Reviewerly/exHarmony huggingface dataset:
Blockchain interoperability: the state of heterogenous blockchain‐to‐blockchain communication

Abstract Blockchain technology has been increasingly adopted over the past few years since the introduction of Bitcoin, with several blockchain architectures and solutions being proposed. Most proposed solutions have been developed in isolation, without a standard protocol or cryptographic structure to work with. This has led to the problem of interoperability, where solutions running on different blockchain platforms are unable to communicate, limiting the scope of use. With blockchains being adopted in a variety of fields such as the Internet of Things, it is expected that the problem of interoperability if not addressed quickly, will stifle technology advancement. This paper presents the current state of interoperability solutions proposed for heterogenous blockchain systems. A look is taken at interoperability solutions, not only for cryptocurrencies, but also for general data‐based use cases. Current open issues in heterogenous blockchain interoperability are presented. Additionally, some possible research directions are presented to enhance and to extend the existing blockchain interoperability solutions. It was discovered that though there are a number of proposed solutions in literature, few have seen real‐world implementation. The lack of blockchain‐specific standards has slowed the progress of interoperability. It was also realized that most of the proposed solutions are developed targeting cryptocurrency‐based applications.
An obstacle avoidance path planner for an autonomous tractor using the minimum snap algorithm

Autonomous tractors use a GNSS-based approach combined with other sensors to provide higher efficiency and minimize human intervention. The basis of field automated navigation is the agricultural route planning of the entire field. One of the main aspects of agricultural route planning is obstacle avoidance path planning, which can provide a safe continuous trajectory for autonomous tractors to pass obstacles. In this study, a collision-free path planning method was proposed using the minimum snap algorithm to enable the tractor to safely navigate from one point to another. To authenticate the proposed algorithm, a tripartite experimental scheme was designed, encompassing a ROS-based simulation, robot platform validation, and a field test. Simulation results demonstrate that the collision-free path generated by the proposed algorithms can effectively guide the tractor to follow the given path with a lateral error of 1.75 cm. For the robot platform validation test, the mean lateral deviations were 2.63 cm, 5.01 cm, and 6.80 cm at speeds of 0.5 m/s, 0.8 m/s, and 1.0 m/s, respectively. The feasibility of using the developed algorithms was followed by a field test with a 162-KW CVT autonomous tractor. The mean lateral deviations were 5.17 cm, 5.13 cm, and 6.10 cm at speeds of 0.6 m/s, 0.8 m/s, and 1.0 m/s, respectively. The test results showed that the model could provide a safe and stable collision-free path for agricultural machines operating in the field.
Multi-dimensional Graph Neural Network for Sequential Recommendation

Graph neural networks (GNNs) technology has been widely used in recommendation systems because most information in recommendation systems has a graph structure in nature, and GNNs have advantages in graph representation learning. In sequential recommendation, the relationships between interacting items can be constructed as an isomorphic graph, and (GNNs) can capture high-order information between graph nodes. Many models have used graph-based methods for sequential recommendation, and achieved great success. However, the existing research only considers the number of interactions between items when constructing the item graph. As such, revisions are needed to capture the multi-dimensional transformation relationships between items. Hence, we emphasize the importance of multi-dimensional information, and we propose a Category and Time information integrated Graph Neural Network (CTGNN), which combines the item category and interaction time information with a multi-layer graph convolution network to form multi-dimensional fine-grained item representations. In addition, we design a temporal self-attention network to model the dynamic user preference and make the next-item recommendation. Finally, we conduct extensive experiments on three real-world datasets, and the results demonstrate the excellent performance of the proposed model.
Multivariate time series anomaly detection with adversarial transformer architecture in the Internet of Things

Many real-world Internet of Things (IoT) systems contain various sensor devices. Operating the devices generates a large amount of multivariate time series data, which reflects the changing trends of the devices and the physical environment and provides data services for upstream applications. Given that the quality of IoT services usually depends on the accuracy and integrity of the data, we must guarantee the data's accuracy. The massive sensor data can be continuously monitored to infer normal and abnormal behaviors through anomaly detection. Therefore, to ensure the stability of IoT infrastructure operation, anomaly detection of sensor data has high research value. In this paper, we propose a new multivariate time series anomaly detection structure that can effectively detect anomalies through an adversarial transformer structure. Additionally, the fused anomaly probability strategy can increase the discrimination between normal and abnormal; the reconstruction error of the first stage as the prior knowledge of the second stage can better detect anomalies. The evaluation experiments are conducted on four public datasets and achieve an average anomaly detection F1-Score higher than 0.89, which validates the effectiveness of our proposed method.
Analytic thinking as revealed by function words: What does language really measure?

Abstract Understanding how people think is a key interest in psychology, and recent advances in automated text analysis have used a verbal analytic thinking index to approximate Kahneman's System 2 (e.g., deliberate, rational thinking). That is, prior work used a style word index to assess university student admissions essays and observed that those who used more articles and prepositions relative to storytelling words (e.g., pronouns) had higher grades at the end of college. This work presumed that verbal analytic thinking represented one's cognitive ability or intellectual potential, but this presumption has remained untested. The current research evaluated if verbal analytic thinking is indeed a reflection of cognitive ability or one's interest and motivation to engage in thinking, called need for cognition. Across 500 participants and two writing samples, the most reliable link to verbal analytic thinking was need for cognition, addressing an unexamined empirical question in psychology of language research.
Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middleincome countries.An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth.There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images.However, these are still lacking high precision in oral cancer detection.Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy.In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN).The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER).Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%.In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach.The proposed methodology is solid and efficient, and specialists can adopt it.However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.
Globally Coupled Private Image Encryption Algorithm Based on Infinite Interval Spatiotemporal Chaotic System

This article proposes a new infinite parameter range spatiotemporal chaotic system named improved sinusoidal dynamic non-adjacent coupled mapping lattice (ISDNCML). The system performance test shows that ISDNCML has more random lattice interaction, better chaos and excellent cryptography. Based on the characteristics of ISDNCML, the globally coupled private image encryption algorithm is proposed. The algorithm first identifies the private area of an image and then couples the private and non-private areas for encryption. While coupling encryption, ill-conditioned dynamic diffusion is introduced into the private area, which avoids the repeated encryption of the private area on the premise of ensuring the security of private information. The security and practicability of this proposed cryptographic system are verified by the analysis of ISDNCML and various tests of encryption effect.
FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline

When training a deep learning (DL) model, input data are pre-processed on CPUs and transformed into tensors, which are then fed into GPUs for gradient computations of model training. Expensive GPUs must be fully utilized during training to accelerate the training speed. However, intensive CPU operations for input data preprocessing (input pipeline) often lead to CPU bottlenecks; correspondingly, various DL training jobs suffer from GPU under-utilization. We propose FastFlow, a DL training system that automatically mitigates the CPU bottleneck by offloading (scaling out) input pipelines to remote CPUs. FastFlow carefully decides various offloading decisions based on performance metrics specific to applications and allocated resources, while leveraging both local and remote CPUs to prevent the inefficient use of remote resources and minimize the training time. FastFlow's smart offloading policy and mechanisms are seamlessly integrated with TensorFlow for users to enjoy the smart offloading features without modifying the main logic. Our evaluations on our private DL cloud with diverse workloads on various resource environments show that FastFlow improves the training throughput by 1 ~ 4.34X compared to TensorFlow without offloading, by 1 ~ 4.52X compared to TensorFlow with manual CPU offloading (tf.data.service), and by 0.63 ~ 2.06X compared to GPU offloading (DALI).
International research collaboration: is Africa different? A cross-country panel data analysis

Abstract International research collaboration (IRC) has been relevant for the development of national scientific systems. In Africa, given the limited resources devoted to research and development (R&amp;D) activities and the crucial role that scientific knowledge generated through research activities can have in socioeconomic development, IRC may be an opportunity to strengthen scientific capabilities. While geographical, economic, political/governance, cultural, intellectual and excellence distance hampers IRC in other regions, we argue that economic and excellence distances actuate differently in Africa. We explored the impact of the variables above in addition to the information and communication technologies (ICTs), and social distances on the IRC of these countries. Using panel data for 54 African economies, our results show that economic distance fosters IRC while governance and excellence distances are non-significant. Past collaborations (one out of two proxies for social distance) and speaking the same language have the highest effect on IRC, and ICTs distance the lowest. The results have implications for science policy in Africa. For instance, we argue that science policies need to be adapted to each environment as the scientific landscape in each country is unique.
Animal Detection and Counting from UAV Images Using Convolutional Neural Networks

In the last decade, small unmanned aerial vehicles (UAVs/drones) have become increasingly popular in the airborne observation of large areas for many purposes, such as the monitoring of agricultural areas, the tracking of wild animals in their natural habitats, and the counting of livestock. Coupled with deep learning, they allow for automatic image processing and recognition. The aim of this work was to detect and count the deer population in northwestern Serbia from such images using deep neural networks, a tedious process that otherwise requires a lot of time and effort. In this paper, we present and compare the performance of several state-of-the-art network architectures, trained on a manually annotated set of images, and use it to predict the presence of objects in the rest of the dataset. We implemented three versions of the You Only Look Once (YOLO) architecture and a Single Shot Multibox Detector (SSD) to detect deer in a dense forest environment and measured their performance based on mean average precision (mAP), precision, recall, and F1 score. Moreover, we also evaluated the models based on their real-time performance. The results showed that the selected models were able to detect deer with a mean average precision of up to 70.45% and a confidence score of up to a 99%. The highest precision was achieved by the fourth version of YOLO with 86%, as well as the highest recall value of 75%. Its compressed version achieved slightly lower results, with 83% mAP in its best case, but it demonstrated four times better real-time performance. The counting function was applied on the best-performing models, providing us with the exact distribution of deer over all images. Yolov4 obtained an error of 8.3% in counting, while Yolov4-tiny mistook 12 deer, which accounted for an error of 7.1%.
Computer Science Curricula 2023 (CS2023)

A Joint Task Force of the ACM, IEEE-Computer Society, and AAAI commenced work in 2021 to revise the Computer Science curricular guidelines that were last updated in 2013. Planned for publication in 2023, the revised guidelines (CS2023) cover curricular content and curricular practices. Curricular content includes updates to the CS2013 knowledge areas, a new sunflower model of what constitutes core computer science topics, a proposal for packaging knowledge areas into courses, and a competency model of the curriculum. Curricular practices cover computer science program design and delivery issues, including social aspects, professional practices, and programmatic considerations. In the special session, the latest CS2023 draft will be presented and feedback solicited. The session is targeted toward educators, administrators, and professionals interested in computer science curricular issues.
Deployment of distributed ledger and decentralized technology for transition to smart industries

Abstract Industries are now deploying smart initiatives and innovative business models towards digital transformation. One of such initiatives is the adoption of distributed ledger technology (DLT) which promises to support smart industrial revolution. DLT facilitates non trusted entities to communicate and achieve a consensus in a fully distributed method through an immutable and cryptographically secure ledger. DLT ensures traceability and secure exchange of information while ensuring confidentiality and portability of data. However, only fewer studies have explored the extent to which DLT can support digitalization to achieve smart industrial process. Besides, the governance role of DLT in industrial sectors is still considered a nascent domain of research and DLT governance design and archetypes for smart industries are still in the early stage. Also, there are fewer studies in the literature that presents consensus mechanisms for integrating DLT for digitalization of smart industries. Grounded on the secondary data this study examines the practical benefits and challenges faced in achieving a smart industrial operation. Findings from this study identifies governance and security issues that influences DLT deployment in industrial sectors. More importantly, several factors that impacts the deployment of DLT for smart industries are presented. Implications from this study will be useful for industrial regulators, practitioners and researchers interested in gaining innovative insights about how smart industries can leverage DLT to create value for competitive advantage.
MFID-Net: Multi-scaled feature-fused image dehazing via dynamic weights

Methods based on convolutional neural networks have achieved excellent performance in the image dehazing task. Unfortunately, most of the dehazing methods that exist suffer from loss of detail in the convolution and activation operations and failure to consider the effects of superimposing different intensities of haze, such as under-exposed and over-exposed images. To address these issues, we propose a dynamic dehazing convolution (DDC) based on attentional weight calculation and dynamic weight fusion and a dynamic dehazing activation (DDA) based on the input global context encoding function to address the problem of detail loss. And we propose a multi-scaled feature-fused image dehazing network (MFID-Net) based on DDC and DDA to address the effects of haze superposition. We also design a loss function based on the physical model with dynamic weights. Extensive experimental results demonstrate that the proposed MFID-Net performs favorably against the state-of-the-art algorithms on the hazy dataset while improving further on hazy images with large differences in haze concentration, and producing satisfactory dehazing results. The code is available at https://github.com/awhitewhale/MFID-Net.
Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network

We address the solution of large-scale Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fields. The OED problem seeks to find sensor locations that maximize the expected information gain (EIG) in the solution of the underlying Bayesian inverse problem. Computation of the EIG is usually prohibitive for PDE-based OED problems. To make the evaluation of the EIG tractable, we approximate the (PDE-based) parameter-to-observable map with a derivative-informed projected neural network (DIPNet) surrogate, which exploits the geometry, smoothness, and intrinsic low-dimensionality of the map using a small and dimension-independent number of PDE solves. The surrogate is then deployed within a greedy algorithm-based solution of the OED problem such that no further PDE solves are required. We analyze the EIG approximation error in terms of the generalization error of the DIPNet and show they are of the same order. Finally, the efficiency and accuracy of the method are demonstrated via numerical experiments on OED problems governed by inverse scattering and inverse reactive transport with up to 16,641 uncertain parameters and 100 experimental design variables, where we observe up to three orders of magnitude speedup relative to a reference double loop Monte Carlo method.
Neural Architecture Search Benchmarks: Insights and Survey

Neural Architecture Search (NAS), a promising and fast-moving research field, aims to automate the architectural design of Deep Neural Networks (DNNs) to achieve better performance on the given task and dataset. NAS methods have been very successful in discovering efficient models for various Computer Vision, Natural Language Processing, etc. The major obstacles to the advancement of NAS techniques are the demand for large computation resources and fair evaluation of various search methods. The differences in training pipeline and setting make it challenging to compare the efficiency of two NAS algorithms. A large number of NAS Benchmarks to simulate the architecture evaluation in seconds have been released over the last few years to ease the computation burden of training neural networks and can aid in the unbiased assessment of different search methods. This paper provides and extensive review of several publicly available NAS Benchmarks in the literature. We provide technical details and a deeper understanding of each benchmark and point out future directions.
STWD-SFNN: Sequential three-way decisions with a single hidden layer feedforward neural network

The three-way decisions strategy has been employed to construct network topology in a single hidden layer feedforward neural network (SFNN). However, this model has a general performance, and does not consider the process costs, since it has fixed threshold parameters. Inspired by the sequential three-way decisions (STWD), this paper proposes STWD with an SFNN (STWD-SFNN) to enhance the performance of networks on structured datasets. STWD-SFNN adopts multi-granularity levels to dynamically learn the number of hidden layer nodes from coarse to fine, and set the sequential threshold parameters. Specifically, at the coarse granular level, STWD-SFNN handles easy-to-classify instances by applying strict threshold conditions, and with the increasing number of hidden layer nodes at the fine granular level, STWD-SFNN focuses more on disposing of the difficult-to-classify instances by applying loose threshold conditions, thereby realizing the classification of instances. Moreover, STWD-SFNN considers and reports the process cost produced from each granular level. The experimental results verify that STWD-SFNN has a more compact network on structured datasets than other SFNN models, and has better generalization performance than the competitive models. All models and datasets can be downloaded from https://github.com/wuc567/Machine-learning/tree/main/STWD-SFNN.
Differential privacy for symbolic systems with application to Markov Chains

Data-driven systems are gathering increasing amounts of data from users, and sensitive user data requires privacy protections. In some cases, the data gathered is non-numerical or symbolic, and conventional approaches to privacy, e.g., adding noise, do not apply, though such systems still require privacy protections. Accordingly, we present a novel differential privacy framework for protecting trajectories generated by symbolic systems. These trajectories can be represented as words or strings over a finite alphabet. We develop new differential privacy mechanisms that approximate a sensitive word using a random word that is likely to be near it. An offline mechanism is implemented efficiently using a Modified Hamming Distance Automaton to generate whole privatized output words over a finite time horizon. Then, an online mechanism is implemented by taking in a sensitive symbol and generating a randomized output symbol at each timestep. This work is extended to Markov chains to generate differentially private state sequences that a given Markov chain could have produced. Statistical accuracy bounds are developed to quantify the accuracy of these mechanisms, and numerical results validate the accuracy of these techniques for strings of English words.