Reviewerly/exHarmony huggingface dataset:
Video Frame Interpolation via Multi-scale Expandable Deformable Convolution

Video frame interpolation is a challenging task in the video processing field. Benefiting from the development of deep learning, many video frame interpolation methods have been proposed, which focus on sampling pixels with useful information to synthesize each output pixel using their own sampling operation. However, these works have data redundancy limitations and fail to sample the correct pixel of complex motions. To solve these problems, we propose a new warping framework to sample called multi-scale expandable deformable convolution(MSEConv) which employs a deep fully convolutional neural network to estimate multiple small-scale kernel weights with different expansion degrees and adaptive weight allocation for each pixel synthesis. MSEConv covers most prevailing research methods as special cases of it, thus MSEConv is also possible to be transferred to existing works for performance improvement. To further improve the robustness of the whole network to occlusion, we also introduce a data preprocessing method for mask occlusion in video frame interpolation. Quantitative and qualitative experiments show that our method shows a robust performance comparable to or even superior to the state-of-the-art method. Our source code and visual comparable results are available at https://github.com/Pumpkin123709/MSEConv.
An Exploratory Study on the Design of Emergency First Aid Privacy Protection Computing System Based on Blockchain

The COVID-19 epidemic at the end of 2019 is a typical global public health event. It has spread to more than 200 countries around the world. In the face of sudden illness, the emergency department is the first choice for most patients, and it is often the first to grasp the basic situation of patients. In the process of processing the data of the emergency big data center, how to ensure the privacy of patients while using medical data reasonably. This paper mainly takes the data of the emergency big data center as the research object. Put important health data on the block chain to ensure that important data remains traceable throughout the process, cannot be tampered with, multi-point maintenance, unified standards, and realize the formation of emergency block chains, and use important health data based on privacy applications, Realize the historical traceability of data usage. At the same time, the calculation process of privacy calculation can also be uploaded to the chain, which can be used for data storage, auxiliary calculation, etc.
Historical Language Models in Cryptanalysis: Case Studies on English and German

In this paper, we study the impact of language models (LM) on decipherment of historical homophonic substitution ciphers.In particular, we investigate if decipherment by using hill-climbing and simulated annealing can benefit from LMs generated from historical texts in general and century-specific texts in particular.We carry out experiments on homophonic substitution ciphers with English and German as plaintext languages.We take into account ciphertext length as well as n-gram size of the LMs.We compare the results on decipherment based on historical LMs with large LMs generated from modern texts.The results show that using historical LMs in decipherment of homophonic substitution ciphers leads to significantly better performance on ciphertext produced in the 17th century or earlier, and centuryspecific language models yield better results on longer and older ciphertexts.
Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is an advanced NLP application that aims to identify aspect terms present in the given review and predict the sentiment associated with those aspect terms. ABSA is better than sentence-based sentiment classification because it considers the aspect terms present in the reviews to determine the sentiment rather than considering the individual sentence. Entrepreneurs could make use of ABSA to understand the customers' opinions about different aspects of their products or services. The task of Aspect-based sentiment analysis can be divided into two subtasks: Aspect Term Extraction (ATE) and Aspect Term Sentiment Classification (ATSC). In this paper, an SVM model is proposed for the task of ATE, and an Attention based LSTM model is proposed for the task of ATSC. The proposed models will be trained and tested on SemEval-2014 dataset.
Robust Pipeline for Detection of Adversarial Images

Deep neural networks (DNN’s) have achieved state-of-the-art (SOTA) for many image-related tasks like classification, object detection, etc. The authors have shown that DNN’s can be easily fooled by adversarial examples that can be generated by adding small distortions or perturbations to the images. Several attack methods to add these perturbations in images have been proposed like the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), etc. to create distortions in images that cause the DNN’s to misclassify instances. It is essential to come up with strategies to identify such fake images. Keeping this in mind, we propose an end-to-end pipeline capable of detecting adversarially attacked images, i.e., images with perturbations. For a given image, we generate images with manipulation masks using Mantra-Net and pass them through a trained binary classifier that distinguishes between perturbed and original images. Experiments on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 dataset have shown that our pipeline achieves an accuracy of 0.986 on the task of detecting adversarially crafted images.
Diffusion Models for Realistic CT Image Generation

Generative networks, such as GANs, have been applied to the medical image domain, where they have demonstrated their ability to synthesize realistic-looking images. However, training these models is hard, as they show multiple challenges like mode collapse or vanishing gradients. Recently, diffusion probabilistic models have revealed to outperform GANs in the context of natural images. However, only a few early works have attempted their use in medical images. Hence, in this work we aim at evaluating the potential of diffusion models to generate realistic CT images. We make use of the Frechet Inception Distance to quantitatively evaluate the trained model at different epochs, as a metric to provide information about the fidelity of the synthetic samples, displaying a clear convergence of the value in the advance of the training process. We quantitative and qualitatively show to successfully achieve coherent and precise CT images, compared to real images.
Gestural Denotators: A First Overview

We discuss the formalism of denotators in digraph categories to represent gestures generalizing the already successful denotator approach. This is also a first step to a software-oriented implementation of gestural perspectives. This generalization generates a powerful conceptual symmetry between compositions and gestures. Both contexts start with simple forms, be it over the category of modules or the category of digraphs. Presheaves over these categories then introduce higher compound concepts by one and the same topostheoretical technique starting from Yoneda’s Lemma.
Provision and Characterization of a Corpus for Pharmaceutical, Biomedical Named Entity Recognition for Pharmacovigilance: Evaluation of Language Registers and Training Data Sufficiency

Machine learning (ML) systems are widely used for automatic entity recognition in pharmacovigilance. Publicly available datasets do not allow the use of annotated entities independently, focusing on small entity subsets or on single language registers (informal or scientific language). The objective of the current study was to create a dataset that enables independent usage of entities, explores the performance of predictive ML models on different registers, and introduces a method to investigate entity cut-off performance. A dataset has been created combining different registers with 18 different entities. We applied this dataset to compare the performance of integrated models with models created with single language registers only. We introduced fractional stratified k-fold cross-validation to determine model performance on entity level by using training dataset fractions. We investigated the course of entity performance with fractions of training datasets and evaluated entity peak and cut-off performance. The dataset combines 1400 records (scientific language: 790; informal language: 610) with 2622 sentences and 9989 entity occurrences and combines data from external (801 records) and internal sources (599 records). We demonstrated that single language register models underperform compared to integrated models trained with multiple language registers. A manually annotated dataset with a variety of different pharmaceutical and biomedical entities was created and is made available to the research community. Our results show that models that combine different registers provide better maintainability, have higher robustness, and have similar or higher performance. Fractional stratified k-fold cross-validation allows the evaluation of training data sufficiency on the entity level.
Study of Sub-pixel Level Edge Extraction for Graphics Under Different Environmental Conditions

Edge features are an important feature in our study of graphics, so accurate edge detection is of great importance for image recognition, processing of images and applications in areas such as video surveillance. While the recognition accuracy of our research images often only stays at the pixel level, there is still a lot of room for exploring the sub-pixel level edge extraction of images. In this paper, we compare the extraction principles of various mainstream operators and finally apply the Devernay algorithm based on the Canny operator. Using Gaussian filtering and Gaussian bilateral filtering for image pre-processing, combined with a reasonable threshold setting for the distance between the line segment set and the next detected line segment set and the double thresholding method, completes the sub-pixel level edge extraction for images in various environments, which improves the extraction clarity by more than 40%.
Depth cue enhancement and guidance network for RGB-D salient object detection

Depth maps have been proven profitable to provide supplements for salient object detection in recent years. However, most RGB-D salient object detection approaches ignore that there are usually low-quality depth maps, which will inevitably result in unsatisfactory results. In this paper, we propose a depth cue enhancement and guidance network (DEGNet) for RGB-D salient object detection by exploring the depth quality enhancement and utilizing the depth cue guidance to generate predictions with highlighted objects and suppressed backgrounds. Specifically, a depth cue enhancement module is designed to generate high-quality depth maps by enhancing the contrast between the foreground and the background. Then considering the different characteristics of unimodal RGB and depth features, we use different feature enhancement strategies to strengthen the representation capability of side-output unimodal features. Moreover, we propose a depth-guided feature fusion module to excavate depth cues provided by the depth stream to guide the fusion of multi-modal features by fully making use of different modal properties, thus generating discriminative cross-modal features. Besides, we aggregate cross-modal features at different levels to obtain the final prediction by adopting a pyramid feature shrinking structure. Experimental results on six benchmark datasets demonstrate that the proposed network DEGNet outperforms 17 state-of-the-art methods.
Investing in Information Systems Security Under Asymmetric Information Sharing

When firms are in an information sharing alliance, the amount of information that the firms provide may vary due to multiple factors, such as size, technology investment capacity, and the information security environment for the firm.Hence, in many cases even the well-intended firms are unable to share the same level of information as they receive from other firms.We refer to such a situation as asymmetric information sharing.Given this setting, information sharing alliances are confronted with the challenges of maintaining a fair information sharing environment, which would ensure proportional returns for firms in the alliance.In the current practice, firms are typically charged a membership fee by Information Sharing and Analysis Centers for participating in information sharing activities.However, the application of a membership fee does not totally address the problems that may arise due to asymmetric information sharing.First, the level of membership fee being charged does not reflect the level of information provided or received by individual firms.Second, no monetary compensation to firms that share more information is provided, which forms a barrier for continuous information sharing.Lastly, the membership fees for ISACs do not consider the willingness to pay attitudes of firms that may prefer to purchase information from other firms.In this study we aim to address these issues by seeking answers to the following research questions: (1) What fair price should a firm pay participating information sharing in asymmetric sharing environment?and, (2) How would the price of information vary under different pricing strategies and other influencing factors?To this end, we develop analytical expressions of a firm's payoffs under an asymmetric information sharing environment.We also analyze the pricing of information as a function of a firm's technology investment level, its information sharing level, and the marginal cost of information sharing.
Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems

To get around the drawbacks of conventional classification algorithms that required manual feature extraction and the high computational cost of neural networks, this paper introduces a deep convolutional neural network with multiple instance learning approaches, namely dynamic max pooling and sparse representation. For the categorization of tuberculosis lung illness, this model combines deep convolutional neural networks and multiple instance learning. The design was composed of four phases: pre-processing, instance production, feature extraction, and classification. To perform feature extraction, a model based on a customized version of the VGG16 architecture was trained from scratch. Multiple instance learning techniques such as Diverse Density (DD) and the Maximum pattern bag formulation of the Support Vector Machine were used to evaluate how well the proposed classification algorithm performed in comparison (SVM).The numerical findings demonstrated that the new method offered a higher level of accuracy than the methods that had been used in the past. When evaluating the efficacy of the current method, accuracy, specificity, sensitivity, and error rate were all taken into consideration. The accuracy of the max-pooling based framework and the sparse representation framework was found to be greater than that of the other multiple instance strategies, coming in at 91.51% and 89.84%, respectively, when compared to that of the other methods. The improved accuracy of the present system that makes use of deep neural networks is mostly attributable to the contributions made by features such as transfer learning and automatic feature extraction.
Bitcoin Works In Practice, But Does It Work In Theory?

Hinzen, John, and Salah (2022) argue that the design of the Bitcoin protocol results in a negative network effect. In their view, additional users create network congestion and settlement delays that discourage adoption. This is an interesting theoretical result, but rests on faulty assumptions about how bitcoin actually works. In particular, the authors fundamentally misunderstand how Bitcoin achieves consensus and how the entry and exit of miners affects the timing of new transaction blocks. The authors also ignore existing, widely-implemented scaling solutions.
Preface to the Special Issue “Mathematical Optimization and Evolutionary Algorithms with Applications”

It is recognized that many real-world problems can be interpreted and formulated as optimization problems [...]
Study on Reduction of Machining Lead Time of Double Surface Machining Machine using FOM

The digital transformation of small and medium enterprises is necessary for the improvement of quality and productivity through the collection and analysis of manufacturing data and to improve delivery compliance for competitiveness. For example, it implements a smart factory operation management solution by analyzing data to reduce long lead times in machining processes and efficiently operate machines in manufacturing large vacuum chambers. The improvement effect is improved after the loss reduction for the long processing lead time of the processing machine. Therefore, many processing companies that manufacture small quantities of small items must achieve the process optimization of custom-made products and continuously promote activities to improve productivity, cost reduction, the total lead time, and profitability through fast manufacturing-level smart factory data flexibility.
Comments on a dynamic cloud data integrity verification scheme: security analysis and improvement

To ensure the integrity and reliability of the data on cloud servers, many public data verification schemes have been presented to allow data auditors to remotely audit the data integrity without fetching the data back. Recently, Zhao et al. (2021) proposed a dynamic data integrity audition scheme for cloud computing based on the dynamic array multi-branch tree (MBT). In their scheme, each leaf node x stores a hash value H(x) which relates to all the hash values of blocks in its block array. Because the value H(x) is always used to generate the integrity proof when any block linked to x is challenged, the attacker can deceive the auditor by replacing the challenged block with another one in the same block array. To address this problem of Zhao's scheme, we add a new index i for each block in the block array, which can ensure the uniqueness of each block to resist the replacement attack. Moreover, the algorithms of label generation and proof verification in Zhao's scheme are also upgraded to keep the soundness of the scheme. We give the security proof of the new scheme and make comprehensive performance analyses showing that compared with the original algorithms in Zhao's scheme, only negligible costs such as hash are brought to the new ones.
Measuring Cyber Security Awareness: A Comparison between Computer Science and Media Science Students

Internet users who behave carelessly or badly increase the possibility of a security attack on the information systems they use. Although the technical security of devices is constantly improving, the human factor still represents a significant vulnerability in the system. Awareness of user misbehavior in cybersecurity allows sufficient security countermeasures to be taken. This study examines level of attitudes and awareness of university students towards cyber security. Based on quantitative research, the survey contrasts the attitudes of students in two disciplines - Computer Science and Media Studies - on a sample of 570 students. The results collected from the statistical analysis show similarities and differences in the responses of students in the two disciplines.
Is cyber security too hard?

The threat landscape is growing, attacks are becoming more frequent and more sophisticated and security budgets are being squeezed. Is information security becoming just too difficult for some organisations? In this interview, Srinivas Mukkamala, chief product officer at Ivanti, explains that there are ways to overcome these challenges.
Implementation of Cloud Computing Based on Infrastructure as a Service (IaaS) to Improve Transaction Quality (Case Study Shop of Central Mart Pekanbaru)

An virtually infinite number of connected information and communication technology (ICT) resources can be found using a method known as cloud computing. Customers can use these resources on-demand over a network in the form of a public IP because both infrastructure and applications are fully owned and managed by third parties.To enhance staff performance and services in the context of transactions made by parties engaged in the buying and selling industry, a computer-based system is required, particularly for cashiers who handle customer payment transactions. There are still a lot of cashier programs available today that can only be accessed via a device linked to the same network or over the local network.In order to facilitate transactions and enable remote control, this research makes use of cloud computing technology that employs Infrastructure as a Service (IaaS) offerings. IaaS is a service that "rents" out fundamental information technology resources, such as storage space, computing power, memory, operating systems, network capacity, and others, so that customers can use them to execute their applications.Azure gives developers access to tools like Visual Studio and the ability to construct applications in a variety of languages, including.NET, Java, and Node.js. Because businesses don't have to worry about the expense of server equipment, implementing cloud computing can make it simpler for them to manage their business apps and finances. The ability for store administrators to use this program remotely (online) may then be aided or made simpler by this IaaS solution.
Enhancing Handwritten Digit Recognition Through Convolutional Neural Networks: A Comprehensive Study

The development of a handwritten digit recognition system is the main subject of the discussion. In particular, the Convolution Neural Network (CNN) technique is used in the proposed topic. The MNIST dataset is used to create the model. The “Modified National Institute of Standards and Technology dataset” has 60,000 grayscale photographs, which are tiny squares, comprises of hand-written single digits between digit Zero and digit Nine and each measuring 28 by 28. Placing a handwritten digit picture among any one of ten classes corresponding to integer values from digit Zero to digit Nine, inclusively is the assignment here. The system employs a camera to take photos made up of images produced by the MNIST test data set and samples supplied by other authors. It then continually processes the images and updates the output every 0.5 seconds. Accuracy for top-performing models is typically 99.