Reviewerly/exHarmony huggingface dataset:
Automatic assignment of microgenres to movies using a word embedding-based approach

Abstract Streaming services are increasingly leveraging Artificial Intelligence (AI) technologies for improved content cataloging, user experiences in content discovery, and personalization. A significant challenge in this domain is the automated assignment of microgenres to movies. This study introduces and evaluates approaches based on clustering, topic modeling, and word embedding to address this task. The evaluation employs a preprocessed dataset containing movie-related data—title tags, synopses, genres, and reviews—alongside a predefined microgenre list. Comparisons of three activation functions (binary step, ramp, and sigmoid) gauge their effectiveness in augmenting microgenre tags. Results demonstrate the superiority of the word embedding approach over clustering and topic modeling in terms of mean accuracy. Even more, the word embedding approach stands as the sole fully automated solution. Analysis indicates that incorporating review-based tags introduces noise and undermines accuracy. Besides, the word embedding approach yields optimal outcomes using the sigmoid function, effectively doubling assigned tags while maintaining matching quality. This sheds light on the potential of word embedding methods within the movie domain.
Training on Pivot Tables and Dashboard Creation for Medical Record Professionals at 'Aisyiyah General Hospital Padang

Pivot tables are one of the features found in Microsoft Excel. With pivot tables, data can be presented based on the selected categories. Data in a pivot table is presented in columns or rows. To make the data easily understandable for anyone reading it, the data can be displayed in the form of a dashboard. There is a lot of patient data stored in the database of 'Aisyiyah General Hospital Padang. This data can be utilized by medical record personnel as reports for management decision-making in the hospital. To ensure easy understanding, the data is presented in an attractive dashboard format. However, the professional medical record staff in the hospital and other medical personnel do not yet have the ability to create dashboards using pivot tables. Therefore, training is needed to implement this. The training will be held at 'Aisyiyah General Hospital Padang for two days, consisting of theoretical material as well as practical exercises based on data obtained from the hospital's database. The training conducted should proceed smoothly, and participants should be able to understand pivot tables, dashboards, and how to create them. Initially targeted at medical record personnel, the training program was also attended by other medical staff, including doctors, training department heads, and the hospital director. The training that has been conducted has expanded the participants' understanding of pivot tables, dashboards, and the significant benefits of Microsoft Excel's features. In the future, it is hoped that this training can be extended with a longer duration and cover other cases within the hospital.
An improved SSD lightweight network with coordinate attention for aircraft target recognition in scene videos

Accurate identification and monitoring of aircraft on the airport surface can assist managers in rational scheduling and reduce the probability of aircraft conflicts, an important application value for constructing a "smart airport." For the airport surface video monitoring, there are small aircraft targets, aircraft obscuring each other, and affected by different weather, the aircraft target clarity is low, and other complex monitoring problems. In this paper, a lightweight model network for video aircraft recognition in airport field video in complex environments is proposed based on SSD network incorporating coordinate attention mechanism. First, the model designs a lightweight feature extraction network with five feature extraction layers. Each feature extraction layer consists of two modules, Block_A and Block_I. The Block_A module incorporates the coordinate attention mechanism and the channel attention mechanism to improve the detection of obscured aircraft and to enhance the detection of small targets. The Block_I module uses multi-scale feature fusion to extract feature information with rich semantic meaning to enhance the feature extraction capability of the network in complex environments. Then, the designed feature extraction network is applied to the improved SSD detection algorithm, which enhances the recognition accuracy of airport field aircraft in complex environments. It was tested and subjected to ablation experiments under different complex weather conditions. The results show that compared with the Faster R-CNN, SSD, and YOLOv3 models, the detection accuracy of the improved model has been increased by 3.2% , 14.3% , and 10.9% , respectively, and the model parameters have been reduced by 83.9% , 73.1% , and 78.2% respectively. Compared with the YOLOv5 model, the model parameters are reduced by 38.9% when the detection accuracy is close, and the detection speed is increased by 24.4% , reaching 38.2fps, which can well meet the demand for real-time detection of aircraft on airport surfaces.
Image Inpainting for Large and Irregular Mask Based on Partial Convolution and Cross Semantic Attention

Image inpainting is inferring missing pixels in images using known content and surrounding regions. To achieve this task, existing methods have utilized convolutional neural networks and adversarial generative networks. However, these approaches produce blurry and unrealistic completion results when the region of the missing pixels is large and irregular. This is because there is insufficient information to fill the missing region, and the networks fail to utilize known pixel information. Therefore, this study proposes a coarse-to-fine strategy using a pre-trained partial convolution-based encoder-decoder network. Our two-stage image inpainting strategy consists of a coarse inpainting network using partial convolution and a fine inpainting network using cross-attention layers based on an Unsupervised Cross-space Generative Adversarial Network (UCTGAN). In the first stage, the coarse network completes the missing region to feed the clue for inpainting into the following stage. Then, in the second stage, the fine network projects instance images and the coarse clue image into a low-dimensional manifold space and combines low-dimensional spatial features across semantic attention layers. Finally, the generator reconstructs a reconstructed image conditioned on the coarse completion. In the experiments, we qualitatively and quantitatively evaluated generated images in terms of image quality and similarity. As a result, our framework can generate precise completion results for a large and irregular missing region better than the previous methods, such as the methods based on partial convolution or the original UTCGAN.
Evaluation of online learning readiness in the new distance learning normality

The COVID-19 pandemic influenced teaching and learning in higher education. The transformation towards digital education challenged Faculty and students. This research examines the online learning readiness of students in a Higher Education Institution in Mexico. Specifically, we investigated how much prior digital skills, as well as having used the digital resources available by the university, influenced their academic achievement in distance learning settings. Seven dimensions of online learning readiness were selected to evaluate the student's preparation for the online learning process. Questionnaires were applied before the start and at the end of digital courses. Follow-up tools were offered to support the student, and two groups were observed, users and non-users of the digital devices. It was observed that students who used the support developed significantly better critical thinking, problem-solving, and time organization skills than non-users. On the other hand, although the evaluations were not significantly different, the lowest averages were found in the non-user group. Our results indicate that prior training in the use of digital tools is essential for the success of online education; in the same way, a timely follow-up with technical and pedagogical assistance is necessary for developing competencies. Training more autonomous and independent students capable of distance learning in a global world demands experts in digital education urgently. Educational institutions must embrace new technologies and teaching methods to meet the ever-changing needs of students. This research is expected to play a crucial role in promoting constructive discussions and facilitating informed decisions concerning the creation of future educational models.
Knowledge Graph Completion with Information Adaptation and Refinement

At present, knowledge graph completion (KGC) is mainly divided into structure-based methods and language-based methods, which characterize the structural information and semantic information of knowledge graphs, respectively. Though existing works have developed methods to integrate both information, we argue their end-to-end training manner suffers discrepancy, compatibility, resources redundancy issues. Therefore, we propose a novel two-stage training paradigm for tackling KGC task, i.e. information adaptation and refinement (KGCIAR). Specifically, KGCIAR has two stages, 1) adaptation and 2) refinement. In the adaptation stage, we fine-tune the PLM with the input of descriptive information and supervised by the KG structural information. In the second refinement stage, we freeze the adapted PLM model and infer the description embeddings of entities and relations. Then, those embeddings are leveraged as the entity/relation initial embeddings. Finally, we train a lightweight KGC model. Moreover, we devise two novel objectives for knowledge adaptation, which are self-supervised adaptation and structure-aware contrastive adaptation. Furthermore, we systematically compare the performance of different lightweight KGC models for information refinement. The experiments on KGC task and various variants analyses demonstrate that KGCIAR is effective in harnessing both structure and language information in KG.
CTKM: Crypto-Based User Clustering on Web Transaction Data

User transaction data are rich, valuable, but sensitive. With the huge amounts of transaction data, data mining algorithms can make many applications practical, such as customer-behavior analysis, marketing, and forensics. The value behind the transaction data analysis on the other hand raises the risk of data leak. In this paper, we introduce a Crypto-based KMeans clustering algorithm (CTKM) on the Transaction data of web users for user clustering and data protection as well. Considering the categoricalness of user transaction data, a taxonomy-based distance has been employed, which is applicable to the data encryption process also. In order to obtain efficient computations on the distance, a distance batch computing(DBC) protocol is designed and deployed in a two-server platform. We theoretically estimate both the computation and communication costs of the algorithm. Experimental results on a real data set demonstrate its practical value on web user clustering.
Research on Improved Method based on YOLOV5s Target Detection Model

Aiming at the problem of low detection accuracy of small targets, an object detection method based on average pooling improved YOLOV5s model is proposed. The algorithm introduces the Squeeze Excitation attention module and the Efficient Intersection Over Union loss function to comprehensively improve the detection calculation efficiency and accurate deployment ability. With the development of deep learning technology, which is of great significance to improve the detection accuracy and detection rate. YOLO greatly improves detection performance, three times faster than retinanet and 2 times faster than faster-rcnn. YOLO has strong generalization ability, can be applied to different application scenarios, and is also easy to deploy. The steel surface defect public dataset was selected for verification. The results show that the improved YOLOV5s model is better than the original YOLOV5s model, the test average accuracy mAP can reach 81.8%, the average accuracy mAP of the model is increased by 7.4%, and the overall performance of the improved model is better than other conventional models.
Don’t Make Your Guests Wait on Line

This report compares hotel reservation website load times to best practice. Long load times can cause users to abandon websites, resulting in lost revenue. Results from our research indicate load times for hotel reservation websites exceed best practice times for both desktop and mobile devices. Site design must consider load time with competing design requirements.
Human behavior data extension method for mine scene

At present, there is no dedicated large-scale video data set for the task of monitoring unsafe behavior in mine scenes. In this paper, the monitoring data set of miners’ unsafe behavior is built by ourselves. The data set is divided into extended group and real group. The extended group and the real group, the extended group of clear video samples from 18 public data sets, for each clear video in the extended group with a particle simulation mask, to achieve a similar dust masking effect as the mine scene. The real group comes from the site monitoring of mine operations. This paper uses the MCTN(Muti-scenic Cyclic Translation Network) to complete cross-scenario knowledge transfer, so as to verify the validity of the mine unsafe behavior monitoring dataset constructed in this paper.
Design of motion pattern recognition system based on artificial intelligence methods

This paper builds a wearable device that can be used for motion movement recognition based on artificial intelligence technology. Firstly, a human motion data collection device is designed to realise data collection and pre-processing; secondly, the collected data is classified based on artificial intelligence technology to realise pattern recognition of motion states; finally, the results of recognition are analysed and the results show that motion pattern recognition based on artificial intelligence methods is feasible.
An efficient multi-view and multi-channel disease risk prediction model

With the development of medical information, machine learning are applied on clinical aid to decision-making, for example disease prediction, mortality risk prediction, and so on. For the multi-dimensional, and multi-modality and sequential clinical data, there are being some challenges on feature fusion and representation which would impact the performance of prediction models. In addition, model interpretation attracts more attention in medical AI application field. According to the difference among the contribution from different examination indicators to predict the disease risk, we need consider the relation between indicators and disease as well as the time-dependence of indicators. In this work, we design a Multi-View and Multi-Channel (MVMC) method to construct patient record and propose an ensemble multi-view and multi-channel disease risk prediction model. In this work, we construct visit-view, feature-view and modality-view to represent the patient clinical features. Meanwhile multi-channel data processing mehod is used to extract temporal and spatial characteristics among features. We adopt GRU+CNN and Bi-GRU and transformer in our ensemble models for feature-view, visit-view and modality-view respectively. Attention mechanism is used for computation of correlation coefficient among each view to simulate physician diagnosis process. Experimental results show that our ensemble MVMC model achieves advanced performance in Precision, AUC, Recall and F1-score on the two data sets. Then we give the model interpretation by heatmap.
Effect of Valsalva Maneuver on Pain Perception During Blood Sample Collection Among Patients

Background: Blood sample collection is the most frequent intrusive practise that hurts patients in hospital settings. A non-pharmacological and economical way to lessen pain during blood sample collection is the Valsalva Maneuver. Objectives; The aim of this study was to evaluate the effect of Valsalva Maneuver during blood sample collection. Methods: A quasi-experimental design (post-test only control group design). Self-structured questionnaire on Socio demographic information, clinical parameter and Wong baker face pain scale was used to observe the pain score of participants. The research population includes all the adult patients between the ages of 18 and 50 admitted in IPD. The sample size for the study comprises of 500 Participants who met the inclusion criteria. Purposive sampling technique was used to identify adult patients between the ages of 18 and 50 who were having blood sample collection admitted in IPD in IMS &amp; SUM hospital, Bhubaneswar, Odisha. Results: The results revealed that there is significant reduction in pain during blood sample collection in experimental group with (p=0.000). No association was found between level pain and socio-demographic and clinical parameters. Discussion: The Valsalva manoeuvre is a non-invasive, non-pharmacological, and efficient way to lessen pain related to drawing blood samples. During the collection of blood samples, nurses should demonstrate the Valsalva manoeuvre to patients. In-service education programmes for nurses and students should be included by hospital administration in order to promote the non-pharmacological technique of pain alleviation during blood sample collection.
YOUTUBE COMMENT ANALYSIS USING MACHINE LEARNING

Analyzing comments on YouTube may yield invaluable insights on the demographics, hobbies, and preferences of the viewership. By looking into comment patterns, we may identify recurring themes, intriguing topics, and even potential collaborations. We may group comments using natural language processing techniques into three categories: good, negative, and neutral. This research helps identify key trends, gauge viewer sentiment, and assess how well a video is received overall. Material providers may use the information gathered from comments to better tailor their material to the specific needs and tastes of their audience, which will ultimately boost viewer satisfaction and engagement. KEY WORDS: Machine learning, Bidirectional Encoder Representation from Transformers (BERT), YouTube comments, Transformers.
PAF-Tracker: A Novel Pre-Frame Auxiliary and Fusion Visual Tracker

Relying on a large amount of data, recent object trackers achieve superior performance. However Siamese-like trackers expose considerable shortcomings in the case of brief occlusion. To address these shortages, the paper proposes a novel pre-frame auxiliary and fusion tracking framework. Within this framework, a retained variable is first introduced to avoid some additional twin branches while retaining the previously obtained deep features of the search frames. Based on such a variable, a pre-frame auxiliary module is constructed to establish the relationship between encoding features and the retained pre-frame information and a decoding fusion module is designed to fuse the generated similarity relationship. Moreover, the Efficient IoU (EIoU) loss is employed to increase the precision of predicted bounding boxes by adding three penalty terms for the differences in the center point, length, and width of the two bounding boxes. Finally, the superiority over state-of-the-art methods is verified by numerous tests on visual tracking benchmarks.
Privacy-Preserving Learning via Data and Knowledge Distillation

In the current era of data science, deep learning, computer vision and image analysis have become ubiquitous across various sectors, ranging from government agencies and large corporations to small end devices, due to their ability to simplify people’s lives. However, the widespread use of sensitive image data and the high memorization capacity of deep learning present significant privacy risks. Now, a simple Google search can yield numerous images of a person, and the knowledge that a specific patient’s record was utilized for training a specific model associated with a disease may reveal the patient’s ailment, potentially leading to membership privacy leakage and other advanced attacks in the future. Furthermore, these unprotected models may also suffer from poor generalization due to this overfitting to train data. Previous state-of-the-art methods like differential privacy (DP) and regularizer-based defenses compromised functionality, i.e., task accuracy, to preserve privacy. Such an imbalanced trade-off raises concerns about the practicability of such defenses. Other existing knowledge-transfer-based methods either reuse private data or require more public data, which could compromise privacy and may not be viable in certain domains. To address these challenges, where membership privacy is of utmost importance and utility cannot be compromised, we propose a novel collaborative distillation approach that transfers the private model’s knowledge based on a minimal amount of distilled synthetic data, leading to a compact private model in an end-to-end fashion. Empirically, our proposed method guarantees superior performance compared to most advanced models currently in use, increasing utility by almost 8%, 34%, and 6% for CIFAR-10, CIFAR-100, and MNIST, respectively. The utility resembles non-private counterparts almost closely while maintaining a respectable level of membership privacy leakage of 50-53.5%, despite employing a smaller model with 50% fewer parameters.
Conceptual model of design in the context of semiotic-interactive methodology

The subject of the research is a modern graphic and communicative design, in the context of changing existing and forming new roles of communicators, transforming the communication model, forming a semiotic communication format. The object of the research is modern graphic and communicative design, as well as their traditional, digital and generative subdirections. Design beginning to have digital, semiotic-interactive and artificially intelligent characteristics. The author examines in detail such aspects of the topic as: dialogical, semiotic-interactive qualities of design solutions; digital, semiotic-interactive design-software expressive means; features of semiotic-deconstructivity (analyticity) and semiotic-synthetics (constructiveness) of design solutions in the context of design activity; the influence of technical and technological features of tools and design-environments (design systems) for the formation of automated and artificially intelligent qualities of design solutions. The study of the conceptual model of a semiotic-interactive design is carried out in the context of a semiotic-interactive methodology (a set of principles and strategies of design activity). The main conclusions of the study are the description of the conceptual model of a semiotic-interactive design solution, in the context of a semiotic-interactive methodology implemented in the conceptual-generative subdirection of modern graphic and communicative design. The author's main contribution to the research of the topic is to identify the main components of the conceptual model of a semiotic-interactive design solution and demonstrate their relationship with each other in the form of a holistic model. The novelty of the research lies in the description of the characteristics of the components, the conceptual model of a semiotic-interactive design solution: a dialogical semiotic-interactive format for creating invariant concepts and design solutions; versioning of design solutions based on the semiotic synthesis of design programs; semiotic deconstruction of design solutions at the macro and micro levels; semiotic synthesis of design and artistic design programs; application of design programs as decision tools; use of hyperconnected capabilities of the design environment: the history of actions, integrity and continuous change and development.