Reviewerly/exHarmony huggingface dataset:
Research on the effect of government media and users’ emotional experience based on LSTM deep neural network

Different government media have different communication effects and users' emotional experience. It carries on a comparative research on government media selecting three different types of government media which include China's Police Online, Central Committee of the Communist Youth League, and China's Fire Control in the context of public health emergencies. Based on the deep learning technique, the emotion classification model of long-term memory network is constructed to analyze the emotion of the users' comments of different government media; taking the number of contents, the number of retweets, the number of praises, and the number of comments as evaluating indicators to do comparative analysis to cross platform government medias. Through the comparative results, it is found that different types and platforms of government media have great differences in users' emotional experience; the emotion performance of users' comments is strongly related to the information communication power and effectiveness of government media.
Accounting Professionals and Digital Maturity: insight from the reflections of digital transformation / Profissionais Contábeis e Maturidade Digital: insights sobre os reflexos da transformação digital

The digital transformation has introduced new challenges and significant changes for society, intensified in this time of (post) pandemic (COVID-19). Among these challenges, the process of learning new technology by professionals, known as digital maturity, has taken on a critical role. It is a fact that digital maturity and digital transformation are not the same thing, but are intimately connected, as both are essential in the current business context.Despite the relevance of the matter, state of the art focuses, mainly, on building models to evaluate the digital maturity of organizations, reducing the prospect of impacts on the professionals. The goal of this research seeks firstly to evaluate studies on digital technology and their impacts on accounting. For this, bibliographical research was carried out based on Scopus and Science Direct examining the relevant publications on digital technology and accounting. Next, we analyzed the results found on the research conducted with accounting postgraduate students in order to understand their level of digital maturity regarding the technologies in evidence in the current market. The analyses revealed that, despite the impact, accounting professionals are still quite unaware of or have limited knowledge about these technologies and consequently the reflexes on their profession.
Uniform Interpolation via Nested Sequents

A modular proof-theoretic framework was recently developed to prove Craig interpolation for normal modal logics based on generalizations of sequent calculi (e.g., nested sequents, hypersequents, and labelled sequents). In this paper, we turn to uniform interpolation, which is stronger than Craig interpolation. We develop a constructive method for proving uniform interpolation via nested sequents and apply it to reprove the uniform interpolation property for normal modal logics \(\mathsf{K}\), \(\mathsf{D}\), and \(\mathsf{T}\). While our method is proof-theoretic, the definition of uniform interpolation for nested sequents also uses semantic notions, including bisimulation modulo an atomic proposition.
The effect of digital literacy towards the selection of social science teacher learning media

Selection of the proper media is one of the teacher's duties in teaching.It must be followed by several aspects, one of which is teacher's ability in digital literacy to keep up with the developments of today's all-digital era.The study aimed to examine the effects of digital literacy levels on the selection of learning media for social science teachers.In the study, correlational quantitative method was used, with data collection applying questionnaires and document studies.The participants of the study were 22 social science teachers from 11 junior high schools selected via total sampling method.The obtained data were analyzed using simple linear regression.The results revealed that there was a significant effect between digital literacy on the selection of social science teacher learning media.The result of the regression equation is Y = 0.553 + 0.119X.Meanwhile, the result of hypothesis testing from the T-Test and F-Test showed that Ho was rejected and Ha was accepted.
Self-Supervised Pretraining of 3D Features on any Point-Cloud

Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like 2D object recognition, video classification etc. However, pretraining is not widely used for 3D recognition tasks where state-of-the-art methods train models from scratch. A primary reason is the lack of large annotated datasets because 3D data is both difficult to acquire and time consuming to label. We present a simple self-supervised pertaining method that can work with any 3D data - single or multiview, indoor or outdoor, acquired by varied sensors, without 3D registration. We pretrain standard point cloud and voxel based model architectures, and show that joint pretraining further improves performance. We evaluate our models on 9 benchmarks for object detection, semantic segmentation, and object classification, where they achieve state-of-the-art results and can outperform supervised pretraining. We set a new state-of-the-art for object detection on ScanNet (69.0% mAP) and SUNRGBD (63.5% mAP). Our pretrained models are label efficient and improve performance for classes with few examples.
A Practical and Economical Bayesian Approach to Gas Price Prediction

On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapidly, these oracles often underestimate or overestimate the price. In this paper, we demonstrate how Gaussian process models can predict the distribution of the minimum price in an upcoming block when transaction volumes are increasing. This is effective because these processes account for time correlations between blocks. We performed an empirical analysis using the Gaussian process model on historical block data and compared the performance with GasStation-Express and Geth gas price oracles. The results suggest that when transactions volumes fluctuate greatly, the Gaussian process model offers a better estimation. Further, we demonstrated that GasStation-Express and Geth can be improved upon by using a smaller training sample size which is properly pre-processed. Based on the results of empirical analysis, we recommended a gas price oracle made up of a hybrid model consisting of both the Gaussian process and GasStation-Express. This oracle provides efficiency, accuracy, and better cost.
Mitigating errors by quantum verification and postselection

Correcting errors due to noise in quantum circuits run on current and near-term quantum hardware is essential for any convincing demonstration of quantum advantage. Indeed, in many cases it has been shown that noise renders quantum circuits efficiently classically simulable, thereby destroying any quantum advantage potentially offered by an ideal (noiseless) implementation of these circuits. Although the technique of quantum error correction (QEC) allows correcting these errors very accurately, QEC usually requires a large overhead of physical qubits which is not reachable with currently available quantum hardware. This has been the motivation behind the field of quantum error mitigation, which aims at developing techniques to correct an important part of the errors in quantum circuits, while also being compatible with current and near-term quantum hardware. In this work, we present a technique for quantum error mitigation which is based on a technique from quantum verification, the so-called accreditation protocol, together with postselection. Our technique allows for correcting the expectation value of an observable $O$, which is the output of multiple runs of noisy quantum circuits, where the noise in these circuits is at the level of preparations, gates, and measurements. We discuss the sample complexity of our procedure and provide rigorous guarantees of errors being mitigated under some realistic assumptions on the noise. Our technique is tailored to time-dependent behaviors, as we allow for the output states to be different between different runs of the accreditation protocol. We validate our findings by running our technique on currently available quantum hardware.
A low-cost and unobtrusive system for fall detection

Nowadays, the amount of elder people living alone is increasing, with all the risks that it involves, maybe the most dangerous threat that they face is fall off with nobody around to help them. A fall at an advanced age usually leads to consequences like bones and hip fracture, which in addition to the low mobility that these people present, make to stand up impossible for them. This situation can get worse if after the fall a person loses consciousness, making it impossible to contact to a third party for help by means of a mobile phone or something similar. Different solutions have been developed in order to accomplish this problem, but some of them are not realistic enough, for example some video solutions invade our privacy, and those which are based on mobile phones expect the user to go everywhere with it. In this paper, it is proposed a low cost solution based on an 9-axis IMU (Inertial Measure Unit), which counts with an accelerometer, gyroscope and magnetometer that will give us the needed information to build a fall detector supported by machine learning. This system will include a gateway, which will be responsible of the data collection and the most complex computations.
Discussion of Agile Software Development Methodology and its Relevance to Software Engineering

Agile Software Development Methodology is a lesser-known and infrequently utilized methodology in academia. In reality, though, content developer software practitioners frequently employ this technique. This journal was created to give readers an overview of agile techniques and their use at various stages of software development in general.
Linguistic Steganography Based on Automatically Generated Paraphrases Using Recurrent Neural Networks

Linguistic steganography is used for hiding information in the multimedia data in a traditional approach. For that, we are taking an image and converting the text into bits like 0’s and 1’s. In this paper, we are using both image and text dataset. Nowadays, providing secure data with confidentiality and integrity has become a big challenge in the present world. To improve the security of these existing parameters, we have proposed a methodology by referring through various reference papers and existing methodologies that can provide and attain confidentiality and integrity by utilizing various algorithms to provide a collective result. We have used algorithms like support vector machine (SVM), recurrent neural network (RNN), and convolutional neural network (CNN). For higher security, steganography algorithm is employed to mix with this algorithm. By applying the above algorithms, we will be doing encoding for providing more security. Image quality will not be loosed and there will be efficiency in decoding the information by doing encoding. If any users attack the code, due to the techniques that we used in our encoding, enhancing more security will be provided.
Improving geological logging of drill holes using geochemical data and data analytics for mineral exploration in the Gawler Ranges, South Australia

(2021). Improving geological logging of drill holes using geochemical data and data analytics for mineral exploration in the Gawler Ranges, South Australia. Australian Journal of Earth Sciences. Ahead of Print.
Cross-Level High-Utility Itemset Mining Using Multi-core Processing

Among the useful tools for the retail stores to analyze their customer behaviors is through the task of mining high-utility itemset (HUIM), which is to reveal the combinations of items which offer high. However, most of them different abstraction levels of items. The CLH-Miner algorithm was presented to solve this problem. It adopts categorization of items with the HUIM to discover interesting itemsets not contained in traditional HUIM approaches. Whereas CLH-Miner discovers itemsets from different levels of abstraction efficiently, the algorithm is sequential. It cannot, therefore, use powerful, easily available, multi-core processors. This work tackles this drawback through the use of a parallel method called the pCLH-Miner algorithm to significantly reduce mining times. The algorithm proposes a way to split the search space into separate parts and assign them to each different core. The pCLH-miner is shown to high efficiency compared CLH-Miner by experiments on real-world databases.
An Effective Algorithm for the Synthesis of Irreducible Polynomials over a Galois Fields of Arbitrary Characteristics

The known algorithms for synthesizing irreducible polynomials have a significant drawback: their computational complexity, as a rule, exceeds the quadratic one. Moreover, consequently, as a consequence, the construction of large-degree polynomials can be implemented only on computing systems with very high performance. The proposed algorithm is base on the use of so-called fiducial grids (ladders). At each rung of the ladder, simple recurrent modular computations are performers. The purpose of the calculations is to test the irreducibility of polynomials over Galois fields of arbitrary characteristics. The number of testing steps coincides with the degree of the synthesized polynomials. Upon completion of testing, the polynomial is classifieds as either irreducible or composite. If the degree of the synthesized polynomials is small (no more than two dozen), the formation of a set of tested polynomials is carried out using the exhaustive search method. For large values of the degree, the test polynomials are generating by statistical modeling. The developed algorithm allows one to synthesize binary irreducible polynomials up to 2Kbit on personal computers of average performance
The Semiring of Dichotomies and Asymptotic Relative Submajorization

We study quantum dichotomies and the resource theory of asymmetric distinguishability using a generalization of Strassen’s theorem on preordered semirings. We find that an asymptotic variant of relative submajorization, defined on unnormalized dichotomies, is characterized by real-valued monotones that are multiplicative under the tensor product and additive under the direct sum. These strong constraints allow us to classify and explicitly describe all such monotones, leading to a rate formula expressed as an optimization involving sandwiched Rényi divergences. As an application we give a new derivation of the strong converse error exponent in quantum hypothesis testing.
A Hybrid Max–Min Ant System by Levy Flight and Opposition-Based Learning

The max–min ant system (MMAS) is a modified ant colony optimization (ACO) algorithm. Its convergence speed is effectively improved by setting the upper and lower bounds of the pheromone and updating it in the optimal path. However, MMAS still has drawbacks, such as long search time and local extremums. In this paper, the hybrid max–min ant system (HMMAS) is proposed to deal with the shortcomings of MMAS. Employing Levy flight strategy, HMMAS can dynamically adjust the parameters to increase the diversity of solutions and expand the search range. Besides, HMMAS uses the OBL strategy to generate opposite solutions in the early stage. In this way, the convergence is accelerated. When HMMAS falls into a local extremum, the path reorganization strategy is utilized. With its help, HMMAS can redistribute the pheromone in each path and achieve global optimum. To verify the effectiveness, HMMAS is first compared with the three conventional ACO algorithms of AS, ACS, and MMAS in 20 sets of experiments. The results indicate that the average results of HMMAS in the 19 sets of TSP instances are better than the other three algorithms, and the standard deviation in the 14 sets of calculation instances is the smallest. Then, HMMAS is compared with some state-of-the-art algorithms, and the results show that HMMAS is better than other comparison algorithms, either by the minimum or the average value.
Torrente, a micropayment based Bittorrent extension to mitigate free riding

We propose Torrente, a distributed file-sharing solution with economic incentives. Its implementation is built as an extension of BitTorrent protocol, in such a manner that user access to file-sharing swarms is controlled by peers that verify micropayments receipts in a blockchain-based ledger. In addition, by using payment commitments, Torrente facilitates off-chain transactions for faster content sharing. The solution is created as a tool to enhance Amazon Biobank application security, but can be used isolated in cases such as file sharing with monetary incentives.
Adolescents’ communication on the Internet: the boundaries of normativity

The emergence and spread of the Internet has led to the fact that the virtual space has become a new source of communication, especially for adolescents, active users. However, such communication differs significantly from real interaction in the absence of various restrictions that affect the quality of communication and can lead to complete disregard of generally accepted moral norms. The purpose of the article is to analyze the features of adolescents ' communication on the Internet and the boundaries of normativity that they tend to cross, as well as the reasons for their violation. The article considers the positive and negative impact of Internet communication on the personality of schoolchildren, communication models, revealing the distinctive features of network interaction of young people and their negative consequences. The problems of compliance with the norms of communication and etiquette are analyzed, which, among other things, can develop into a more aggressive form – cyberbullying. Statistical data on violations of the boundaries of normativity when communicating with young people on the Internet in a number of countries are presented. The main directions of prevention of non-normative interaction of adolescents in the virtual space are highlighted. The study of the peculiarities of communication of adolescents in the digital environment will reveal in more detail the understanding of the causes of the spread of virtual aggression and will be able to contribute to the development of the level of communication culture of young people on the Internet.
Explainable artificial intelligence and machine learning [Guest Editors’ introduction]

Explainable artificial intelligence (XAI) is a crucially important aspect of improving trust and reliability of artificial intelligent and machine learning (AI/ML) systems. In this special issue, we have included a diverse group of articles addressing different aspects of XAI in a variety of domains. From real-time systems to human-in-the-loop fault detection, the articles here have looked into AI explanation from varying perspectives and for multiple groups of audience. These articles shed light on some of the important and useful aspects of XAI and its application.
The Road to an Archival Data Format—Data Structures

The current data formatting and labeling standards for the Planetary Data System (PDS) are known as the PDS4 Standards. They supersede the PDS3 Standards, but they represent a complete redesign of the requirements and implementation, rather than even a major incremental revision, from the previous standard. At the heart of the PDS4 Standards lies a fundamental, philosophical change from the PDS3 paradigm: the PDS4 Standards clearly and specifically constrain the way that the bytes comprising observational data may be stored in their data files—that is, the data structures—to a much greater degree than the PDS3 Standards ever did, even in their most mature realization. In PDS4, the PDS has defined data structures optimized for the long-term preservation of observational data. We explore the history of the PDS and its standards through the examination of a single, simple data structure (the 2D image), to understand the evolutionary pressures on the data and on the PDS that led to the development of the archival data structure requirements for observational data at the core of the PDS4 standards.
ANONYMIZATION OF SENSITIVE DATA IN UNSTRUCTURED DOCUMENTS USING NLP

Lot of researchers have worked for the progress of anonymization of structured data through spread-sheets and database tools.Masking of sensitive information in structured data and data anonymization is possible through algorithms or techniques.But anonymizing unstructured data is a real challenge since data currently exists in different form.The study which ensures to cope with the interactions between human language and computers is called NLP.Natural Language Processing is the sub-field of AI which focuses on enabling computers to understand and process human languages.Further, we provide the deeper insight on how NLP works and show how a system can understand unstructured text, extract sensitive data and perform anonymization.The proposed anonymization procedure provides a system to apply text anonymization on unstructured original medical-records of an individual and release the anonymized document to help researchers for further study or investigation by preserving the privacy of the concerned individual.
Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion

Bootstrapping has become the mainstream method for entity set expansion.Conventional bootstrapping methods mostly define the expansion boundary using seed-based distance metrics, which heavily depend on the quality of selected seeds and are hard to be adjusted due to the extremely sparse supervision.In this paper, we propose BootstrapGAN, a new learning method for bootstrapping which jointly models the bootstrapping process and the boundary learning process in a GAN framework.Specifically, the expansion boundaries of different bootstrapping iterations are learned via different discriminator networks; the bootstrapping network is the generator to generate new positive entities, and the discriminator networks identify the expansion boundaries by trying to distinguish the generated entities from known positive entities.By iteratively performing the above adversarial learning, the generator and the discriminators can reinforce each other and be progressively refined along the whole bootstrapping process.Experiments show that Boot-strapGAN achieves the new state-of-the-art entity set expansion performance.