Reviewerly/exHarmony huggingface dataset:
Three language political leaning text classification using naturallanguage processing methods

In this article, the problem of political leaning classificationof the text resource is solved. First, a detailed analysis of ten stud-ies on the work’s topicwas performed in the form of comparative characteristicsof the used methodologies.Literary sources were compared according to the problem-solvingmethods,the learning that was carried out, the evaluation metrics, and according to the vectorizations.Thus, it was determined that machine learning algorithms and neural networks, as well as vectorizationmethods TF-IDF and Word2Vec, were most often used to solve the problem.Next, various classification models of whether textual information is pro-Ukrainian or pro-Russian were built based on a dataset containing messages from social media users about the events of the large-scale Russian invasion of Ukraine from February 24, 2022.The problem was solved with the help of Support Vector Machines, Decision Tree, Random Forest, Naïve Bayes classifier,eXtreme Gradient BoostingandLogistic Regressionmachine learning algo-rithms, Convolutional Neural Networks, Long short-term memory and BERT neural networks, techniques for working with unbal-anced dataRandom Oversampling, Random Undersampling , SMOTE and SMOTETomek, as well as stacking ensembles of models.Amongthe machine learning algorithms, LR performed best, showing a macro F1-scorevalue of 0.7966 when features were trans-formed by TF-IDF vectorization and 0.7933 when BoW.Among neural networks, the best macro F1-scorevalue of 0.76was ob-tained using CNN and LSTM.Applying data balancing techniques failed to improve the results of machine learning algorithms.Next, ensembles of models from machine learning algorithms were determined. Two of the constructed ensembles achieved the same macro F1-scorevalue of 0.7966 as with LR. Ensembles that wasable to do so consisted of the TF-IDF vectorization, the B-NBC meta-model, and the SVC, NuSVC LR, and SVC, LR base models, respectively.Thus, three classifiers, the LR machine learning algorithmand two ensembles of models, which were defined as a combination of existing methods of solving the problem, demon-strated the largest macro F1-score value of 0.7966. The obtained models can be used for a detailed review of various news publica-tions according to the political leaning characteristic, information about which can help people identify being isolated by a filter bubble.
Path Smoothing with Deterministic Shortcuts

Path smoothing is an important operation in a number of path planning applications. While several approaches have been proposed in the literature, a lack of simple and effective methods with quality-based termination conditions can be observed. In this paper we propose a deterministic shortcut-based smoothing method that is simple to be implemented and achieves user-specified termination conditions based on solution quality, overcoming one of the main limitations observed in traditional random-based approaches. We present several benchmarks demonstrating that our method produces higher-quality results when compared to the traditional random shortcuts approach.
Arabic Hate Speech Identification by Enriching MARBERT Model with Hybrid Features

The propagation of hate speech has grown more particularly apparent on social media sites as their usage for communication and unrestricted thought has increased on a worldwide scale. However, this could cause disagreement and antagonism among users, creating an unattractive online environment. Countries, companies, and academic institutions have all invested heavily in finding an effective solution to this challenge. There has been less study done in Arabic compared to other languages to develop automated systems for recognizing hate speech. Additionally, Arabic research on the correlation between personality traits and hate speech still remains rather limited. In this paper, we propose a novel method for enriching MARBERT model that incorporates static word embedding (AraVec 2.0) and personality trait features for Arabic hate speech detection. The experimental results indicate that the suggested methodology exceeds in terms of macro-F1 score by achieving 86.4% compared to previous research reported in the literature.
Adsorption of strontium (Sr2+) in seawater using adsorbent Fe3O4/MnO2/fulvic acid nanocomposite

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ferdy Nanlohy, Anugrah Ricky Wijaya, Bambang Semedi, Eli Hendrik Sanjaya, Husni Wahyu Wijaya, Desi Wulandari; Adsorption of strontium (Sr2+) in seawater using adsorbent Fe3O4/MnO2/fulvic acid nanocomposite. AIP Conference Proceedings 24 January 2023; 2634 (1): 020100. https://doi.org/10.1063/5.0112262 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Restricted access data in the neurosciences: Are the restrictions always justified?

OPINION article Front. Neurosci., 24 January 2023Sec. Neural Technology Volume 16 - 2022 | https://doi.org/10.3389/fnins.2022.975795
Corrigendum: Impact of climate change and rubber (Hevea brasiliensis) plantation expansion on reference evapotranspiration in Xishuangbanna, Southwest China

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.In the published article, there are errors for The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.In the published article, there was an error. We mixed the data of other research by mistake. We apply to delete this sentence. "[Wind speed in Xishuangbanna showed the largest change which is followed by sunshine duration, average relative humidity, and air temperature with 11.71%, 4.39%, 4.32%, and 2.84%, respectively. ]"The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated. "[According to formula (1), we selected six meteorological factors for correlation analysis with the annual ET0 in the rubber plantation area of Xishuangbanna, as shown in The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated. 5).]" The corrected sentence appears below:"[It was significantly negatively correlated with RH, and the correlation coefficients of 0.52-0.89 ( shown in Table 4).
Dynamic Analogical Association Algorithm Based on Manifold Matching for Few-Shot Learning

At present, deep learning has been well applied in many fields.However, due to the high complexity of hypothesis space, numerous training samples are usually required to ensure the reliability of minimizing experience risk.Therefore, training a classifier with a small number of training examples is a challenging task.From a biological point of view, based on the assumption that rich prior knowledge and analogical association should enable human beings to quickly distinguish novel things from a few or even one example, we proposed a dynamic analogical association algorithm to make the model use only a few labeled samples for classification.To be specific, the algorithm search for knowledge structures similar to existing tasks in prior knowledge based on manifold matching, and combine sampling distributions to generate offsets instead of two sample points, thereby ensuring high confidence and significant contribution to the classification.The comparative results on two common benchmark datasets substantiate the superiority of the proposed method compared to existing data generation approaches for few-shot learning, and the effectiveness of the algorithm has been proved through ablation experiments.
Video Transmission Secrecy Improvement Based on Fractional Order Hyper Chaotic System

In the Digital World scenario, the confidentiality of information in video transmission plays an important role.Chaotic systems have been shown to be effective for video signal encryption.To improve video transmission secrecy, compressive encryption method is proposed to accomplish compression and encryption based on fractional order hyper chaotic system that incorporates Compressive Sensing (CS), pixel level, bit level scrambling and nucleotide Sequences operations.The measurement matrix generates by the fractional order hyper chaotic system strengthens the efficiency of the encryption process.To avoid plain text attack, the CS measurement is scrambled to its pixel level, bit level scrambling decreases the similarity between the adjacent measurements and the nucleotide sequence operations are done on the scrambled bits, increasing the encryption.Two stages are comprised in the reconstruction technique, the first stage uses the intra-frame similarity and offers robust preliminary retrieval for each frame, and the second stage iteratively improves the efficiency of reconstruction by integrating inter frame Multi Hypothesis (MH) estimation and weighted residual sparsity modeling.In each iteration, the residual coefficient weights are modified using a mathematical approach based on the MH predictions, and the Split Bregman iteration algorithm is defined to resolve weighted l1 regularization.Experimental findings show that the proposed algorithm provides good compression of video coupled with an efficient encryption method that is resistant to multiple attacks.
Hybrid Watermarking and Encryption Techniques for Securing Medical Images

Securing medical data while transmission on the network is required because it is sensitive and life-dependent data.Many methods are used for protection, such as Steganography, Digital Signature, Cryptography, and Watermarking.This paper introduces a novel robust algorithm that combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) digital image-watermarking algorithms.The host image is decomposed using a two-dimensional DWT (2D-DWT) to approximate low-frequency sub-bands in the embedding process.Then the sub-band low-high (LH) is decomposed using 2D-DWT to four new sub-bands.The resulting sub-band low-high (LH 1 ) is decomposed using 2D-DWT to four new sub-bands.Two frequency bands, high-high (HH 2 ) and high-low (HL 2 ), are transformed by DCT, and then the SVD is applied to the DCT coefficients.The strongest modified singular values (SVs) vary very little for most attacks, which is an important property of SVD watermarking.The two watermark images are encrypted using two layers of encryption, circular and chaotic encryption techniques, to increase security.The first encrypted watermark is embedded in the S component of the DCT components of the HL2 coefficients.The second encrypted watermark is embedded in the S component of the DCT components of the HH 2 coefficients.The suggested technique has been tested against various attacks and proven to provide excellent stability and imperceptibility results.
Is sized typing for Coq practical?

Contemporary proof assistants such as Coq require that recursive functions be terminating and corecursive functions be productive to maintain logical consistency of their type theories, and some ensure these properties using syntactic checks. However, being syntactic, they are inherently delicate and restrictive, preventing users from easily writing obviously terminating or productive functions at their whim. Meanwhile, there exist many sized type theories that perform type-based termination and productivity checking, including theories based on the Calculus of (Co)Inductive Constructions (CIC), the core calculus underlying Coq. These theories are more robust and compositional in comparison. So why haven't they been adapted to Coq? In this paper, we venture to answer this question with CIC$\widehat{*}$, a sized type theory based on CIC. It extends past work on sized types in CIC with additional Coq features such as global and local definitions. We also present a corresponding size inference algorithm and implement it within Coq's kernel; for maximal backward compatibility with existing Coq developments, it requires no additional annotations from the user. In our evaluation of the implementation, we find a severe performance degradation when compiling parts of the Coq standard library, inherent to the algorithm itself. We conclude that if we wish to maintain backward compatibility, using size inference as a replacement for syntactic checking is wildly impractical in terms of performance.
A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization

Abstract Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty method. In the co-evolution stage, the population is divided into multiple subpopulations, each of which is associated with a penalty factor. Through the co-evolution of these subpopulations, the performance of penalty factors can be evaluated. Since different penalty factors are used, the subpopulations will evolve along different directions. Thus, exploration can be enhanced. In the shuffle stage, all subpopulations are merged into a population and the best penalty factor from the co-evolution stage is used to guide the evolution. In this manner, the information interaction among subpopulations can be facilitated; thus, exploitation can be promoted. By executing these two stages iteratively, the feasible optimum could be obtained finally. In the two-stage evolutionary process, the search algorithm is designed based on two trial vector generation strategies of differential evolution. Additionally, a restart mechanism is designed to help the population avoid stagnating in the infeasible region. Extensive experiments demonstrate the effectiveness of the proposed method.
In-Store Personal Navigation and Dynamic Pricing

This paper contributes to the existing field of research about self-organized logistics (SOL). Self-organized logistics is a system that consists of agents that rely on strong horizontal connections rather than on vertical ones while executing tasks. The agents do this without strong intervention of humans or a controlling software. SOL can be utilized in retail as an enhancement to customer experience in store. Self-driven smart carts equipped with sensors can provide guidance and assist shoppers.
Data Integrity for Dynamic Big Data in Cloud Storage: A Comprehensive Review and Critical Issues

Cloud storage services provide vast storage space to solve the bottleneck of the data generated by different big data applications. However, the nature of big data in terms of its massive volume and rapid velocity, needs to be considered when designing data integrity schemes to provide security assurance for data stored in the cloud. The state of the art of data integrity in the cloud includes two primary schemes: (i) Proof of Retrievability (POR) and (ii) Provable Data Possession. Both techniques are designed to achieve the same goal in ensuring data integrity of outsourced data in cloud storage; However, PoR varies from PDP by error-correcting feature to retrieve the damaged outsourced data. This paper focuses on the proof of data retrievability technique (POR) for dynamic data. Dynamic data is defined as data under different update operations. The paper surveys the state of the art data integrity techniques for cloud storage (CS) and previous work on basic requirements for an effective data integrity technique for big data applications. Methods used to provide dynamic PoR are discussed before summarizing the classification of the POR state-of-the-art. The recently proposed techniques and their limitations are also discussed with issues to consider for future POR scheme design.
Effects of haptic imagery on purchase intention

Abstract The COVID‑19 pandemic has increased the popularity of online shopping, and companies are looking for ways to provide consumers with experiences that online shopping cannot provide, such as touching products and imagining them in use. In this context, the importance of haptic imagery of products showcased online is increasing. This study replicated and extended Peck et al.’s (2013, Journal of Consumer Psychology , 23 , 189–196) finding that physical control and psychological ownership mediate the influence of haptic imagery on purchase intention. This study showed that imagining touching a product increased purchase intention through the mediation of physical control and psychological ownership compared with not imagining, conceptually replicating Peck et al.’s study. This study also examined the moderating effect of product involvement and showed that there was no moderator role of product involvement. The findings would have a practical application in marketing, such as encouraging consumers to imagine touching the product.
Evaluation of Semantic Interoperability of Automotive Service API Models Based on Metamodels Similarity Metrics Using a Semi-automated Approach

In recent years, mapping of application software components’ ontologies semantically emerged as a big research challenge in automotive application domain that manipulates several cross-enterprise synergy knowledge application frameworks. The same knowledge formalized by different experts in different vehicle application frameworks leads to heterogeneous representations of components’ interface data. Consequently, this causes the most daunting impediment in semantic interoperability between the service components in cooperative automotive systems. From a modeling perspective, in the absence of standardized domain-based unified modeling techniques, the orchestration and resolution of semantic data interoperability between various vehicle application frameworks’ components’ interface models remain a challenge. However, this challenge could be addressed using ontological metamodeling by specifying semantic associations between components’ interface model concepts based on the domain knowledge. Apart from the semantic mapping of interface ontological metamodels, this work also defines quality metrics to determine the degree of semantic alignment achieved between the various interface ontologies. Additionally, to reduce development time and cost towards semantic interoperability, this work proposes a semi-automated plugin tool for the applicability of the evaluated quality metrics to semantic mapping of real-world components’ interface models.
CHARACTERISTICS OF FLOOD INUNDATION IN WATARI AREA DUE TO HEAVY RAINFALL IN JULY 2020

令和2年7月豪雨によって,人吉・球磨盆地において大規模な浸水被害が発生した.特に球磨郡球磨村渡地区では,浸水に加えて氾濫流による多くの家屋被害も確認されている.本研究では,球磨郡球磨村渡地区において氾濫流が河川と一体的に流れる流下型氾濫の挙動を把握し,家屋倒壊等氾濫想定区域の検証を行った.その結果,家屋倒壊等氾濫想定区域によると,渡地区では山地以外の箇所がすべて指定されていた.今回の水害で渡地区茶屋集落の家屋流失が目立ったのは,堤防と鉄道に挟まれた,流体力の大きい領域であった.また,球磨川の渡地区における氾濫流の挙動は流下型氾濫形態であった.支川からの流入を考慮しない計算からは,氾濫流は連続した蛇行部の上流から堤防を乗り越えて堤内地に侵入したことが確認された.
Aaron

AI applications powered by deep learning are increasingly running on edge devices. Meanwhile, many real-world IoT applications demand multiple real-time tasks to run on the same device, for example, to achieve both object tracking and image segmentation simultaneously on an augmented reality glass. However, the current solutions can not yet support such multi-tenant real-time DNN inference on edge devices. Techniques such as on-device model compression trade inference accuracy for speed, while traditional DNN compilers mainly focus on single-tenant DNN model optimization. To fill this gap, we propose Aaron, which leverages DNN compiling techniques to accelerate multi-DNN inference on edge GPU based on compile-time kernel adaptation with no accuracy loss. Aaron integrates both DNN graph and kernel optimization to maximize on-device parallelism and minimize contention brought by concurrent inference.
PFed-LDP

Recent advancements in deep learning techniques have shown great potential for smart Internet of Things (IoT) applications. However, the edge devices of IoT applications often collect and store only limited data, which is insufficient for training modern deep learning models. Collaborative training methods such as cloud computing and federated learning set steps to build robust models for IoT applications, yet these methods bring the concern of data privacy (e.g., untrusted central server, model inversion). On the other hand, directly applying privacy-preserving techniques such as differential privacy can dramatically degrade the performance of IoT applications. Inspired by the development of model personalization, we aim to design a federated learning framework in a personalized fashion to reduce the accuracy loss caused by privacy-preserving techniques. In this paper, we present PFed-LDP, a private and accurate federated local differential privacy (LDP) framework for IoT sensing data. We first design a dynamic layer sharing mechanism to separate the local model into global layers and personalized layers. Second, we apply LDP noise to the global layers and transmit them to the federated learning framework for aggregation. Third, each local client updates their model with local personalized layers and aggregated global layers to perform IoT tasks. Our experiments on real-world datasets show that we only sacrifice 1.6% of accuracy to achieve privacy-preserving IoT applications. We also observe that our method has the smallest accuracy range, which means we can achieve the best performance for the worst performed client.
Dynamic Trust-Based Resource Allocation Mechanism for Secure Edge Computing

Edge computing plays an important role in processing and storing data. By offloading tasks to the edge server, mobile users can access necessary computing resources on demand. However, security of edge computing service is still a major concern. This paper proposes an edge computing resource allocation mechanism based on dynamic trust. First, security problems due to lack of reliability in the resource allocation process are solved based on the trust mechanism. This mechanism considers the resource allocation process between the mobile user node and the edge server as a transaction, according to the trading behavior in the transaction process of server to give its corresponding trust. Second, a trust mechanism is used for dynamic credit granting. Mobile users with similar behaviors form a group, where a representative is elected to trade resources and bundle information into a block and attach it on the chain. At the same time, the delay problem is added as a constraint to the trust calculation. Finally, the simulation experiment shows that the mechanism improves security of edge computing.