Reviewerly/exHarmony huggingface dataset:
Improving Performance of Long Short-Term Memory Networks for Sentiment Analysis Using Multicore and GPU Architectures

A massive amount of data is generated on the Internet through websites, blogs, and social networks, with all kinds of content, including comments about companies and their products. Sentiment analysis (SA) is the interpretation of emotions in texts. It is essential for different companies as it helps identify customer’s opinions. It is also beneficial to understand people’s responses to new content, providing audience insights to help make decisions. However, current technological advances enable us to efficiently store and retrieve these immense amounts of data to better insight into different application areas. Companies use this information to make marketing decisions. In response, we propose a performance optimization LSTM for SA using multicore and GPUs to keep the accuracy. To validate our proposal, we have applied it over a public database with 50,000 film records. The results showed a performance improvement of 3.17 times on the multicore and 12.15 on the GPU.
Exploring a Reinforcement Learning Agent with Improved Prioritized Experience Replay for a Confrontation Game

Deep Q-network (DQN) is used successfully in dealing with many reinforcement learning situations and challenging tasks with real-world complexity. The current limits are the unacceptable training time to obtain satisfactory results like a human. To address this obstacle, I propose a new reinforcement learning strategy. This paper focuses on the confrontation game environment for two players with sparse reward and no direct hindsight reward function and no fixed goals. According to some strategies, algorithm can put them into reinforcement learning with reward functions and replay to give the abilities of judging in the middle of the games as references. To demonstrate the effectiveness of the proposed strategy, a new game is designed. Fence game is a confrontation game for two players that one tries their best to fence the other one in Die Иow. The custom environment of this game will give the only reward functions at the end: win, lose or draw. In conclusion, these factors include performance and results proved that 1) Prioritized Experience Replay with Dynamic Hindsight reward function (DH-PER) and 2) Prioritized Experience Replay with Dynamic Hindsight reward function and Sharing (DHS-PER) both let the RL agents converge more quickly.
“Stronger than Hate”: On the Dissemination of Hate Speech during the 2020 Vienna Terrorist Attack

In this paper, we present an analysis of 36,685 tweets related to the 2020 Vienna terror attack. We used a Convolutional Neural Network (CNN) approach to identify hateful and non-hateful tweets. Our findings indicate that users who post hateful content are predominantly anonymous. Moreover, we found that hateful messages can spread widely across the network and that hateful communication forms characteristic structural patterns.
Design-Based Research for Virtual Learning

This design-based research dissertation study provides a holistic perspective on the pedagogical and contextual factors of interactive mobile technology on English learners. Specifically, the study explored current virtual technologies designed to model face-to-face EL instruction due to the COVID-19 pandemic-prolonged school closures. After evaluating three cycles of interventions founded on Stephen Krashen's and Lev Vygotsky's language learning theories and utilizing a teacher survey and researcher observations, the study identified an onslaught of key takeaways including the need for investment of necessary infrastructure, ed-tech policies and standards, and comprehensive pedagogical frameworks to effectively operationalize virtual learning and leverage best practices of interactive mobile technology. The study discusses the impact of the sudden shift in educational contexts and provides suggestions for overcoming challenges in educational technology integration within the computer-assisted language learning contexts.
Blockchain-based Access Control Model to Preserve Privacy for Students’ Credit Information

In the process of sharing students’ credit information across schools and departments, there are some problems such as tampering and leaking of students’ credit information.In this paper, combined with the characteristics of blockchain traceability and difficult to tamper, a credit information access control method based on blockchain is proposed, which not only protects students’ privacy, but also realizes the cross school access control of students’ credit information.This paper designs a multi blockchain architecture that combines consortium blockchain and private blockchain of colleges and universities. It stores credit information summary on the blockchain and original records off the blockchain to relieve the storage pressure of blockchain; Then, the multi authorization attribute encryption technology is used to set the access policy for fine-grained access control.Finally, the simulation results show that the scheme can achieve fine-grained access control of students’ credit information while protecting students’ privacy.
Listening to Sounds of Silence for Audio replay attack detection

Automatic Speaker Verification (ASV) is a biometric authentication system identifying a person based on the voice presented to a system. Nowadays, due to the widespread use of these systems, various attacks are carried out on them. These attacks are in four different formats, which are impersonation, speech synthesis, voice conversion and replay attack. One of the most commonly used attacks is replay attack due to its simplicity. The purpose of this study is to provide a countermeasure system against replay attacks. We found that the effect of noises generated by different recorders and playback devices on the spoof samples can be used as a criterion for attack detection. So this study analyzes the silent parts of the speech signal that include the noises of various recording and playback devices. Also due to the proper operation of deep convolutional neural networks in classification applications, we propose an ensemble classifier based on end to end neural networks architecture and residual structures to accurately distinguish spoof utterances from genuine ones. We have decreased the t-DCF metric on ASVspoof2019 database by almost 16&#x0025; compared to similar models that have processed on full speech signals.
Knowledge Graph Representation via Hierarchical Hyperbolic Neural Graph Embedding

Knowledge graph enhanced information retrieval systems have attracted considerable attention due to their ability to improve performance and provide additional explainability. As the knowledge graphs usually include fruitful facts, they are also good sources of side information. However, recent studies have shown that the usefulness of knowledge graphs depends highly on their representation, e.g., the embeddings of entities and relations. Embedding entities and relations in low-dimensional space is a successful knowledge graph representation solution. Most of the works lie in modeling symmetry/asymmetry/composition/inversion relations but pay less attention to the hierarchical relations. Recent studies have observed the fact that there exist rich semantic hierarchical relations in knowledge graphs such as Freebase (entities are connected in a taxonomic hierarchy) and WordNet (entities are synsets linked together in a hierarchy).To address the above problems, we propose Hierarchical Hyperbolic Neural Graph Embedding (H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E), a new knowledge graph representation approach, which is able to better preserve hierarchical relations. Specifically, the entities/relations representations are learned in a hyperbolic polar embedding space. In a hyperbolic polar embedding space, the entity and relation are modeled as a dual-embedding with modulus embedding part and phase embedding part, enabling the explicitly modeling of two types of hierarchies: inter-level hierarchy and intra-level hierarchy. As the polar embedding is defined i n hyperbolic space, the ability of modeling and inferring hierarchical relations are mutual enhanced. In addition, by noticing the existence of the rich relational context, we propose an attentional neural context aggregation to adaptively integrate the relational context for further enhancing the ability to preserve the hierarchical relations. The empirical study on three benchmark datasets for the link prediction task demonstrates significant performance gains compared to some existing state-of-the-art methods and verifies the effectiveness of the proposed method on hierarchical relations.
Transition Information Enhanced Disentangled Graph Neural Networks for Session-Based Recommendation

Session-based recommendation (SBR) is a practical recommendation task that predicts the next item based on an anonymous behavior sequence, and its performance relies heavily on the transition information between adjacent items in the sequence. The current state-of-the-art methods in SBR employ graph neural network to model neighboring item transition information from global (i.e, other sessions) and local (i.e, current session) contexts. However, most existing methods treat neighbors from different sessions equally without considering that the neighbor items from different sessions may share similar features with the target item on different aspects and may have different contributions. (e.g., color , style , size of clothes). In other words, they have not explored finer-granularity transition information between items in the global context, leading to sub-optimal performance. In this paper, we fill this gap by proposing a novel method called Transition Information Enhanced Disentangled Graph Neural Network (TIE-DGNN) to capture finer-granular transition information between items and try to interpret the reason of the transition by modeling the various factors of the item. Specifically, we first propose a position-aware global graph at item-level, which utilizes the relative position information to distinguish the different types of neighbors, to model the neighboring item transition in the global context. Then, we slice item embeddings into blocks, each of which represents a factor, and use global-level disentangling layers equipped with position-aware embedding propagation to separately learn the factor embeddings over the global graph. Moreover, we employ distance correlation to encourage independence between each pair of factors. After obtaining the items of independent factor embeddings from the global context, we train local-level item embeddings by using attention mechanisms to capture transition information from the current session. Then, generating factor-aware inter-session embedding (global context) and intra-session embedding (local context) from two types of item embeddings, respectively. Finally, we use contrastive learning techniques to enhance the robustness of two types of session embeddings. To this end, our model considers two levels of transition information. Especially in global text, we not only consider finer-granularity transition information between items but also take user intents at factor-level into account to interpret the key reason for the transition. Extensive experiments on three real-world benchmark datasets demonstrate the superiority of our method over the SOTA methods.
Laplacian Smoothing Stochastic ADMMs With Differential Privacy Guarantees

Many machine learning tasks such as structured sparse coding and multi-task learning can be converted into an equality constrained optimization problem. The stochastic alternating direction method of multipliers (SADMM) is a popular algorithm to solve such large-scale problems, and has been successfully used in many real-world applications. However, existing SADMMs fail to take into consideration an important issue in their designs, i.e., protecting sensitive information. To address this challenging issue, this paper proposes a novel differential privacy stochastic ADMM framework for solving equality constrained machine learning problems. In particular, to further lift the utility in privacy-preserving equality constrained optimization, a Laplacian smoothing operation is also introduced into our differential privacy ADMM framework, and it can smooth out the Gaussian noise used in the Gaussian mechanism. Then we propose an efficient differentially private variance reduced stochastic ADMM (DP-VRADMM) algorithm with Laplacian smoothing for both strongly convex and general convex objectives. As a by-product, we also present a new differentially private stochastic ADMM algorithm with DP guarantees. In theory, we provide both private guarantees and utility guarantees for the proposed algorithms, which show that Laplacian smoothing can improve the utility bounds of our algorithms. Experimental results on real-world datasets verify our theoretical results and the effectiveness of our algorithms.
User Privacy Protection Strategy under Mobile Peer-to-Peer Network Propagation Environment

At present, LBS privacy protection algorithm cannot be widely used because of its vulnerability in the face of continuous attacks. Therefore, this paper proposes a new location service privacy protection method. The algorithm broadcasts its own location information according to the change of user speed. Then, based on the regional similarity partition algorithm and user balance partition algorithm, the search of k-1 peer nodes is realized by using the imprecise location response mechanism. It enables the location service provider to generate multiple indistinguishable location data, to protect the real location information of location service users. Finally, it is tested on MATLAB platform. The experimental results show that compared with the traditional privacy location protection algorithm, the improved LBS privacy protection algorithm can protect user location information and has better comprehensive performance.
Entity Recognition of Chinese Electronic Medical Record Based on Gated Graph Neural Network

Named entity recognition for Chinese electronic medical records aims to identify and classify clinical terms. At present, most methods based on deep learning have weak points, such as insufficient feature extraction, ineffective identification of rare entities, and inaccurate boundary division. A novel approach to solve these problems is proposed in this paper. The approach uses graph neural networks with a multidigraph structure to capture the features that the medical dictionary offers; a convolutional neural network (CNN) is employed to obtain the boundary features between words. These two features are concatenated into one feature vector and input to the Bi-Mogrifier LSTM-Attention module. The proposed model integrates dictionary features into the deep neural network to better deal with rare clinical named entities. The Multi-Head Attention module emphasizes the correlation and dependence between current information and context information in many aspects. It weakens the weight of semantic features irrelevant to medical and clinical treatment. The experimental results show that the proposed model is superior to the mainstream sequence labeling model, and the FI value of 95.31% is achieved.
Machine learning for design and optimization of photonic devices

This chapter discusses how we can apply deep learning to photonic device design problems, by focusing on three types of modeling approaches. In forward modeling, the trained deep neural network (DNN) is integrated in the optimization loops of classical optimizers. In inverse modeling, the trained DNN directly constructs a structure with the desired target performance given as input. Generative modeling produces a series of optimized designs for a target performance. We take an example of designing compact silicon-on-insulator (SOI)-based 1×2 power splitters with various target splitting ratios, which can be a building block for various integrated photonic circuits. We also give an overview of their applications to metasurfaces/plasmonics, other types of photonic devices in these three categories. These approaches are expected to be applicable to a broad range of design for various types of photonic devices.
On Differential Uniformity and Nonlinearity of Permutations on $$\mathbb {Z}_{n}$$

In this paper, we present an alternate expression for differential uniformity of permutations on $$\mathbb {Z}_{n}$$ , the ring of integers modulo n, which may be useful for computational purposes. We also explore links between nonlinearity and differential uniformity of bijections on $$\mathbb {Z}_{n}$$ . Further, we deduce some results on the graph of permutations on $$\mathbb {Z}_{n}$$ . Based on our observation and experimental results, we present a couple of conjectures also.
Concept Drift Detection for Social Media: A Survey

The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions.
A brief review of Data Analytics approach for Small and Medium Scale Enterprises over Clouds

In the computing & IT industry, cloud computing is the latest model for developing methods. By providing ready-to-consume cloud-based IT services, cloud computing provides the latest path for company fickleness & helps a quicker time to market. Without require for upfront fees, SMEs could wisely take benefit from cloud computing services. The cognition of cloud computing from an SME stance is investigated. Small & medium enterprises (SMEs) are a powerful driving force for industrialized expansion &, indeed, total financial improvement in all economies, particularly in emerging & transition economies, according to state policymakers, development economists, & also foreign improvement partners. Huge Data has currently been viewed as the latest answer to aid in policy & practice in a variety of app contexts & domains. The highlights of research in a field too far are an imbalanced use of quantitative methods & a lack of in-depth use of case studies to build a foundation of theorizing in a region, which were examined various study techniques in this article. This article examined essential research associated with conceptual approaches, techniques, Apps, issues & concerns addressed, & then offer pointer research directions in the cloud & huge data.
Computer Assisted College English CBI Teaching Mode in MOOC Environment

Computer assisted CBI teaching mode with the main idea of “content and language integrated teaching” is a new foreign language teaching method in the Internet environment. It lays more emphasis on the cultivation of students' innovative spirit, imagination and thinking ability. MOOC, with the advantages of high-quality course resources and highly interactive learning process, is leading the global trend of ecological education and learning, and bringing opportunities to the reform of higher education. In order to meet the English learning needs of college students in the “post CET-4” stage, this paper constructs a new College English teaching mode based on CBI in MOOC environment. The results show that the new mode is conducive to improving students' comprehensive English level and critical thinking ability.
Construction and Promotion of Reading Service Platform of University Library Based on Computer Network Cloud Platform

This paper analyzes the basic framework, cloud service mode, and cloud service approach of digital library cloud service platform under cloud computing environment and studies the future development trend of cloud service platform and cloud service mode of digital library under cloud computing environment. This paper introduces the advantages of cloud service platform construction in digital reading promotion of university library in detail, expounds the construction and application of cloud service platform for digital reading promotion of university library, and puts forward the construction and promotion strategy of reading service platform of university library and the framework model of personalized information service of digital library. By describing the technical characteristics of cloud platform, the advantages of digital reading based on cloud platform are revealed, such as massive data storage, personalized service virtualization, and big data statistical analysis. On this basis, the innovation of digital reading promotion mode in colleges and universities is discussed. This study is conducive to promoting the standardized management of reading and information services and the rational allocation of resources in university libraries in order to provide some reference for the practice of building and popularizing the reading service platform of university library.
Online Learning Based Long-Term Feature Existence State Prediction for Visual Topological Localization

Visual localization of autonomous robots in dynamic scenes is a critical problem to be solved. Unlike the existing methods that regard the dynamic changes as outliers, we explore the hidden regularities of the long-term dynamic changes, and propose a new visual topological localization system. According to the regular pattern of feature existence changing with time, its feature existence matrix is constructed incrementally, which is applied for modeling the time-varying states of each feature. In particular, a new online learning-based modeling method is proposed, whose parameters are online updated by constrained Newton step adaptively. The feature sets with the largest existence possibilities are predicted for accurate topological localization. Further, extensive experiments are performed on both simulated and real measured datasets, the results verify that our method outperforms the state-of-the-art methods in the prediction and localization accuracy, and it achieves competitive real-time performance.
A Novel Elite Information Interaction Based Differential Evolution

Meta-heuristic algorithms (MHAs) have emerged in recent years to handle optimization difficulties. Due to the limitations of these algorithms, the improvement of MHAs has become a research hotspot. As one of the most representative and powerful MHA, differential evolution (DE) has become our improvement object. In this study, we improved the parent individual selection mechanism in DE mutation operation and proposed a novel elite information interaction-based differential evolution (EDE). According to the experimental results of IEEE CEC2017 benchmark functions, EDE demonstrates its superior performance.