Reviewerly/exHarmony huggingface dataset:
A Knowledge Graph-based Interactive Recommender System Using Reinforcement Learning

To achieve the personalisation recommendation, modem recommendation models should consider the user’s preference for item attributes and dynamic changes in preferences. 1RS has attracted attention to dealing with dynamic user preferences. However, current 1RS models share a common issue of sparse user-interaction data for training an effective recommendation policy. In this paper, we propose a knowledge graph-based interactive recommender system (KGIRS) to improve the recommendation by considering the users’ dynamic preference for item attributes’ weight. This interactive recommender system incorporates the knowledge graph as the source of the auxiliary information to increase the user-item interaction data efficiency and utilises the Q-learning technique to detect the user’s preference drifting. In this model, Q-learning is used to learn the user preference on item attribute dimensions and detect the user’s preference drifting through the user’s every feedback. A user’s every interaction with items is modelled as a ripple in the knowledge graph. Their previous interest and dimensional preferences activate their potential interest in items. It propagates along the links in the knowledge graph from the ripple centre with the learned speed. Then obtain the candidate recommendation items in the unit propagation time according to the user’s preference. Through the experiments on the real-world dataset MovieLens-1M, we demonstrate that KGIRS can achieve efficient, personalised recommendations.
Teachers' and Students' Perceptions of Android-based Mathematics Learning Media Development by Integrating GeoGebra

The utilization of technology as a learning resource or media has been widely used as an innovation to promote learning. It is expected to stimulate the thoughts, feelings, interests, and attention of students in learning. The purpose of this study is to find out teachers’ and students’ perceptions of the need for Android-based mathematics learning media on geometry transformation. This research used a descriptive qualitative approach. Data was collected using validated questionnaires for teachers and students. The subjects of this study were mathematics teachers and 2 nd -grade students at a senior high school in Aceh, Indonesia. The data was analyzed using descriptive statistical methods. The results showed that the geometry transformation subject was difficult for students. The various formulas and the material's abstractness make understanding the transformation subject challenging. Less attractive learning sources or media and a lack of variety in learning approaches are also influential factors. This study also revealed that teachers were still not optimal in utilizing the various available technological media. Thus, teachers and students need interesting and innovative learning media that can assist students in exploring abstract mathematical concepts, especially geometry transformation. These results became an important reference in the development of Android-based learning media assisted by GeoGebra Software.
Peran Taman Bacaan Masyarakat (TBM) untuk Menumbuhkan Minat Baca Masyarakat:

One of the community library gardens is the smart stock community public library in Deli Serdang, Medan City. This research is to find out how the reading community in the reading garden community, the smart stock community, the city of Deli Serdang in triggering interest in reading. This research applies qualitative research procedures through descriptive and investigative approaches, questions and answers, and documentation as a technique for gathering materials. The findings of this study indicate that TBM Saham Pinang will function as a source of community education and is expected to be of benefit to the community. The increasing number of visitors to TBM shows interest. reading that grew among those who lived around. There are official and repeated visits. It can be concluded that TBM Smart Stock contributes to increasing public interest in reading. and dissemination of information.&#x0D; Keywords: TBM, Reading Gardens, The Role of TBM
Jaringan Kerja Sama Perpustakaan Dalam Negeri

&#x0D; Domestic library cooperation network is intended as a type of network that occurs in libraries so that cooperative relationships between other libraries are established in an effort to fulfill what is needed by users so that they can satisfy users and the purpose of library services can be achieved. The method used to carry out this research is to conduct a literature study which is further developed in a qualitative descriptive form so that the contents of the journal will provide a more in-depth explanation. The results of the study explain that it is necessary to implement a collaboration network between libraries in either conventional or digital form.&#x0D; Keywords: network, cooperation, library
Perusal of Camera Trap Sequences Across Locations

The current rate of decline in biodiversity exclaims ecological conservation. In response, camera traps are being increasingly deployed for the perlustration of wildlife. The analyses of camera trap data can aid in curbing species extinction. However, a substantial amount of time is lost in the manual review curtailing the usage of camera traps for prompt decision-making. The insuperable visual challenges and proneness of camera trap to record empty frames (frames that are natural backdrops with no wildlife presence) deem wildlife detection and species recognition a demanding and taxing task. Thus, we propose a pipeline for wildlife detection and species recognition to expedite the processing of camera trap sequences. The proposed pipeline consists of three stages: (i) empty frame removal, (ii) wildlife detection, and (iii) species recognition and classification. We leverage vision transformer (ViT), DEtection TRansformer (DETR), vision and detection transformer (ViDT), faster region based convolutional neural network (Faster R-CNN), inception v3, and ResNet 50 for the same. We examine the adroitness of the leveraged algorithms at new and unseen locations against the challenges of domain generalisation. We demonstrate the effectiveness of the proposed pipeline using the Caltech camera trap (CCT) dataset.
Gesture Recognition and Multi-modal Fusion on a New Hand Gesture Dataset

We present a baseline for gesture recognition using state-of-the-art sequence classifiers on a new freely available multi-modal dataset of free-hand gestures. The dataset consists of roughly 100,000 samples, grouped into six classes of typical and easy-to-learn hand gestures. The dataset was recorded using two independent sensors, allowing for experiments on multi-modal data fusion at several depth levels and allowing research on multi-modal fusion for early, intermediate, and late fusion techniques. Since the whole dataset was recorded by a single person we ensure a very high quality of data with little to no risk for incorrectly performed gestures. We show the results of our experiments on unimodal sequence classification using a LSTM as well as a CNN classifier. We also show that multi-modal fusion of all four modalities results in higher precision using late-fusion of the output layer of an LSTM classifier trained on a single modality. Finally, we demonstrate that it is possible to perform live gesture classification using an LSTM-based gesture classifier, showing that generalization to other persons performing the gestures is high.
Similarity Constrained Conditional Generative Auto-encoder with Generalized Dilated Networks

Recent advancements in Generative Adversarial Networks have made it possible to generate plausible results. But what these models fail to learn is to disentangle different factors of variations to have a better control over the generated images. This makes generating images with specific features difficult. Typically, it is possible to do so, but with limited success, because models are prone to mix features from different classes together when generating images. For successful image generations with control over the content of the generated images, it is necessary that the model is interpretable conditionally disentangled. In fact, disentanglement is considered to be an important property of interpretability and is our focus point in this work. We introduce a novel idea to generate disentangled representations. In this approach, we add a Convolutional Encoder to the Conditional GAN structure and scale down the latent features to a vector. Labels are used at different levels in the Decoder with help of a separate network called Label-Scaler, which can be seen as a convolutional projection. Doing this, we drastically increase the disentanglement, which we visualize by performing traversals in both the feature latent space as well as in the conditional latent space. Additionally, we improve our intermediate results significantly by using Generalized Dilation Convolutions, increasing the reconstruction- and generation-performance of our framework without a change in disentanglement.
Deep learning applications in visual data for benign and malignant hematological conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.
MiniGAN: Toward Informative and Uninformative Image Transferring

This article proposes a generative adversarial networks (MiniGAN) to tackle both informative and uninformative image transferring. The generator of MiniGAN is based on the structure of StyleGANv2, in which the encoder and style transform block are proposed to extract the high-level feature maps of the source image and capture the latent representation of the target image, respectively. This information guides the generator for the final image generation. The proposed MiniGAN outperforms other models in style transferring while preserving the color information on the informative images. To test the performance of MiniGAN on the uninformative images, a new data set consisting of 10,000 fashion hand drawings is proposed. Extensive experiments and detailed analysis are presented to demonstrate the performance of MiniGAN.
Enhancement of video demosaicing techniques using optimization algorithm

Summary Demosaicing and denoising are the most significant steps in the imaging process for both images and videos. The correlation noise corrupts the image quality, such that an accurate interpretation of the image is avoided. Thus, demosaicing is utilized to restructure the full‐color image from imperfect color samples. In this article, cat shuffled shepherd optimization‐based local polynomial approximation and intersection of confidence intervals (Cat SSO‐based LPA‐ICI) approach is developed for video demosaicing. The LPA‐ICI model is employed for frame demosaicing in which the second‐order polynomial interpolation filter coefficients are produced using the devised optimization algorithm, named cat SSO technique. Similarly, generative adversarial network classifier is also applied for frame demosaicing. In addition, initialization and flow estimation is performed for the demosaicing process. Besides, spatio temporal demosaicing model is utilized in order to get a noise‐free sequence. However, the developed cat SSO technique is newly devised by the incorporation of the shuffled shepherd optimization algorithm and cat swarm optimization technique. The created video demosaicing model outperformed with peak signal‐to‐noise ratio, root mean square error, and second derivative‐like measure of enhancement values of 56.85 dB, 0.46, and 60.62, respectively.
Survey on Gesture Recognition for Sign Language

Abstract: From the total world population, more than 5% of the population has problems with speaking and hearing, which creates a barrier in communication as they communicate only using sign language. This paper presents a survey on all components which are required to build a device that helps to reduce this barrier and has studied many inventions in this field, which include all the possible methods through which the device can be built. They include Vision-based methods, device-based methods, and many more. And survey on different hardware like flex sensor, accelerometer, IMU sensor, Arduino, and EMG sensors. An even survey is done on different algorithms used for processing data captured during gesture recognition.
Analysis of top box office film poster marketing scheme based on data mining and deep learning in the context of film marketing

With the development of science and technology and the continuous changes of social environment, the development prospect of traditional cinema is worrying. This work aims to improve the publicity effect of movie posters and optimize the marketing efficiency of movie posters and promote the development of film and television industry. First, the design concept of high grossing movie posters is discussed. Then, the concept of movie poster analysis based on Deep Learning (DL) technology is analyzed under Big Data Technology. Finally, a movie poster analysis model is designed based on Convolutional Neural Network (CNN) technology under DL and is evaluated. The results demonstrate that the learning curve of the CNN model reported here is the best in the evaluation of model performance in movie poster analysis. Besides, the learning rate of the model is basically stable when the number of iterations is about 500. The final loss value is around 0.5. Meanwhile, the accuracy rate of the model is also stable at the number of iterations of about 500, and the accuracy rate of the model is around 0.9. In addition, the recognition accuracy of the model designed here in movie poster classification recognition is generally between 60% and 85% in performing theme, style, composition, color scheme, set, and product recognition of movie posters. Moreover, the evaluation of the model in the movie poster style composition suggests that the style composition of movie poster production dramatically varies in different films, in which movie posters focus most on movie product, style, and theme. Compared with other models, the performance of this model is more outstanding in all aspects, which shows that this work has achieved a great technical breakthrough. This work provides a reference for the optimization of the design method of movie posters and contributes to the development of the movie industry.
Improving Conversational Recommender Systems via Knowledge Graph-based Semantic Fusion with Historical Interaction Data

Conversational recommender systems (CRS) use interactive discussions to recommend high-quality items to users. Two essential components in a good CRS are the recommendation module that makes pertinent product recommendations to consumers and a conversation component that creates text-based sentences with product recommendations. The most commonly used dataset to train CRS models is ReDial. In this paper, we found that using the INSPIRED dataset in place of the ReDial dataset significantly improves model performance in terms of effectiveness. Along with the INSPIRED dataset, the inclusion of historical data in the input improves efficiency. The accuracy and efficiency of the model increase when we include the historical data into the system in the form of DialoGPT corpus and Gutenberg books. The paper further extends to compare three versions of state-of-the-art knowledge graph based conversational recommender systems called KGSF â one with the INSPIRED dataset with history, one with the INSPIRED dataset without historical data and the last with the ReDial dataset without historical data, which is the original version of the KGSF model. The comparison between three versions of the KGSF model shows that the change of the dataset and the inclusion of historical data can promote the performance of this conversational recommendation system.
Ill-fated interactions: modeling complaints on a food waste fighting platform

The redistribution of surplus food is a challenging problem, yet a crucial one to address given the urgent nature of climate change. However, designing computer-mediated food sharing systems is made even harder due to failed interactions between users and ensuing complaints, which can dissuade others from participating when shared within a public forum. To examine the phenomenon of complaints within such data, we analyze the public forum of a food sharing platform, OLIO. We characterize complaining behaviour and augment it through qualitative labeling and a machine learning approach to model complaints using affective indicators of dissatisfaction across a corpus of 3,195 forum posts. Results emphasize that linguistic features yield high prediction accuracies, with negative, nonconstructive sentiment being of greatest relevance. We discuss how machine learning can further enrich qualitative understandings and validation of complaints in the sharing economy.
Building a Patient-Centered Virtual Hospital Ecosystem Using Both Access Control and CNN-Based Models

Virtual hospitals empower traditional hospitals to deliver more accessible, affordable, and comprehensive patient-centered (PC) care services. However, the legacy information systems of traditional hospitals are ill-equipped to support the needs of virtual hospitals. We propose a holistic virtual hospital ecosystem design that addresses these issues. We have developed two models. The first is a VHealth-CNN model that extracts PC knowledge from multi-sourced biomedical big data by (1) extracting disease health-related features; (2) structuring the relevant health-related features as per the pre-identified factors; (3) training a convolutional neural network (CNN) double-layer structure, where we select significant health-related features in the first layer, and classify the positively and negatively correlated features in the second one; and (4) generating disease class outputs representing the PC knowledge. The second model is a granular VHealth-AC model that seamlessly grants healthcare practitioners at a hub hospital remote access to PC knowledge at the right point of care. We have deployed a granular 5-tier PC information classification scheme to enforce information security rules across hospitals. In addition, we examined the feasibility of the proposed design through a tele-monitoring service experimental case study for predicting obesity, hypertension, and diabetes. The experimental results show that the proposed model predicts obesity, hypertension, and diabetes diagnoses with 91.3%, 93.5%, and 95% accuracy, respectively. Finally, our ecosystem design should encourage the adoption of virtual hospitals and the adoption of virtual healthcare services as a new norm.
An insulator feature-enhanced segmentation method based on the residual-type attention mechanism

As an crucial component of power line, the timely check for the state of insulator is necessary for the normal running of transmission lines. In view of debasement of insulator segmentation accuracy in current transmission line caused by complex background, low contrast, and the quality of images is not guaranteed, we improve U-Net by combining attention mechanism and residual connection. Residual connection is added to the encoder part to improve the extraction of low-level semantic information, and the attention mechanism is added to the decoder part for integrating high and low level features better and reduce the error between them. We confirm the effectiveness of the improved module through experiments, while the results showing that the improved U-Net model segmentation performance on insulator dataset is improved from 0.875 to 0.912. And our method also outperforms previous segmentation work on insulator dataset.
Exploring the Possibility of Higher-Order Fault Attacks

Higher-order side-channel attacks have been widely studied. In this paper, the principles and preprocessing methods of higher-order side-channel attacks are analyzed, and the essential consistency between higher-order side-channel attacks and higher-order fault attacks is defined at the algorithmic and physical levels. Higher-order leakage point in fault attacks and the possibility of higher-order attacks is confirmed, and several possible attack methods are proposed. Finally, the current problems of higher-order side-channel attacks are analyzed, and hot issues worth further research in the field of higher-order fault attacks, such as promoting first-order attack methods, constructing a unified physical leakage model, carrying out combined attacks, and designing a common defense strategy, are proposed.
Randomize Adversarial Defense in a Light Way

The ultimate goal in adversarial defense is to build a universally robust defense against all types of attacks, but ongoing arms race between adversarial attacks and defenses show the difficulty in building a deterministic defense to work towards the goal. Leveraging the idea of a mixture model, in this paper, we introduce a new Randomized Adversarial Defense method (RAD) to increase the robustness against adversarial examples. RAD is designed as a simple, yet effective random mixture of a global model and one or more local models. The mixture is able to create a random decision boundary for a test instance, making it harder for an adversarial example to succeed, and thus increasing the robustness of the defense. The global model is adversarially trained to provide the baseline robustness. The local models are aimed to supplement the global model and thus the decision boundary of a local model is expected to be adjacent to the decision boundary of the global model. These models then together form a random mixture to create a randomized (non-deterministic) decision boundary for each test instance at the end. Such a randomization scheme reduces the adversarial risk since the adversary has to approximate the best attack despite of the given complete knowledge of the parameters of the individual classifiers. By proposing the notion of having a global and local models with different focuses in the mixture and the way of creating a local model which has minimum dependency on the base (global) model, RAD provides a simpler and more flexible, yet effective approach building a randomized defense, compared with the existing randomization based methods. Experimental results show that our simple randomization approach outperforms the most robust deterministic defense method and performs competitively upon the existing randomized defense method against strong adaptive attacks on CIFAR10 and CIFAR100.