Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition System on a Distributed Edge-Cloud Infrastructure

Time-sensitive AI applications usually pre-process the raw data on edge devices without having to offload them all to the cloud. However, deploying the AI applications on a distributed edge-cloud infrastructure is still an open issue since separating the roles between the edge and the cloud has no existing rule to follow. In this paper, we implemented a Facial Expression Recognition (FER) system, as a case study AI application, on an edge-cloud infrastructure to bridge the gap. FER system is distributed, fault tolerant, performant and completely edge-cloud separated. FER performs light-weight algorithms such as extracting facial feature points on the edge, while it performs heavy-weight algorithms such as deep neural network inference on the cloud. We performed experiments on different cloud providers, and we have seen that we reduced the network overhead significantly and improved the performance by 25% compared with deploying it solely on the cloud, with only the feature data being transferred to the cloud instead of all the raw data.
Sarcasm Detection in English Text using Tweets and Headlines

Online sarcasm is usually used to express something opposite to the literal meaning of the phrase or sentence and is hard to catch since it thrives in ambiguous situations. This paper revolves around natural language processing of samples in the English language to generalize the structure of a sarcastic sentence. Through this study, we develop a well-defined model for the automatic identification of sarcastic sentences in various usages. We intend for this study to find use in opinion mining, information categorization and sentiment analysis. Through the LSTM model along with a plethora of data used, this study achieved an accuracy of 93.02% without overfitting.
Keyframe Selection with Information Occupancy Grid Model for Long-term Data Association

As the basics of Visual Simultaneous Localization And Mapping (VSLAM), keyframes play an essential role. In previous works, keyframes are selected according to a series of view change-based strategies for short-term data association (STDA). However, the texture enrichment of frames is always ignored, resulting in the failure of long-term data association (LTDA). In this paper, we propose an information enrichment selection strategy with an information occupancy grid model and a deep descriptor. Frame is expressed by a deep global descriptor for a statistical explainable abstraction, in which the texture enrichment is indicated. Based on the abstraction, an information occupancy grid model is established to measure the information enrichment and the potential LTDA ability. Evaluations on variant datasets are conducted, showing the advantage of our proposed method in terms of keyframe selection and tracking precision. Also, the statistical explainability of the deep descriptor is provided. The proposed keyframe selection strategy can improve LTDA and tracking precision, especially in situations with repeated observations and loop-closures.
Parameter Update Framework based on Unitized Stochastic Gradient Descent Algorithm in Federated Learning

Although the aim of federated learning is to solve the problem of data island and privacy preserving, it is found that federated learning itself also has the risk of privacy leakage due to the corrupted participants. In this paper, a collaborative federated learning framework is proposed to address the problem of privacy leakage during model training caused by honest-but-curious servers. The framework is based on the unitized stochastic gradient descent algorithm to selectively update the parameters to protect the gradient. The experimental results indicate that the proposed framework makes the server failed to infer explicit information from the model, which further improves the degree of privacy protection and ensures the accuracy of model training.
SQL Server 2022 on Azure Virtual Machines

Today when you deploy SQL Server 2022, you have choices to run SQL Server on a laptop, a “bare-metal” server, or even a container. The most common choice to deploy and run SQL Server is a virtual machine. While running SQL Server with virtualization technologies such as Hyper-V and VMWare is extremely popular, many customers are now turning to the cloud to get the Infrastructure-as-a-Service (IaaS) experience. I believe using an Azure Virtual Machine provides you with a great experience.
Kicking Analysts Out of the Meeting Room: Supporting Future Data-driven Decision Making with Intelligent Interactive Visualization Systems

Today's data-driven decisions are largely dependent on professional analysts conducting analysis and generating visualizations for decision makers. These middlemen between data and decision makers may induce cost and trust issues in the generated visualizations. To overcome these issues, I envision a future scenario where intelligent interactive visualization systems may replace analysts in the decision-making process when the analyses and visualizations are relatively simple. However, three gaps need to be addressed before the future scenario could be realized. In this paper, I will discuss these gaps, propose potential solutions, and hope to raise a discussion on the future role of visualization systems for data-driven decision-making.
Diminished Reality for Sense of Movement with XR Mobility Platform

This paper proposes a method to reduce a sense of movement by controlling a passenger's sense of movement using a multimodal XR system mounted on an autonomous vehicle. The passenger should not be given the sense of movement from the accelerating stimulus, in order to make the space comfortable during self-driving. In the field of VR, technologies have been developed to simulate a sense of movement that does not occur, such as in a driving simulator. However, the purpose of this paper is to solve the inverse problem to reduce the sense of movement generated by real autonomous driving. By using a multimodal XR mobility platform that consists of an immersive display and a motion platform with a tilting seat, we develop a state-of-the-art method to reduce the passenger's perceived sense of movement. This method enables one to perceive the vehicle as if they are not moving, even though they are in automated driving. In the experiments, we evaluated 20 participants under the condition of the combination of visual and force acceleration stimuli. From experimental results, it was confirmed that the sense of movement was reduced significantly when both visual and force acceleration stimuli were not presented in the condition.
A Satellite-Based Method for Forecasting Solar Radiation Part II: Radiative Transfer and Model Validation

In a previous work we presented a methodology for forecasting the future cloud distribution in the atmosphere by using the capabilities of the Advanced Himawari Imager (AHI) which provides high frequency imagery (every 10min) of the Asian hemisphere. This capability is used to detect and characterize cloud physical and optical characteristics over a fixed geographical location centered over Singapore. In this present work, we report on the forecast (1-3Hrs) of short-wave solar radiation by using a radiative transfer model, that includes aerosols and clouds, and applied to future cloud distribution scenarios resulting from the cloud motion scheme reported in our earlier work.
Blockchain-Based Public Key Authentication of IoT Devices for Electrical Energy Systems

Information and communication technologies (ICT) support the digital evolution of energy networks, enabling automated and smart operations. Internet of Things (IoT) devices allow automated remote measurements and data collection improving anomaly detection, predictive maintenance, and operational efficiency. When IoT devices communicate with each other they shall be authenticated within the network. Traditional authentication methods rely on certificates issued by a centralized Certification Authority (CA), that may be affected by a single point of failure and may be computationally heavy for resource constrained IoT devices. The paper presents a prototype implementation of a blockchain based public key authentication for IoT devices in the context of electrical monitoring systems. In the proposed solution, the IoT devices communicate on a secure channel via a publisher subscriber protocol (MQTT - Message Queue Telemetry Transport), exploiting the Transport Layer Security (TLS) and the use of an Algorand Blockchain system. The results of the functional testing are encouraging a deeper investigation in the adoption of Blockchain systems for the authentication of IoT devices in typical scenarios targeted by the energy transition.
HTD: A Fast Human-centered Text-locating Method for Auxiliary Reading

As computer vision and human-computer interaction technologies are getting mature, vision-based auxiliary text reading has become the mainstream method to optimize learning and reading experience. To overcome the drawbacks of the common multi-step detection way and further improve the text reading experience, we propose a new human-centered fast text-locating method, named HTD. It transforms the relatively time-consuming serial procedure to a hybrid object detection problem, utilizing the hand-text hybrid object detection model to instantly locate the text of interest to readers. Considering the particularity of this task, we choose to perform the weighted NMS to infer final predicted bounding box. To verify the effectiveness of the proposed method, we tested the performance on the homemade hand-text hybrid object detection dataset HTDD. The experiment indicated that our proposed method is more efficiency and can produce satisfying results, which could meet the needs of human-computer interactive auxiliary reading in text reading scenarios.
Credit card transaction data analysis and performance evaluation of machine learning algorithms for credit card fraud detection

Fraud is defined as any malicious behavior with the intent of causing financial loss to a third party[1]. Due to the unprecedented pandemic scenario prevailing over the world in the past 2 years, there has been rapid participation in online-based transactional activities using credit cards and it has coerced many people into the digital world[2].This increased utilization of advanced digital cash or plastic cash even in new nations has highlighted increased fraud cases related to credit cards[3]. Obviously, this elevated activity has resulted in criminals getting more attracted to it[4]. As part of comprehensive fraud prevention, fraud detection helps to reduce the manual aspects of the screening/checking process and makes the process automatic. This is now one of the most well-known industry/government issues[5]. Detection of potentially fraudulent transactions is the main goal, so as the customers are not charged for the products which were not purchased by them. Additionally, we have to reduce the business costs and make it profitable for credit cards companies. The many strategies used to identify credit card frauds are investigated in this study. This paper examines the algorithms used for detecting fraud related to credit cards, as well as their advantages and disadvantages, along with performance comparisons that will aid in the evaluation of their usability and accuracy.
Public Sentiment Analysis with Opinion Mining on Rohingya Refugee Crisis in Bangladesh

Twenty-first century, there has been moderate forced migration where it articulates human civilization. This forced migration is the identity of various territories and demography. According to this issue, Bangladesh is now the largest populated country for being stateless refugees in the world. The Rohingya community is hopeless about placing their lives, therefore, stateless refugees possessions in the southeastern border region of Bangladesh. Due to the part of Bangladesh addressing that stateless refugees and Bangladeshi peoples and other countries people have a tremendous debate on this issue. Tropically, according to these stateless refugees the victim of hate speech. This study aims to detect the daily report of negativity predicting actual analysis among machine learning approaches using hate speech features. Employed 5 classifiers like SVM, DT, KNN, RF, and NB. Necessary cleaning steps accommodate the model using preprocessing. NB shows the results outcome accuracy of 79% which is more than all other models. The approach of predicting hate speech on stateless refugees took 310 comments data which can rule as a global application..
Analysis and application of clustering validity indexes

Nowadays, data mining has become an important means of processing massive amounts of data, and the clustering process is unknown to the data structure, so it is difficult to clarify the optimal number of clusters in the clustering algorithm. The number of clusters is often closely related to the performance of the clustering algorithm and can be used to estimate the quality of the clustering. This paper briefly introduces the different types of clustering effectiveness indicators, follows the classification standards of the indicators, and summarizes the differences, advantages and disadvantages of the existing clustering validity index clustering validity indicators through experiments. The problems and challenges that may arise in the future of clustering effectiveness index research are discussed.
Vision-based Moving Target Tracking of Certain Target for Quadruped Robots

Due to the high flexibility of quadruped robots compared with some traditional robots, it has become an important branch in the field of mobile robot research. Target detection and tracking technology is important for the environment perception and autonomous motion tasks of quadruped robots. To achieve effective tracking of a specific target, the premise is that the target is accurately detected. In this article, a target detection algorithm based on deep learning for quadruped robot person tracking is proposed. After the targets are detected, a simple but accurate selection strategy is used to extract the certain target to be tracked from other targets. The visual distance and angle information from the target is used to realize the autonomous tracking. In the part of experiment, we conducted experiments based on the changeable outdoor environment, which included interference factors such as light changes, people crossing, obstruction by obstacles. During the experiments, the SDUQuad-48 quadruped robot can simultaneously realize the stable tracking of a specific moving target based on our algorithm with a speed of 0.8 m/s.
Embedded Spherical Topic Models for Supervised Learning

Probabilistic topic models are powerful techniques for analyzing and understanding large collections of text documents to learn meaningful patterns of words. Their supervised extensions also capture topics conditioned on the response metadata associated with each document such as user rating. However, inferring such information from data often comes at the detriment of topics quality, leading to uninterpreted and meaningless topics. In this paper, we propose a novel Supervised-Embedded Spherical Topic Model (S-ESTM) that balances two goals: interpretable and coherent topics explaining the data and accurate prediction of the associated response values. Our model combines word embeddings and knowledge graph embeddings to effectively encode the semantic information of text and the related background knowledge to guide the inference of supervised topics. In S-ESTM, document constituents are drawn as points on spherical manifolds along with topics using the von Mises-Fisher distribution. Efficient variational inference methods for posterior approximation and latent parameter estimation are derived and various empirical studies on real-world datasets are also provided. Our experiments demonstrate that our model can discover discriminative and coherent topical patterns associated with regression tasks, while achieving improved prediction quality.
Research and Analysis of Recommendation Technology

With the continuous development of the network, a variety of resources can be spread through the Internet, a large number of data will cause information overload in the network, how to efficiently obtain the information they need has gradually become a subject that can be studied independently. The recommendation system can filter out effective information according to the needs of users. this paper describes the current research situation of recommendation system. The traditional recommendation methods, recommendation based on deep learning and recommendation based on reinforcement learning are analyzed in detail. Finally, the problems and challenges faced by the recommendation system are discussed.
RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues

The gap between simulation and the real-world restrains many machine learning breakthroughs in computer vision and reinforcement learning from being applicable in the real world. In this work, we tackle this gap for the specific case of camera-based navigation, formulating it as following a visual cue in the foreground with arbitrary backgrounds. The visual cue in the foreground can often be simulated realistically, such as a line, gate or cone. The challenge then lies in coping with the unknown backgrounds and integrating both. As such, the goal is to train a visual agent on data captured in an empty simulated environment except for this foreground cue and test this model directly in a visually diverse real world. In order to bridge this big gap, we show it's crucial to combine following techniques namely: Randomized augmentation of the fore- and background, regularization with both deep supervision and triplet loss and finally abstraction of the dynamics by using waypoints rather than direct velocity commands. The various techniques are ablated in our experimental results both qualitatively and quantitatively finally demonstrating a successful transfer from simulation to the real world. Code will be made available on publication <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Project page: github.com/kkelchte/tgbg.
Statistical analysis of query processing time in cache-based cloud database systems

With the proliferation of data in cloud-based systems, the performance of the data retrieval process from the database management system is becoming indispensable. Caching is one of the techniques to retrieve the data faster. It reduces the number of database accesses for similar queries, which in turn, reduces the processing time. It also facilitates in reducing the load on database servers, which results in the reduction of the overall response time. This paper is based on our caching framework for NOSQL datastores, which seeks to speed up the processing of many requests. The frequently used queries, which are expensive to reevaluate, are cached by this framework on top of a column-based datastore. In order to speed up query processing, certain queries are cached. In this study, query processing times without the influence of the database or system cache are used to evaluate the framework’s performance.
Leisure Activities as a Driver for the Development of Libraries in the Modern World

Social, economic, political, digital transformations in society lead to changes in Russian social and cultural space. Leisure becomes a significant part of the modern cultural process. The problem of the study of cultural and leisure activities (CLA) remains open. Leisure activities are undergoing changes. The article provides an empirical analysis of cultural and leisure activities to comprehend the current stage of the cultural development process in Russia. The authors give the examples of the dependence of the forms of library practice on changing reader needs in a historical retrospective. It is noted that various forms of CLA are commonly implemented into the work of cultural institutions. Intellectual and leisure activities becomes a significant part of CLA. It is necessary for the inclusion of an individual into modern educational, professional, cultural spaces. New digital combined forms of work and project activities are popular tools that increase efficiency of cultural and leisure activities of cultural institutions. The article provides examples of the active involvement of the State Universal Scientific Library of Krasnoyarsk region in the intellectual leisure industry. The authors come to the conclusion that the content and technological change of CLA as a systemic phenomenon occurs under the influence of the realities of modern life and contributes to the functions evolution in cultural institutions.
IPR: Integrative Policy Recommendation Framework Based on Hybrid Semantics

Policy recommendations aim to inform people who are faced with policy decisions on specific issues about how research and evidence can assist them in making the best decisions possible. This paper proposes an ontology-focused semantical driven integrative system for policy recommendation. The recommendation is user query-centric and uses Structural Topic Modelling to find topics that can be correlated. The semantic similarities are computed using Resnik and concept similarity methods to achieve ontology alignment, and for the alignment of principle classes, three models, normalized compression distance, Twitter semantic similarity, and Hiep’s Evenness Index, are used. The IPR achieves the best-in-class accuracy of 94.72% and precision of 93.14% for a wide range of recommendations over the other baseline models, making it an efficient and semantically compliant system for the policies recommendation.
Transparency in Digital-Citizens Interfaces Through Blockchain Technology: BBBlockchain for Participation Processes in Urban Planning

Despite the new opportunities created by digital technologies, today's participation processes in urban planning face major problems mainly regarding transparency, trust, and accountability. Therefore, the aim of the paper is to investigate how the implementation of blockchain technologies can impact the transparency of urban planning processes. Specifically, the study adopts an empirical approach through a case study analysis of an App BBBlockchain for citizen participation in urban planning decisions based on blockchain technology. From a preliminary investigation on the use cases called timestamping and document management, both addressing the first layer of participation which is information, the study draws some important conclusions on the positive and negative effects that blockchain has on transparency of urban development processes and repercussions on stakeholders' engagement. In particular, the findings suggest that blockchain alone cannot support transparency as conceived in this study as, there are some transparency dimensions which are beyond blockchain's influence.