Reviewerly/exHarmony huggingface dataset:
The Quantum Path Kernel: A Generalized Neural Tangent Kernel for Deep Quantum Machine Learning

Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing.A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear.This problem has been variously approached in the literature, principally via the introduction of measurements between layers of unitary transformations.In this paper, we introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning.Our approach generalizes the notion of Quantum Neural Tangent Kernel, which has been used to study the dynamics of classical and quantum machine learning models.The Quantum Path Kernel exploits the parameter trajectory, i.e. the curve delineated by model parameters as they evolve during training, enabling the representation of differential layer-wise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function.We evaluate our approach with respect to variants of the classification of Gaussian XOR mixtures -an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation.
Public-Key Encryption With Tester Verifiable Equality Test for Cloud Computing

Public-key encryption with equality test (PKEET) provides cloud servers with an effective way to check the equality of outsourced encrypted data without decryption. This enables PKEET to attract much attention and be widely researched in cloud computing. However, we claim that the existing PKEET schemes suffer from an inherited problem, called message-consistency unverifiability of testers (MCUT). Applying the MCUT problem, outsourcers can fool cloud servers into outputting incorrect testing results of encrypted data, which negates the practicability of PKEET in cloud computing. We investigate the PKEET literature and find the main reason for the MCUT problem is the independence between the messages inserted in the decryption and testing modules in their ciphertexts. To bridge the technical gap between PKEET and its practical applications, we present a new notion, called PKE with tester verifiable equality test (PKE-TVET), which solves the MCUT problem by allowing testers to verify the message consistency in two modules. We then instantiate the PKE-TVET and give a specific construction in the standard model. In our PKE-TVET scheme, the testing module is integrated into the decryption module so that there is only one message inserted in the ciphertext for both decryption and testing. This special setting lets our scheme directly get rid of the MCUT problem. For better applications in actual scenarios, we further extend the scheme to support authorization and tester designation. Finally, we analyze the tradeoff of parameter sizes and computation costs for the security against MCUT attacks in our PKE-TVET scheme.
Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

The particle swarm optimization (PSO) algorithm is a swarm intelligence (SI) algorithm used to solve optimization problems.Owing to its advantages in simplicity, using only a few parameters, PSO has become one of the most popular optimization algorithms.However, the single structure of PSO leads to challenges in finding the appropriate optima, resulting in low convergence accuracy.To solve the defects of PSO, it is necessary to increase the diversity of the populations involved as well as enhance the ability of the algorithm to develop locally.In this study, we propose a PSO algorithm with an adaptive two-population strategy (PSO-ATPS), which adaptively divides a population into two groups representing excellent and ordinary populations.Inspired by animal hunting behavior, a new velocity-position update method is proposed for the general population.A velocity update formulation with decreasing inertia weights based on logistic chaotic mapping is applied to the excellent population.The algorithm increases the diversity of the population by continuously changing the search strategy of the particles.In addition, a new neighborhood search strategy (oscillation strategy) is proposed, in which a particle searches randomly in its own adaptive neighborhood when its motion is stagnant and updates the particle position using an elite strategy.Among several optimization strategies, PSO-ATPS achieved first place in 7, 8, and 9 groups of tests involving 10 test functions in 3 dimensions, indicating the accuracy and effectiveness of PSO-ATPS.The results show that the performance of PSO-ATPS is competitive, and many improvements developed for PSO can be applied to PSO-ATPS, demonstrating the potential for further development.
Scalable and Efficient Full-Graph GNN Training for Large Graphs

Graph Neural Networks (GNNs) have emerged as powerful tools to capture structural information from graph-structured data, achieving state-of-the-art performance on applications such as recommendation, knowledge graph, and search. Graphs in these domains typically contain hundreds of millions of nodes and billions of edges. However, previous GNN systems demonstrate poor scalability because large and interleaved computation dependencies in GNN training cause significant overhead in current parallelization methods. We present G3, a distributed system that can efficiently train GNNs over billion-edge graphs at scale. G3 introduces GNN hybrid parallelism which synthesizes three dimensions of parallelism to scale out GNN training by sharing intermediate results peer-to-peer in fine granularity, eliminating layer-wise barriers for global collective communication or neighbor replications as seen in prior works. G3 leverages locality-aware iterative partitioning and multi-level pipeline scheduling to exploit acceleration opportunities by distributing balanced workload among workers and overlapping computation with communication in both inter-layer and intra-layer training processes. We show via a prototype implementation and comprehensive experiments that G3 can achieve as much as 2.24x speedup in a 16-node cluster, and better final accuracy over prior works.
High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations

Approximate K nearest neighbor (AKNN) search in the high-dimensional Euclidean vector space is a fundamental and challenging problem. We observe that in high-dimensional space, the time consumption of nearly all AKNN algorithms is dominated by that of the distance comparison operations (DCOs). For each operation, it scans full dimensions of an object and thus, runs in linear time wrt the dimensionality. To speed it up, we propose a randomized algorithm named ADSampling which runs in logarithmic time wrt the dimensionality for the majority of DCOs and succeeds with high probability. In addition, based on ADSampling we develop one generic and two algorithm-specific techniques as plugins to enhance existing AKNN algorithms. Both theoretical and empirical studies confirm that: (1) our techniques introduce nearly no accuracy loss and (2) they consistently improve the efficiency.
A Step Toward Deep Online Aggregation

For exploratory data analysis, it is often desirable to know what answers you are likely to get before actually obtaining those answers. This can potentially be achieved by designing systems to offer the estimates of a data operation result-say op(data)-earlier in the process based on partial data processing. Those estimates continuously refine as more data is processed and finally converge to the exact answer. Unfortunately, the existing techniques-called Online Aggregation (OLA)-are limited to a single operation; that is, we cannot obtain the estimates for op(op(data)) or op(...(op(data))). If this Deep OLA becomes possible, data analysts will be able to explore data more interactively using complex cascade operations. In this work, we take a step toward Deep OLA with evolving data frames (edf), a novel data model to offer OLA for nested ops-op(...(op(data)))-by representing an evolving structured data (with converging estimates) that is closed under set operations. That is, op(edf) produces yet another edf; thus, we can freely apply successive operations to edf and obtain an OLA output for each op. We evaluate its viability with Wake, an edf-based OLA system, by examining against state-of-the-art OLA and non-OLA systems. In our experiments on TPC-H dataset, Wake produces its first estimates 4.93× faster (median)-with 1.3× median slowdown for exact answers-compared to conventional systems. Besides its generality, Wake is also 1.92× faster (median) than existing OLA systems in producing estimates of under 1% relative errors.
All-optical spiking neural network and optical spike-time-dependent plasticity based on the self-pulsing effect within a micro-ring resonator

In this paper, we proposed an all-optical version of photonic spiking neurons and spike-time-dependent plasticity (STDP) based on the nonlinear optical effects within a micro-ring resonator. In this system, the self-pulsing effect was exploited to implement threshold control, and the equivalent pulse energy required for spiking, calculated by multiplying the input pulse power amplitude with its duration, was about 14.1 pJ. The positive performance of the neurons in the excitability and cascadability tests validated the feasibility of this scheme. Furthermore, two simulations were performed to demonstrate that such an all-optical spiking neural network incorporated with STDP could run stably on a stochastic topology. The essence of such an all-optical spiking neural network is a nonlinear spiking dynamical system that combines the advantages of photonics and spiking neural networks (SNNs), promising access to the high speed and lower consumption inherent to optical systems.
Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this paper would study the pre-training language model based on NLP technology, which aimed to use NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10G and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This paper opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning

This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential to achieve satisfactory results. Existing approaches based on deep neural networks typically use second-order statistics to generate the output. However, these hand-crafted features computed from a single image cannot leverage style information sufficiently, which leads to artifacts such as local distortions and style inconsistency. To address these issues, we learn style representation directly from a large number of images based on contrastive learning by considering the relationships between specific styles and the holistic style distribution. Specifically, we present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components: a parallel contrastive learning scheme for style representation and transfer, a domain enhancement (DE) module for effective learning of style distribution, and a generative network for style transfer. Qualitative and quantitative evaluations show the results of our approach are superior to those obtained via state-of-the-art methods. The code is available at https://github.com/zyxElsa/CAST_pytorch .
A Polarized Structured Light Method for the 3D Measurement of High-Reflective Surfaces

The reflection phenomenon exhibited by highly reflective surfaces considerably affects the quality of captured images, thereby rendering the task of structured light (SL) 3D reconstruction. In this paper, a polarized SL method is proposed to address the reconstruction issues on high-reflectance surfaces. The SL system we build in this paper involves a four-channel polarizing camera and a digital light processing (DLP) projector equipped with a polarizer in the lens. The built system enables the simultaneous acquisition of four groups of fringe images, each with different brightness differences. Then, a binary time-multiplexing SL method is adopted to obtain four distinct point clouds. Additionally, a fusion algorithm is proposed to merge the four point clouds into a single, precise, and complete point cloud. Several experiments have been conducted to demonstrate that the proposed method is capable of achieving excellent reconstruction outcomes on highly reflective surfaces.
Modern foreign studies of information needs of students of higher educational institutions (review of publications

Educating of the culture of information needs of future specialists and teaching effective ways of how to meet them are among the urgent tasks of higher education. The goal of the paper is to characterize the status of research in the field of academic information needs. The author reviews the publications of 2015–2021, included into the Scopus and Web of Science databases. She characterizes the document flow, discusses the basic contexts of the studies, highlights the organizational and methodological aspects of research, and reveals the influence of the research base on the results of studying the hierarchy of student information needs. The relationship of and differences between the concept of academic information needs and the concept of information needs related to educational activities are demonstrated. The academic information needs are stipulated by the need to maintain academic performance, to adapt to academic work and future career, to overcome learning difficulties, to master learning methods, to get involved in student activities and academic life, to live on or off campus, and to participate in international programs, etc. The extrinsic factors affecting the formation, expression and satisfaction of academic information needs include: environment, area of study, the functions performed by the student and the timing for the assignment. The intrinsic factors are: level of education, previous experience, age, personal and physical characteristics, and level of motivation. The author argues that the specific character of academic information needs of older students is studied insufficiently. She also formulates the proposals on how to educate the culture of academic information needs.
Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzing user opinions and applying these to guide choices, making it one of the most popular areas of research in the field of natural language processing. Despite the fact that several languages, including English, have been the subjects of several studies, not much has been conducted in the area of the Arabic language. The morphological complexities and various dialects of the language make semantic analysis particularly challenging. Moreover, the lack of accurate pre-processing tools and limited resources are constraining factors. This novel study was motivated by the accomplishments of deep learning algorithms and word embeddings in the field of English sentiment analysis. Extensive experiments were conducted based on supervised machine learning in which word embeddings were exploited to determine the sentiment of Arabic reviews. Three deep learning algorithms, convolutional neural networks (CNNs), long short-term memory (LSTM), and a hybrid CNN-LSTM, were introduced. The models used features learned by word embeddings such as Word2Vec and fastText rather than hand-crafted features. The models were tested using two benchmark Arabic datasets: Hotel Arabic Reviews Dataset (HARD) for hotel reviews and Large-Scale Arabic Book Reviews (LARB) for book reviews, with different setups. Comparative experiments utilized the three models with two-word embeddings and different setups of the datasets. The main novelty of this study is to explore the effectiveness of using various word embeddings and different setups of benchmark datasets relating to balance, imbalance, and binary and multi-classification aspects. Findings showed that the best results were obtained in most cases when applying the fastText word embedding using the HARD 2-imbalance dataset for all three proposed models: CNN, LSTM, and CNN-LSTM. Further, the proposed CNN model outperformed the LSTM and CNN-LSTM models for the benchmark HARD dataset by achieving 94.69%, 94.63%, and 94.54% accuracy with fastText, respectively. Although the worst results were obtained for the LABR 3-imbalance dataset using both Word2Vec and FastText, they still outperformed other researchers’ state-of-the-art outcomes applying the same dataset.
Cognitive management and artificial intellect in libraries: Possibilities and highlights

The author discusses use of cognitive management in libraries and examines the possibilities of artificial intellect (AI) technologies for library and information processes (LIP). The systemic functional method is used for the analysis. The article is based on the research work in the area of libraries digital transformation and will be of interest to library managers. The author examines the key vectors of cognitive management: а) library collection management and preservation; b) library bibliographic user services; and c) research in libraries. These vectors correspond to the LIP functions as fixed in the professional standard “Specialists in library information work”. The author characterizes in brief the potential problems, risks and regional specifics of AI application in public libraries. Library managers have to be aware of the problems and to make AI systems implementation effective and ethical. AI may revolutionize the libraries and their user services. Meanwhile, the consequences of such implementation have to be analyzed: algorithm accuracy and reliability, possible bias, as well as the impact it may have on the library personnel.
Blockchain-based decentralized supply chain system with secure information sharing

Supply chain management (SCM) has become an important way for the companies to improve the production efficiency and reduce the management cost. However, the existing SCM system is usually built and performed with the semi-trusted certificate authority, which may be susceptible to the collusion attack and tampering attack launched by the malicious certificate authority, resulting in illegal modification of product provenance record. Moreover, the lack of transparent and mutual trust among the stakeholders in the SCM system remains a great challenging issue. In this paper, we propose a blockchain-based decentralized supply chain system with secure information sharing, which can ensure the security of product provenance record without relying on any fully trusted intermediary. In our system, two validation mechanisms, i.e., one-way validation mechanism and transaction-based validation mechanism are built to help the stakeholders to perform the validation of product provenance record without accessing their original content. Furthermore, the blockchain-based smart contract is used to achieve secure registration, proper authentication and fair payment for all stakeholders, where the valid stakeholders can pay for the purchased products, automatically and reliably. Security analysis and performance evaluation demonstrate that our proposed scheme is secure, feasible and efficient with a limited and reasonable computation cost.
On multi-step randomized extended Kaczmarz method for solving large sparse inconsistent linear systems

In order to improve the convergence property and computational behavior of the randomized extended Kaczmarz method, we propose a multi-step randomized extended Kaczmarz method, in which we repeatedly update the iterate several times at each iteration step, obtaining a nonstationary inner-outer iteration scheme for solving large-scale, sparse, and inconsistent system of linear equations. For this multi-step randomized extended Kaczmarz method, we prove its convergence, derive an upper bound for its convergence rate, and demonstrate that this upper bound can be smaller than that of the randomized extended Kaczmarz method for several typical choices of the numbers of inner iteration steps. Numerical experiments also show that the multi-step randomized extended Kaczmarz method can perform better than the randomized extended Kaczmarz method if we choose the numbers of inner iteration steps appropriately.