Reviewerly/exHarmony huggingface dataset:
Library Digitalisation as a Mainstream of Scientific Discourse

Since 2017, the Russian State Library (RSL) has held annually scientific and practical seminars of the cycle “The Role of science in the development of libraries: theoretical and practical aspects”, addressed to library specialists and scientists in the field of library science, bibliography science and book studies. The main targets of the events of the cycle are to activate research work in the field of library science, bibliography science and book studies; to discuss current problems of scientific activity of federal and regional library centres; to acquaint library specialists with the results of scientific research. One of the thematic blocks of the cycle consists of seminars devoted to the problems of digitalisation of librarianship. The latest scientific and practical seminar of this block devoted to the topic “Digital transformation of libraries as a subject of scientific research” was held on February 10, 2022. The speakers of the seminar were the leading scientists of the Centre for Study of Library Development Problems in the Information Society of the RSL. The seminar program included two questions. M.Y. Neshcheret presented the results of studying the remote reference and bibliographic services offered on the websites of national libraries around the world. Y.N. Stolyarov analysed the content of publications on the study of digitalisation problems in Russian professional journals. The seminar noted the significant role of practical librarians in the implementation of digital technologies in the work of libraries, as well as demonstrated achievements, proving the need for social library institute. The speakers called on contemporary researchers to intensify scientific research aimed at theoretical understanding of the ongoing processes of digitalisation. The seminar aroused considerable interest of library specialists.
Development of General Issues of Library Science: Modern Problem and Situational Discourse

The author presents the problems of general library science that need profound development. First, it is necessary to determine the relationship between the concepts of “library science” and “general library science”, as well as the content of the concepts of “partial library science”, “special library science”, “electronic library science”, etc. The author emphasizes to bring the Standard GOST 7.0—99 “Information and library activities, bibliography. Terms and definitions or coming to replace it the State Standart GOST “Library and information activities, bibliography. Terms and defenitions” into compliance with the Federal Law No. 78-FZ of 29.12.1994 “On Librarianship”, primarily in terms of the relationship between the concepts of “information and library” and “library and information”. Both of these terms are incorrect. Then, it is necessary to give the scientific definition of the concept of “library”, which differs from its normative-legal definition, as well as to abandon the terms and concepts artificially introduced into the library science thesaurus. It is required to separate the concepts of “librarianship” and “library activity”, to abandon the idea of library as a social institution for the provision of services. The existing approach leads to the steady displacement of cultural activities from the sphere of government responsibility into purely market relations, forms a false understanding of the phenomenon of cultural values, from which the spiritual component is derived. In the field of services, being a type of marketing, albeit non-commercial, as it is in librarianship, the preservation, production, transmission and dissemination of spiritual values cannot be carried out. The author proposes in place of the concept of “service” as the ultimate goal of librarianship to put the concept of “goodness” and to understand the library as a social institution providing socially significant goodness. The system approach requires the development of each of the library components as a four-element system. The author declares the need to correct almost the entire conceptual apparatus of library stock studies and catalogue science, littered with the introduction of the term “information resource” instead of the concept of “document”, which, in turn, needs considerable clarification. The author makes proposal for the implementation of the current actual tasks of general library science.
Ensemble classifier based big data classification with hybrid optimal feature selection

Big data is turning out to be well-liked and enviable amongst numerous users for storing, analyzing, and handling larger quantities of data. Nevertheless, clustering these larger data has turned into more multifaceted owing to its data size. A number of machine learning (ML) approaches have been developed recently to extract information from Big Data. These existing techniques, on the other hand, do not meet the accuracy criterion. Proposed LDA, PCA, and LSR-based features are first calculated. The optimal features are then chosen using a new SSI-CSA model. These optimal features are then classified via ensemble classifier (EC) that includes SVM, RF, DT and NN and the precise classified outcomes are obtained. This work employs the Shark Smell Integrated Cat Swarm Algorithm (SSI-CSA) model for precise feature selection. In the end, the improvement of deployed scheme is confirmed regarding diverse metrics like FNR, MCC, and accuracy and so on.
A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling

Spaced repetition is a mnemonic technique where long-term memory can be efficiently formed by following review schedules. For greater memorization efficiency, spaced repetition schedulers need to model students' long-term memory and optimize the review cost. We have collected 220 million students' memory behavior logs with time-series features and built a memory model with Markov property. Based on the model, we design a spaced repetition scheduler guaranteed to minimize the review cost by a stochastic shortest path algorithm. Experimental results have shown a 12.6% performance improvement over the state-of-the-art methods. The scheduler has been successfully deployed in the online language-learning app MaiMemo to help millions of students.
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences

It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. Indeed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer CAT, for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.
Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization

Curbing online hate speech has become the need of the hour; however, a blanket ban on such activities is infeasible for several geopolitical and cultural reasons. To reduce the severity of the problem, in this paper, we introduce a novel task, hate speech normalization, that aims to weaken the intensity of hatred exhibited by an online post. The intention of hate speech normalization is not to support hate but instead to provide the users with a stepping stone towards non-hate while giving online platforms more time to monitor any improvement in the user's behavior. To this end, we manually curated a parallel corpus - hate texts and their normalized counterparts (a normalized text is less hateful and more benign). We introduce NACL, a simple yet efficient hate speech normalization model that operates in three stages - first, it measures the hate intensity of the original sample; second, it identifies the hate span(s) within it; and finally, it reduces hate intensity by paraphrasing the hate spans. We perform extensive experiments to measure the efficacy of NACL via three-way evaluation (intrinsic, extrinsic, and human-study). We observe that NACL outperforms six baselines - NACL yields a score of 0.1365 RMSE for the intensity prediction, 0.622 F1-score in the span identification, and 82.27 BLEU and 80.05 perplexity for the normalized text generation. We further show the generalizability of NACL across other platforms (Reddit, Facebook, Gab). An interactive prototype of NACL was put together for the user study. Further, the tool is being deployed in a real-world setting at Wipro AI as a part of its mission to tackle harmful content on online platforms.
The Role of Chatbots in Enhancing Customer Experience: Literature Review

Today's competitive world uses different marketing opportunities to achieve business goals. Customer experience is a core component in marketing studies; firms need to create strong and enduring customer experiences. To achieve this goal, enterprises can use technology, especially since it reshapes the nature of service, customers’ experiences, and customer relationship management. In this sense, the purpose of the present article is to highlight the role of AI throughout chatbot in enhancing customer experience. We try to bring some answers to our following problematic: how do AI based Chatbots influence the customer experience? To do so, we conducted a systematic literature review. 23 marketing journals were reviewed from two prominent databases (Elsevier and Web of Science). Finally, Fourteen peer-reviewed journal publications in marketing field were analysed. The study found that the analysed marketing journals paid less attention to intelligent agents within the field of customer experience. Another observation is that most of research on chatbots and customer experience in these two databases were conducted between 2020 and 2021. Research implications and directions for future research across marketing field were suggested.
Smart farming architectures based on IoT review: comparative study

The amount of data generated by internet of things devices multiplied day after day, and the use of cloud computing to store and process the data is very challenging due to the congestion of network, which negatively affects the processing time. The thing that led to the emergence of edge computing to solve these problems besides with the use of recent technologies such as artificial intelligence, blockchain and many more. The objective of this paper is to present the comparative study of the several proposed architectures on smart farming based on IoT according to different features since protocols, technologies, security, interoperability, accessibility, resilience etc., the advantages and limitations of each solution. Index Terms
Automatic Phenotyping by a Seed-guided Topic Model

Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand and predict patient disease risks with suitable machine learning methods such as topic models. However, existing topic models do not generate identifiable topics each predicting a unique phenotype. One promising direction is to use known phenotype concepts to guide topic inference. We present a seed-guided Bayesian topic model called MixEHR-Seed with 3 contributions: (1) for each phenotype, we infer a dual-form of topic distribution: a seed-topic distribution over a small set of key EHR codes and a regular topic distribution over the entire EHR vocabulary; (2) we model age-dependent disease progression as Markovian dynamic topic priors; (3) we infer seed-guided multi-modal topics over distinct EHR data types. For inference, we developed a variational inference algorithm. Using MixEHR-Seed, we inferred 1569 PheCode-guided phenotype topics from an EHR database in Quebec, Canada covering 1.3 million patients for up to 20-year follow-up with 122 million records for 8539 and 1126 unique diagnostic and drug codes, respectively. We observed (1) accurate phenotype prediction by the guided topics, (2) clinically relevant PheCode-guided disease topics, (3) meaningful age-dependent disease prevalence. Source code is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Seed.
ReprBERT: Distilling BERT to an Efficient Representation-Based Relevance Model for E-Commerce

Text relevance or text matching of query and product is an essential technique for e-commerce search engine, which helps users find the desirable products and is also crucial to ensuring user experience. A major difficulty for e-commerce text relevance is the severe vocabulary gap between query and product. Recently, neural networks have been the mainstream for the text matching task owing to the better performance for semantic matching. Practical e-commerce relevance models are usually representation-based architecture, which can pre-compute representations offline and are therefore online efficient. Interaction-based models, although can achieve better performance, are mostly time-consuming and hard to be deployed online. Recently BERT has achieved significant progress on many NLP tasks including text matching, and it is of great value but also big challenge to deploy BERT to the e-commerce relevance task. To realize this goal, we propose ReprBERT, which has the advantages of both excellent performance and low latency, by distilling the interaction-based BERT model to a representation-based architecture. To reduce the performance decline, we investigate the key reasons and propose two novel interaction strategies to resolve the absence of representation interaction and low-level semantic interaction. Finally, ReprBERT can achieve only about 1.5% AUC loss from the interaction-based BERT, but has more than 10% AUC improvement compared to previous state-of-the-art representation-based models. ReprBERT has already been deployed on the search engine of Taobao and serving the entire search traffic, achieving significant gain of user experience and business profit.
Fast and accurate 3D face reconstruction based on facial geometry constraints and fringe projection without phase unwrapping

Phase unwrapping is a critical process and a challenging step in fringe projection profilometry. Methods employing additional auxiliary patterns or embedding signals in the projected fringe pattern can successfully address this issue, but most of them suffer from increased number of structured patterns or depressed signal-to-noise ratio (SNR) of fringe images, which are not expected in real-time high-accuracy application such as 3D face reconstruction. In this paper, a 3D face reconstruction method that completely sidesteps the phase unwrapping process is proposed. Phase order lines (POLs) are first extracted from the left and right wrapped phase map of the stereo vision system respectively, and then the left and right POLs are matched by automatically drawing polylines on the face with facial geometry constraints as prior knowledge. Finally, based on the matched POL pairs, fine matching is performed directly employing the wrapped phase to generate a dense disparity map. Compared with the conventional, the processing time of the proposed method is further improved without sacrificing accuracy. Experiments demonstrate the effectiveness and robustness of the method.
Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know

Emerging technologies powered by artificial intelligence (AI) have sparked hope of achieving better clinical outcomes among patients. One of the trends is the use of medical image recognition systems to screen, diagnose, or stratify risks of diseases. This technology may enhance sensitivity and specificity and thus, improve the accuracy and efficiency of disease diagnosis. Therefore, it is important and beneficial for healthcare providers to understand the basic concepts of AI so that they can develop and provide their own AI-powered technology. The purpose of this literature review is to provide (1) a simplified introduction to AI, (2) a brief review of studies on medical image recognition systems powered by AI, and (3) discuss some challenging aspects in this field. While there are various AI-powered medical image recognition systems, this paper mainly discusses those integrated in smartphone apps. Medical fields that have implemented image recognition models in smartphones include dermatology, ophthalmology, nutrition, neurology, respiratology, hematology, gynecology, and dentistry. Albeit promising, AI technology may raise challenges from the technical and social aspects of its application. Notable technical issues are limited dataset access and small datasets, especially for rare diseases. In a social context, the perspectives of all involved parties (physicians, patients, and engineers) must be considered.
Recognizing the Damaged Surface Parts of Cars in the Real Scene Using a Deep Learning Framework

Automatically recognizing the damaged surface parts of cars can noticeably diminish the cost of processing premium assertion that leads to providing contentment for vehicle users. This recognition task can be conducted using some machine learning (ML) strategies. Deep learning (DL) models as subsets of ML have indicated remarkable potential in object detection and recognition tasks. In this study, an automated recognition of the damaged surface parts of cars in the real scene is suggested that is based on a two-path convolutional neural network (CNN). Our strategy utilizes a ResNet-50 at the beginning of each route to explore low-level features efficiently. Moreover, we proposed new mReLU and inception blocks in each route that are responsible for extracting high-level visual features. The experimental results proved the suggested model obtained high performance in comparison to some state-of-the-art frameworks.
Mutual-Feed Learning for Super-Resolution and Object Detection in Degraded Aerial Imagery

The resolution degradation poses a huge challenge for object detection (OD) in the aerial imagery. Existing methods utilize super resolution (SR) based on Generative Adversarial Network (GAN) to restore texture details in degraded images. However, constrained detection results are still acquired due to the object feature difference between restored and clear images. Therefore, we propose a simple-yet-effective learning method called Mutual-Feed Learning (MFL) to solve the problem in this paper. A closed-loop structure is designed via building the feedback connection based on the feedforward connection between the two tasks. It effectively delivers the object spatial and feature information from OD to SR, and provides restoration-enhanced images from SR to OD. Specifically, a Feedback of Region of Interest (FROI) module is introduced to realize a region-level discrimination under the guidance of object information. It guides the discrimination process of super resolution. Furthermore, a Multi-Scale Object Information (MSOI) module is developed to implement a feature-level restoration by narrowing differences in object-related features. It improves the generation process of super resolution. Then object detection can be performed in restoration-enhanced images to obtain more accurate results. Extensive experiments over NWPU VHR-10, COWC, and FAIR1M dataset show that the method can achieve state-of-the-art results.
Three-Dimensional Trajectory Design for Energy-Efficient UAV-Assisted Data Collection

While fixed-wing unmanned aerial vehicles (UAVs) provide an effective means to collect data from sensor nodes (SNs), the design of energy-efficient three-dimensional (3D) trajectories has been deterred by the lack of adequate propulsion energy models. This paper develops a new UAV-assisted data collection scheme to save the energy of the UAV and SNs by holistically designing the 3D trajectory and data collection schedule of a fixed-wing UAV. The key aspect is that a new propulsion energy model is established for a fixed-wing UAV flying a 3D trajectory. Another important aspect is that a new approach is developed to save the energy of UAV-based data collection by jointly optimizing the 3D trajectory and the data collection schedule. Difference-of-convex functions and successive convex approximation are employed to tackle the non-convexity pertaining to the new model. Simulations show the validity of the new model and considerable energy saving by optimizing the 3D trajectories for the UAV, as compared to two-dimensional (2D) trajectories.
Local Loss-Assisted Dynamic Client Selection for Image Classification-Oriented Federated Learning

In this paper, we put forward a novel local loss-assisted client selection approach for communication-efficient federated learning. Unlike many prior works that require all clients to upload compressed local gradients or model parameters in each round, the proposed approach intelligently selects a small subset of clients to send local model parameters to the server in each round. Specifically, in each round, clients send their latest local model losses to the server and the server picks up the clients with the smallest losses to upload the best set of local model parameters. Although different clients are selected in different rounds, the number of selected clients in a round is fixed and therefore the communication cost of a round is time-invariant. We use collaborative image classification as the application for evaluating the proposed approach of federated learning. Simulation results reveal that the proposed local loss-assisted client selection approach could significantly reduce the communication cost of federated learning at the cost of slightly reducing the accuracy of image classification.
An Efficient and Privacy-Preserving Federated Learning Scheme for Flying Ad Hoc Networks

In a flying ad hoc network (FANET), unmanned areal vehicles (UAV) communicate to route the message from the source node to the destination. Often, these UAVs carry sensitive information that needs to be transmitted with full confidentiality without revealing the identity of the sender node. Federated learning (FL) is an emerging machine learning approach that can protect the privacy of nodes in the network. However, implementing FL in FANET is challenging due to the highly dynamic network and limited resources. In the existing distributed FL, clients need to communicate with other clients participating in FL to exchange key information, which increases the overhead significantly. Therefore, we propose a group signature-based federated algorithm that can enhance the protection of the nodes’ identity and significantly reduce the overheads by eliminating the need to exchange key information among the drones. Our simulation results show that the proposed algorithm achieves significantly lower computation cost, communication cost, and signaling overhead than existing works. We also verify the security of our algorithm for various known attacks using AVISPA.