Reviewerly/exHarmony huggingface dataset:
Regional Transformer for Image Super-Resolution

In the image super-resolution algorithm model, a large receptive field can provide more valuable features, so the Transformer with strong information interaction ability has achieved excellent results in image super-resolution processing applications. However, when the range of the receptive field reaches a certain critical value, the restoration performance of the super-resolution algorithm also reaches a certain critical value, which indicates that unconditionally increasing the receptive field will not continue to promote the improvement of the restoration performance. At the same time, the larger the receptive field range, the more data the model needs to process, which also seriously increases the computational complexity of the algorithm. In order to exchange information in a wider range more effectively, in this paper, a new type of super-resolution network based on Transformer, namely Regional Transformer, is designed. The key element in the newly designed network structure is the Region Block (RB) with the Boundary Restriction (BR) mechanism. In addition, the paper designs a Boundary Restriction based on coarse-to-fine pipes. This paper conducts a large number of experiments on multiple datasets, and the experiments show that the network structure designed in this paper has a significant improvement in performance.
Data Analysis and Prediction Based on Video Platform

Nowadays, there are video platforms emerging and data virtualization and prediction on those platforms draw little attention. Related research on it is beneficial for both content creators and advertisers as it helps decision-making. In this study, a video platform in China called Bilibili was selected. This paper acquired the dataset from a number of top-rated video uploaders in their field from a data share website and preprocess them to combine them for each month. For each frame of the data, the number of likes, shares, comments, archives, and bullet charts was used to predict the number of views. In the analysis, this study mainly used random forest and sklearn to achieve the prediction. 368 data points were utilized, and in each data point the amount of favorite, share, save and play are combined to make the prediction. The prediction that is made by using random forest is much more realistic than that made by using sklearn because some of the statistics in the sklearn graph reach negative, which is not realistic in the real situation. According to the experimental results, it can be found that the play rate has a positive relationship with the amount of favorite which is the most important factor in play rate. The amount of share is the secondary factor, and the least effective factor is the amount of collecting.
Design of Chinese Speech Intelligibility Evaluation System Based on Machine Learning Algorithm

With the acceleration of globalization and the popularization of computer technique, the links between different parts of the world are getting closer and closer, and the translation between different languages has become a key research topic of domestic and foreign scholars. Speech recognition is to identify the original speech signal into the corresponding text or other forms of message that can be processed by the computer. Speech recognition technique is an important research direction in the domain of artificial intelligence, which has high research value and commercial value. The method adopted in this paper is machine learning algorithm, and the deep learning technique among machine learning algorithms is selected. In this paper, the influence of high-order harmonic distortion, high frequency and mixed elements on the quality of speech signals caused by human vocal organs and speech signal acquisition equipment is studied. Windowing and framing are performed, so that the whole speech waveform will be divided into many small speech segments with overlapping 25ms, and then appropriate acoustic feature extraction algorithm is used to extract the corresponding acoustic features from the 25ms speech segments. After research, this method ameliorates the efficiency of speech recognition, which is higher than that of traditional methods.
Reducing QoE Loss and Data Wastage of Short-form Video Streaming with HTTP/3 Prioritization

With the introduction of the novel HTTP/3 prioritization scheme, the video player can conveniently assign a different priority for every download of a video segment in order to allocate suitable bandwidths for them. This work applies such a mechanism to reduce the QoE loss and data wastage of mobile short-form video streaming. Specifically, the proposed method buffers the viewing video and prebuffers (i.e., buffer in advance) pending videos simultaneously with respect to an optimized threshold. Such a threshold is based on an optimization of minimizing the risk of QoE loss caused by playback stalls and the risk of data wastage caused by downloading never-watched content. In order to prevent the prebuffering process from deteriorating the bandwidth of the buffering process, the prebuffering process is assigned the lowest priority and the buffering process is assigned the highest priority. Through evaluation, the proposed method is proven to provide the least QoE loss, data wastage, and a very balanced trade-off between them. Moreover, the crucial role of HTTP/3 prioritization in preventing bandwidth drop and avoiding playback stalls is clearly shown.
Mean estimation of electricity consumption data based on PID and local differential privacy

In the collection and analysis of electricity consumption data, malicious attacks from untrusted third party are prone to incomplete protection of user privacy. However, the periodic and correlation of the electricity consumption data will be ignored with direct random perturbation under local differential privacy, causing the potential for leakage of user life details. Therefore, this paper proposed a mean estimation method for electricity consumption data based on proportion-integral-derivative (PID) and local differential privacy. Firstly, the data was divided into a smooth period and a peak period; Secondly, the classified data was sampled with PID updating the sampling period. Finally, the sampled data was randomly perturbed by the Piecewise Mechanism (PM) and aggregated for mean estimation. The theoretical analysis shows that the method satisfies the local differential privacy. The experimental results demonstrate that the mean estimation error can be controlled within 0.008. As the privacy budget increases, the error becomes smaller and can reach 0.0001 when the privacy budget is maximum with better data utility.
Usability Evaluation of Communication Tools Used as Remote Support in Machine Service Industry: A Comparison between Microsoft Teams and Skype

Due to pandemic Covid-19, there is an increase and rough transition into remote work. Communication tools/platforms are softwares used by industries to facilitate different functionalities like remote technical support and communication. Especially for service industries, coordinators and engineers adapted on using these tools to support internal and external communications. For a certain machine service company, Skype and Microsoft Teams are the main communication tools used. The objective of this study is to determine the usability/user experience of these tools of engineers and coordinators on their daily activities. System Usability Scale were used to evaluate their user experience. As interpreted using the Curved Grading Scale, only the use of Microsoft Teams of Coordinators is graded as D, and the rest are graded as F. Based on the results on the user experience of coordinators and engineers using platforms Microsoft Teams and Skype, the low scores show that they are struggling with the usability of these tools due to drastic change and adaptability and limitation of activities that these tools can only perform.
SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation

Converting text into the structured query language (Text2SQL) is a research hotspot in the field of natural language processing (NLP), which has broad application prospects. In the era of big data, the use of databases has penetrated all walks of life, in which the collected data is large in scale, diverse in variety, and wide in scope, making the data query cumbersome and inefficient, and putting forward higher requirements for the Text2SQL model. In practical applications, the current mainstream end-to-end Text2SQL model is not only difficult to build due to its complex structure and high requirements for training data, but also difficult to adjust due to massive parameters. In addition, the accuracy of the model is hard to achieve the desired result. Based on this, this paper proposes a pipelined Text2SQL method: SPSQL. This method disassembles the Text2SQL task into four subtasks——table selection, column selection, SQL generation, and value filling, which can be converted into a text classification problem, a sequence labeling problem, and two text generation problems, respectively. Then, we construct data formats of different subtasks based on existing data and improve the accuracy of the overall model by improving the accuracy of each submodel. We also use the named entity recognition module and data augmentation to optimize the overall model. We construct the dataset based on the marketing business data of the State Grid Corporation of China. Experiments demonstrate our proposed method achieves the best performance compared with the end-to-end method and other pipeline methods.
Sports Video Image Analysis Application Based on BP Neural Network Algorithm

Human motion analysis is one of the main directions in the field of computer vision in recent years. Especially in the emerging field of competitive sports analysis. The purpose of this paper is to study the application of sports video image analysis based on BP neural network algorithm. The node number of input layer, output layer and hidden layer of BP neural network classifier is selected to establish the neural network classifier model. Select parameters such as learning rate and convergence error. The fusion features of LBP histogram reflecting motion behavior and motion speed are taken as the input of BP neural network, and the type of motion behavior is taken as the output of the neural network. Through experimental research, it is concluded that the sports behavior recognition algorithm proposed in this paper can achieve good results.
Research on the Topic Content Prediction of Online Reviews: from the Perspective of Bi-directional Sentiment Classification

To solve the problem of coarse-grained results in the research of topic content prediction, this paper proposes a prediction method for the topic content from the perspective of bi-directional sentiment classification. Firstly, the method uses SnowNLP to classify the sentiment of online reviews; secondly, LDA model is applied to extract the topics and entropy is used to sort topics; finally, Word2Vec is applied to achieve the prediction of the topic content. Example calculation shows that this method effectively solves the problem of coarse-grained prediction results of online reviews’ topic content, and presents the prediction results from positive and negative sentiments. The average precision of positive topics is 86.67%, and the average precision of negative topics is 80.00%.
Basic Research on Machine Vision Underpinned by Image Frame Algebra (VFA) and Visual Semantic Algebra (VSA)

Computer vison [1], [2], [3], [4], [5] studies properties of machine vision, its semantic understanding, and general manipulations by Intelligent Mathematics (IM) [6], [7], [8], [9], [10] [11], [12], [13], [14], [15] [16], [17]. Computer vison has been studies from various aspects such as algorithmic methods, analysis methods, pattern recognitions, and neural-network-regression (AI) technologies [2], [3]. However, there is a lack of fundamental theories for enabling autonomous image recognition and processing by machines. Basic research on contemporary IM has revealed that formal manipulations of visual objects by intelligent machines may be rigorously implemented by Image Frame Algebra (IFA) [8], [18] in the front-end and Visual Semantic Algebra (VSA) [19] in the backend. IFA formally manipulates visual images as general 2D matrixes by a set of algebraic operators such as modeling, analyses, syntheses, feature elicitation, and pattern recognition [4], [5], [18]. Then, its counterpart, VSA, transforms the geographic relations of visual objects to their semantic interpretations by algebraic analyses and compositions. The coherent theory of IFA and VSA provides a formal methodology for machine-enabled image processing and comprehension. This keynote presents a theoretical framework of machine vision underpinned by IFA and VSA for the structural denotations of visual objects and functional manipulations of visual mechanisms [3], [8], [9]. It demonstrates how the persistent challenges to machine vision may be rigorously and efficiently solved by the IFA/VSA methodology. Case studies on applying IFA/VSA for rigorous visual pattern detection, recognition, analysis, and composition in real world will be demonstrated [5], [18], [20]. As two coherent paradigms of IM, among others [21], [22], [23], [24], [25] [26], [27], [28], [29], [30], IFA and VSA have been applied not only in robot visual and spatial reasoning, but also in computational intelligence and AI for rigorously representing and manipulating of visual objects and patterns by machine recognition and cognition [31], [32], [33], [34], [35] [36], [37], [38], [39], [40], [41], [42], [43], [44], [45] [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65] [66], [67], [68], [69], [70], [71], [72], [73], [74], [75] [76].
A Novel Cross Grouping CG MLP based on local mechanism

Recently, Google proposed the MLP-Mixer – a simple multi-layer fully connected network, proving that convolutional and attention mechanisms are not irreplaceable. Although MLP-Mixer is simple, training it requires a lot of resources. In this paper, a network model——Cross Grouping MLP(CG MLP) based on local mechanism is proposed. The CG MLP module is a general visual task backbone that replaces the original MLP’s spatial mixing module. CG MLP introduces vertical and horizontal bar grouping in different channels of feature map to extract local information. CG MLP also introduces pyramid structure. For the input image, this model reduces the computational complexity of MLP from the square of the area(the fourth power of the side length) to the third power of the side length. CG MLP with 64M parameters achieved 82.5% accuracy on Imagenet-1K, and it reaches the SOTA performance of MLP models.
Max-Pooling Based Self-Attention with Transformer for Speaker Verification

Transformer has become predominant in many natural language processing (NLP) tasks for its powerful long-term sequence processing abilities. As the central idea of Transformer, self-attention mechanism is originally proposed to model global information for textual sequences. However, discriminating acoustic feature sequences from different speakers mostly rely on local information, which makes Transformer less competitive in the speaker verification task. We alleviate this limitation with a max-pooling based self-attention mechanism to enlarge the receptive field of the attention heads thus to better capture local information. Besides, we also introduce and compare position-based and content-based self-attention mechanism to self-attention and explore different frame-level pooling methods for speaker embeddings. Experiments conducted on AISHEL-1 and LibriSpeech datasets demonstrate that the method we proposed accomplishes the most excellent performance with statistic attentive pooling (SAP) compared with the original Transformer baseline systems.
Automated Photomontage Generation with Neural Style Transfer

Applying relevant processing methods to an image to extract its style and generate a new image with target text is a complicated task and manually doing it is time-consuming. This paper illustrates the practical approach of extracting the style of an image and applying it to the content of another image by the method of neural style transfer (NST) in an automated way. This method is generally used for applying an artistic or abstract filter to the content image. Applying relevant processing methods to an image to extract its style and generate a new image with target text is a complicated task and manually doing it is time-consuming. This paper illustrates the practical approach of extracting the style of an image and applying it to the content of another image by the method of neural style transfer (NST) in an automated way. This method is generally used for applying an artistic or abstract filter to the content image. The proposed work reciprocates the NST method for text images and is used for combining the text as content of one image to the style of another image. The proposed approach is supplemented with experimentation carried out using VGG-19 pre-trained over the ImageNet dataset. The implementation incorporates the transfer learning approach for the neural style transfer (NST) method. Content and style loss have been also analyzed for some sample styles and contents.
Resource-Based Prediction in Cloud Computing Using LSTM with Autoencoders

Cloud computing is currently the most dynamic and evolutionary segment in our computational world. Since it is widely deployed by many clients from across the industry, the cloud service providers need to keep the Quality of Service (QoS) up to the Service level Agreements (SLA). Therefore, proper management of its resource functioning is the key objective to make profit as well as to meet the client’s optimal amount of services needed to complete a job/task. In this paper we predict one of the most important resources required for the smooth functioning of the jobs with one of its kind LSTM architectures. The objective is obtaining better fidelity in less time with the real-world Google Cluster Trace Dataset 2011. For data preprocessing and extracting the essential features from the original high-dimensional workload data, we use a custom autoencoder. Long short-term memory (LSTM) is used as our deep learning algorithm to facilitate the highly variable time series workload prediction for all the jobs/tasks based on certain prediction lengths like per hour & day. For validating we compare with the previously used approaches of deep learning techniques using Facebook’s Prophet model and ARIMA models with the same dataset.
Defining the Convergence of Artificial Intelligence and Cyber Security with a Case Study of Email Spam Detection

In the current era where we deal with a lot of technological advancements, the cyber security frameworks should also be restructured with the help of various artificial intelligence (AI)-based algorithms. As we know currently, there are a number of security threats to the transmitted and stored data. Therefore, we cannot secure the data with manual security or loosely coupled algorithms. We need certain automatic and frequent checkups, which can enhance its security. AI is a term which can help us in achieving the same results as we expect. In this paper, we describe certain techniques, which can be used in the field of cyber security. The challenges of implementation of cyber security are highlighted. Further, we discuss the benefits of AI in the field of cyber security. We also provide the AI tools used in cyber security. We have considered the scenario of email spamming and presented a mechanism for the detection of spam emails. The presented scheme achieved better accuracy as compared to the other existing schemes.
A Hybrid Approach on Lexical Indian Sign Language Recognition

India is one of the countries with an increasing deaf population. The needs and problems of deaf communities are not adequately addressed. The majority of the previous approaches on Indian Sign Language (ISL) Recognition are based on convolutional neural networks (CNNs), whose performance depends on factors like lighting and the clothing of the sign language performer. The facial key points along with arm and body key points play a significant role in interpreting the ISL. The proposed approach identifies the facial key points and other body key points using the OpenPose model to generate pose video, which removes information unfavorable to CNN networks. The generated pose video is used by the pre-trained CNN model to extract spatial features and recurrent neural networks (RNNs) to extract temporal features necessary for ISL interpretation. We have used pre-trained models like Inception V3, DenseNet121 and MobileNetV2 to extract spatial features along with GRU, LSTM and bidirectional LSTM networks to extract the temporal features. Our model using the pre-trained MobileNetV2 model combined with GRU gave an accuracy of 99%. We also extended our approach using a combination of pose videos with background information.
Enhancing DevOps Using Intelligent Techniques

Change is an inevitable part of any business. Customer satisfaction and building good will is the primary goal. The real success lies in the above two factors rather than money. Different businesses operate in different ways. Each one focuses on a different set of criteria and thus follows a different set of models. There are various models in the software development life cycle, such as the waterfall model, spiral model, V-model, and so on. These models have advantages and disadvantages and aid in the improvement of a company's workforce. They overcome the disadvantages of the previous model with each model. DevOps is the most recent model that is widely used. This chapter deals with DevOps, including the need, working, and how it differs from other models. This also looks into how intelligent techniques can be used to enhance the DevOps process for better productivity in the businesses (i.e., AIOps). It summarizes the different phases in DevOps, the corresponding machine learning or artificial algorithms that can be applied in the phases.
Герой эпохи новой искренности: попытка культурологического, психологического и литературоведческого анализа монодрам Евгения Гришковца

The paper presents a study of the nature of the experiencing expression in monodramas by E. Grishkovets. This analysis is based on the idea of the human consciousness layering and the hierarchical principle of the organization of “mental spaces” in it, which was considered by F. E. Vasilyuk in relation to experiencing and its expression. The ideas about the work of experiencing as a movement between different life worlds, which are reflected in the monodramas by Grishkovets, are studied in the paper as a feature of the writer’s idiostyle. The narrator of the one-man theatrical shows is considered as a literary hero of the “New Sincerity” era, which replaced the period of postmodern literature. The article traces the features of the experiencing expression in the monodramas by Grishkovets, the associative nature of the author’s consciousness slipping into other life worlds, and the ways of creating (virtualizing) these life worlds.