Reviewerly/exHarmony huggingface dataset:
Self-paced ARIMA for robust time series prediction

For time series prediction tasks, the autoregressive integrated moving average (ARIMA) model is one of the most classical and popular linear models, and extended applications have achieved satisfying prediction accuracy in many domains. However, less work has focused on the influence of noisy data, which causes the instability of ARIMA and a sharp decline in performance. In this paper, we propose a robust time series prediction framework named spARIMA. To reduce noise interference in the training process, we design a sequential training scheme in batches based on the degree of noise and the contribution to the correct modeling. spARIMA relies on the differential prediction model in ARIMA and absorbs the advantages of the gradual training scheme in self-paced learning (SPL) so that spARIMA can effectively address the instability caused by noise. Moreover, considering that the similarity of selected samples may generate local optimal solutions, we extend spARIMA to diversity selection, which utilizes a weighted local complexity–similarity distance expression to represent the diversity of noisy data. We test the performance of spARIMA on twelve datasets compared with existing ARIMA models on two gradient descent algorithms. The results show that our method is not only suitable for noisy data but also efficient for normal data, which indicates the generalization ability of spARIMA.
Regulation of Digital Healthcare in India: Ethical and Legal Challenges

In the wake of the COVID-19 pandemic, digital healthcare has gained an influx of interest and global investment. The WHO has published guidelines and recommendations for countries to successfully implement telemedicine on a large, nationwide scale. This is not only helpful for patients who wish to protect themselves from COVID-19 and related illnesses that they may be vulnerable to, but it also has great potential to increase access to healthcare. In India, a country without universal healthcare grappling with a high level of distrust in the public health system, there are several implementation challenges for digital healthcare across the country. The current laws in India that regulate technology do not explicitly address telehealth, nor are there adequate data protection laws in place that could manage the significant amount of data that would be generated by digital healthcare if applied on a large scale. Further, there are concerns at the level of patient privacy, which could be compromised through digital healthcare. In addition to the legal concerns surrounding privacy, there is no framework in place to ensure informed consent in a digital healthcare context. In this paper, I analyze the legal, structural, and ethical concerns around digital health and provide an understanding of the problems these shortcomings pose, as well as policy recommendations for overcoming these problems.
PGA: A New Hybrid PSO and GA Method for Task Scheduling with Deadline Constraints in Distributed Computing

Distributed computing, e.g., cluster and cloud computing, has been applied in almost all areas for data processing, while high resource efficiency and user satisfaction are still the ambition of distributed computing. Task scheduling is indispensable for achieving the goal. As the task scheduling problem is NP-hard, heuristics and meta-heuristics are frequently applied. Every method has its own advantages and limitations. Thus, in this paper, we designed a hybrid heuristic task scheduling problem by exploiting the high global search ability of the Genetic Algorithm (GA) and the fast convergence of Particle Swarm Optimization (PSO). Different from existing hybrid heuristic approaches that simply sequentially perform two or more algorithms, the PGA applies the evolutionary method of a GA and integrates self- and social cognitions into the evolution. We conduct extensive simulated environments for the performance evaluation, where simulation parameters are set referring to some recent related works. Experimental results show that the PGA has 27.9–65.4% and 33.8–69.6% better performance than several recent works, on average, in user satisfaction and resource efficiency, respectively.
Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking

Complete autonomous systems such as self-driving cars to ensure the high reliability and safety of humans need the most efficient combination of four-dimensional (4D) detection, exact localization, and artificial intelligent (AI) networking to establish a fully automated smart transportation system. At present, multiple integrated sensors such as light detection and ranging (LiDAR), radio detection and ranging (RADAR), and car cameras are frequently used for object detection and localization in the conventional autonomous transportation system. Moreover, the global positioning system (GPS) is used for the positioning of autonomous vehicles (AV). These individual systems' detection, localization, and positioning efficiency are insufficient for AV systems. In addition, they do not have any reliable networking system for self-driving cars carrying us and goods on the road. Although the sensor fusion technology of car sensors came up with good efficiency for detection and location, the proposed convolutional neural networking approach will assist to achieve a higher accuracy of 4D detection, precise localization, and real-time positioning. Moreover, this work will establish a strong AI network for AV far monitoring and data transmission systems. The proposed networking system efficiency remains the same on under-sky highways as well in various tunnel roads where GPS does not work properly. For the first time, modified traffic surveillance cameras have been exploited in this conceptual paper as an external image source for AV and anchor sensing nodes to complete AI networking transportation systems. This work approaches a model that solves AVs' fundamental detection, localization, positioning, and networking challenges with advanced image processing, sensor fusion, feathers matching, and AI networking technology. This paper also provides an experienced AI driver concept for a smart transportation system with deep learning technology.
The use of chatbots in university EFL settings: Research trends and pedagogical implications

This mini-review aims to identify major research trends, models, and theories and provide specific pedagogical implications for teaching when using chatbots in EFL classes. This study follows the guidelines of the PRISMA methodology and searches for open-access empirical studies in two reputable databases, Web of Science and Scopus. The results of this mini-review confirm the findings of other research studies, which show that the present research on the use of chatbots in university EFL settings focuses on their effectiveness, motivation, satisfaction, exposure, and assessment. The key contribution of this study lies in its evaluation of the chatbot's potential in applying and integrating the existing theories and concepts used in EFL teaching and learning, such as CEFR, mind mapping, or self-regulatory learning theory. This will address the gap in the literature because no previous review study has conducted such an analysis. Overall, the findings of this mini-review contribute with their specific pedagogical implications and methods to the effective use of chatbots in the EFL environment, be it formal or informal.
Innovation Intervention: Cyber Stalking

Today, there is a growing acceptance of the Internet of Things (IoT). In addition, for Technology devices to better serve the expanding need for network devices or the services provided, a few major obstacles still require special attention. This new technology has enormous promise in the most varied technical fields and may considerably aid in realizing the IoT technologies vision in several ways, including by boosting the capacity for decentralization, simplifying interaction, enabling transaction models, or promoting autonomous device coordination. In order to help the issues related to cyberstalking technology plays important role like blockchain, big data. The objective of this article is to explain the fundamentals of big data & blockchain technology, as well as how they may be utilized to protect stalkers security and safety. Then, introduce the stalker, as selfish miner variation whose goal is to stop a node from publishing any blocks mostly on main chain.
Cryptography Algorithms for Enhancing IoT Security

In today's fast-paced world, a new technology paradigm known as the Internet of Things (IoT) is advancing every business. It provides communication between the digital and physical worlds, transforming the way people do business quickly. The IoT is a network of physical objects (things) equipped with sensors, intelligent networking, radio-frequency identification (RFID), and other technologies that communicate and exchange data with other systems and devices online. IoT applications are now significantly more prevalent worldwide. IoT is driving the real world to become more intelligent in several industries, including smart grids for electricity, intelligent traffic, and smart homes and buildings. As a result, predictions and research point to over 50 billion connected devices by 2020. It will be required to safeguard this growing volume of data during exchange because the amount of data being transmitted will be quite enormous. Data security is represented by cryptography, an essential tool (algorithms) in security, which is why many researchers are developing cryptographic algorithms to improve the Security of IoT. This survey paper introduced an overview of IoT technology, architecture, and applications and a detailed analysis comparing all cryptographic algorithms and their use in day-to-day life activities. This paper discusses lightweight block ciphers, stream ciphers, and hybrid ciphers. The report evaluates security algorithms, comparing performance and robustness with the computational complexity of these techniques. Finally, the survey presents IoT security challenges, threats, and attacks with their mitigation techniques.
Quantum computation for periodic solids in second quantization

In this work, we present a quantum algorithm for ground-state energy calculations of periodic solids on error-corrected quantum computers. The algorithm is based on the sparse qubitization approach in second quantization and developed for Bloch and Wannier basis sets. We show that Wannier functions require less computational resources with respect to Bloch functions because: (i) the L$_1$ norm of the Hamiltonian is considerably lower and (ii) the translational symmetry of Wannier functions can be exploited in order to reduce the amount of classical data that must be loaded into the quantum computer. The resource requirements of the quantum algorithm are estimated for periodic solids such as NiO and PdO. These transition metal oxides are industrially relevant for their catalytic properties. We find that ground-state energy estimation of Hamiltonians approximated using 200--900 spin orbitals requires {\it ca.}~$10{}^{10}$--$10^{12}$ T gates and up to $3\cdot10^8$ physical qubits for a physical error rate of $0.1\%$.
Audio Classification with Skyrmion Reservoirs

Physical reservoir computing is a computational paradigm that enables spatiotemporal pattern recognition to be performed directly in matter. The use of physical matter leads the way toward energy‐efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. Proposed herein is a high‐performance “skyrmion mixture reservoir” that implements the reservoir computing model with multidimensional inputs. This implementation solves spoken digit classification tasks with an overall model accuracy of 97.4% and a &lt; 1% word error ratethe best performance ever reported for in materio reservoir computers. Due to the quality of the results and the low‐power properties of magnetic texture reservoirs, it is evident that skyrmion fabrics are a compelling candidate for reservoir computing.
Informative Evaluation Metrics for Highly Imbalanced Big Data Classification

We conduct experiments that show the Area Under the Precision Recall Curve (AUPRC) metric provides a more meaningful insight into the impact of Random Undersampling than Area Under the Receiver Operating Characteristic Curve (AUC). Evaluating experiments with multiple metrics is a robust method for overcoming challenges in Machine Learning, such as class imbalance. Random Undersampling is a technique to deal with class imbalance. We find Random Undersampling may provide an improvement to AUC scores. However, at the same time, Random Undersampling may be detrimental to AUPRC scores. AUPRC is a metric that involves precision, whereas AUC does not. In the classification of imbalanced Big Data, an increase in false positive counts has a more noticeable drop in precision scores. Therefore, in application domains where false positives are undesirable, optimizing models for AUPRC is a wise choice. Our contribution is to compare the performance of models in terms of AUPRC and AUC to show the impact of Random Undersampling on the classification of imbalanced Big Data. We compare the performance via experiments in the classification of highly imbalanced Big Data. Models are built with data in its original class ratio, and with data undersampled into 5 distinct class ratios. We report the results of 600 experiments where we apply Random Undersampling to a dataset with about 175 million instances. To the best of our knowledge we are the first to utilize Medicare Part D data which became available in 2021.
Inventory management and information sharing based on blockchain technology

This paper presents a structural framework for blockchain-based supply chains (SCs) and demonstrates how blockchain technology can help realize five crucial changes: common platform, end-to-end information sharing and traceability, real-time update, speedy and efficient asset transfer, and contract automation. The most unique change is that a blockchain-based supply chain operates on a decentralized common platform, in contrast to the tiered structure of a traditional supply chain. Using fundamental analytical models, inventory management and information-sharing benefits from the abovementioned critical changes were quantified. Therefore, the three managerial implications for supply chain resilience are as follows. (1) A blockchain-based SC essentially transforms a multi-tier SC into a one-platform SC, effectively shortening the length of SC tiers and therefore eliminating problems and challenges in multi-tier supply chain management (SCM). (2) A blockchain-based SC can be both time- and cost-efficient. (3) A company on the blockchain platform can reduce the cost and risk/variance associated with its SC partners. These results can substantially contribute to the industry, given that blockchain is in the early stage, thus rendering supply chains smarter and more efficient.
Two-phase Defense Against Poisoning Attacks on Federated Learning-based Intrusion Detection

The Machine Learning-based Intrusion Detection System (ML-IDS) becomes more popular because it doesn't need to manually update the rules and can recognize variants better, However, due to the data privacy issue in ML-IDS, the Federated Learning-based IDS (FL-IDS) was proposed. In each round of federated learning, each participant first trains its local model and sends the model's weights to the global server, which then aggregates the received weights and distributes the aggregated global model to participants. An attacker will use poisoning attacks, including label-flipping attacks and backdoor attacks, to directly generate a malicious local model and indirectly pollute the global model. Currently, a few studies defend against poisoning attacks, but they only discuss label-flipping attacks in the image field. Therefore, we propose a two-phase defense mechanism, called Defending Poisoning Attacks in Federated Learning (DPA-FL), applied to intrusion detection. The first phase employs relative differences to quickly compare weights between participants because the local models of attackers and benign participants are quite different. The second phase tests the aggregated model with the dataset and tries to find the attackers when its accuracy is low. Experiment results show that DPA-FL can reach 96.5% accuracy in defending against poisoning attacks. Compared with other defense mechanisms, DPA-FL can improve F1-score by 20∼64% under backdoor attacks. Also, DPA-FL can exclude the attackers within twelve rounds when the attackers are few.
High-Resolution Swin Transformer for Automatic Medical Image Segmentation

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuously exchanging feature map information with different resolutions generated by the Transformer blocks. The proposed Transformer-based network is named the high-resolution Swin Transformer network (HRSTNet). Extensive experiments demonstrated that the HRSTNet can achieve performance comparable with that of the state-of-the-art Transformer-based U-Net-like architecture on the 2021 Brain Tumor Segmentation dataset, the Medical Segmentation Decathlon's liver dataset, and the BTCV multi-organ segmentation dataset.
Light Field Image Super-Resolution Network via Joint Spatial-Angular and Epipolar Information

This paper proposes a novel efficient convolutional neural network (CNN) for light field (LF) spatial super-resolution (SR). Due to the high dimensionality of LF data, it is important to decompose the original 4D LF data structure into a lower data subspace helping to reduce memory and computational costs. In this context, we propose a novel hybrid CNN structure working on different 2D data representations to fully handle the 4D structure. Our proposed network can simultaneously exploit information from three different types of 4D LF representations: spatial, angular, and epipolar plane image (EPI) information. Based on pixel similarities of each data subspace, we propose an inter-intra spatial feature extractor (II-SFE) and an inter-intra angular feature extractor (II-AFE) that can exploit the pixel correlations among the spatial-angular domain. Moreover, based on the correlated epipolar geometry among pixels, we also propose a multi-orientation epipolar feature extractor (MO-EFE) by decomposing an LF image into four orientation stacks of views and feeding them into different network branches to learn rich sub-pixel information in the EPI domain. The extracted features of each 2D subspace are integrated by our proposed attention fusion module (AFM), which adaptively rescales the embedding weights based on their correlations. Experimental results on both small- and wide-baseline benchmark LF datasets confirm the effectiveness of the proposed method compared to the well-known state-of-the-art methods in terms of objective and subjective performance.
Impact of Tokenization on Language Models: An Analysis for Turkish

Tokenization is an important text preprocessing step to prepare input tokens for deep language models. WordPiece and BPE are de facto methods employed by important models, such as BERT and GPT. However, the impact of tokenization can be different for morphologically rich languages, such as Turkic languages, where many words can be generated by adding prefixes and suffixes. We compare five tokenizers at different granularity levels, i.e. their outputs vary from smallest pieces of characters to the surface form of words, including a Morphological-level tokenizer. We train these tokenizers and pretrain medium-sized language models using RoBERTa pretraining procedure on the Turkish split of the OSCAR corpus. We then fine-tune our models on six downstream tasks. Our experiments, supported by statistical tests, reveal that Morphological-level tokenizer has challenging performance with de facto tokenizers. Furthermore, we find that increasing the vocabulary size improves the performance of Morphological and Word-level tokenizers more than that of de facto tokenizers. The ratio of the number of vocabulary parameters to the total number of model parameters can be empirically chosen as 20% for de facto tokenizers and 40% for other tokenizers to obtain a reasonable trade-off between model size and performance.
Planarized sentence representation for nested named entity recognition

One strategy to recognize nested entities is to enumerate overlapped entity spans for classification. However, current models independently verify every entity span, which ignores the semantic dependency between spans. In this paper, we first propose a planarized sentence representation to represent nested named entities. Then, a bi-directional two-dimensional recurrent operation is implemented to learn semantic dependencies between spans. Our method is evaluated on seven public datasets for named entity recognition. It achieves competitive performance in named entity recognition. The experimental results show that our method is effective to resolve nested named entities and learn semantic dependencies between them.
Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

In recent years, considerable work has been conducted on the development of synthetic medical images, but there are no satisfactory methods for evaluating their medical suitability. Existing methods mainly evaluate the quality of noise in the images, and the similarity of the images to the real images used to generate them. For this purpose, they use feature maps of images extracted in different ways or distribution of images set. Then, the proximity of synthetic images to the real set is evaluated using different distance metrics. However, it is not possible to determine whether only one synthetic image was generated repeatedly, or whether the synthetic set exactly repeats the training set. In addition, most evolution metrics take a lot of time to calculate. Taking these issues into account, we have proposed a method that can quantitatively and qualitatively evaluate synthetic images. This method is a combination of two methods, namely, FMD and CNN-based evaluation methods. The estimation methods were compared with the FID method, and it was found that the FMD method has a great advantage in terms of speed, while the CNN method has the ability to estimate more accurately. To evaluate the reliability of the methods, a dataset of different real images was checked.