Reviewerly/exHarmony huggingface dataset:
Ext4 and XFS File System Forensic Framework Based on TSK

Recently, the number of Internet of Things (IoT) devices, such as artificial intelligence (AI) speakers and smartwatches, using a Linux-based file system has increased. Moreover, these devices are connected to the Internet and generate vast amounts of data. To efficiently manage these generated data and improve the processing speed, the function is improved by updating the file system version or using new file systems, such as an Extended File System (XFS), B-tree file system (Btrfs), or Flash-Friendly File System (F2FS). However, in the process of updating the existing file system, the metadata structure may be changed or the analysis of the newly released file system may be insufficient, making it impossible for existing commercial tools to extract and restore deleted files. In an actual forensic investigation, when deleted files become unrecoverable, important clues may be missed, making it difficult to identify the culprit. Accordingly, a framework for extracting and recovering files based on The Sleuth Kit (TSK) is proposed by deriving the metadata changed in Ext4 file system journal checksum v3 and XFS file system v5. Thereafter, by comparing the accuracy and recovery rate of the proposed framework with existing commercial tools using the experimental dataset, we conclude that sustained research on file systems should be conducted from the perspective of forensics.
Application of Adaptive Virtual Reality with AI-Enabled Techniques in Modern Sports Training

Virtual reality technology has many characteristics, such as high immersion, dynamic interactive response, and multidimensional information digitization. These characteristics are needed by the combination of modern sports development and scientific and technological progress. The introduction of virtual reality technology into the field of sports will be conducive to the scientific training of sports competition and the digital research of technical theory and to the rapid development of modern sports. This paper constructs the framework of sports training based on virtual reality technology and designs a motion capture data algorithm based on behavior string, which successfully improves the advantages of virtual reality technology. In addition, the training experiment method is used to verify the effectiveness and superiority of the system, focusing on the high-school students learning tennis for the first time as the experimental object to study the differences in training effects between the traditional training method and the training method using virtual reality technology. The training effect of tennis is better than the traditional training method, and the difference is significant; the internal motivation of training psychology is better than the traditional training method, and the difference is significant. Compared with the traditional training method, this training method can stimulate students’ interest in training, improve students’ training effect, and promote students’ psychological internal motivation to continue training. This study provides useful enlightenment for the further application of virtual reality technology and in the modern sports training for various sports events.
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities.The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils).Additionally, users migrate to new forums when one is closed further increasing the difficulty of linking users across multiple forums.We develop a novel stylometry-based multitask learning approach for natural language and model interactions using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution.We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2X on Recall@10.
Environmental, Social and Governance (ESG) Disclosure and Its Impact on Financial Performance of Top 100 Companies in Malaysia and Australia

The definition of Environmental, Social and Governance (ESG) is not obscure. ESG involvement is the proceeding dedication by commerce to act ethically and contribute to economic growth while improving the standard of life of the employees and their families as well as the people around them. In the attempt to investigate the impact of ESG disclosure on financial performance of top 100 companies in Malaysia and Australia, this research scrutinises the annual reports of top 100 companies in Malaysia and Australia based on market capitalisation in 2017. This research has considered a comparison between Malaysia, a developing country and Australia, a developed country due to the purpose of evaluating the levels of disclosure based on different regulatory requirements on ESG while assessing the impact of ESG disclosure on Company Financial Performance (CFP). The reason being Australia is chosen as a benchmark for Malaysia to enhance their regulatory requirement for level of ESG disclosure. Overall, it is found that there is a positive impact of ESG disclosure on CFP.
When Revision-Based Case Adaptation Meets Analogical Extrapolation

Case-based reasoning, where cases are described in terms of problem-solution pairs \(\mathtt{case}^{}=(\mathtt{x}, \mathtt{y})\), amounts to propose a solution to a new problem on the basis of past experience made of stored cases. On the one hand, the building of the solution to a new problem may be viewed as a form of belief revision of the solution of a retrieved case (whose problem part is similar to the new problem) constrained by domain knowledge. On the other hand, an extrapolation mechanism based on analogical proportions has been proposed. It exploits triplets of cases \((\mathtt{case}^{a}, \mathtt{case}^{b}, \mathtt{case}^{c})\) whose descriptions of problem parts \(\mathtt{x}^{a}\), \(\mathtt{x}^{b}\), \(\mathtt{x}^{c}\) form an analogical proportion with the new problem \(\mathtt{x}^{\mathtt{tgt}}\), in such a way that “\(\mathtt{x}^{a}\) is to \(\mathtt{x}^{b}\) as \(\mathtt{x}^{c}\) is to \(\mathtt{x}^{\mathtt{tgt}}\)”. Then, the analogical inference amounts to compute a solution \(\mathtt{y}^{\mathtt{tgt}}\) of \(\mathtt{x}^{\mathtt{tgt}}\) by solving (when possible) an equation expressing that “\(\mathtt{y}^{a}\) is to \(\mathtt{y}^{b}\) as \(\mathtt{y}^{c}\) is to \(\mathtt{y}^{\mathtt{tgt}}\)” (where \(\mathtt{y}^{a}\), \(\mathtt{y}^{b}\) and \(\mathtt{y}^{c}\) are respectively the solution parts of \(\mathtt{case}^{a}\), \(\mathtt{case}^{b}\) and \(\mathtt{case}^{c}\)). The paper investigates how the belief revision view and analogical extrapolation relate. Besides that it constitutes an unexpected bridge between areas which ignore each other, it casts some light on the adaptation mechanism in case-based reasoning. The paper is illustrated by a running example.
SPARQLing Database Queries from Intermediate Question Decompositions

To translate natural language questions into executable database queries, most approaches rely on a fully annotated training set.Annotating a large dataset with queries is difficult as it requires query-language expertise.We reduce this burden using grounded in databases intermediate question representations.These representations are simpler to collect and were originally crowdsourced within the Break dataset (Wolfson et al., 2020).Our pipeline consists of two parts: a neural semantic parser that converts natural language questions into the intermediate representations and a non-trainable transpiler to the SPARQL query language (a standard language for accessing knowledge graphs and semantic web).We chose SPARQL because its queries are structurally closer to our intermediate representations (compared to SQL).We observe that the execution accuracy of queries constructed by our model on the challenging Spider dataset is comparable with the state-of-the-art text-to-SQL methods trained with annotated SQL queries.Our code and data are publicly available.
Location and solar system parameter extraction from power measurement time series

Abstract Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying PV system parameters, such as location, panel orientation and peak power. In most open PV generation databases, these parameters are missing or are inaccurate.In this paper, we present a framework based on particle swarm optimisation and the PVWatts model to estimate PV system parameters using only power feed-in measurements and satellite-based ERA5 climate reanalysis data. Our sensitivity analysis points out the most relevant PV system parameters, which are panel and inverter peak power, panel orientation, system location and a small but not negligible influence of ambient temperature and albedo. The detailed evaluation on one exemplary PV system shows an acceptable accuracy in panel azimuth and tilt for the use in microgrid PV system simulation. The extracted location has less than 25 km of positioning error in the best case, which is more than satisfying with respect to the underlying data resolution of the ERA5 dataset. Similar results are observed for 10 systems in Europe and the USA.
Photo-Realistic Image Dehazing and Verifying Networks via Complementary Adversarial Learning

Physical model-based dehazing methods cannot, in general, avoid environmental variables and undesired artifacts such as non-collected illuminance, halo and saturation since it is difficult to accurately estimate the amount of the illuminance, light transmission and airlight. Furthermore, the haze model estimation process requires very high computational complexity. To solve this problem by directly estimating the radiance of the haze images, we present a novel dehazing and verifying network (DVNet). In the dehazing procedure, we enhanced the clean images by using a correction network (CNet), which uses the ground truth to learn the haze network. Haze images are then restored through a haze network (HNet). Furthermore, a verifying method verifies the error of both CNet and HNet using a self-supervised learning method. Finally, the proposed complementary adversarial learning method can produce results more naturally. Note that the proposed discriminator and generators (HNet &amp; CNet) can be learned via an unpaired dataset. Overall, the proposed DVNet can generate a better dehazed result than state-of-the-art approaches under various hazy conditions. Experimental results show that the DVNet outperforms state-of-the-art dehazing methods in most cases.
Software development in disruptive times

Creating a software solution with fast decision capability, agile project management, and extreme low-code technology.
Neural-mechanism-driven image block encryption algorithm incorporating a hyperchaotic system and cloud model

An image encryption algorithm is proposed in this paper based on a new four-dimensional hyperchaotic system, a neural mechanism, a Galois field and an improved Feistel block structure, which improves the efficiency and enhances the security of the encryption algorithm. Firstly, a four-dimensional hyperchaotic system with a large key space and chaotic dynamics performance is proposed and combined with a cloud model, in which a more complex and random sequence is constructed as the key stream, and the problem of chaotic periodicity is solved. Then, the key stream is combined with the neural mechanism, Galois field and improved Feistel block structure to scramble and diffuse the image encryption. Finally, the experimental results and security analysis show that the encryption algorithm has a good encryption effect and high encryption efficiency, is secure, and can meet the requirements of practical applications.
Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud

The increase in transactions on the Internet related to the purchase of products or services can provide facilities for the parties involved in these acquisitions, but they also generate uncertainties and possibilities of attacks that can originate from fraud.This work seeks to explore and extract knowledge of auction fraud by using an evolving fuzzy neural network model based on n-uninorms.This new model uses a fuzzification technique based on Typicality and Eccentricity Data Analysis operators and a parallel processor for stream samples.To test the model in solving auction fraud problems, stateof-the-art neuro-fuzzy models were used to compare a public dataset on the topic.The results of the model proposed in this paper were superior to the other models evaluated (close to 96% accuracy) in the test, and the fuzzy rules demonstrate the model's ability to extract knowledge.
Skeleton-Based Action Recognition With Low-Level Features of Adaptive Graph Convolutional Networks

Skeleton-based action recognition is a typical classification problem which plays a significant role in human-computer interaction and video understanding. Since a human skeleton has natural graphic features, methods based on graph convolutional networks (GCN) are widely applied in skeleton-based action recognition. Previous studies mainly focus on structural links in GCN to generate high-level features of human skeleton. However, low-level features are also important in many applications. For instance, low-level edge gradient and color information are important for image classificaion. This paper introduces a multi-branches structure to capture different low-level features of human skeleton. We combine both high-level and low-level features to recognize human action. We validate our method in action recognition with two skeleton datasets, NTU-RGB+D and Kinetics. Experiment results indicate that the proposed method achieves considerable improvement over some state-of-the-art methods.
Salience-Aware Event Chain Modeling for Narrative Understanding

Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process.It is desirable to extract the event chains that represent such processes.However, this extraction remains a challenging problem.We posit that this is due to the nature of the texts from which chains are discovered.Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated.We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences.We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks.We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering.The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains. 1
Contribution of Digital Literacy to Students' Science Learning Outcomes in Online Learning

Online learning has a negative impact on students, especially students who cannot use online learning support facilities. This has an impact on the decline in the quality of student learning outcomes. This study aims to analyze the effect of implementing digital literacy on students' science learning outcomes in online learning during the Covid-19 pandemic. This research is research in the field of education using a quantitative approach and correlation method. The population in this study amounted to 348 people. Determination of the sample in this study using the random sampling technique. The data collection methods used are questionnaires and science learning outcomes tests. The instrument used has gone through the stages of testing the validity and reliability of the instrument. The data analysis used in this research is simple linear regression analysis, which previously carried out several analysis prerequisite tests in the form of normality and homogeneity tests. The results showed that the calculation of the correlation coefficient of 4.412 is greater than 2.034 with a significance value of 0.000 less than an alpha value of 0.05, a coefficient of determination of 0.371 x 100% of 37.1%. Based on these results, it can be concluded that there is a positive and significant effect on the application of digital literacy on students' science learning outcomes.
Trace Element Distribution in the Snow Cover of Different Functional Zones in Berezniki-Solikamsk Industrial Hub, Russia

The current study considered the distribution of trace elements in snow cover taking into account the functional zoning of the territory of Berezniki-Solikamsk industrial hub, Perm Region, Russia.The concentrations of 22 trace elements were determined in the dissolved phase of snowmelt using ICP-MS method.On the basis of on the background approach, it was found that Ni, Se, Cu, and Sn are actively accumulated in the snow cover.Snowmelt surface runoff during snow melting period significantly contributes to the total watershed discharge of rivers; therefore, the compliance with the Russian fishery quality standards was assessed.It was found that meltwater is the source of Cu, Mn, Se, Zn, V in surface waters.Significant concentrations of Pb, Cd, W, As, Se in snow are characteristic of conditionally background sites in comparison with average values of global concentrations of dissolved trace elements in river waters, and Se, W, Pb, Ni, As, Cd are characteristic of all functional zones.This study presented the possible sources of priority pollutants.The greatest technogenic impact was observed in the area of transport infrastructure development.Upon that, recreational and residential functional zones also experience significant anthropogenic impact.In order to create a comfortable and healthy urban environment it is necessary to implement the measures to restore these areas.
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet

Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine translation, and dialog response generation. Previous work usually used Markov Chain Monte Carlo (MCMC) sampling to generate lexically constrained sentences, but they randomly determined the position to be edited and the action to be taken, resulting in many invalid refinements. To overcome this challenge, we used a classifier to instruct the MCMC-based models where and how to refine the candidate sentences. First, we developed two methods to create synthetic data on which the pre-trained model is fine-tuned to obtain a reliable classifier. Next, we proposed a two-step approach, “Predict and Revise”, for constrained sentence generation. During the predict step, we leveraged the classifier to compute the learned prior for the candidate sentence. During the revise step, we resorted to MCMC sampling to revise the candidate sentence by conducting a sampled action at a sampled position drawn from the learned prior. We compared our proposed models with many strong baselines on two tasks, generating sentences with lexical constraints and text infilling. Experimental results have demonstrated that our proposed model performs much better than the previous work in terms of sentence fluency and diversity. Our code, pre-trained models and Appendix are available at https://github.com/NLPCode/MCMCXLNet.
Early Detection and Stimulation of Multiple Intelligences in Kindergarten

This research aims at detecting and stimulating multiple intelligences of kindergarten children early by applying collaborative action research by academics and practitioners.This research moves from the most fundamental problem in early childhood education activities especially in kindergartens namely the teachers' mistreatment in learning activities.This research was conducted using collaborative action research between researchers and teachers using a qualitative approach.The subjects in this research were 40 kindergarten childrens in groups 1 and 2. The research results produced multiple intelligences instruments for kindergarten children whose validity and reliability were tested.guidelines for the detection learning model and stimulation of multiple intelligences for kindergarten children.The measuring tool used is a multiple intelligences measuring tool for kindergarten children developed by Lazear (1995) based on four key instruments as follows: (a) Behavior Log; (b) Skill Games; (c) Intelligence Function; (d) Complex Problems and (e) Discovery.Also adopted from the concept of multiple intelligences developed by Armstrong (2004).Result of this research that the average stimulation of multiple intelligences of group 1 was 28.81 and group 2 was 29.38.Group 1 the largest gain was linguistic intelligence (33.84) and the lowest was interpersonal intelligence (23.89).Meanwhile.Group 2 the largest gain was linguistic intelligence (32.27) and the lowest were physical kinesthetic intelligence (24.50).
Image-Based Insider Threat Detection via Geometric Transformation

Insider threat detection has been a challenging task over decades; existing approaches generally employ the traditional generative unsupervised learning methods to produce normal user behavior model and detect significant deviations as anomalies. However, such approaches are insufficient in precision and computational complexity. In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised image classification task, and therefore the performance can be boosted via computer vision techniques. To illustrate, our IGT uses a novel image-based feature representation of user behavior by transforming audit logs into grayscale images. By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then trains a behavior classifier to detect anomaly in a self-supervised manner. The motivation behind our proposed method is that images converted from normal behavior data may contain unique latent features which remain unchanged after geometric transformation, while malicious ones cannot. Experimental results on CERT dataset show that IGT outperforms the classical autoencoder-based unsupervised insider threat detection approaches, and improves the instance and user based Area under the Receiver Operating Characteristic Curve (AUROC) by 4% and 2%, respectively.
Data-to-text Generation by Splicing Together Nearest Neighbors

We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" sourcetarget pairs.Unlike recent work that conditions on retrieved neighbors but generates text token-by-token, left-to-right, we learn a policy that directly manipulates segments of neighbor text, by inserting or replacing them in partially constructed generations.Standard techniques for training such a policy require an oracle derivation for each generation, and we prove that finding the shortest such derivation can be reduced to parsing under a particular weighted context-free grammar.We find that policies learned in this way perform on par with strong baselines in terms of automatic and human evaluation, but allow for more interpretable and controllable generation.