Reviewerly/exHarmony huggingface dataset:
Environmental Impact Assessment of Heavy Metals in Surface Disposed Drilling Waste

Saudi Arabia has some of the biggest oil and gas reserves in the world, and has expanded its drilling operations gradually to meet the global demand.Improper handling of contaminated drilling wastes may cause serious negative environmental impacts.The current study investigates the toxicological effects of heavy metals in drilling waste by quantification and developing different contamination indices.Solid and liquid samples from different drilling waste pits were collected and analyzed for heavy metals.The average concentration varies significantly and decreases in the order of Fe > Al > Sr > Mn > Cu > Cr > Zn > Ni > Pb > Cd > Ag > Co.The spatial variation showed the highest concentration at SDGM Site 1, West.Comparison of current data showed continental crust average values within the specifications for most of the sites.For metals like Cd, Cr and Cu, the concentration is higher than the continental crust value.Indices such as pollution load index, modified degree of contamination etc. were calculated from the available data.The contamination level for different site calculations showed nil to a very low degree of contamination.Spatial variation of the contamination level indicated comparatively higher values for sites UTMN-4 and SDGM-1 North, which indicates the necessity of precautionary methods.The metal concentration in the pit water samples exceeded generally accepted standards, if this water was to be discharged/leaked from the pit.The current study concluded presence of different heavy metals in samples from the drilling waste pits, whereas the degree of contamination is minute.
The Relationship between Social Emotional Learning Competencies and Life Satisfaction in Adolescents: Mediating Role of Academic Resilience

Social-emotional learning (SEL) competencies has been the subject of many studies.The purpose of this study was to examine the mediating role of academic resilience in the relationship between adolescents' social-emotional learning competencies and life satisfaction.The study group was composed of 371 adolescents.Self-report measurement tools were used in the study.Structural equation modeling was used to investigate the mediating role of academic resilience in the relationship between social-emotional learning and life satisfaction.As a result of the study, it was concluded that social-emotional learning competencies predicted life satisfaction and academic resilience.Also, academic resilience predicted life satisfaction.As a result of this study, it has been concluded that academic resilience has a mediating effect on the relationship between socialemotional learning competencies and life satisfaction.It is suggested that include academic resilience activities in SEL programs to be developed to increase the quality of life, happiness, well-being and life satisfaction.
Applications and Challenges of Blockchain with IoT in Food Supply Chain Management System: A Review

Food security has become the most important thing now a days.People are getting infected by food coming from unprotected sources.Many technologies have been developed for this problem but a very successful technology is blockchain due to its nature of decentralization and transparency which helps in protecting food.Blockchain protects the food and also trace the source of the food from where it came without involving third party.According to present studies blockchain still needs more advancement.This paper carries out the systematic literature review based on the applications and challenges of blockchain in food supply chain management combined with IoT to provide more transparency, traceability and transparency with pros and cons.Our study indicates few barriers and challenges in the safety of food which are still existing in food supply chain management and faced by people now a days all these challenges have been discussed in this paper.This comprehensive literature review provides a useful perspective on many issues of food supply chain without blockchain.
Nonground Abductive Logic Programming with Probabilistic Integrity Constraints

Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are a suitable framework to handle uncertain information, and in the last decade many probabilistic logical languages have been proposed, as well as inference and learning systems for them. In the realm of Abductive Logic Programming (ALP), a variety of proof procedures have been defined as well. In this paper, we consider a richer logic language, coping with probabilistic abduction with variables. In particular, we consider an ALP program enriched with integrity constraints `a la IFF, possibly annotated with a probability value. We first present the overall abductive language, and its semantics according to the Distribution Semantics. We then introduce a proof procedure, obtained by extending one previously presented, and prove its soundness and completeness.
Poster: Lessons from Doing Fieldwork with Bandwidth-constrained communities Online

research-article Open AccessPoster: Lessons from Doing Fieldwork with Bandwidth-constrained communities Online Share on Authors: Sarina Till University of Cape Town and Independent Institute of Education, South Africa University of Cape Town and Independent Institute of Education, South AfricaView Profile , Melissa Densmore University of Cape Town, South Africa University of Cape Town, South AfricaView Profile , Toshka Lauren Coleman University of Cape Town, South Africa University of Cape Town, South AfricaView Profile , Nervo Verdezoto Dias Cardiff University, South Africa Cardiff University, South AfricaView Profile Authors Info & Claims COMPASS '21: ACM SIGCAS Conference on Computing and Sustainable SocietiesJune 2021 Pages 445–448https://doi.org/10.1145/3460112.3471979Online:23 September 2021Publication History 0citation50DownloadsMetricsTotal Citations0Total Downloads50Last 12 Months50Last 6 weeks20 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
AI Conversational Chatbot for Primary Healthcare Diagnosis Using Natural Language Processing and Deep Learning

Artificial Intelligence is becoming an important Computer Science field that is being leveraged periodically, to be collaborated with everyday applications. Health care is a major part of our lives and it is inevitable. It is not always easy to await a doctor’s appointment to find out what the health problem or disease could be, for various reasons. The period of COVID-19 has especially been challenging in having access to doctors and hospital visits; despite minor health issues, people refrain from visiting medical institutions. chatbots have progressed to become quite handy in the medical industry for various purposes––predicting diseases, medications, pathology queries, and even just for general medical awareness at much lesser cost and resources. A conversational chatbot, like the one aimed to be created here, is a prototypical model for providing users a pre-diagnosis based on symptoms and concerns mentioned by the user. The model utilizes NLP and Neural Networks together, and Decision tree classifiers separately for two different ways of diagnosis. This can, therefore, assist users to get an initial idea and how to proceed about it further.
Internal Video Inpainting by Implicit Long-range Propagation

We propose a novel framework for video inpainting by adopting an internal learning strategy. Unlike previous methods that use optical flow for cross-frame context propagation to inpaint unknown regions, we show that this can be achieved implicitly by fitting a convolutional neural network to known regions. Moreover, to handle challenging sequences with ambiguous backgrounds or long-term occlusion, we design two regularization terms to preserve high-frequency details and long-term temporal consistency. Extensive experiments on the DAVIS dataset demonstrate that the proposed method achieves state-of-the-art inpainting quality quantitatively and qualitatively. We further extend the proposed method to another challenging task: learning to remove an object from a video giving a single object mask in only one frame in a 4K video.
Training and pattern recognition by an opto-magnetic neural network

Neuromorphic computing aims to mimic the architecture of the human brain to carry out computational tasks that are challenging and much more energy consuming for standard hardware. Despite progress in several fields of physics and engineering, the realization of artificial neural networks, which combine high operating speeds with fast and low-energy adaptability, remains a challenge. Here, we demonstrate an opto-magnetic neural network capable of learning and classification of digitized 3 × 3 characters exploiting local storage in the magnetic material. Using picosecond laser pulses, we find that micrometer sized synapses absorb well below 100 picojoule per synapse per laser pulse, with favorable scaling to smaller spatial dimensions. We, thus, managed to combine the speed and low-dissipation of optical networks with the low-energy adaptability and non-volatility of magnetism, providing a promising approach to fast and energy-efficient neuromorphic computing.
An Extension of the Brouwer–Zimmermann Algorithm for Calculating the Minimum Weight of a Linear Code

A modification of the Brouwer–Zimmermann algorithm for calculating the minimum weight of a linear code over a finite field is presented. The aim was to reduce the number of codewords for consideration. The reduction is significant in cases where the length of a code is not divisible by its dimensions. The proposed algorithm can also be used to find all codewords of weight less than a given constant. The algorithm is implemented in the software package QextNewEdition.
Parametrized Hamiltonian simulation using quantum optimal control

Building on known quantum control methods, the authors propose a technique to scalably use optimal control to simulate quantum dynamics. They validate this approach with numerical simulation of a system of superconducting transmons.
HBDCA: A Toolchain for High-Accuracy BRAM-Defined CNN Accelerator on FPGA with Flexible Structure

In recent years FPGA has become popular in CNN acceleration, and many CNN-to-FPGA toolchains are proposed to fast deploy CNN on FPGA. However, for these toolchains, updating CNN network means regeneration of RTL code and re-implementation which is time-consuming and may suffer timing-closure problems. So, we propose HBDCA: a toolchain and corresponding accelerator. The CNN on HBDCA is defined by the content of BRAM. The toolchain integrates UpdateMEM utility of Xilinx, which updates content of BRAM without re-synthesis and re-implementation process. The toolchain also integrates TensorFlow Lite which provides high-accuracy quantization. HBDCA supports 8-bits per-channel quantization of weights and 8-bits per-layer quantization of activations. Upgrading CNN on accelerator means the kernel size of CNN may change. Flexible structure of HBDCA supports kernel-level parallelism with three different sizes (3×3, 5×5, 7×7). HBDCA implements four types of parallelism in convolution layer and two types of parallelism in fully-connected layer. In order to reduce access number to memory, both spatial and temporal data-reuse techniques were applied on convolution layer and fully-connect layer. Especially, temporal reuse is adopted at both row and column level of an Input Feature Map of convolution layer. Data can be just read once from BRAM and reused for the following clock. Experiments show by updating BRAM content with single UpdateMEM command, three CNNs with different kernel size (3×3, 5×5, 7×7) are implemented on HBDCA. Compared with traditional design flow, UpdateMEM reduces development time by 7.6X-9.1X for different synthesis or implementation strategy. For similar CNN which is created by toolchain, HBDCA has smaller latency (9.97µs-50.73µs), and eliminates re-implementation when update CNN. For similar CNN which is created by dedicated design, HBDCA also has the smallest latency 9.97µs, the highest accuracy 99.14% and the lowest power 1.391W. For different CNN which is created by similar toolchain which eliminate re-implementation process, HBDCA achieves higher speedup 120.28X.
Zip Trees

We introduce the zip tree, a form of randomized binary search tree that integrates previous ideas into one practical, performant, and pleasant-to-implement package. A zip tree is a binary search tree in which each node has a numeric rank and the tree is (max)-heap-ordered with respect to ranks, with rank ties broken in favor of smaller keys. Zip trees are essentially treaps (Seidel and Aragon 1996), except that ranks are drawn from a geometric distribution instead of a uniform distribution, and we allow rank ties. These changes enable us to use fewer random bits per node. We perform insertions and deletions by unmerging and merging paths ("unzipping" and "zipping") rather than by doing rotations, which avoids some pointer changes and improves efficiency. The methods of zipping and unzipping take inspiration from previous top-down approaches to insertion and deletion (Stephenson 1980; Mart\'inez and Roura 1998; Sprugnoli 1980). From a theoretical standpoint, this work provides two main results. First, zip trees require only $O(\log \log n)$ bits (with high probability) to represent the largest rank in an $n$-node binary search tree; previous data structures require $O(\log n)$ bits for the largest rank. Second, zip trees are naturally isomorphic to skip lists (Pugh 1990), and simplify the mapping of (Dean and Jones 2007) between skip lists and binary search trees.
Multi-label Chaining with Imprecise Probabilities

We present two different strategies to extend the classical multi-label chaining approach to handle imprecise probability estimates. These estimates use convex sets of distributions (or credal sets) in order to describe our uncertainty rather than a precise one. The main reasons one could have for using such estimations are (1) to make cautious predictions (or no decision at all) when a high uncertainty is detected in the chaining and (2) to make better precise predictions by avoiding biases caused in early decisions in the chaining. We adapt both strategies to the case of the naive credal classifier, showing that this adaptations are computationally efficient. Our experimental results on missing labels, which investigate how reliable these predictions are in both approaches, indicate that our approaches produce relevant cautiousness on those hard-to-predict instances where the precise models fail.
Handling Class Imbalance in Online Transaction Fraud Detection

With the rise of internet facilities, a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction. However, the fraud cases have also increased causing the loss of money to the consumers. Hence, an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time. Generally, the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem. In this research work, an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e., fraud transactions. A novel loss function named Weighted Hard- Reduced Focal Loss (WH-RFL) has been proposed which has achieved maximum fraud detection rate i.e., True Positive Rate (TPR) at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate (TNR) in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional datasets. Also, Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.
Possibilty of applying Bull algebra in creating an accounting information system

Modern business organizations are facing a multidimensional problem of creating an accounting information system is a multidimensional problem. The presence of mathematics in solving such problems is often a necessity, which is why, in this case, mathematical relations Presence of mathematics in solving such problems is often a necessity, which is why, in this case, mathematical relations using Boolean algebra represent the basis for solving problems related to the development and functioning of an adequate accounting information system.
Automatic Segmentation of Prostate Cancer using cascaded Fully Convolutional Network

In this paper we proposed a prostate segmentation and also tumour detection using deep neural networks. The cutting-edge deep learning techniques are useful compared to the challenges of machine learning based feature extraction techniques. Here we proposed a strategy that contains an FCN model that incorporates data from several MRI images, allowing for faster convergence and more accurate segmentation. T1 and DWI volumes may be used together to delineate the prostate boundary, according to this study. Second, we investigated whether this method might be utilized to provide voxel-level prostate tumor forecasts. The cascaded learning method and performed tests to demonstrate its effectiveness.
Face Mask Detection Using CNN

The COVID-19 pandemic has become an evolving situation all around the globe and the spread is at an alarming rate. Adapting public health-informed hygiene practices can control the transmission of COVID-19, basic measure being wearing a mask. Face detection is the process of identifying faces in a given input image or video and indicating it by drawing a bounding box around the face. In this paper we introduce a deep learning computer vision model to recognise if a person visible through the camera is wearing a mask or not. The deep learning algorithm administered in our work is a transfer learning based Convolutional Neural Network (CNN) and the face detection task is done using the Viola-Jones algorithm approach.
High-definition object detection technology based on AI inference scheme and its implementation

Video artificial intelligence (AI) applications for edges/terminals, such as non-visual drone flight, require object detection in high-definition video in order to detect a wide range of objects with one camera at once. To enable satisfying this requirement, we propose a new high-definition object detection technology based on an AI inference scheme and its implementation. In the technology, multiple object detectors cooperate to detect small and large objects in high-definition video. The evaluation results show that our technology can achieve 2.1 times higher detection performance in full HD images thanks to the cooperation of three object detectors.
Tangent Graph Convolutional Network

Most Graph Convolutions (GCs) proposed in the Graph Neural Networks (GNNs) literature share the principle of computing topologically enriched node representations based on the ones of their neighbors.In this paper, we propose a novel GNN named Tangent Graph Convolutional Network (TGCN) that, in addition to the traditional GC approach, exploits a novel GC that computes node embeddings based on the differences between the attributes of a vertex and the attributes of its neighbors.This allows the GC to characterize each node's neighbor by computing its tangent space representation with respect to the considered vertex.
DEEPAGÉ: Answering Questions in Portuguese About the Brazilian Environment

The challenge of climate change and biome conservation is one of the most pressing issues of our time - particularly in Brazil, where key environmental reserves are located. Given the availability of large textual databases on ecological themes, it is natural to resort to question answering (QA) systems to increase social awareness and understanding about these topics. In this work, we introduce multiple QA systems that combine in novel ways the BM25 algorithm, a sparse retrieval technique, with PTT5, a pre-trained state-of-the-art language model. Our QA systems focus on the Portuguese language, thus offering resources not found elsewhere in the literature. As training data, we collected questions from open-domain datasets, as well as content from the Portuguese Wikipedia and news from the press. We thus contribute with innovative architectures and novel applications, attaining an F1-score of 36.2 with our best model.
A blockchain-based privacy-preserving asynchronous federated learning

Federated learning enables the joint training of machine learning models by utilizing distributed data and computing resources while protecting local data privacy.The existing asynchronous federated learning can effectively solve the problems such as waste of computing resources and low training efficiency caused by synchronous learning.However, it aggregates local models from different nodes and updates the global model through the central server,which makes it endogenously subject to the centralized trust mode and suffers from some issues such as single point of failure andprivacy leakage. In this paper, we propose a blockchain-based privacy-preserving asynchronous federated learning,which ensures the trustability by storing local models into the blockchain and generating the global model through the consensus algorithm.In order to guarantee the privacy of federated learning and improve the model utility,the exponential mechanism of differential privacy is used to select model gradients with high contribution at high probability,and a lower privacy budget is allocated to ensure the model privacy.In addition, in order to solve the problem of clock desynchronization in asynchronous federated learning,we propose a two-factor adjustment mechanism to further improve the global model utility. Finally,theoretical analysis and experimental results demonstrate that our proposed scheme can effectively guarantee the trustability and privacy of the asynchronous federated learning while improving the model utility.
Analysis of the applicability of artificial neural networks for the post-quantum cryptography algorithms development

This article discusses the mathematical apparatus of artificial neural networks (ANNs) and analyzes the possibility of its application in the implementation of post-quantum asymmetric cryptosystems. The authors show that ANNs can be used for development such systems and analysis basic terms and processes in ANNs.