireless Network

The need to ensure the quality of video streaming transmitted over wireless networks is growing every day. Video streaming is typically used for applications that are sensitive to poor quality of service (QoS) due to insufficient bandwidth, packet loss, or delay. These challenges hurt video streaming quality since they affect throughput and packet delivery of the transmitted video. To achieve better video streaming quality, throughput must be high, with minimal packet delay and loss ratios. A current study, however, found that the adoption of the adaptive multiple TCP connections (AM-TCP), as a transport layer protocol, improves the quality of video streaming by increasing throughput and lowering packet delay and loss on high QoS wireless networks. The main objective of this study is to develop a Quality of Service (QoS) method for enhancing video streaming quality over wireless networks. For that, this paper proposes a bandwidth aggregation adaptive multiple TCP-connection (BAM-TCP) scheme to enhance the video streaming quality. BAM-TCP combines AM-TCP with a bandwidth aggregation (BAG) method to aggregate bandwidth from numerous connections. Simulation experiments demonstrate that the BAM-TCP method enhances throughput while minimizing packet loss ratio and end-to-end delay.
Robust Classification with Noisy Labels for Manufacturing Applications: A Hybrid Approach Based on Active Learning and Data Cleaning

Classification is an important machine learning technique that attracts growing interests in various manufacturing applications. Learning an accurate classifier generally requires a large-scale perfectly-labeled training dataset. However, such "golden" labels are not only expensive but also difficult to collect in practice. To facilitate accurate classification in the presence of noisy labels, we propose a novel hybrid method based on active learning and data cleaning. Specifically, we first train an initial classifier with noisily- labeled data. Based on its prediction outcomes, a set of most informative samples is queried for manual annotation. To effectively correct other incorrect labels, we further self-label the unqueried samples based on the true labels provided by human experts and the estimated labels predicted by the initial classifier. As demonstrated by the experimental results based on two industrial datasets, the proposed approach achieves superior accuracy over other conventional methods.
Developing an Integrated Virtual Assistant (IVA): Feasibility of a Behavior Tracking and Reminder Prototype Solution to Assist Persons with Dementia and Their Care Partners

The number of people living with Alzheimer’s and related dementias is increasing worldwide. Much of the care that persons with dementia receive is from informal, family care partners, and solutions that help support the dyad are needed. The purpose of this study was to design and test the feasibility of an application that can track behavior and provide reminders for persons with cognitive impairment or dementia and their care partners. Participants (N=20) included ten family care partners and ten persons with mild cognitive impairment. Participants engaged in a presentation of the application user interface (UI) and accompanying care reports that aggregate data collected by the software. Reactions to the UI and care reports were generally positive, with most participants expressing they would find the application useful. Qualitative themes were identified based on improving the application and care reports’ usability. Overall, results support utilizing this technology to facilitate aging in place and reduce care partner strain.
Reinforcement Learning Based Accurate Detection of Malicious URLs with Multi-Feature Analysis

Malicious URLs result in malware installation, privacy leakage and illegal funding of mobile devices and computers. However, attackers frequently change domain names of URLs to avoid static detection and the malicious URL detection has to address variance in structure of domain names, which seriously degrades the detection accuracy in fixed detection policy selection and impedes optimal policy selection with theoretical analysis. In this paper, we propose an accurate detection of malicious URLs to protect Internet users from accessing malicious URLs, which designs a multi-feature analysis architecture to exploit lexical and content-based features and applies reinforcement learning (RL) to choose the detection mode and parameter. We provide a lightweight RL-based detection with transfer learning and a deep RL-based detection to further improve the detection accuracy for the server with sufficient computation resources. Malicious URLs that have specific domain name features including long numeric string or high percentage of the numeric character or alphabetic string without syllables are considered and simulation results show that this scheme improves the detection accuracy and increases the utility compared with the benchmark scheme.
Abnormal Detection to Big Data Using Deep Neural Networks

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.
Coherent Moiré and Moiré Interferometry

GeometricCoherent moiré moiréMoirécoherent is aMoirégeometric well-established full-field method for measuring displacements. A serious drawback of the method is its low sensitivity. The density of the gratings cannot exceed 40 lines/mm or the pitch cannot be smaller than 2.5 × 10–2 mm. The displacement difference between two successive fringes is equal to the pitchGratingpitch of the grating and this limits the sensitivity of the method. For higher density gratingsGratingdensity diffraction effects enter which alter the nature of the geometric moiré effect. For measuring small displacements moiré methods with much higher sensitivity than geometric moiré have been developed. They use coherent illumination as opposed to geometric moiréMoirégeometric which uses ordinary light.
PFG-YOLO: A Safety Helmet Detection based on YOLOv4

Developing effective helmet detection is very useful in construction sites. To explore the applicability of YOLOv4 in helmet testing, a new helmet detection system PFG-YOLO based on the YOLv4 network is proposed. The up-sampling using interpolation method is replaced by PixelShuffle which solves the losing information on fusing low-resolution with high-resolution feature maps. The ghost module is introduced to reduce the FLOPs and parameters of the network. The fire module is introduced to reduce the model size. When comparing the performance of the two models, the PFG-YOLO we proposed, not only increases the accuracy but also decreases the complexity of the network model. The mAP of class helmet and class no-helmet increased about 5%, both parameters and FLOPs at the corresponding layers were reduced by about a half, and the model size reduced about 20%.
Application of computer‐generated images to train pattern recognition used in semiquantitative immunohistochemistry scoring

This study aimed to clarify whether the pattern recognition involved in scoring proliferation fractions can be trained by abstract computerized images of virtual tissues. Twenty computer-generated images with randomly distributed blue or red dots were scored by 12 probands (all co-workers or collaborators of the Institute of Pathology, University of Bonn). Afterward, the probands underwent a training phase during which they received an immediate feedback on the actual rate of positivity after each image. Finally, the initial testing series was rescored. In a second round with 15 different probands, 20 Ki-67 immunohistochemistry images of tonsil tissue were scored, followed by the same training phase with computer-generated images, before the immunohistochemistry slides were scored again. Paired t-tests were used to compare the differences in mean rates pre- and post-training. Concerning computerized images, untrained probands scored the percentages of positive dots with a mean deviation from the true rates of 8.2%. Following training, the same testing series was scored significantly better with a mean deviation of 4.9% (mean improvement 3.3%, p < 0.001). Scoring real immunohistochemistry slides, the training with computerized images also improved correct estimations, albeit to a lesser degree (mean improvement 1%, p = 0.03). Abstract computerized images of virtual tissues may be a useful tool to train and improve the accuracy of pattern recognition involved in semiquantitative scoring of immunohistochemistry slides. As a side results, this study highlights the value of computer-generated images to verify the performance of image-analysis software.
Dense Packings via Lifts of Codes to Division Rings

We obtain algorithmically effective versions of the dense lattice sphere packings constructed from orders in Q-division rings by the first author. The lattices in question are lifts of suitable codes from prime characteristic to orders <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> in Q-division rings and we prove a Minkowski–Hlawka type result for such lifts. Exploiting the additional symmetries under finite subgroups of units in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> , we show that this leads to effective constructions of lattices approaching the best known lower bounds on the packing density Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>n</i></sub> in a variety of new dimensions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . This unifies and extends a number of previous constructions.
Analysing a UI’s Impact on the Usability of Hands-free Interaction on Smart Glasses

As smart glasses and other head-mounted devices (HMD) are becoming more developed, the number of different use cases and settings where they are deployed have also increased. This includes scenarios where the hands of the user are not available to interact with a system running on such hardware, which precludes some interaction designs from these devices, such as free-hand gestures or the use of a touchpad attached to the device (e.g., on the frame). Alternative modalities include head gestures and speech-based input. However, while these interfaces leave the hands of their users free, they are not as intuitive: common metaphors like touching, pointing, or clicking do not apply. Hence there is an increased need to explain these mechanisms to the user and to make sure they can be used to operate such a device. However, there is no work available on how this should be done properly.In the research presented here, we conducted a study on different ways to support the use of head gestures and voice control on HMDs. For each modality, an abstract as well as an explicit UI design for communicating their usage to users were designed and evaluated in a care setting, where hands-free interaction is necessary to interact with patients and for hygienic reasons. First results from a within-subjects analysis show that surprisingly there does not seem to be much of a difference in performance when comparing these approaches to each other as well as when comparing them to a baseline implementation which offered no additional help. User preferences between the designs diverged: participants often had one clear favourite for the head-gesture UIs while barely noticing the difference between the speech-based UIs. Preferences on certain designs did not seem to impact performance in objective and subjective measures such as error rates and questionnaire results. This suggests that either implementations’ support for these modalities should adapt to individual preferences or that there is a need to focus on other areas of support to increase usability.
The Role of Mediation in Resolving Conflict Situations in the System of Educational Institutions

The article discusses mediation – one of the modern methods of conflict resolution. It is noted that mediation plays an important role in the management process, because if you properly organize meditative assistance in your educational institution then the socio-psychological climate will become favorable. The authors also identified a number of difficulties associated with the implementation of mediation from the point of view of the management process in an educational institution.
Learning the Impedance Entanglement for Wireline Data Communication

In wireline communication networks, a line impedance entanglement exists since changes of the line impedance at one network port cause a change of the line impedance at the other port. This physical phenomenon can be constructively exploited to realize a form of digital modulation that is referred to as impedance modulation (IM). IM is an alternative method to more conventional voltage modulation (VM). In this paper, the impedance entanglement is studied and learned through a supervised machine learning (ML) approach which enables the implementation of a ML based receiver. Numerical results are obtained in a data set of measured power line communication channels, which is among the most challenging environments for such a modulation approach. The resulting system can have practical implementation, for instance in a smart building automation network where monitoring-control of sensors and devices enables the efficient energy management. Comparisons with the optimal maximum-likelihood (MaxL) receiver that perfectly knows the impedance entanglement transfer function are made. It is found that the ML based receiver performs close to the optimal genie receiver.
Coimbatore Destination Chatbot: A Study on Customer Preference

Numerous technologies are being integrated to enhance the services and tourist experiences in the field of tourism in which a digital support is provided to travelers before during and after the travel activity. Artificial Intelligence is the human intelligence technology to perform particular tasks. Artificial Intelligence offers travel services that are automated, customized and insightful to make tourism as Digital Tourism. The application of artificial intelligence in people's life have a significant role and so this article focuses on how AI technology can lead to the next mile stone in the tourism industry. Here we studied on Chatbot a revolution that has emerged in Artificial Intelligence which can solve our queries easily. This study is structured into two phases, first Identifying the customer preferences on the destination Coimbatore, second phase is developing a chatbot on Coimbatore as a destination Ask me WHW(What, How, Where). Bot covers 3 A’s of Coimbatore-Access, Attraction and Accommodation The rapid digitalization and vast potential of technologies will help in tourism development which make travel ease and feasible. It also interacts with customers and function as a virtual tour guide.
A New Approach for Detecting Activities of Daily Living Detection Using Smartphone and Wearable Sensors

In the last years, the pervasive use of smartphones and wearable devices, with digital sensors, like accelerometers, gyroscopes, and magnetometers, makes it easy to assess activities of daily living, with several applications and benefits for human beings, as: monitoring elderly patients for the occurrence of falls; monitoring diabetic and Parkinson's disease patients; monitoring of the rehabilitation process of patients, etc. Major of the works published in literature use a single machine lo classify all activities. In this work we propose a separate machine learning process to detect group of activities. The main advantages of our approach are the following enables customizing the sampling window size and overlapping for detecting each activity group, enables a separated adjustment of the parameters and hyperparameters of each classifier, and enables using different signals for detection of each activity group. Therefore, in this work we used different signals, sampling window size, overlapping and classifiers for detecting each activity group. The mean accuracy obtained in activities of daily living detection, using 5-fold-cross validation situated between 97.32% and 99.76%. In literature, using a single classifier, the reported accuracies in detecting activities of daily living situated in the range between 52% to 99.15%.