Reviewerly/exHarmony huggingface dataset:
The Influence of Herd Mentality on Rating Bias and Popularity Bias: A Bi-Process Debiasing Recommendation Model Based on Matrix Factorization

To reduce the impact of rating bias and popularity bias in recommender system, and make the recommender system reach a balance between recommendation utility and debias effect at the same time, we propose a bi-process debiasing recommendation model based on matrix factorization. Firstly, considering the problem that the user's ratings are affected by the herd mentality, which leads to a consistency between the rating and the selection of rating items, resulting in the power-law distribution, the k-times parabolic fuzzy distribution was used to fuse the user's age to redistribute the ratings. Secondly, the loss function is optimized by the continuously increasing flow and popularity of items. Finally, user emotion and item popularity are combined to construct user psychological tendency, which is divided into three levels: strong, medium and weak, and different levels are given different weights. To verify the performance of the model, the experimental results on real datasets show that the model proposed in this paper not only effectively reduces the recommendation bias but also ensures the recommendation utility.
A General Noninterference Policy for Polynomial Time

We introduce a new noninterference policy to capture the class of functions computable in polynomial time on an object-oriented programming language. This policy makes a clear separation between the standard noninterference techniques for the control flow and the layering properties required to ensure that each “security” level preserves polynomial time soundness, and is thus very powerful as for the class of programs it can capture. This new characterization is a proper extension of existing tractable characterizations of polynomial time based on safe recursion. Despite the fact that this noninterference policy is Π 1 0 -complete, we show that it can be instantiated to some decidable and conservative instance using shape analysis techniques.
Non-episodic and Heterogeneous Environment in Distributed Multi-agent Reinforcement Learning

Reinforcement learning (RL) is a efficient intelligent algorithm when solving radio resource management problems in the wireless communication network. However, for large-scale networks with limited centralization (i.e., high latency connection to center-server or capacity-limited backbone), it is not realistic to employ a centralized RL algorithm to perform joint real-time decision-making for the entire network, which calls for scalable algorithm designs. Multi-agent RL, which allows separate local execution of policy, has been applied to large-scale wireless communication areas. However, it has performance issue which largely varies with different system settings. In this paper, we study a multi-agent algorithm for a coordinate multipoint (CoMP) scenario, which requires cooperation between base stations. We show that the common settings of user distribution, the design of reward, and episodic in the environment can significantly ease the learning of the algorithm and obtain beautiful converge results. However, these settings are not realistic in wireless communication. By validating the performance difference between these settings with our algorithm in a coordinate multipoint (CoMP) scenario, we introduce several possible solutions and highlight the necessity of further study in this area.
Self-Taught Black-Box Adversarial Attack to Multilayer Network Automation

Although the idea of multilayer network automation (MLy-NA) has gained its initial success due to the advances on software-defined networking (SDN) and machine learning (ML), the security vulnerabilities brought by the reduction of human intervention in network operation should not be ignored. In this work, we study how to mislead the ML-based classifiers for anomaly detection in MLy - NA of packet-over-optical networks, and propose a self-taught black-box adversarial attack (BB-AdA) scheme. Specifically, we design novel algorithms to synthesize and label training data for the substitute classifier used by the BB-AdA. The algorithms not only generate synthetic data to cover all the anomaly types based on a small set of legitimate telemetry data only containing the “Normal” type, but also label the data with minimized queries to the target classifier in MLy-NA. Extensive simulations are conducted with the telemetry data collected from a real-world packet-over-optical network testbed. The results show that with our self-taught BB-AdA, an attacker can interact with an MLy-NA system quietly and efficiently to train itself adaptively, generate well-crafted adversarial samples to mislead the target classifiers in different ML architectures and severely affect their performance on anomaly detection, and disturb the operation of MLy-NA in the hard-to-detect manner.
One Picture is Worth a Thousand Words: A New Wallet Recovery Process

We introduce a new wallet recovery pro-cess. Our solution associates 1) visual passwords: a photograph ofa secretly picked object (Chabanne et aI., 2013) with 2) ImageNet classifiers transforming images into binary vectors and, 3) obfuscated fuzzy matching (Galbraith and Zobernig, 2019) for the storage of visual passwords/retrieval of wallet seeds. Our experiments show that the replacement of long seed phrases by a photograph is possible.
An Efficient Blockchain-Based Time-Stamping Scheme Using Commitment Signatures

Blockchain-based time-stamping services are widely used in file archiving systems, which can prove a file existed at a given time point by inserting the file hash into the blockchain. However, existing data insertion methods are not satisfactory in terms of efficiency, concealment, and scalability. Besides, to save the on-chain cost of inserting data, existing time-stamping services generate a Merkle tree to aggregate files and then utilize Merkle paths to verify the existence of files. However, as the number of files increases, the Merkle path grows in size, leading to a significant communication overhead for the file existence proof. To solve the above problems, we design a novel blockchain-based time-stamping scheme. First, we design a commitment signature scheme to embed data in the addresses and signatures of blockchain transactions, which can insert our data into public blockchains in an efficient, concealed, and scalable fashion without modifying the data structures and signature verifying schemes of the current public blockchains. Second, instead of using a Merkle tree to aggregate files, we utilize a bilinear pairing accumulator to achieve a constant communication overhead for the file existence proof. Finally, we implement a prototype on the Bitcoin blockchain to show that our scheme is more efficient without any security compromise compared with existing blockchain-based time-stamping schemes.
New Quantum Burst-Error Correcting Codes from Interleaving Technique

Quantum memory is essential for the development of many devices in quantum information processing, including a synchronization tool that can match the various processes in a quantum computer, a quantum gate that maintains the identity of any state, and a mechanism for converting predetermined photons into on-demand photons. Quantum memory can be used in many aspects, such as quantum computing and quantum communication. Continuous research and experiments have enabled quantum memory to realize the storage of qubits. On the other hand, interleaving techniques are used to deal with burst of errors. Many works from literature provide effective interleaving techniques for combating burst of errors by using classical error-correcting codes. However, to the best of our knowledge, little is known regarding interleaving techniques for combating cluster of errors in topological quantum error-correcting codes. Thenceforward, it is proposed in this work a new interleaving technique by using good toric quantum codes, namely Bombin and Martin-Delgado toric codes and another class of toric codes from literature which is described in this work. As a consequence, by applying this new interleaving method to such toric quantum codes, new quantum burst-error correcting codes are obtained which improve the coding gain. In addition to the proposed quantum interleaving technique improves such parameter, it can be used for burst-error correction in errors which are located, quantum data stored and quantum channels with memory.
Pairwise Gaussian Graph Convolutional Networks: Defense Against Graph Adversarial Attack

As a research hotspot for graph mining technology, Graph Convolutional Networks (GCN) have achieved remarkable performance in the fields of wireless networks, Internet of Things, and edge computing. However, recent studies have shown that GCN is vulnerable to adversarial attack; that is, even imperceptible intentional perturbations on graph structure or node attributes can significantly change classification results. This paper proposes a novel graph convolutional network, Pairwise Gaussian Graph Convolutional Networks (PGGCN), in which a pairwise architecture is designed for GCN model construction and training. This elegant design enables PGGCN to mitigate the effects of adversarial attack and thus improve model robustness while guaranteeing classification accuracy. The performance of PGGCN is validated through extensive experimental results, which confirm that PGGCN can effectively improve the robustness of GCN while ensuring classification accuracy.
Accelerating GNN Inference by Soft Channel Pruning

Graph Neural Networks (GNNs) are effective models for processing graph-structured data. With the continuous growth of graph data scale and the deepening of graph neural network layers, the heavy cost of GNN inference has greatly limited its application in real-time tasks. This paper focus on accelerating the performance of GNN inference. We first measures the execution time ratio of each stage in the inference process for commonly used GNN models, and analyzes the performance characteristics of different stages. We find out that the critical performance factor of GNN inference is the feature dimension, which is different to CNN and NLP models. Therefore, we propose a soft channel pruning method with a ladder pruning pattern. It reduces the calculation from unimportant graph node features and achieve performance acceleration. Meanwhile, it reserves inference accuracy of GNNs. According to experimental validation on graph datasets of different scales, our method can effectively reduce the inference latency and achieve 2×–7× speedup. Also, compared with existing pruning methods, higher inference accuracy can be obtained with comparable speedup ratio.
An Order-Theoretic Analysis of Universe Polymorphism

We present a novel formulation of universe polymorphism in dependent type theory in terms of monads on the category of strict partial orders, and a novel algebraic structure, displacement algebras, on top of which one can implement a generalized form of McBride’s “crude but effective stratification” scheme for lightweight universe polymorphism. We give some examples of exotic but consistent universe hierarchies, and prove that every universe hierarchy in our sense can be embedded in a displacement algebra and hence implemented via our generalization of McBride’s scheme. Many of our technical results are mechanized in Agda, and we have an OCaml library for universe levels based on displacement algebras, for use in proof assistant implementations.
From SMT to ASP: Solver-Based Approaches to Solving Datalog Synthesis-as-Rule-Selection Problems

Given a set of candidate Datalog rules, the Datalog synthesis-as-rule-selection problem chooses a subset of these rules that satisfies a specification (such as an input-output example). Building off prior work using counterexample-guided inductive synthesis, we present a progression of three solver-based approaches for solving Datalog synthesis-as-rule-selection problems. Two of our approaches offer some advantages over existing approaches, and can be used more generally to solve arbitrary SMT formulas containing Datalog predicates; the third—an encoding into standard, off-the-shelf answer set programming (ASP)—leads to significant speedups (∼ 9× geomean) over the state of the art while synthesizing higher quality programs. Our progression of solutions explores the space of interactions between SAT/SMT and Datalog, identifying ASP as a promising tool for working with and reasoning about Datalog. Along the way, we identify Datalog programs as monotonic SMT theories, which enjoy particularly efficient interactions in SMT; our plugins for popular SMT solvers make it easy to load an arbitrary Datalog program into the SMT solver as a custom monotonic theory. Finally, we evaluate our approaches using multiple underlying solvers to provide a more thorough and nuanced comparison against the current state of the art.
Emerging and Disruptive Technologies: The Metaverse. Implications on Global Security

Abstract This paper analyzes the metaverse as a multi-faceted example of emerging and disruptive technologies and its potential impact on global security. After a brief presentation of the concept, including some socio-economic implications on the civilian life, asymmetric risks such as terrorism and crypto-laundering in the metaverse/darkverse are addressed. Moreover, aspects related to the potential impact of the military applications on the metaverse are underlined. Last, but not least, beside the inherent opportunities and the risks of this still incipient version of the metaverse, a series of current limitations is presented.
On maximal autocorrelations of Rudin–Shapiro sequences

In this paper, we present an alternative proof showing that the maximal aperiodic autocorrelation of the mth Rudin–Shapiro sequence is of the same order as λm, where λ is the real root of x3+x2−2x−4. This result was originally proven by Allouche et al. (2019) and Choi (2020) using a translation of the problem into linear algebra. Our approach simplifies this linear algebraic translation and provides another method of dealing with the computations given by Choi. Additionally, we prove an analogous result for the maximal periodic autocorrelation of the mth Rudin–Shapiro sequence. We conclude with a discussion on the connection between the proofs given and joint spectral radius theory, as well as a couple of conjectures on which autocorrelations are maximal.
Robust anchor-based multi-view clustering via spectral embedded concept factorization

Multi-view clustering (MVC) often provides superior effectiveness to single-view clustering due to the integration of information from diverse views. Nonetheless, existing MVC methods are limited to large-scale real-world data by the drawbacks of low efficiency and poor robustness. To address these issues, we propose a novel robust anchor-based MVC model via spectral embedded concept factorization (RAMCSF). RAMCSF builds anchor graphs to approximate full-sample graphs and decomposes these anchor graphs by concept factorization (CF). To improve the clustering effectiveness, factor matrices of CF are constrained as orthogonal matrices to reduce the freedom of decomposition, and a novel small-scale anchor-based spectral embedding is designed to explore the high-order neighbor relationships. To restrain complex noises distributed in real-world data, we employ correntropy to measure the error between the original data and the learned representation. Moreover, RAMCSF can get a clustering indicator matrix directly, avoiding additional post-processing and ensuring that changes in data dimensions have a limited impact on efficiency. The model is then optimized by a novel fast half-quadratic-based optimization strategy that combines the orthogonal properties and the traces of matrices. Extensive experiments indicate that RAMCSF can achieve higher efficiency and robustness while maintaining comparable effectiveness to other state-of-the-art methods.
Color printing based on 3MF: Color diffusion from the surface to the interior of voxel model

Abstract Full‐color 3D printing technology has become more and more popular for industrial manufacturing applications. The voxelization of color 3D models and the expression of appearance colors become more and more important. To illustrate the complex appearance and internal characteristics of 3D models, we present a voxelization algorithm of the color 3D models for color 3D printing in this paper. Specifically, we use the 3D model of 3D Manufacturing Format (3MF) with color appearance features for surface voxelization first. Then, we propose an improved scan line filling algorithm to realize the voxelization of the interior of the model and obtain the color voxel model. Finally, to improve the high quality of color printing, we propose a surface color inward diffusion algorithm, which can make the surface color of the voxel model diffuse inward and further express the surface color of the voxel model. The experimental results show our proposed method can obtain an effective color voxel model and accurately express the color of the voxel model surface, which can enhance the color effect of the color model and realize the control of voxels.
Conventional Methods of Energy System Evaluation

The data on solar radiation may be used to calculate the amount of energy that is delivered by the sun to the surface of the planet at a certain place and during a particular time. These kinds of data are essential for effectively researching solar energy. Because of the high cost and limited capacity of physically in-situ data harvesting of solar radiation, we are unable to get a regular dataset that is also made accessible to the public. As a result, we need to develop alternative ways to acquire this data. The current part of the investigation involves doing an in-depth analysis of solar energy modelling approaches, which are arranged following the kind of information they provide. In addition to this, a variety of models and methods that were created or sponsored by a variety of organizations to evaluate, analyse, and optimize energy efficiency and renewable energy systems have also been included. Some of these tools may be used on a worldwide scale, on a regional scale, on a local scale, or for a particular purpose. These approaches provide a scientific solution to the issue at hand while also shedding light on how scientific research is carried out as a discipline. In addition to that, a variety of different data processing methods were developed and put into practice to deal with the raw data or intermediate data that were produced during the process of modelling. Following that, all the data for the region’s sample solar maps were integrated and plotted or otherwise created. In a word, what we did was study the many methods that a researcher may use to investigate this kind of research challenge, in addition to the reasoning behind those methods. The researcher must be familiar with not only the study methodologies and procedures, but also the ideas since similar aspects of different problems might provide unique challenges.
Color changes in yardang strata sediment in the Dunhuang Yardang National Geopark, Northwest China: Controlling factors and significance for sedimentary environments

Introduction: Yardang stratum is the basic material unit of yardang landforms. Due to their protracted age and complex sedimentary structure, formative yardang stratum mechanisms remain poorly understood. Color, being one of the most intuitive, easily accessible, and important lithological indicators, can be used to identify yardang strata sedimentary environments. However, the lack of quantitative analysis and systematic research have limited how chromatic environmental proxies can be extracted and interpreted. Materials and methods: The Dunhuang Yardang National Geopark was selected for this experiment. Based on chromaticity, grain-size, chemical element, organic matter, and carbonate content measurements of yardang strata sediment samples, this study used correlation analysis and chromaticity proxy extraction to quantitatively characterize associated color changes, controlling factors, and sedimentary environments. Results and discussion: Results found significant cyclic changes in chromaticity parameter curves were observed in the profiles, which is likely indicative of a dry/wet evolutionary process that occurred during different stages of the yardang sedimentary environment. Organic matter content was the main factor affecting changes in brightness, while iron oxide content directly affected yardang sediment redness and yellowness values. Due to its low value, effects of carbonate content on brightness were obscured. The grain-size characteristics indirectly affect the brightness value through the content of quartz, feldspar and organic matter. Moreover, based on modern analogy method, the brightness and redness values are proved to be effective proxies for distinguishing the yardang sedimentary environment. Accordingly, it is concluded that the sedimentary environment of Dunhuang yardang landforms is composed of sedimentation that derived from lacustrine, aeolian, and alluvial environments, and that the chromaticity parameter range and characteristic color for sedimentary environments are extracted and quantified. Findings from this study not only rectify the lack of quantitative research on yardang sediment color theory but also provide an important theoretical basis for identifying sedimentary environments in the field.
Research on Intelligent Evaluation of College Students’ Mental Health Based on Decision Tree Algorithm

Students' mental health evaluation is an important part of mental health research. In recent 10 years, domestic colleges and universities have conducted a lot of research on students' mental health evaluation, but there is generally no mature model from theory to practice. In order to solve the problems of high error rate and low efficiency in mental health evaluation, an intelligent mental health evaluation system based on decision tree algorithm is proposed. This paper first analyzes the research status of mental health intelligent evaluation, constructs a mental health intelligent evaluation system, and then uses decision tree algorithm to collect mental health intelligent evaluation data. Analyze and classify the mental health intelligence evaluation data, get the mental health intelligence analysis results, and finally analyze the feasibility and benefits of the mental health intelligence evaluation system through specific simulation experiments. The results show that the system can overcome the shortcomings of the existing mental health evaluation system, improve the accuracy of mental health evaluation, improve the effectiveness and stability of mental health evaluation, and meet the actual needs of modern mental health.
Research on English Language Learning Algorithm Based on Speech Recognition Confidence

Speech recognition technology is the key to the realization of a language learning system. The research of large-scale continuous speech recognition has been carried out for more than 20 years. Although significant progress has been made, it is still far from wide application. Because the current English learning software can not fully meet the requirements of users, this paper proposes a method to introduce the confidence of speech recognition into language learning. After discussing the basic principle of confidence and its role in language blind learning, a new algorithm is proposed, and based on this algorithm, an English language learning system based on confidence technology is finally established.