Reviewerly/exHarmony huggingface dataset:
Making Higher-Order Superposition Work

Superposition is among the most successful calculi for first-order logic. Its extension to higher-order logic introduces new challenges such as infinitely branching inference rules, new possibilities such as reasoning about Booleans, and the need to curb the explosion of specific higher-order rules. We describe techniques that address these issues and extensively evaluate their implementation in the Zipperposition theorem prover. Largely thanks to their use, Zipperposition won the higher-order division of the CASC-J10 competition.
ROSSIO Infrastructure: A Digital Humanities Platform to Explore the Portuguese Cultural Heritage

The ROSSIO Infrastructure is developing a free and open-access platform for aggregating, organising, and connecting the digital resources in the Social Sciences, Arts and Humanities provided by Portuguese higher education and cultural institutions. This paper presents an overview of the ROSSIO Infrastructure, its main objectives, the institutions involved, and the services offered by the infrastructure’s aims through its platform—namely, a discovery portal, digital exhibitions, collections, and a virtual research environment. These services rely on a metadata-aggregation solution for bringing the digital objects’ metadata from the providing institutions into ROSSIO. The aggregated datasets are converted into linked data and undergo an enrichment process based on controlled vocabularies, which are developed and published by ROSSIO. The paper will describe this process, the applications involved, and how they interoperate. We will further reflect on how these services may enhance the dissemination of science, considering the FAIR principles.
PROSPECTS OF INVESTMENT IN DIGITAL ART: CASE OF ETHEREUM AND NON-FUNGIBLE TOKEN (NFT)

This paper focuses on the technology impact via Blockchain to change consumer behavior in the digital art industry. The paper objectives are three folds: first, analyze the impact of Ethereum in selling digital artwork and on the bank world. Second, investigate the effects of Non-Fungible Token on the art industry. Third, highlight how people can sell their artworks, tweets, or even memes for thousands of dollars. Fourth, explain the effect of Ethereum in creating value for artwork and why a meme that includes nothing unique to be sold for an unbelievable price? The paper provides insights on the future of the digital arts industry and the rational behavior of the developers, artists, and even customers, as the three parties shape the business of the NFL’s success. It reflects on the investment behavior in digital arts and the importance of a secure long-term investment for a niche segment that seeks to satisfy their need of acquiring unique products. The paper serves as a guide for digital art and focuses on how AI applications create profitable markets.
A Novel Deep Ensemble Learning Framework for Classifying Imbalanced Data Stream

Machine learning has emerged as one of the most indispensable fields of this era in mining stream data. The stream data have the tendency to change their characteristics over time. Mining imbalanced stream is a research demanding subfield of this area. In imbalanced data, one of the target classes has much less instances than other class. The imbalanced data may differ either in their ratio between majority and minority class or dimension or the number of classes. The performance of the classifier is affected by variety of imbalanced data set used for training the classifier. Because, the learning of classifier is different for different types of data sets. Imbalanced data can result due to rare events which can have negative impact on society. Most traditional data mining algorithm misclassifies the minority class of the imbalance data sets or considers it as noise. Therefore, the decision is biased toward majority class and hence reduces the accuracy and overall performance of the algorithm. The algorithms for classifying imbalanced data sets thus demand high adaptability to changes in the majority and minority class ratios. The performance of traditional machine learning algorithms is enhanced by applying ensemble method and deep learning approaches. Purpose: This paper proposes a framework based on deep ensemble learning for classifying imbalanced stream of data. Methodology: The deep ensemble methods ensemble multiple base learners. While deep learning approach is applied to improve the performance by extracting lower-level features and feeding them forward for the next layer to identify higher level features. Results: In this method, the effect of highly imbalanced classes is reduced by uniting the ensemble method with deep learning. The accuracy of the classifier is improved in terms of not only accuracy but also categorical accuracy. Conclusion: In addition to accuracy, other performance measures like categorical prediction accuracy, training accuracy and prediction accuracy have also been compared which are crucial metrics for evaluating imbalance stream of data.
Methodological Problems of Complex Researches of a Digital Transformation in Scientific Communication

The article substantiates the need for methodological reflection for an orderly polydisciplinary study of the digital transformation of scientific communications, based on identifying four possible options for studying the transformation; a possibility to present a system of digital scientific communications (DSC) as an object of complex research and the proposed approach to create its possible model is underlined. A brief outline of the methodological specifics of each of the research clusters describing digital transformation is given: scientometric, sociological, economic, as well as a cluster related to information and communicationtechnologies. Their historical features are briefly described, as well as the most important milestones in the development of the digital transformation of scientific communications. A number of general scientific concepts (platform, network, blog, repository, database) used in digital transformation research are also presented, and their terminological specificity within the studied clusters is indicated. The article also proposes a model of the DSC system, based on the theoretical developments of the studied clusters, primarily - scientometric, sociological and economic ones. The DSC system model would contribute to the development of a holistic, more accurate and complete representation of the digital transformation of the field of research and development, including research activities, the information environment of science and the system of scientific communications.
An Evaluation on Speech Recognition Technology based on Machine Learning

Speech is the basic way of interaction between the listener to the speaker by voice or expression. Humans can easily understand the speakers' message, but machines can't understand the speaker's word. Nowadays, most of our lives are occupied by machines; but we can't interact with machines. The human brain, like machine learning technology, is essential for speech recognition to interact with machines to humans. The language used for speech recognition must be a global language, so English has been used in this paper. The machine learning methodology is used in a lot of assignments through the feature learning capability. The data modelling capability results attained supplementary than the performance of normal learning methodology. So, in this work, the speech signal recognition is based on a machine-learning algorithm to merge the speech features and attributes. As a result of voice as a bio-metric implication, the speech signal is converted into a significant element of speech improvement. A new speech and emotion recognition technology is introduced. In this paper, discriminated speaking technology are spotlighted on the feature extraction, improvement, segmentation and progression of speech emotion recognition. Initially, the trained RNN layer-based feature extraction is done to get the speech signal's high-level features. From the generated high-level features are used for generating the new speech feature for the capsule network. Finally, the obtained speech features and attribute features are combined into the same RNN with Caps Net framework through the fully connected network. The experimental result shows the improved proposed speech recognition algorithms accuracy with another state-of-the-art method.
Federated Learning Incentive Mechanism Design via Enhanced Shapley Value Method

Federated learning (FL) is an emerging collaborative machine learning method. In FL processing, the data quality shared by users directly affects the accuracy of the federated learning model, and how to encourage more data owners to share data is crucial. In other words, how to design a good incentive mechanism is the key problem in FL. In this paper, we propose an incentive mechanism based on the enhanced Shapley value method for FL. In the proposed mechanism, the enhanced Shapley value method is proposed to measure income distribution, which takes multiple influence factors as weights. The analytic hierarchy process (AHP) is used to find the corresponding weight value of the influence factors. Finally, the numerical experiments are carried to verify the performance of the proposed incentive mechanism. The results show that compared with the Shapley value method considering the single factor, the income distribution of all participants can better reflect multiple factor contribution when using the enhanced Shapley value method.
Video Based Transcript Summarizer for Online Courses using Natural Language Processing

Online education has become an effective way to deliver quality education to students. They have become more popular because of their high graphical and pictorial content, delivered by experts in the subjects and convenient for learning at anytime and anywhere. But sometimes, students may not be able to go through the course content due to shortage of time. Video transcript summarizer has got a lot scope in this situation. It highlights the important topics from the video. The idea of summarizing the videos can be extended to online courses videos. This will help students save a lot of time as they can understand the gist of the class within less time without actually watching the video and by just going through the summary. Our system focuses on the development of a module using Natural Language Processing with python to summarize an online class video. The methodology adopted in this project uses Natural Language Processing (NLP) algorithms such as Term Frequency-Inverse Document Frequency (TF-IDF) and Gensim to obtain the summary of video of online course. The model takes URL of a video from user as input. We have implemented summarization process with the help of two algorithms. TF-IDF is an information retrieval algorithm which uses frequency of a term and its inverse document frequency. Gensim is a NLP package that deals with topic modeling. The model also gives the flexibility to the user to decide on as to what percentage of summary is needed compared to the original lecture. The summarization technique is a subjective process. We have incorporated two prominent methods. One is cosine similarity and the other one is ROUGE score. The former does not require human generated summary for reference, whereas latter requires it. The efficiency obtained using Cosine similarity is greater than 90% in both the cases: TF-IDF and Gensim. The efficiency obtained in case of ROUGE score is in between 40-50%.
Pyramidal Image Segmentation Based on U-Net for Automatic Multiscale Crater Extraction

To extract craters with a radius greater than 10 km more effectively from lunar digital elevation maps, pyramidal image segmentation based on the U-Net model is proposed, and the conversion relationship between the multilayer image pyramid and the geographic coordinates of the crater is established.The crater image pyramid method ensures the full coverage of the study area with a small number of images and that each crater exists in several images with different resolutions.The proposed method can effectively improve the detection performance of large-scale craters and solve the migration problem when stitching together craters from largescale images.This method recovered 85.48% of the craters with a radius greater than 10 km in an artificially annotated dataset, found 1044 new craters, and extended the maximum radius of detected craters from 72 km in randomly cropped image segmentation to 200 km.It was estimated by visual interpretation that approximately 82.09% of these new craters are real.Also, the recall reaches 90.17% when the new real craters are added to the true craters.
Estimating High Probability Reachable Sets using Gaussian Processes

We present a method for computing reachable sets and forward invariant sets for systems with dynamics that include unknown components. Our main assumption is that, given any hyperrectangle of states, lower and upper bounds for the unknown components that hold with high probability are available. We then show that this assumption is well-suited when the unknown terms are modeled as state-dependent Gaussian processes. Under this assumption, we leverage the theory of mixed monotone systems and propose an efficient method for computing a hyperrectangular set that over-approximates the reachable set of the system with high probability. We then show a related approach that leads to sufficient conditions for identifying sets that are forward invariant for the dynamics with high probability. These theoretical results lead to practical algorithms for efficient computation of high probability reachable sets and invariant sets. A major advantage of our approach is that it leads to tractable computations for systems up to moderately high dimension that are subject to low dimensional uncertainty modeled as Gaussian Processes, a class of systems that appears often in practice. We demonstrate our results on an example of a six-dimensional model of a multirotor aerial vehicle.
A Comparative Analysis of Object Detection Algorithms in Naturalistic Driving Videos

Abstract Intelligent vehicle research has been rapidly developing. Object detection is one of the most critical study areas for intelligent vehicle’s driving safety. This paper conducts a comparative analysis for two popular real-time object detection algorithms: Yolo-v4 and deconvolutional single shot multibox detector (DSSD) under a naturalistic driving environment. An 80-classes COCO dataset trains each neural network at first, then fine-tuned by the BDD100k dataset. The detection results are compared by True/False Positive Results, Precision-Recall Curve, and average precision @ intersection of union 50 and average precision @ intersection of union 75 results. According to the analysis results, the Yolo-v4 outperforms the DSSD algorithm in bad weather, nighttime conditions, and small object detections. The Yolo-v4 and DSSD mean average precision for the BDD dataset is 22.63 and 11.86, respectively. The Yolo-v4 precision is 90.81% better than the DSSD algorithm, proving that the Yolo-v4 is a better fit for real-world driving environment studies.
Application of Security System Legal Documents Reviewer Letters Using Blockchain Technology

the popularity of blockchain technology has been widely used in aspects of life in various fields and industries. Because previously blockchain technology was used as crypto or known as digital currency which has developed towards technology and digital is growing rapidly. Where the use of blockchain technology is a new innovation that is increasingly developing in data security systems to suit the times and become one of the modern technologies in the world of education. However, nowadays there are often crimes in the illegal falsification of reviewer letters that can be carried out by irresponsible people for personal interests and can harm the parties concerned. For that we need a data security technology without the need for a third party called blockchain that can maintain the authenticity of the reviewer's letter from the criminal actions of irresponsible parties in forging intentionally or unintentionally. Raharja College has applied blockchain technology to a tool called ABC (data security) and has been implemented on the adimas website which has been going on for two years. Blockchain technology has an encryption hash code or a unique code that is a sign that a letter that has been verified with the blockchain can be seen as different from the original or fake. Because blockchain technology has a level of data security with a block system that is interconnected and has an encryption hash code. This paper uses the literature study method by looking at previous papers with the same theme. This research has the aim that future researchers can produce new innovations in improving data security and improving the quality of technology that can produce new findings in more modern data security systems.
Representation of the coherent state for a beam splitter operator and its applications

Abstract A beam splitter operator is a very important linear device in the field of quantum optics and quantum information. It can not only be used to prepare complete representations of quantum mechanics, entangled state representation, but it can also be used to simulate the dissipative environment of quantum systems. In this paper, by combining the transform relation of the beam splitter operator and the technique of integration within the product of the operator, we present the coherent state representation of the operator and the corresponding normal ordering form. Based on this, we consider the applications of the coherent state representation of the beam splitter operator, such as deriving some operator identities and entangled state representation preparation with continuous-discrete variables. Furthermore, we extend our investigation to two single and two-mode cascaded beam splitter operators, giving the corresponding coherent state representation and its normal ordering form. In addition, the application of a beam splitter to prepare entangled states in quantum teleportation is further investigated, and the fidelity is discussed. The above results provide good theoretical value in the fields of quantum optics and quantum information.
Distributed Ledger Technologies Based Microgrid Energy Management Using IOTA Tangle

Microgrids' role in the decentralization of energy systems keeps growing and becoming more and more important. Trading mechanisms have been improved by the incorporation of Distributed Ledger Technologies, mainly Blockchain, offering transactions with improved security, and reduced cost, among other benefits. The study of the implementation of Distributed Ledger Technologies as part of Microgrid Transactive Energy Systems has been narrowed down to the use of Blockchain as trading platform, given the maturity and global acceptance of the technology. Nonetheless, other existing Distributed Ledger Technologies, such as IOTA, Tempo, Holochain, Hashgraph, offer significant improvements in transaction cost, scalability and latency. Therefore, this paper presents the implementation and simulation of an IOTA Tangle based micromarket looking forward to increase the research efforts in other Distributed Ledger Technologies, besides Blockchain. A successful simulator is presented, which offers the possibility of evaluating the results and convenience of implementing a microgrid energy manager based on IOTA Tangle.
Encoder-Decoder Based Multi-Feature Fusion Model for Image Caption Generation

Image caption generation is an essential task in computer vision and image understanding. Contemporary image caption generation models usually use the encoder-decoder model as the underlying network structure. However, in... | Find, read and cite all the research you need on Tech Science Press
Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual

Image and video manipulation has been actively used in recent years with the development of multimedia editing technologies. However, object-based video tampering, which adds or removes objects within a video frame, is posing challenges because it is difficult to verify the authenticity of videos. In this paper, we present a novel object-based frame identification network. The proposed method uses symmetrically overlapped motion residuals to enhance the discernment of video frames. Since the proposed motion residual features are generated on the basis of overlapped temporal windows, temporal variations in the video sequence can be exploited in the deep neural network. In addition, this paper introduces an asymmetric network structure for training and testing a single basic convolutional neural network. In the training process, two networks with an identical structure are used, each of which has a different input pair. In the testing step, two types of testing methods corresponding to two- and three-class frame identifications are proposed. We compare the identification accuracy of the proposed method with that of the existing methods. The experimental results demonstrate that the proposed method generates reasonable identification results for both two- and three-class forged frame identifications.
Highly Vectorized SIKE for AVX-512

It is generally accepted that a large-scale quantum computer would be capable to break any public-key cryptosystem used today, thereby posing a serious threat to the security of the Internet’s public-key infrastructure. The US National Institute of Standards and Technology (NIST) addresses this threat with an open process for the standardization of quantum-safe key establishment and signature schemes, which is now in the final phase of the evaluation of candidates. SIKE (an abbreviation of Supersingular Isogeny Key Encapsulation) is one of the alternate candidates under evaluation and distinguishes itself from other candidates due to relatively short key lengths and relatively high computing costs. In this paper, we analyze how the latest generation of Intel’s Advanced Vector Extensions (AVX), in particular AVX-512IFMA, can be used to minimize the latency (resp. maximize the hroughput) of the SIKE key encapsulation mechanism when executed on Ice Lake CPUs based on the Sunny Cove microarchitecture. We present various techniques to parallelize and speed up the base/extension field arithmetic, point arithmetic, and isogeny computations performed by SIKE. All these parallel processing techniques are combined in AvxSike, a highly optimized implementation of SIKE using Intel AVX-512IFMA instructions. Our experiments indicate that AvxSike instantiated with the SIKEp503 parameter set is approximately 1.5 times faster than the to-date best AVX-512IFMA-based SIKE software from the literature. When executed on an Intel Core i3-1005G1 CPU, AvxSike outperforms the x64 assembly implementation of SIKE contained in Microsoft’s SIDHv3.4 library by a factor of about 2.5 for key generation and decapsulation, while the encapsulation is even 3.2 times faster.