Reviewerly/exHarmony huggingface dataset:
A Simplified Benchmark for Non-ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks

Relational Graph Convolutional Networks (RGCNs) identify relationships within a Knowledge Graph to learn real-valued embeddings for each node and edge. Recently, researchers have proposed explanation methods to interpret the predictions of these black-box models. However, comparisons across explanation methods is difficult without a common dataset and standard evaluation metrics to evaluate the explanations. In this paper, we propose a method, including two datasets (Royalty-20k and Royalty-30k), to benchmark explanation methods on the task of explainable link prediction using Graph Neural Networks. We report the results of state-of-the-art explanation methods for RGCNs.
OPTIMIZED PCA BASED FACE RECOGNITION

In this paper an algorithm to solve the problem of automatic face recognition is presented. The novelty of the algorithm is the ability to combine the principal component analysis (PCA) with Modified Particle Swarm Optimization (MPSO) to improve the execution time and to obtain better face recognition results. The efficiency of face recognition system is imp measure the similarity of an input face compared with a database of faces. The use of the fitness function helps to obtain more accurate results in a faster way. The results obtained are excellent even when the system w A comparison of the results obtained with the algorithm without MPSO versus the algorithm using MPSO is also presented. The algorithm is also implemented on the colored images of the human faces. Keywords: Particle Swarm Optimization, Face Recognition, Eigenfaces, Evolutionary Computer Vision.
Proof Transfer for Neural Network Verification

We introduce the novel concept of proof transfer for neural network verification. We show that by generating proof templates that capture and generalize existing proofs, we can speed up subsequent proofs. In particular we create these templates from previous proofs on the same neural network and consider two cases: (i) where the proofs are created online when verifying other properties and (ii) where the templates are created offline using a dataset. We base our methods on three key hypotheses of neural network robustness proofs. Our evaluation shows the potential of proof transfer for benefitting robustness verification of neural networks against adversarial patches, geometric, and $\ell_{\infty}$-perturbations.
Socio-pedagogical study of educational transformation in the period of crisis changes

The relevance of the article is due to a change in the philosophy of the very paradigm of human development, provokes changes in the structure of society, namely: new requests appear that require adequate expression in educational systems. In this regard, the article is aimed at theoretical substantiation and practical implementation of the idea of developing soft skills as basic needs in the “knowledge economy”. The key approach to solving the problem was the use of logical methods, namely deductive inferences, inferences by analogy, made it possible to generalize the accumulated experience and present it in the model of soft skills formation. The article presents theoretical generalizations on the change in the philosophy of the vector of education development in the period of crisis changes, on the importance of lifelong education in this aspect, motivation for self-realization, value orientations.
Optimum Strategies for the Game Super Six

In the game "Super Six", after successfully getting rid of a stick by rolling with the die a number that is not occupied, the player has the choice to continue to roll the die or to stop and to hand over the die to their opponent. The probability to win depends on the situation and on the chosen strategy. In this paper, the optimum strategy to maximise the winning probability is derived. If there are 1 or 2 sticks on the lid, it is always best to continue to roll the die. If there are 5 sticks on the lid it is best to stop. If there are 4 sticks on the lid it is best to stop unless both players have one stick in their hands. If there are 3 sticks on the lid, the optimum strategy depends on the number of sticks in the hands of the two players.
A Semantic Indexing Structure for Image Retrieval

In large-scale image retrieval, many indexing methods have been proposed to narrow down the searching scope of retrieval. The features extracted from images usually are of high dimensions or unfixed sizes due to the existence of key points. Most of existing index structures suffer from the dimension curse, the unfixed feature size and/or the loss of semantic similarity. In this paper a new classification-based indexing structure, called Semantic Indexing Structure (SIS), is proposed, in which we utilize the semantic categories rather than clustering centers to create database partitions, such that the proposed index SIS can be combined with feature extractors without the restriction of dimensions. Besides, it is observed that the size of each semantic partition is positively correlated with the semantic distribution of database. Along this way, we found that when the partition number is normalized to five, the proposed algorithm performed very well in all the tests. Compared with state-of-the-art models, SIS achieves outstanding performance.
Variational Disentanglement for Domain Generalization

Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain. In this paper, we propose to tackle the problem of domain generalization by delivering an effective framework named Variational Disentanglement Network (VDN), which is capable of disentangling the domain-specific features and task-specific features, where the task-specific features are expected to be better generalized to unseen but related test data. We further show the rationale of our proposed method by proving that our proposed framework is equivalent to minimize the evidence upper bound of the divergence between the distribution of task-specific features and its invariant ground truth derived from variational inference. We conduct extensive experiments to verify our method on three benchmarks, and both quantitative and qualitative results illustrate the effectiveness of our method.
On the intelligent development of unmanned platforms in high-risk industries

With the development of AI technology and “New Infrastructure Construction, the intelligent development of unmanned platforms becomes significantly important for high-risk industries such as rescue activities in disasters, public emergencies, and emergency treatments. It will promote the safety and health of people and enhance capabilities for the national emergency and governance. This paper analyzes the current intelligent development of unmanned platforms for high-risk industries such as counter-terrorism, rescue activities in disasters, and emergency treatments. This paper summarizes the domestic and foreign current situations and development trends in four aspects: basic theories, key technologies, major equipment, and talent development. Based on the current situations, needs, and deficiencies of the intelligent development of unmanned platforms for high-risk industries of China, this paper proposes future challenges and problems for basic theories, key technologies, major equipment, and talent development. Moreover, this paper proposes high-level strategic planning and suggestions for the intelligent development of unmanned platforms for high-risk industries.
Towards effective shaping of large-amplitude high-fidelity Schrodinger cat states by inefficient photon number resolving detection

We propose an efficient way to generate optical analogs of both even and odd Schrodinger cat states (SCSs) with high fidelity, large amplitude and reasonable generation rate. The resource consumed are a single-mode squeezed vacuum state (SMSV) and possibly a single photon or nothing. We report the generation of even (odd) SCS with amplitude 4.2, fidelity higher than 0.99 and reasonable generation rate by subtraction of 30(31) photons from SMSV by ideal photon number detection. In the case of inefficient detectors, maintaining SCS size and its fidelity at the same level as in the case of ideal detectors results in a dramatic decrease in the success probability. In order to have certain harmony between the three characteristics (large amplitude, high fidelity and acceptable success probability for the generation scheme) in the case of imperfect detection, highly transmitting beam splitters should not be used and number of the subtracted photons must be reduced to 10(11).
Remote Data Integrity Check

In Cloud Storage Server, data integrity plays an important role, given cloud clients might not be aware whether the data is safe or has been tampered with. This system introduces identity-based signature algorithms to protect data that belongs to the data owner and gets the status of cloud data by means of verification through signatures. Since it is practically not possible for the data owner to be available online all the time for checking cloud data integrity, Third party auditor is tasked with verifying the data integrity every time instead of data owner. The Third party auditors should not read the cipher text data while verifying and must authenticate itself to cloud server by performing Proof of Knowledge operation; then cloud server can reveal the sensitive data as block wise and the third party auditor can verify the signature without knowledge of cipher text data. Finally, an audit report is sent to the data owner. This work demonstrates data security and integrity in the cloud..
Relating Neural Text Degeneration to Exposure Bias

This work focuses on relating two mysteries in neural-based text generation: exposure bias, and text degeneration. Despite the long time since exposure bias was mentioned and the numerous studies for its remedy, to our knowledge, its impact on text generation has not yet been verified. Text degeneration is a problem that the widely-used pre-trained language model GPT-2 was recently found to suffer from (Holtzman et al., 2020). Motivated by the unknown causation of the text degeneration, in this paper we attempt to relate these two mysteries. Specifically, we first qualitatively quantitatively identify mistakes made before text degeneration occurs. Then we investigate the significance of the mistakes by inspecting the hidden states in GPT-2. Our results show that text degeneration is likely to be partly caused by exposure bias. We also study the self-reinforcing mechanism of text degeneration, explaining why the mistakes amplify. In sum, our study provides a more concrete foundation for further investigation on exposure bias and text degeneration problems.
RAFT: A Real-World Few-Shot Text Classification Benchmark

Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied settings, and so don't directly answer this question. The RAFT benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment. Baseline evaluations on RAFT reveal areas current techniques struggle with: reasoning over long texts and tasks with many classes. Human baselines show that some classification tasks are difficult for non-expert humans, reflecting that real-world value sometimes depends on domain expertise. Yet even non-expert human baseline F1 scores exceed GPT-3 by an average of 0.11. The RAFT datasets and leaderboard will track which model improvements translate into real-world benefits at this https URL .
MultiAzterTest: a Multilingual Analyzer on Multiple Levels of Language for Readability Assessment

Readability assessment is the task of determining how difficult or easy a text is or which level/grade it has. Traditionally, language dependent readability formula have been used, but these formulae take few text characteristics into account. However, Natural Language Processing (NLP) tools that assess the complexity of texts are able to measure more different features and can be adapted to different languages. In this paper, we present the MultiAzterTest tool: (i) an open source NLP tool which analyzes texts on over 125 measures of cohesion,language, and readability for English, Spanish and Basque, but whose architecture is designed to easily adapt other languages; (ii) readability assessment classifiers that improve the performance of Coh-Metrix in English, Coh-Metrix-Esp in Spanish and ErreXail in Basque; iii) a web tool. MultiAzterTest obtains 90.09 % in accuracy when classifying into three reading levels (elementary, intermediate, and advanced) in English and 95.50 % in Basque and 90 % in Spanish when classifying into two reading levels (simple and complex) using a SMO classifier. Using cross-lingual features, MultiAzterTest also obtains competitive results above all in a complex vs simple distinction.
PAC Learnability of Approximate Nash Equilibrium in Bimatrix Games

Computing Nash equilibrium in bimatrix games is PPAD-hard, and many works have focused on the approximate solutions. When games are generated from a fixed unknown distribution, learning a Nash predictor via data-driven approaches can be preferable. In this paper, we study the learnability of approximate Nash equilibrium in bimatrix games. We prove that Lipschitz function class is agnostic Probably Approximately Correct (PAC) learnable with respect to Nash approximation loss. Additionally, to demonstrate the advantages of learning a Nash predictor, we develop a model that can efficiently approximate solutions for games under the same distribution. We show by experiments that the solutions from our Nash predictor can serve as effective initializing points for other Nash solvers.
INSTAGRAM AND THE MEMED SELF

The irreverent, bizarre and deeply political work of Instagram meme artists serves as a potent rejoinder to the claim that “the left can’t meme” In their commitment to a constantly moving mix of politics and absurdism, IG memers have something in common with the Alt-Right despite their completely opposed politics. Many of these well-followed accounts have developed a signature brand of humor and aesthetics while cultivating loyal fans. These “meme lords” are part of a recent wave in online culture I’m calling “weird Instagram.” Named for Facebook precursor, weird Instagram might best be differentiated by the much greater inclusion of self in meme production. In this paper I’ll discuss this phenomenon in its platform specificity, using the work of two prominent meme artists to exemplify the phenomenon.
COC

In recent years, more and more applications involving large-scale data analytics tend to be geo-distributed [6]. Those applications distribute intermediate data into different data centers, and generate traffic going through WAN links between data centers, which are expensive resources for service providers. Many global service providers like Microsoft, Google, and Facebook have already developed their SD-WAN traffic engineering systems [5, 7, 8] to optimize the bandwidth allocation across their inter-data centers.
THE SEER AND THE SEEN: A SURVEY OF TOPICS FOUND IN PALANTIR PATENTS

Palantir is one of the most secretive technology firms in the US. The company supplies information technology (IT) solutions to governments, nonprofits, and corporations, focusing on data integration and surveillance services. To investigate Palantir’s opaque technology practices, this article presents findings from a topic modeling of Palantir patents (n=155) filed from 2006-2019 in the US, Germany, Australia, UK, and EU. This approach follows recent literature that uses patents as data for researching opaque IT firms. We begin by summarizing scholarship on Palantir and IT for policing, intelligence, and security. Our findings show that Palantir’s IT produces infrastructural layers of meaning/context via metadata that are wrapped ‘on top’ of previously held legacy data. We thus use the concept of infrastructuring to understand Palantir’s practices, where information standards like metadata are theorized as phenomena for structuring social worlds. The paper ends by offering action items for future research into opaque IT firms.
Smart Contract: Blockchain-based Energy Exchange Framework with reduced Transaction Cost

Smart grid is an innovation in a communication network, interconnected power framework, advanced control technology, and smart metering has been applied to work on the usage of renewable energy resources and alleviate the energy emergency somehow.Blockchain is basically a decentralized accounting ledger with the potential to enable, manage, track and verify thousands of energy transactions per second.The blockchain energy market is proposed to be utilized by networks that share a nanogrid.The application permits nanogrid participants that have an abundance of electrical energy, to offer that energy to different clients of the nanogrid.The application gives a decentralized marketplace for executing electrical energy.Since this blockchain energy market is implemented by Ethereum smart contract, it suffers from the high operating costs that result from the contract's high gas consumption.Ethereum smart contracts are implemented by reworking on acceptoffer function to verifies its validity and withdraw function which uses a single function call for multiple energy transfer contracts to decrease the operation cost.
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling

Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. This paper studies the practical yet challenging source-free unsupervised domain adaptation problem, in which only an existing source model and the unlabeled target data are available for model adaptation. We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels. Importantly, considering that the pseudo labels generated from source model are inevitably noisy due to domain shift, we further introduce two complementary pixel-level and class-level denoising schemes with uncertainty estimation and prototype estimation to reduce noisy pseudo labels and select reliable ones to enhance the pseudo-labeling efficacy. Experimental results on cross-domain fundus image segmentation show that without using any source images or altering source training, our approach achieves comparable or even higher performance than state-of-the-art source-dependent unsupervised domain adaptation methods.
The practice of using the electronic information and educational environment of a university in the organization of intermediate control of current progress (on the example of the Pskov branch of the Academy of the FPS of Russia)

The article discusses the issues of the practical use of the Moodle electronic information and educational environment in the organization of the educational process of full-time students. The subject of the article was the methods built into the Moodle environment, which allowed for the current monitoring of progress in the studies. The purpose of the study is to show the possibility of the Moodle electronic information and educational environment in the process of current control, to formulate guidelines for the formation of this process and to assess the impact of this methodology on the educational process quality.formed by the analysis, synthesis and formal-logical methods. As a result of the work carried out, the components that make it possible to objectively assess the knowledge of students have been considered, analyzed and studied. Recommendations have been formulated for the use and configuration of the selected components included in the built-in toolkit of the Moodle environment. The statistical data showing the positive impact of these decisions regarding the use of the Moodle environment on the quality of the educational process are presented. Conclusions are made about the importance of introducing new pedagogical technologies into the educational process. The methodological basis of the research was
Distilling GHZ States using Stabilizer Codes

Entanglement distillation is a well-studied problem in quantum information, where one typically starts with $n$ noisy Bell pairs and distills $k$ Bell pairs of higher fidelity. While distilling Bell pairs is the canonical setting, it is important to study the distillation of multipartite entangled states because these can be useful for realizing distributed algorithms on quantum networks. In this paper, we study the distillation of GHZ states using quantum error correcting codes (QECCs). Using the stabilizer formalism, we begin by explaining the QECC-based Bell pair distillation protocol in arXiv:0708.3699, which relies particularly on the transpose symmetry between Alice's and Bob's qubits in Bell states. Extending this idea, we show that, given $n$ GHZ states, performing a matrix on Alice's qubits is equivalent to performing a version of the transpose of the matrix on the qubits of Bob and Charlie. We call this mapping to the stretched version of the matrix the GHZ-map, and show that it is an algebra homomorphism. Using this property, we show that Alice projecting her qubits onto an $[[n,k]]$ stabilizer code implies the simultaneous projection of Bob's and Charlie's qubits onto an induced $[[2n,k]]$ stabilizer code. Guided by this insight, we develop a GHZ distillation protocol based on local operations and classical communication that uses any stabilizer code. Inspired by stabilizer measurements on GHZ states, we also develop a new algorithm to generate logical Pauli operators of any stabilizer code and use it in the protocol. Since quantum codes with finite rate and almost linear minimum distance have recently been discovered, this paper paves the way for high-rate high-output-fidelity GHZ distillation. We provide simulation results on the $5$-qubit perfect code to emphasize the importance of the placement of a certain local Clifford operation in the protocol.
Augmented Reality Technology and Its Application in Aviation Industry

With the development of digital technology, the wide application of augmented reality technology in aviation manufacturing industry is also possible. Through the introduction of the development and key components of augmented reality technology, this paper summarizes the application of augmented reality technology in aircraft assembly, maintenance and inspection of Boeing, Airbus and other aircraft manufacturers, and analyzes the research on augmented reality technology in domestic aviation industry The current situation and development trend provide certain technical support for the specific application of augmented reality technology in the whole life cycle of civil aircraft.