Reviewerly/exHarmony huggingface dataset:
Tourist Sentiment Analysis Using Natural Language Processing

Social media has become a platform for the users to raise their opinions. It has given birth to Sentiment analysis, which aims at determining the user’s opinion which will be helpful for others to find which is good. People usually interested in seeking positive and negative opinions for particular product or features which got significant role in analysing sentiments. Opinions of the people are taken from the tourism review website for better performance. The main objective of this paper is to extract data from the large amount of dataset and performing various machine learning algorithms and show the best result. Feature extraction in sentiment analysis is an active area of research. Feature extraction algorithms like TFIDFVectorization and CountVectorization along with various classification algorithms like Naive Bayes classifier, Decision Tree and Random Forest classifier are compared and finally show which algorithm is best for opinion mining. Accuracy, recall, precision and f1-score were taken as parameters to differentiate the performance of the algorithms used. Graphical representation of the various aspects is depicted for a particular resort. So that people can get a clear opinion of that particular resort.
Research on short text classification method based on semantic fusion and BiLSTM-CNN

Aiming at the problems of short text length, less information contained in text and poor classification performance caused by easy interference of deep learning model, combined with the advantages of CNN and BiLSTM, a text classification method based on fusion semantics, attention mechanism and BiLSTM-CNN model is proposed. Firstly, the Skip-gram model in word2vec is used to map the words in the data and convert them into vectors with fixed dimensions as the input of the model. The local features of the text vector are extracted by CNN and the global features related to the text context are extracted by BiLSTM. The features extracted by the two complementary models are fused, It solves the problem that the single convolution neural network model ignores the semantic and grammatical information of words in the context, and effectively avoids the gradient disappearance or gradient dispersion of the traditional cyclic neural network. This paper makes an experimental analysis on R8 and Ohsumed datasets. The results show that the proposed feature fusion model effectively improves the accuracy of text classification.
Data centers with quantum random access memory and quantum networks

In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantum sensing, with a primary focus on QDC for $T$-gate resources, QDC for multi-party private quantum communication, and QDC for distributed sensing through data compression. We show that QDC will provide efficient, private, and fast services as a future version of data centers.
Media Projects as a Culture-Saving Communication Technology

The role of media projects in the modern world is growing. Today they are used in various spheres of activity of subjects of sociocultural communication. Theaters and museums, striving to remain a living part of society, are increasingly using media projects, providing their visitors with the opportunity to interact with the cultural achievements of the present and past. The article considers media projects as a promising communication technology in the development of cultural institutions. The article describes the transformation of the museum and theatrical space organization, the trend towards the growth of interactivity as a form of open dialogue between the visitor and the cultural institution. The author defines the concept of “media project in the field of art”. Museums and theaters today are participants in the process of mediatization, they actively use digital communication technologies and new models of communication with the audience. There are considered examples of successful media projects using virtual and augmented reality technologies in Russian museums and theaters over the past few years, including in the context of the COVID-19 pandemic. The article focuses on the connection of cultural institutions with new media — both of them disseminate information content, seek to gain and retain the attention of the audience. There are described examples of new media that have a successful experience in the implementation of educational projects. The article also discusses culture-oriented media projects in the field of education, which contributes to the formation and strengthening of the cultural competence of a modern specialist in any field. There are formulated the problems that a cultural institution may face at the stages of planning and implementing a media project. The author comes to the conclusion that media projects help to attract a wide audience to a cultural institution and retain the interest of its regular visitors; and interactivity, gamification and high-quality design will bring media projects in the field of art to a qualitatively new level.
Neural Knowledge Bank for Pretrained Transformers

The ability of pretrained Transformers to remember factual knowledge is essential but still limited for existing models. Inspired by existing work that regards Feed-Forward Networks (FFNs) in Transformers as key-value memories, we design a Neural Knowledge Bank (NKB) and a knowledge injection strategy to introduce extra factual knowledge for pretrained Transformers. The NKB is in the form of additional knowledgeable memory slots to the FFN and the memory-like architecture makes it highly interpretable and flexible. When injecting extra knowledge with the Salient Span Masking (SSM) pretraining objective, we fix the original pretrained model and train only the NKB. This training strategy makes sure the general language modeling ability of the original pretrained model is not influenced. By mounting the NKB onto the T5 model, we verify its strong ability to store extra factual knowledge based on three closed-book question answering datasets. Also, we prove that mounting the NKB will not degrade the general language modeling ability of T5 through two representative tasks, summarization and machine translation. Further, we thoroughly analyze the interpretability of the NKB and reveal the meaning of its keys and values in a human-readable way. Finally, we show the flexibility of the NKB by directly modifying its value vectors to update the factual knowledge stored in it.
An Efficient Security Technique Using Steganography and Machine Learning

With the recent advancement in e-commerce technology and IoT-enabled smart devices, where every bit of information passing through unsecured channel, it became mandatory to secure the information from unethical use. Cryptography and steganography are two significant approaches that have been applied strenuously in various algorithms to prevent the information from unauthorized use. However, every scientific terminology has some deficiencies due to time and advancements in mechanization; it needs to rebound with recent advancements in technology. This paper presents an efficient secure communication framework by using steganography technique. In the proposed framework, information in the form of images, hyperlinks, and text have been concealed using heuristic approach. This framework further has been improved by applying machine learning tools. The significance of proposed framework has been verified by MATLAB tools.
Identifying Reliable Machines for Distributed Matrix-Vector Multiplication

This paper considers a distributed computing framework, where the task of T matrix-vector products is distributed among n worker machines. External adversaries have access to a subset ℒ (the cardinality of which is |ℒ|) of these machines, and can maliciously perturb the result of each of their computations with probability α. To correctly recover each matrixvector product, the master has to identify a set (of a fixed cardinality) of ‘unattacked’ worker machines. Towards this end, this work proposes four schemes that aim at performing such an identification. These schemes are analyzed and compared under different regimes of (|ℒ|,α) for the two cases when |ℒ| is (1) known or (2) unknown at the master.
Using Error-Correcting Codes to Ensure Information Security of Unmanned Vehicles and IoT Systems

This paper discusses ways to ensure the information security of unmanned vehicles and Internet of Things systems under the conditions of a quantum threat. For this, it is proposed to use post-quantum algorithms, a review of the methods for constructing which and works devoted to their application to the considered sphere are given. The construction of a post-quantum algorithm with the help of linear error-correcting codes is considered in detail, for which it is proposed to use algebraic geometry codes with characteristics suitable for implementation in conditions of systems with limited resources. A theorem is proved that makes it possible to guarantee that the automorphism group of some algebro-geometric codes does not allow applying the Sidelnikov-Shestakov attack to them.
Toward Fractal Development of Data Processing-Intensive Artificial Intelligence Systems

Our artificial intelligence (AI) project Automatic Learning for the Rapid Classification of Events (ALeRCE) increasingly suffered from tech stack sprawl. We developed a fractal development methodology to mitigate tech stack sprawl. Our methodology would be applicable to AI engineering in general.
SQCC: Smart Quantum Circuit Cutting

In this paper, we are investigating the idea of cutting the original quantum circuits into multiple sub-circuits and evaluating the fidelity (correctness) amount of the sub-circuits results. We introduce an analysis method, Distributed Quantum Circuits (DQC), to encounter all aspects of Quantum properties from qubit's error data to the topology of Quantum processors on cutting the quantum circuits. Finally, we share our observations and experiments to introduce a new design pattern for managing distributed Quantum circuits on smaller Quantum computers.
Mobile Learning in Science Education to Improve Higher-Order Thinking Skills (HOTS) and Communication Skills: A Systematic Review

Today, the increasing use of technology and mobile applications in education was interesting.This research was a systematic review study with limited 30 articles from 2012 to 2021.It aims to answer research questions about what mobile devices used in learning and what learning approaches are used in science learning to improve higher-order thinking skill and communication skills.The findings of this study were in line with the research objectives: First, the most appropriate mobile devices used to achieve learning objectives are mobile phones, followed by PDAs, tablets, iPad, laptops, e-books, and iPods.Second, the learning approach used in science learning to improve higher-order thinking skills and communication skills are a collaborative learning approach, inquiry learning, projectbased learning, problem-based learning, game-based learning, and flipped classroom learning.It was hoped that this research can be an illustration for other researchers to create innovative learning approaches.Some research that can be done next based on this research is how mobile learning in social learning or comparing the two, further research on the most appropriate learning media for mobile learning, and research on the effectiveness of implementing approach strategies in mobile learning.
Research on Dynamic Deduction of Plans Based on Knowledge Graph

As the guiding text of emergency response, the plan is unstructured information, and its existing static management mode cannot dynamically adapt to the deduction needs of different scenarios. To this end, this paper proposes a dynamic derivation method based on knowledge graph: for large-scale event emergencies, sorting out knowledge of business scenarios, designing and constructing domain knowledge graphs including geographic information ontology, contingency text ontology, and deduction scene ontology. Moreover, use Neo4j to store and manage the constructed knowledge graph. Based on this, the typical scenario of “crowded and stampede event in security check area” is selected. Based on the FSR (Force-Scene-Response) model theory, the inference rules are constructed, the deduction model is designed, and the model is simulated and analyzed. The simulation results show that The disposal instructions generated by the model can effectively mitigate risk situations. Finally, a dynamic deduction auxiliary decision-making system with good interactivity is developed. Taking the crowded stampede event in the security check area as an example, the system generates the disposal strategy and the situation evolution process according to the input parameters, verifying the validity of the proposed model and method. It shows that the system can adapt to different scenarios and support dynamic deduction of plans.
A new type of gold mineralization in the basement of the Aldan-Stanovoi gold-bearing province

A new type of gold mineralization has been revealed in rocks of the basement of the Aldan-Stanovoi goldbearing province, within the Spokoininsky ore cluster of the Tyrkandinsky gold-bearing region of southern Yakutia. A geological model has been created of an object with a bulk-minable nonlinear type gold mineralization related to elements of plicative structures and to the reverse fault type tectonics in the hosting metamorphic rocks of the basement. The modeled object is represented by a combination of lodes with veinlet-disseminated sulfide-(pyrite)quartz mineralization. A complex has been refined of prospecting guides and criteria, that characterizes this mineralization type represented by the ore-bearing lodes with gold-quartz and gold-low-sulfide-quartz veinletdisseminated mineralization in rocks of the basement and Late Jurassic-Early Cretaceous magmatic formations. The structural-geological position, the morphological type, the composition of the ore-hosting rocks and ores, and the principal parametric characteristics of the gold mineralization have been determined. Sampled intervals with the gold grades corresponding to the required parameters for the preliminary estimation have been distinguished in the exploration workings. Forecasted resources of the P2 category have been assessed.
Adaptability between ABR algorithms in DASH video streaming and HTTP/3 over QUIC

This paper contains the research proposal of Sindhu Chellappa that was presented at the MMSys 2022 doctoral symposium. With the ever growing demand for media consumption, HTTP Adaptive Streaming (HAS) is the de-facto standard for adaptive bit rate streaming. Several efforts have been made to increase the Quality of Experience (QoE) for the users by proposing Adaptive Bit Rate (ABR) streaming algorithms to choose the best bitrate for the current network condition. With HAS, the segments are downloaded through Hyper Text Transfer Protocol (HTTP) which operates over Transmission Control Protocol (TCP). HTTP/3 is the recent standardized application protocol which operates over Quick UDP Internet Connection (QUIC). When the protocols changes, it is necessary to revisit the QoE of the ABR algorithms to select the suitable ABR algorithm best suited for the protocol. We present four research questions to address the adaptability between the ABR algorithms and the underlying protocols.
The implementation path of intelligent rehabilitation under the background of healthy China construction

&lt;p&gt;The improvement of rehabilitation service capacity is an important part of the construction of a healthy China, and intelligent technology is a powerful means of rehabilitation development. This paper reviews the background of a series of national policies for the construction of a healthy China, analyzes and summarizes the many shortcomings that currently restrict the improvement of rehabilitation service capabilities, and proposes the implementation path of intelligent rehabilitation. By expounding the service process of intelligent rehabilitation, and analyzing in detail the intelligent technical means suitable for integration from the four key links of real-time health monitoring, remote home intelligent rehabilitation intervention, health classification evaluation standard system and health intervention standard system, the general framework of implementation path of intelligent rehabilitation is built. Taking hypertension rehabilitation as an example, the article introduces the intelligent rehabilitation practice exploration and reference model in three aspects: The research and development of hypertension intelligent equipment, the clinical research of hypertension rehabilitation and the construction of hypertension rehabilitation database. Finally, combined with the concept of intelligent interconnection of all things, the definition of “rehabilitation Internet of things” is proposed, and the time is right for intelligent rehabilitation in the context of building a healthy China.&lt;/p&gt;
Entrepreneurial Mentality Analysis for College Students Based on the Improved BP Neural Network

In this study, the fuzzy comprehensive evaluation model is used to evaluate the characteristics of college students' entrepreneurial psychology, and a prediction model of college students' entrepreneurial psychology characteristics is established, which is simulated by Matlab to achieve good validity. Based on the research on the characteristics of college students' entrepreneurial psychology, this study proposes a design method of indicators and parameters for evaluating the characteristics of college students' entrepreneurial psychology. In this study, the genetic algorithm is used to optimize the BP neural network. The optimized neural network greatly improves the global search and local search capabilities. The performance of the model is tested through simulation tests. Through the simulation comparison test between the improved model and the standard model, the results show that the model can predict the entrepreneurial psychological characteristics of college students. By comparing the improved BP neural network algorithm with the original algorithm simulation experiment, the improved BP neural network improves the sensitivity by 20%, the specificity by 5%, and the accuracy by 8%.
056 Online assessment and monitoring of cognitive decline in neurological conditions

Neurological conditions present with cognitive impairment that greatly affects the quality of life of the patients and should be routinely evaluated. However, it can be difficult to detect and impractical to monitor with classic in person cognitive assessment due to limitations of sensitivity, scalability and cost. Internet- and app-based tools for cognitive assessment are a potential solution of this problem, offering superior sensitivity and being deliverable remotely in the context of clinical registries, providing an acces- sible and cost-effective way to combine large-scale longitudinal cognitive and clinical data. To validate this idea, we administered a superset of 23 internet-deliverable cognitive tasks from the Cognitron platform to people with multiple sclerosis (PwMS) through the UKMSRegister. These tasks come with extensive normative data (N&gt;400,000) collected during previous large-scale studies. We report high sensitivity to cognitive deficits in PwMS and identify the cognitive domains that are most vulnerable. We propose an optimal subset of tasks that can provide an efficient multivariate profile of cognitive decline in PwMS at scale and longitudinally via MSRegisters. This same approach will be extended to other neurological conditions, offering a way to measure and predict the evolution of cognitive impairment over time, and monitor the long-term impacts of interventions.
Towards Adversarial Learning

Although deep neural networks (DNNs) have been successfully deployed in various real-world application scenarios, recent studies demonstrated that DNNs are extremely vulnerable to adversarial attacks. By introducing visually imperceptible perturbations into benign inputs, the attacker can manipulate a DNN model into providing wrong predictions. For practitioners who are applying DNNs into real-world problems, understanding the characteristics of different kinds of attacks will not only help them improve the robustness of their models, but also can help them have deeper insights into the working mechanism of DNNs. In this tutorial, we provide a comprehensive overview of the recent advances of adversarial learning, including both attack methods and defense methods. Specifically, we first give a detailed introduction of various types of evasion attacks, followed by a series of representative defense methods against evasion attacks. We then discuss different poisoning attack methods, followed by several defense methods against poisoning attacks. In addition, besides introducing attack methods working in the digital setting, we also introduce attack methods designed for threatening physical world systems. Finally, we present DeepRobust, a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. Via our tutorial, audience can grasp the main ideas of adversarial attacks and defenses and obtain a deep insight of the robustness of DNNs. The tutorial official website is available at https://sites.google.com/view/kdd22-tutorial-adv-learn/.
RGVisNet

Recent years have witnessed the burgeoning of data visualization (DV) systems in both the research and the industrial communities since they provide vivid and powerful tools to convey the insights behind the massive data. A necessary step to visualize data is through creating suitable specifications in some declarative visualization languages (DVLs, e.g., Vega-Lite, ECharts). Due to the steep learning curve of mastering DVLs, automatically generating DVs via natural language questions, or text-to-vis, has been proposed and received great attention. However, existing neural network-based text-to-vis models, such as Seq2Vis or ncNet, usually generate DVs from scratch, limiting their performance due to the complex nature of this problem. Inspired by how developers reuse previously validated source code snippets from code search engines or a large-scale codebase when they conduct software development, we provide a novel hybrid retrieval-generation framework named RGVisNet for text-to-vis. It retrieves the most relevant DV query candidate as a prototype from the DV query codebase, and then revises the prototype to generate the desired DV query. Specifically, the DV query retrieval model is a neural ranking model which employs a schema-aware encoder for the NL question, and a GNN-based DV query encoder to capture the structure information of a DV query. At the same time, the DV query revision model shares the same structure and parameters of the encoders, and employs a DV grammar-aware decoder to reuse the retrieved prototype. Experimental evaluation on the public NVBench dataset validates that RGVisNet can significantly outperform existing generative text-to-vis models such as ncNet, by up to 74.28% relative improvement in terms of overall accuracy. To the best of our knowledge, RGVisNet is the first framework that seamlessly integrates the retrieval- with the generative-based approach for the text-to-vis task.