Reviewerly/exHarmony huggingface dataset:
The Effect of the Relay Writing Method on Students' Narrative Writing Skills

This writing skill is very necessary for students, because in writing activities students can develop intelligence, and writing also encourages habits as well as fostering the ability to find, collect, and organize information. The formulation of the research problem is whether there is an effect of the relay writing method on the narrative writing skills of grade III students of SDN Balonggarut? How much influence does the relay writing method have on the writing skills of grade III students of SDN Balonggarut? This type of research is quantitative using saturated sampling. The results of this study indicate that there is an effect of the relay writing method on the narrative essay writing skills of students of SDN Balonggarut proven by testing using SPSS 18 with a t count of 2.492 while the t tabel with t is 0.05 of 1.813 and the relay writing method has a major influence on skills. writing narrative essays for third grade students of SDN Balonggarut as proven by the calculation of eta squared with the results of 0.383&gt; 0.14 can be said to have a big effect.
A Novel Video Quality Evaluation Method Based on Coarse-to-fine Strategy in Traffic Scenario

Video Quality Evaluation (VQE) is one of the key technologies for traffic monitoring system since the massive traffic monitoring may produce various distortions in the process of video recording, compression and transmission, which may cause the degradation of the monitoring picture quality. In literature, most of the traditional VQE methods are based on the pixel level, which indicates a limited number of video quality issues. However, the complexity generated by the constant changes of video images in actual scenes can easily lead to misjudgment and omission of this technology. In view of this, current deep learning methods propose to use object detection to solve it. However, since the features of deep learning mainly behave as problems, the performance is limited when there are non-quality targets that approximate the learned texture. Due to the openness of video quality issues, that is, there are various quality situations, the lack of datasets is also the focus of current research. To this end, this study constructs a dataset that can cover a variety of common video quality issues based on traffic monitoring, and proposes a coarse-to-fine quality evaluation method around this dataset. The method is mainly composed of a quality detection module and a quality judgment module. Experimental results show good performance on our proposed dataset.
Performance Comparison of Seven Pretrained Models on a text classification task

Deep-learning-based models are making a great influence in NLP tasks, especially the Text Classification task. Since there are already many existing pretrained models for this task, this paper chose seven representative models to test their performance on a certain benchmark dataset. Original sentences are tokenized and fed into the pretrained models to get their embeddings. These embeddings then are used as input of four simple 3-layer neural networks to get the classification result. We found that the structure of ELECTRA attached with ANN achieved the best performance among all the models we compared.
Online Try-On: GANs and TPS

Style transfer between images has been a research direction gaining considerable attention in the field of image generation. CycleGAN is widely used because it does not require paired image data to train, which greatly reduces the cost of collecting data. In 2018, based on CycleGAN, a new model structure, InstaGAN, was proposed and then applied in the style transfer algorithm in the special part of an image we called instance. From then on, style transfer can transform the instance in the image. Based on CycleGAN and InstaGAN, we transformed the pictures in different domains combined with shape context and thin plate splines (TPS) in the present study. Based on generative adversarial networks (GANs), we designed a fusion network to optimize the results. We combined style transfer with TPS in fashion and got convincing performance by experiments and a fusion net with good performance.
Improving Sequential Recommendation via Subsequence Extraction

The temporal order of user behaviors, which implies the user's preference in the near future, plays a key role in sequential recommendation systems. To capture such patterns from user behavior sequences, many recent works borrow ideas from language models and consider it a next item prediction problem. It is reasonable, but the gap between the user behavior data and the text data is ignored. Generally speaking, user behaviors are more arbitrary than sentences in natural languages. A behavior sequence usually carries multiple intentions, and the exact order does not matter a lot. But a sentence in a text tends to express one meaning and different orders of the words may bring very different meanings. To address these issues, this study considers user behavior as a mixture of multiple subsequences. Specifically, we introduce a subsequence extraction module, which assigns the items in a sequence into different subsequences, with respect to their relationship. Then these subsequences are fed into the downstream sequence model, from which we obtain several user representations. To train the whole system in an end-to-end manner, we design a new training strategy where only the user representation near the target item gets supervised. To verify the effectiveness of our method, we conduct extensive experiments on four public datasets. It is compared with several baselines and achieves better results in most cases. Further experiments explore the properties of our model and we also visualize the result of the subsequence extraction.
Physical Activity Recognition using Deep Transfer Learning with Convolutional Neural Networks

Current wearable devices are capable of monitoring various health indicators as well as fitness and/or physical activity types. However, even on the latest models of many wearable devices, users need to manually enter the type of work-out or physical activity they are performing. In order to automate real-time physical activity recognition, in this study, we develop a deep transfer learning-based physical activity recognition framework using acceleration data acquired through inertial measurement units (IMUs). Towards this goal, we modify a pre-trained version of the GoogLeNet convolutional neural network and fine-tune it with data from IMUs. To make IMU data compatible with GoogLeNet, we propose three novel data transform approaches based on continuous wavelet transform: Horizontal Concatenation (HC), Acceleration-Magnitude (AM), and Pixelwise Axes-Averaging (PA). We evaluate the performance of our approaches using the real-world PAMAP2 dataset. The three approaches result in 0.93, 0.95 and 0.98 validation accuracy and 0.75, 0.85 and 0.91 test accuracy, respectively. The PA approach yields the highest weighted F1 score (0.91) and activity-specific true positive ratios. Overall, our methods and results show that accurate real-time physical activity recognition can be achieved using transfer learning and convolutional neural networks.
Determining Object Color in Images Using Color Thresh-Holding Approach

In this article, we proposed a singular technique to hit upon the user-decided on color from an RGB photo the usage of color thresh-conserving technique. Our proposed technique use the color variations of low- and excessive-color pixel values and the usage of this distinction we expand a color masks photo. This ensuing color masks photo is then implemented at the authentic photo and executed bitwise AND operation to hit upon the photo vicinity having identical color location as of the distinction of the excessive and occasional stage pixel values. All this method is began out from changing the RGB to grey scale photo and the usage of median clear out to compress the noisy data. The proposed technique is applied in Python surroundings giving promising results. The proposed technique is similarly relevant in some of photo processing packages namely, the robots utilized in spy-associated activities, multi-item tracking, separation of the gadgets from the photo historical past primarily based totally at the color information, intrusion detection etc.
The Complexity of the Co-occurrence Problem

Let S be a string of length n over an alphabet $$\varSigma $$ and let Q be a subset of $$\varSigma $$ of size $$q \ge 2$$ . The co-occurrence problem is to construct a compact data structure that supports the following query: given an integer w return the number of length-w substrings of S that contain each character of Q at least once. This is a natural string problem with applications to, e.g., data mining, natural language processing, and DNA analysis. The state of the art is an $$O(\sqrt{nq})$$ space data structure that—with some minor additions—supports queries in $$O(\log \log n)$$ time [CPM 2021]. Our contributions are as follows. Firstly, we analyze the problem in terms of a new, natural parameter d, giving a simple data structure that uses O(d) space and supports queries in $$O(\log \log n)$$ time. The preprocessing algorithm does a single pass over S, runs in expected O(n) time, and uses $$O(d + q)$$ space in addition to the input. Furthermore, we show that O(d) space is optimal and that $$O(\log \log n)$$ -time queries are optimal given optimal space. Secondly, we bound $$d = O(\sqrt{nq})$$ , giving clean bounds in terms of n and q that match the state of the art. Furthermore, we prove that $$\varOmega (\sqrt{nq})$$ bits of space is necessary in the worst case, meaning that the $$O(\sqrt{nq})$$ upper bound is tight to within polylogarithmic factors. All of our results are based on simple and intuitive combinatorial ideas that simplify the state of the art.
Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.
Inclusive interaction in working with children with special learning needs

The education of children with special needs is one of the main tasks for the country. This is a necessary condition for creating a truly inclusive society, where everyone can feel the involvement and relevance of their actions. We are obliged to give every child the opportunity, regardless of their needs and other circumstances, to fully realize their potential, to benefit society and become a full-fledged member of it. The purpose of the article is an applied analysis of working with people with special learning needs. The leading method for studying this problem is the design method, which allows us to consider this problem as a purposeful and organized process for improving professional and special competencies. The structure of the presented work includes target, content, organizational-procedural and effective components. Taking into account the results of this study, we can identify a number of scientific problems and promising areas that require further consideration: deepening and expanding some of the provisions set out in the article related to the formation and accumulation of psychological and pedagogical experience.
Understanding Document Data Sources Using Ontologies with Referring Expressions

We show how JSON documents can be abstracted as concept descriptions in an appropriate Description Logic (DL). This representation allows the use of a DL ontology, which includes naming conventions ("referring expression types (RETs)") for instances of certain primitive concepts, in order to locate (perhaps multiple) subdocuments of the original JSON document capturing information about some particular conceptual entity. Detecting such situations allows for normalizing the JSON document into several separate smaller documents that capture all information about each such conceptual entity. This transformation preserves all the original information present in the input document. The RET assignment enables more refined and normalized capture of documents, and lead to query answers that adhere better to user expectations. We also show how RETs allow checking for a document admissibility condition ensuring that each final subdocument describes a single conceptual entity.
Multi-view Pre-trained Model for Code Vulnerability Identification

Vulnerability identification is crucial for cyber security in the software-related industry. Early identification methods require significant manual efforts in crafting features or annotating vulnerable code. Although the recent pre-trained models alleviate this issue, they overlook the multiple rich structural information contained in the code itself. In this paper, we propose a novel Multi-View Pre-Trained Model (MV-PTM) that encodes both sequential and multi-type structural information of the source code and uses contrastive learning to enhance code representations. The experiments conducted on two public datasets demonstrate the superiority of MV-PTM. In particular, MV-PTM improves GraphCodeBERT by 3.36% on average in terms of F1 score.
Fuzzy Kernel Extreme Learning Machine for Face Recognition

Kernel Extreme Learning Machine (KELM) provides deterministic solution to pattern recognition problems in non-iterative manner. On contrary to ELM, the feature mapping is implicitly accomplished in KELM by practicing kernel matrix function. The performance of a learning technique relies on the capability of feature vectors. Keeping this perspective, we have applied fuzzy concept for feature extraction to develop an enhancement of KELM named as Fuzzy KELM (FKELM). In the introduced technique, the capability of fuzzy set is utilized to obtain the association of pixel elements of facial images with distinct classes. The validity of presented technique is tested on face databases. The empirical results illustrates that the presented technique outperform ELM variants for multi-class classification problem.
Lightweight Vehicle Detection and Recognition Method Based on Improved YOLOv5 in SAR Images

Synthetic aperture radar (SAR) is very widely used in the military and civilian fields, but the target recognition and detection in the SAR images are relatively difficult. The existing deep learning methods for the SAR target detection and recognition has the problem of many model parameters and low efficiency, thus this paper has proposed a lightweight vehicle detection and recognition method based on the improved YOLOv5 in the SAR images. First, the lightweight processing of reducing the number of the channels is carried out, which can reduce many parameters and speed up the inference. Then, because the vehicles in the SAR image are relatively small and the detection head for detecting the large targets is redundant, the lightweight processing of removing the detection head is performed, which can shorten the running time of the proposed algorithm. Finally, the convolutional block attention module (CBAM) has been added to make the training set fit better when the network model parameters are limited, which can avoid the problem of the excessive decline in the detection and recognition performance after the network model is reduced in the parameters. The experimental results are shown to verify the correctness and the effectiveness of the proposed method.
Application Based Access Control for Remote RDBMS

To accelerate business growth, companies nowadays use not only data collected by themselves but also contracted data provided by other companies. Relational data base management systems (RDBMS) are widely used to control provided data at the provider side. However, there are problems in terms of safety and convenience when a data provider accepts access to relational database data from outside data consumers. From the viewpoint of data providers, RDBMS cannot identify which applications attempt to access whereas can identifying which users and applications with IP addresses attempt to access. In addition, data providers need to apply data filtering at row and/or column level to remove specified data such as personal records and this task is bothersome and error-prone. From the perspective of data consumers, remote access to RDBMS brings about increases in the response latency on the provider side. To solve these problems, we propose a mechanism for relational database access control, which is transparent to RDBMSs and their client applications. According to an access control policy, a filtering program linked with a relational database server removes unnecessary row and/or column elements before transferring them to clients on the provider side. Based on the policy and /proc file system, a proxy program controls access from clients at the granularity of applications on the consumer side. We build the prototype system on a container orchestration tool. The experimental results show that overheads due to our access control mechanism are small enough for practical use and our mechanism is scalable with moderate overhead in comparison with operations on relational databases without it.
Resource Management Scheduling-Based on Proximal Policy Optimization

Job scheduling solutions based on traditional heuristics are being severely challenged by the uncertainty and complexity of data center environments. There is an urgent need for data centers to adopt new techniques to optimize job scheduling. Job scheduling is a part of combinatorial optimization, most of which are considered as NP-hard problems. Previous work has effectively demonstrated that reinforcement learning (RL) is significantly effective in solving NP-hard problems. In this paper, we propose a reinforcement learning-based scheduling algorithm RMP to effectively solve different resource management problems. The model combines job scheduling with resource management system optimization, captures resource management models using convolutional neural networks, and makes scheduling decisions using proximal policy optimization (PPO). The results show that our proposed algorithm has faster convergence and better scheduling efficiency in terms of job slowdown compared with current deep reinforcement learning (DRL) algorithms DeepRM and other heuristics algorithms.
bisAUTH: A Blockchain-Inspired Secure Authentication Protocol for IoT Nodes

Some existing methods for authentication of IoT nodes are based on blockchain technology which has limitations that must be taken into account. In this paper, our contribution consists in proposing a new authentication protocol, named bisAUTH, for IoT objects inspired on certain characteristics of the blockchain technology. Our main objective is to propose a mechanism allowing neighboring IoT nodes of a network to authenticate in a decentralized and secure way. Evaluation of our proposed protocol against several criteria, its resistance to various attacks and its comparison with recent protocols, show that it brings significant improvements compared to the existing ones.
The importance of art and architecture regarding the idea that we have about a city

This article intends to make people reflect about the importance of artworks and architecture in the cities, as a way to turn them into memories and icons, regarding the idea we have about a city itself. The importance of new technologies and artificial intelligence have become a vehicle of dissemination of a certain image and attractiveness. The importance of communication in terms of a city image as a whole and of urban sites of specific cultural, artistic or historical interest. This is a contemporary subject, since tourism and the number of visitors to a city have practically disappeared or been suspended due to the current situation in the world. The crisis that has arisen with the emergence of the COVID-19 virus. A very important question arises now, when the pandemic ends, how to restore the attraction of people into certain historic and touristic places, which have meanwhile become deserted? During this period, art and traditional forms of artistic expression have been transformed. Digital and technological resources have been increasingly combined, thus giving rise to new forms of artistic expression combined with new technologies. We can see with great facility, in the most varied internet platforms, museum tours, virtual exhibitions, conferences and lectures. The city as it was known before the pandemic no longer exists. People now move around the city, silently or by car with their glasses tightly closed, they don't go to cultural places as they previously did, and they don't use restaurants in the same way. The city has become a place where people live inside and where they only travel outside for short, occasionally necessary journeys. Therefore, we are in a moment of convergence, a kind of turning of the page, where we will all learn again how to live and enjoy the city. In this article we relate the new paradigm of the increasingly digital city with the conventional city as we knew it. how can the most iconic places in cities help to make cities better known and more attractive? We did some research on some cities, and we tried to verify what were the main icons of these cities? In what way did those icons make them well-known?
Supporting Artificial Intelligence/Machine Learning Security Workers Through an Adversarial Techniques, Tools, and Common Knowledge Framework

This article focuses on supporting artificial intelligence (AI)/machine learning (ML) security workers. It presents AI/ML adversarial techniques, tools, and common knowledge (AI/ML ATT&CK) framework to enable AI/ML security workers to intuitively explore offensive and defensive tactics.
Sentiment Analysis-Based Categorized Opinions Expressed in Feedback Forums Using Deep Learning Technique and Message Queue Architecture

Sentiment analysis is a sub-field of natural language processing (NLP). In sentiment analysis the sentiment behind the piece of data is tried to know, this data can be a review of a product by a customer or a comment on some social media platform. Analysing large amounts of data is still an easy task for small retail websites and business owners. Deep learning (DL) has made a great revolution in the field of speech and image recognition. Mature deep learning neural network i.e. convolution neural network (CNN) has completely changed the field of NLP. This paper proposed a high accuracy, efficient, scalable, reliable and secure solution to cater all the needs of business owners and institutes for sentiment analysis with DL model, a browser based GUI interface for easy accessibility to all the non-technical folks and a dashboard having graphical representations of their results. The proposed sentiment analysis based model has achieved 93.55% accuracy which has outperformed other models.