Reviewerly/exHarmony huggingface dataset:
Deep Learning Technology to Improve the Coding Efficiency of H.266/VVC

H.266/VVC modifies the quadtree structure of HEVC and adopts the Quadtree with nested multi-type tree (QT-MTT) encoding structure to search for the best encoding unit. Although the QT-MTT encoding structure has better encoding efficiency, it also increases the computational complexity and encoding time. This paper mainly focuses on the QT-MTT structure of H.266/VVC intra-frame coding and proposes the use of convolutional neural networks (CNNs) based on deep learning to prematurely terminate the decision of the horizontal binary tree, horizontal ternary tree, vertical binary tree, or vertical ternary tree of $32\times 32$ coding units, and skip the rate distortion optimization (RDO) step to save encoding time of H.266/VVC. Experiments show that this paper only approximately increases BDBR by 0.45 dB, but can reduce% of encoding time.
Exploring Generative Adversarial Networks for Photo-to-Anime Image-to-Image Translation: A Comparative Study

Generative Adversarial Networks (GAN) is a generative modeling approach with the ability to learn highly complex data. Particularly, they don’t require immediate pairing between the data in input and output domains. This property makes it ideal for image translation tasks. Image translation from photo image into anime style images using GAN is a fast and efficient way to generate art for creative industries. Recently, some algorithms such as U-GAT-IT, CycleGAN, AnimeGAN, and CartoonGAN emerged as few algorithms to accomplish this particular task. The purpose of this paper is to compare the performance of these algorithms in photo-to-anime styled image-to-image translation and discuss the results these algorithms in image-to-image translation task between photo image domain into anime image domain.
Apply Masked-attention Mask Transformer to Instance Segmentation in Pathology Images

Instance segmentation can be applied for the discrimination and diagnosis of cancer cells in pathology images. Accurate segmentation of each pathological cell in the pathology images can improve the efficiency of clinical diagnosis. In this paper, we aim to evaluate the state-of-the-art transformer-based instance segmentation method, masked-attention mask transformer (Mask2Former)[1], on pathology datasets. With the pretrained model of Mask2Former on the natural image instance segmentation dataset, we show that Mask2Former can be adaptive to small pathological datasets and achieve comparable or even better instance segmentation performance compared with the state-of-the-art task-specific pathology image instance segmentation methods.
Applying Evolutionary Algorithms to Optimize Hyperparameters for Prediction Model of Solar Power Generation

Because of climate change and global warming, the demand for renewable energy grows continually. Among the renewable energy sources, solar power is the most common type due to its low construction cost and easy parallel connection with existing power grids. The power company can not only dispatch power but obtain better electricity price contracts by forecasting the power generation of photovoltaic panels. In the past, many studies have focused on the research of solar power generation, from statistical regression to mathematical planning models to heuristic meta methods and evolutionary algorithms. Recently, there are more and more literatures using machine learning to establish power generation forecasting models and even the deep learning model of artificial intelligence. However, research on hyperparameter optimization to make ensemble learning algorithms perform better is still scarce. This paper attempts to optimize the hyperparameters in the modeling process of ensemble learning with evolutionary algorithms and construct more accurate solar power prediction models. Gradient boosting regressor is employed as ensemble learning models where the hyperparameters are optimized by differential evolution, Jaya algorithm, particle swarm optimization and genetic algorithm for comparison. The data is based on practical data and weather forecasting data of solar power plants in central Taiwan. The computational results reveal that differential evolution outperforms to explore the optimal hyperparameter combination of the prediction model for solar power generation.
Aggregated Spatio-temporal MLP-Mixer for Violence Recognition in Video Clips

Existing violent behavior datasets are not perfect in quantity and quality due to the difficulty of collecting. Although the state-of-the-art Transformer models had shown their capability in behavior recognition, it is unsuitable for the task of short-term behavior understanding (e.g., violent behavior recognition) due to the need for a large amount of data to achieve their best performance. Recently, a simple deep learning architecture, an all multilayer perceptron (MLP) architecture called MLP-Mixer, was proposed against Transformer in the task of a few-sample dataset to obtain competitive results. Motivated by spatio-temporal features on neurons, we invent a dual-form dataset for MLP-Mixer-based model training called aggregated spatio-temporal MLP-Mixer (ASM) to handle video understanding tasks. We show that ASM outperforms the state-of-the-art Transformer models as well as some of the best-performed convolutional neural network (CNN) approaches on three public datasets, smart-city CCTV violence detection dataset (SCVD), real-life violence situations (RLVS) dataset, and Hockey fight. Experimental results further validate our idea on short-term behavior scene understanding improvement.
Overlapped Context Modeling Using Feature Mapping Functions in the Adaptive Arithmetic Coding Process for Lossless Encoding

Context modeling plays a critical role in the adaptive arithmetic coding process. It classifies the causal part into several classes according to the features extracted from the causal neighboring pixels. However, when the feature value is around the border of the ranges of two adjacent contexts, its corresponding probability model cannot be estimated accurately. In this paper, we propose an advanced way for context assignment. We make the contexts overlapped in both the training phase and the coding phase. With the proposed method, more than one context wm be assigned for each input data. Then, the probability model generated by weighted combination is applied to encode the input data. Then, the frequency table corresponds to the context whose range overlaps with the input data value wm be adjusted. Experimental results on lossless image coding show that, with the proposed algorithm, a high coding efficiency can be achieved.
A Collaboration Federated Learning Framework with a Grouping Scheme against Poisoning Attacks

Federated learning has been regarded as emerging machine learning framework due to its privacy protection. In the IoT trend, federated learning enables edge clients to predict or classify local detected data with a global model that is computed by a FL server through the aggregation of all local models trained by a base FL algorithm. However, meanwhile, its distributed nature also brings several security challenges. Poisoning attacks are the main security risks that can easily and efficiently affect the accuracy of the global learning model. Previous work proposed a voting strategy which can predict the label of the input robustly no matter the attacks the malicious users use. However, its accuracy also easily falls down as the number of malicious user increases while the number of groups is fixed. This paper proposes a new attack defense algorithm against poisoning attacks in federated learning. This paper uses ID-distribution features to group all clients, including normal and malicious ones. The main idea of this proposed scheme is to put those potential malicious clients in specified groups. Hence, the resulting vote output can accurately classify the dataset inputs, regardless of the number of the groups the learning framework has. Our analytical results also show that our scheme exactly perform better compared to original voting scheme.
Creation of Digital Virtual Human Based on GAN2Shape and Kinect 2.0

This study aimed at the problem of the low recognition rate of facial expressions cause by complex environments. Hence, the study examined, digital virtual humans through GAN2Shape and Kinect 2.0 to obtain high-quality three-dimensional (3D) images of human faces. First, we captured and recognized facial expressions through Kinect. Second, we generated high-quality 3D images of the facial expressions through GAN2Shape and transferred the images to Unity 3D for front and back-end development to generate a facial model. Third, we recognized and captured motions through Kinect. Finally, we combined the facial model with the motions to form the virtual human model. Experimental results show that the virtual human model generated through this method has a clear face and a good effect in motion capture.
Quantum-based Detection of Higly Semantically Similar Social bot

Social bot often publish content that has a high degree of similarity in text or semantics. Combining this feature, this paper designs a method to detect social bot. First, the user-published text is used as the input to use sentiment analysis and data cleaning to predict the content. Processing and classification, and then adding the quantum similarity algorithm in the emerging quantum discipline in recent years to the structural system of social bot detection to perform similarity clustering, which greatly improves the operating efficiency of the system, and then obtains the corresponding user attribute characteristics for artificial intelligence. Intelligent algorithm classification, on the collected real dataset, marked two social bot groups for machine learning classification and detection, and listed two traditional similarity algorithms for comparison, the results show that the quantum similarity results Compared with the two traditional similarity algorithms, it has improved, and the average accuracy improvement was about 2%.
PENDETEKSIAN KECURANGAN LAPORAN KEUANGAN DI TINJAU DARI TEKANAN WAKTU DAN INDEPENDENSI

This study aims to examine the effect of time pressure and independence on the ability to detect financial statement fraud. This research was conducted at an independent prosperous cooperative. The sampling technique used was purposive sampling with predetermined criteria. The data used in this study is primary data in the form of questionnaires distributed to respondents. 60 questionnaires were distributed, 48 questionnaires were returned and were feasible to use and could be processed in this study. The research method used is a quantitative research method. The data were obtained by distributing questionnaires and measured using a Likert scale. The data analysis method uses multiple linear regression analysis which is processed through SPSS software. Based on the analysis that has been carried out, the result is that time pressure and independence have a positive effect on the ability to detect fraudulent financial reports.
Recognition Algorithm of Basketball Dynamic Movement Behavior Based on Multimedia Network Technology

In order to realize the recognition of basketball's dynamic sports behavior in the dynamic scene of competition and training based on computer vision technology, combined with video data analysis, and improve the standardization of basketball's dynamic sports behavior, this paper puts forward a recognition method of basketball's dynamic sports behavior in the dynamic scene of competition and training based on multimedia network, and adopts the method of optical flow acquisition to collect RGB image data during the conversion process of basketball's dynamic sports behavior in the dynamic scene of competition and training. The collected optical flow field of basketball dynamic motion in the dynamic scene of competition and training is extracted with normative features, and the video parameters of basketball dynamic motion in the dynamic scene of competition and training are analyzed with the method of multimedia network technology video supervised learning. Under the constraint of dynamic basketball vision and optical flow field changing parameters, the temporal and spatial features extracted from basketball dynamic motion sequence are extracted by two-degree-of-freedom dynamic parameter model parameter learning. Combining the techniques of feature extraction, feature fusion and feature classification, this paper analyzes the optical flow trajectory parameters in the process of basketball dynamic movement behavior transformation, and realizes the dynamic parameter identification of basketball dynamic movement behavior in the dynamic scene of competition and training by combining the scene attributes and the distribution of time and space points of interest. The simulation results show that this method can effectively recognize the static and dynamic characteristics of basketball dynamic movement behavior in the dynamic scene of competition and training, and improve the dimension of basketball dynamic movement behavior recognition, thus effectively realizing the accurate detection and recognition of basketball dynamic movement behavior under complex background and multi-person interaction.
CONSEQUENCES OF PAYING A RANSOM WHILE RANSOMWARE ATTACK

The subject of this article is devoted to the actions taken by personnel of organizations that havebecome the target of a ransomware attack. It should be noted here that within the framework ofthe considerations undertaken, reference was made not to technical - IT undertakings aimed atunlocking the attacked system, but to the harmfulness of ransomware behavior, both in the area ofactivities of public entities and in the business environment. The methodology for thedevelopment of the article was based on the analysis of information on the phenomenon ofransomware, as well as on the ranking and analysis of the legislation governing the issue ofliability in the event of ransomware placement. The research problem of the manuscript wascontained in the question: what are the consequences for those who decide to transferransomware, as well as those who seek a way to finance the ransomware attack? At the same time,it was assumed that this is the responsibility of both those who decide to pay the ransom and thepersonnel who supervise the activities of the entities. It was also attempted to depict that theresponsibility for the ransomware decision arises both in the area of public entities and withingthe scope of companies prospering in the private economic sector.
ANALYSIS OF THE GENERALISABILITY OF THE PARKING TEST

In 1995, George Marsaglia published a collection of randomness tests , "DIE HARD". One of thetests included there was the ,,Parking Lot Test", which was designed around the problem ofrandomly placing hyperspheres in d-dimensional space. Designed in this way, the randomness testworked very well for small amounts of data and was able to detect randomness where other testshad failed. However, due to its statistical properties, it could not cope with longer bit strings.The main objective of the present study was to parameterise the test under investigation so that itcould be used to test longer pseudorandom strings. The statistical properties of the test withalready different parameters were established empirically by performing a series of tests onstrings generated by pseudorandom generators constructed from Trivium and LFSR ciphersencrypted with the AES-128-ECB algorithm.The work also included an analysis of the possibility of optimizing the computational andmemory performance of the test. As a result of the research, the parameters of the test wereselected to allow an increase in the amount of analyzed data, while maintaining the computationaland memory complexity that allows its practical application.This article is an abstract of a Master's thesis of the same title9, which received a distinction in the2nd edition of the competition for the Marian Rejewski Award for the best Master's thesis in thefield of cyber security and cryptology.
Representation Learning and NLP

Abstract Natural language processing (NLP) aims to build linguistic-specific programs for machines to understand and use human languages. Conventional NLP methods heavily rely on feature engineering to constitute semantic representations of text, requiring careful design and considerable expertise. Meanwhile, representation learning aims to automatically build informative representations of raw data for further application and achieves significant success in recent years. This chapter presents a brief introduction to representation learning, including its motivation, history, intellectual origins, and recent advances in both machine learning and NLP.
Knowledge Representation Learning and Knowledge-Guided NLP

Abstract Knowledge is an important characteristic of human intelligence and reflects the complexity of human languages. To this end, many efforts have been devoted to organizing various human knowledge to improve the ability of machines in language understanding, such as world knowledge, linguistic knowledge, commonsense knowledge, and domain knowledge. Starting from this chapter, our view turns to representing rich human knowledge and using knowledge representations to improve NLP models. In this chapter, taking world knowledge as an example, we present a general framework of organizing and utilizing knowledge, including knowledge representation learning, knowledge-guided NLP, and knowledge acquisition. For linguistic knowledge, commonsense knowledge, and domain knowledge, we will introduce them in detail in subsequent chapters considering their unique knowledge properties.
Sentence and Document Representation Learning

Abstract Sentence and document are high-level linguistic units of natural languages. Representation learning of sentences and documents remains a core and challenging task because many important applications of natural language processing (NLP) lie in understanding sentences and documents. This chapter first introduces symbolic methods to sentence and document representation learning. Then we extensively introduce neural network-based methods for the far-reaching language modeling task, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, and Transformers. Regarding the characteristics of a document consisting of multiple sentences, we particularly introduce memory-based and hierarchical approaches to document representation learning. Finally, we present representative applications of sentence and document representation, including text classification, sequence labeling, reading comprehension, question answering, information retrieval, and sequence-to-sequence generation.
Assessing Power Needs to Run a Workload with Quality of Service on Green Datacenters

Datacenters are an essential part of the internet, but their continuous development requires finding sustainable solutions to limit their impact on climate change. The Datazero2 project aims to design datacenters running solely on local renewable energy. In this paper, we tackle the problem of computing the minimum power demand to process a workload under quality of service constraint in a green datacenter. To solve this problem, we propose a binary search algorithm that requires the computation of machine configurations with maximum computing power. When machines are heterogeneous, we face the problem of choosing the machines and their DVFS state. A MILP (Mixed-Integer Linear Programming), to find the optimal solution, and four heuristics that give satisfactory results in a reasonable time are proposed. The bests reach an average deviation from the optimal solution of 0.03% to 0.65%.
FedCML: Federated Clustering Mutual Learning with non-IID Data

Federated learning (FL) enables multiple clients to collaboratively train deep learning models under the supervision of a centralized aggregator. Communicating or collecting the local private datasets from multiple edge clients is unauthorized and more vulnerable to training heterogeneity data threats. Despite the fact that numerous studies have been presented to solve this issue, we discover that deep learning models fail to attain good performance in specific tasks or scenarios. In this paper, we revisit the challenge and propose an efficient federated clustering mutual learning framework (FedCML) with an semi-supervised strategy that can avoid the need for the specific empirical parameter to be restricted. We conduct extensive experimental evaluations on two benchmark datasets, and thoroughly compare them to state-of-the-art studies. The results demonstrate the promising performance from FedCML, the accuracy of MNIST and CIFAR10 can be improved by $$0.53\%$$ and $$1.58\%$$ for non-IID to the utmost extent while ensuring optimal bandwidth efficiency ( $$4.69\times $$ and $$4.73\times $$ less than FedAvg/FeSem for the two datasets).
Extraction of Parallel Sentences

As explained in Chap. 1 and later developed in Chap. 6 , Machine Translation (MT) engines need to be trained with large numbers of parallel sentences or segments. The quantity and diversity of existing parallel text is limited however. This motivates the search for parallel sentences in comparable corpora. By exploring a larger share of the levels of comparability introduced in Sect. 1.2 , a much larger source of multilingual data can be obtained. Strongly comparable corpora such as Wikipedia entries [1, 62] or news text [2] are rife with parallel sentences and have been among the first to be explored.
The Polarizing Microscope

The central equipment in optical mineralogy is the polarizing microscope, also named as petrologic or petrographic microscope. It is essentially a compound microscope with provision for optical studies of minerals. By now, you have learnt basic principles of optical crystallography; hence, you are now capable of appreciating the special features that are added to a compound microscope.
Building Correlation Immune Functions from Sets of Mutually Orthogonal Cellular Automata

Correlation immune Boolean functions play an important role in the implementation of efficient masking countermeasures for side-channel attacks in cryptography. In this paper, we investigate a method to construct correlation immune functions through families of mutually orthogonal cellular automata (MOCA). First, we show that the orthogonal array (OA) associated to a family of MOCA can be expanded to a binary OA of strength at least 2. To prove this result, we exploit the characterization of MOCA in terms of orthogonal labelings on de Bruijn graphs. Then, we use the resulting binary OA to define the support of a second-order correlation immune function. Next, we perform some computational experiments to construct all such functions up to $$n=12$$ variables, and observe that their correlation immunity order is actually greater, always at least 3. We conclude by discussing how these results open up interesting perspectives for future research, with respect to the search of new correlation-immune functions and binary orthogonal arrays.