Reviewerly/exHarmony huggingface dataset:
Develop a Marathi Lemmatizer for Common Nouns and Simple Tenses of Verbs

IR-Information Retrieval! The whole world today is running on this concept—information retrieval. Of Course, data mining, data cleaning, data processing, etc. come hand in hand when we talk about retrieving any information. We are dealing with the text data and hence text processing. During the journey of reaching the root word (destination) from any given input word (source), we need to process that word and this process is called Stemming. As almost every process has different approaches to look at, even stemming has various approaches like truncating, statistical, hybrid, etc. In this paper, we have our focus on rule-based stemmers’ study. It follows the truncating approach. Marathi is written in Devanagari script and even being one of the top 10 languages in the world, playing with Marathi data is a less touched area than other foreign languages. In the proposed method, Marathi—(written script—Devanagari) one of the most spoken languages in India is used and common nouns and simple tenses have been explored for implementation. Variety of rule-based stemmers have been studied in this paper for various Devanagari scripts like Marathi, Hindi, and Kumaoni language. It is observed that a stemmer can get improved by defining new rules as per the standard specified grammar of the language selected.
Optimizing Super-Resolution Generative Adversarial Networks

Image super-resolution is an ill-posed problem because many possible high-resolution solutions exist for a single low resolution (LR) image. There are traditional methods to solve this problem, they are fast and straightforward, but they fail when the scale factor is high or there is noise in the data. With the development of machine learning algorithms, their application in this field is studied, and they perform better than traditional methods. Many Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) have been developed for this problem. The Super-Resolution Generative Adversarial Networks (SRGAN) have proved to be significant in this area. Although the SRGAN produces good results with 4 $$\times $$ upscaling, it has some shortcomings. This paper proposes an improved version of SRGAN with reduced computational complexity and training time. The proposed model achieved an PPSNR of 29.72 and SSIM value of 0.86. The proposed work outperforms most of the recently developed systems.
A Systematic Comparative Study of Handwritten Digit Recognition Techniques Based on CNN and Other Deep Networks

Recognition of handwritten digits has been a popular challenge among research due to its wide applicability in real-life applications. It has been a hard problem, and several efforts have been put to solve it. Deep learning (artificial neural networks)-based methods have proved to be very effective and promising method for handwritten digit classification lately. Most prominently convolutional neural network (CNN) has been deployed in deep networks-based techniques, whereas other deep networks like recurrent neural networks (RNN), generative adversarial networks (GAN), spiking neural networks (SNN), etc. have also resulted in competing accuracy. There are several standard datasets which are frequently used to train and test the networks. In this article, nine techniques based on CNN and six techniques based on other deep networks have been discussed and analyzed. The paper presents a detailed comparative study on the recent techniques based on artificial neural networks. This analysis talks about implementations of the techniques, hyper-parameters used, accuracy achieved, and standard datasets utilized by the technique. Accuracy is used as performance metric to compare the performances of above-mentioned techniques. The average accuracy of CNN-based techniques is above 98%, and average accuracy of other deep networks is above 97%.
Nature-Inspired Hybrid Virtual Machine Placement Approach in Cloud

Many nature-inspired, swarm intelligent, and hybrid algorithms have been designed to figure out an optimal virtual machine (VM) placement solution. In this paper, a hybridized intelligent water drop cycle algorithm (IWCDA) has been proposed that aims to achieve better resource utilization and minimum energy consumption in cloud datacenters. The algorithm works in two phases. First, intelligent water drop (IWD) is adapted to construct an efficient VM placement solution by using its unique heuristic function. In the second phase, the water cycle algorithm (WCA) is applied to bring out the best solution for the VM placement problem. At the end, the solutions of both phases are compared and the best one is taken as an optimal VM placement solution after undergoing several iterations. Energy and resource aware schemes are involved in selecting suitable physical machines (PM) for respective VM in both phases, respectively. The proposed work has been implemented on MATLAB, and simulation results have shown that IWDCA performed better as compared to IWD and WCA by 4% and 7%, respectively, in terms of energy consumption, number of active servers, and resource utilization rate.
Building a Product Recommender System Using Knowledge Graph Embedding and Graph Completion

The rise of data processing techniques has led to the flourishing of information technology, which has now become ever more powerful and accessible. This leads to the challenge of selecting pieces of information relevant to the user’s interests. Digital businesses have overcome this challenge using a subclass of information filtering systems known as recommender systems which aim to influence a user’s decision-making process by predicting their preferences. Traditional recommender systems work based on information filtering systems such as collaborative or content-based filtering. They face several challenges due to a lack of sufficient amounts of user pertinent data points. A knowledge graph (KG) allows for knowledge about data points and their interrelationships to be directly represented. As relevant external data about products like metadata is also readily available, a KG-based recommender system can use this extra information and is not heavily dependent on extensive user interaction. Moreover, by using a KG, the problem of heterogeneity of data sources can also be overcome. Hence, we aim to build a KG-based product recommender system. The dataset we have chosen is the Amazon product review dataset which has over 200 million individual data points about product reviews and product metadata. After turning this data into a KG, graph embeddings are generated, and a graph completion procedure using link prediction is performed to provide recommendations. The quality of the recommendations thus generated is evaluated using various measures, and real-time recommendations for specific users are demonstrated.
A Systematic Study of Application of Cognitive Intelligence in Mphasis – a Case Study

Introduction: The modern definition of intelligence is machines’ capacity to comprehend and adapt to surroundings employing innate skills and acquired knowledge. We have arguably reached a brand-new stage in the development of civilization. In recent years, the advancement of cognitive technology has accelerated rapidly. On many levels, their capabilities—both current and future—are very appealing. Their application in business is intended to save operating expenses while accelerating the rate of business development and improving the quality of services provided. Cognitive systems use logical human processes to interpret data in order to enable computers and robots to precisely mimic human actions, both in terms of repetitive, routine tasks and comprehensive procedures of completing a variety of tasks. Additionally, they carry out diagnostic and predicative tasks. They are used in the most recent technologies because they offer conclusions, shed light on processes, and offer automated actions based on data analysis and contextual human interactions. Since much of the labour done by employees is routine in nature, computers and algorithms may simply take over these activities. Information technology services and integrated solutions are offered by the Mphasis. It covers application services, infrastructure technology, and business process outsourcing. It adopts a customer-in view to transform organisations by utilising the sector-specific X2C2TM framework, which harnesses the power of cloud and cognitive to give hyper-personalized digital experiences to its customers. Background/Purpose: Mphasis is in the industry of: Business Services, Custom Software &amp; IT Services, Management Consulting - leading applied technology services, helping enterprises transform businesses globally by applying next-generation technology. Design/Methodology/Approach: Referred websites and articles related to Mphasis development, services, cognitive applications. Findings: With regard to sales, delivery, and development, Mphasis innovates to produce excellent customer service and successful results. In order to maintain its clients at the top of the heap in a market that is constantly changing, its goal is to be flexible, nimble, and customer-centric. There have been companies with significant AI-related infrastructure in India. IISc has organizations with driving organizations like Robert Bosch, Faurecia; IIIT Bangalore is in association with Mphasis and focus Cognitive Computing; IIT Kharagpur has made AI inquire about focus with Capillary Technologies. Originality: This paper tries to give an overview of how Mphasis focuses on cognitive intelligence applications and anticipates 0the future of applied technology and forecasts tomorrow's trends. Paper Type: A Research Case study paper on the use of cognitive intelligence in Mphasis
Structural learning of functional directed graphical models with incomplete signals

AbstractFunctional directed graph model (DGM) learning has been widely used to analyze complex systems. Most existing works assume that the functional signals are complete, which in reality is not true. To address this problem, in this study, a framework for DGM learning with incomplete signals is proposed. Specifically, a penalty term that integrates information from the graph structure is added to the Maximum Margin Matrix Factorization (MMMF) objective function. The proposed method can be used with a known structure to estimate the functional relationship between nodes or with an unknown structure to estimate the relationship together with the graph structure. Numerical experiments and a real-world case study of monocrystalline silicone manufacturing are performed to verify the effectiveness of the proposed method when the signal matrices are incomplete.Keywords: Functional directed graph modelsfunction-to-function regressiongraph learningmissing datasignal completion Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementDue to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.Additional informationFundingThis work was funded by the Key Program of National Science Foundation of China under Grant No. 71932006.
THE INFLUENCE OF COMIC STRIPS TOWARDS STUDENTS’ READING COMPREHENSION ON RECOUNT TEXT

This research aimed to know the influence of comic strips towards students’ reading comprehension. This technique helped the students to comprehend the story by imagining it with the strip of pictures. The objective of the research was to know the influence of comic strips towards students’ reading comprehension in recount text. In this research the writer used experimental method or known as descriptive quantitative research. The population was 48 students in 2 classes. The sample was taken by using Total Sampling Technique. The writer took all of the classes as the sample of research namely experimental and control class. It consisted of 45 students; the other students were absence. The main technique in measuring students’ reading comprehension was multiple choice which consisted of 40 items test. Each item has four options a,b,c, and d. The score each item was 2.5. In calculating the data analysis, the writer used t-test formula. Based on the data analysis, the writer got the result that was accepted. It was obtained that = 4.45, for α= 0.05 was 1.68 and for α=0.01 was 2.42 (4.45 &gt; 1.68 &lt; 2.42). So, was accepted. It meant that there was significant influence of comic strip toward students’ reading comprehension on recount text at the tenth grade of SMA Negeri 8 Bandar Lampung in 2021/2022. Based on the result of the research, it is clear that comic strips had an influence to the students’ reading comprehension, especially in recount text.
Boosting Server Type Recognition with Multi-Task, Attention and Similarity Methods

Periodic fixed asset inventory work in the data centre room requires server type recognition and number statistics of servers. However, most of the servers are similar in appearance, making the inventory and statistics work heavy. Therefore, this paper proposes a detection and recognition deep learning model based on server similarity. We integrate the detection and recognition tasks of the server into a unified architecture for end-to-end training. At the same time, the model supports open-set testing and use. The model mainly consists of one feature encoder and two decoders for object detection and recognition. Since there is no public dataset in this field, we use the Internet Data Centre (IDC) inspection robot to collect the dataset and conduct training and testing. The speed and accuracy are higher than other detection and recognition models. In addition, it can be achieved real-time processing on the embedded device Jetson Xavier NX. Experiments show that compared with the baseline model (YOLOX model [1]), the accuracy of our model is improved by 6% and the average precision improved by 2.9%. Moreover, it can reduce the oscillation of the learning rate and accelerate the convergence.
Возможности и перспективы использования технологии связанных данных в Президентской библиотеке

The article discusses approaches to publishing library data of the Presidential Library in the LOD (Linked open data) environment as an integral part of a single digital knowledge space. The basic model of data is built taking into account the specifics of the Presidential Library catalogue: the integrated library cata-logue includes library, archival and museum materials; when building the catalog, the national RUSMARC format is used. The proposed scheme for publishing linked data is based on the transformation of MARC records using the IFLA LRM and RiC-CM conceptual models as well as standard ontologies.
Multi-dimensional Equilibrium-depth Hashing Method for Large-Scale Image Retrieval

Aiming at the limited feature extraction capability and inefficient quantization constraint mechanism of existing hashing methods,a deep multi-scale attention hashing network was proposed for large-scale image retrieval . The equilibrium network consists of two sub-networks, the main branch and the object branch, and adds a multi-dimensional significant area extraction module to the main branch network to effectively extract the saliency region in the image, and send the execution results to the object branch network to learn more detailed features. Triplet quantization constraints are introduced to reduce the quantization error and preserve the similarity relationship of pairs of samples. To verify the effectiveness of the method, extensive experiments are performed on two benchmark datasets. Experimental results show that the proposed method outperforms most existing hash retrieval methods.
A secret image sharing method based on block-wise cheating detection and recovery on shadow images

In (k,n) threshold schemes, it is very important to determine which shadow images are cheated on and which shadow images will be used in the recovery of the secret image. Also, some of the cheated shadow images may have not cheated regions. Therefore, authentication of the shadow image’s blocks is necessary. In this paper, a secret image sharing method using block-wise cheating detection and recovery is proposed. With the proposed block-wise method, even if all shadow images have been cheated, recovery of the secret images is performed with the block-based authentication and recovery mechanism. In the proposed recovery process, it is sufficient for k of each paired block group in the n shadow image to be not cheated. Experimental results illustrated that the proposed method successfully recovered the secret image even if n shadow images were cheated, and the randomness of the proposed method is satisfactory.
On some Toeplitz-type matrices over finite fields

In this paper, we study some arithmetic properties of certain Toeplitz-type matrices over finite fields. For example, suppose {α1,…,αn} are all the nonzero k-th powers in a finite field and for 1⩽i,j⩽n, we setxij={1αi−αjif i≠j,0if i=j. We give an explicit formula for the characteristic polynomial of [xij]1⩽i,j⩽n and confirm several conjectures posed by Zhi-Wei Sun.
Equivalence and duality of polycyclic codes associated with trinomials over finite fields

In this paper, all of the conjectures in Aydin et al. (2022) [6] are settled: some proven correct, some proven correct with a modification, and some disproven, involving the equivalence and duality of polycyclic codes associated with trinomials. We also give methods to construct isodual and self-dual polycyclic codes, and study the self-orthogonal and dual-containing polycyclic codes over F2.
A complementary object detection method via integrating CNN with Vision Transformer(ViT)

The object detection method based on CNN is the mainstream method in the field of object detection because of its special structure of hierarchical and gradual extraction of local features, which can simultaneously consider low-level geometric features and high-level semantic features. However, this structure does not make full use of the global information of the image, resulting in classification errors when the features are limited or fuzzy. To solve the above problems, this paper explores a complementary feature extraction backbone via integrating CNN with Vision Transformer(ViT), and designs a shallow ViT structure to interact features of the proposals in a two-stage object detector with the image background feature to realize global modeling and feature alignment. In addition, according to the particularity of the supplementary structure, a segmented training strategy is designed. This strategy ensures that the model can extract the features together, and maximize the independence of their respective structures, giving full play to the advantages brought by different feature extraction methods. The model is verified on the COCO and PASCAL VOC Datasets. Through the experimental results and feature visualization analysis, it can be concluded that the mAP values are higher than CNN-based and ViT based detectors on the premise of adding limited parameters, which proves the effectiveness of the method.
A transformer-based deep learning model for Persian moral sentiment analysis

Moral expressions in online communications can have a serious impact on framing discussions and subsequent online behaviours. Despite research on extracting moral sentiment from English text, other low-resource languages, such as Persian, lack enough resources and research about this important topic. We address this issue using the Moral Foundation theory (MFT) as the theoretical moral psychology paradigm. We developed a Twitter data set of 8000 tweets that are manually annotated for moral foundations and also we established a baseline for computing moral sentiment from Persian text. We evaluate a plethora of state-of-the-art machine learning models, both rule-based and neural, including distributed dictionary representation (DDR), long short-term memory (LSTM) and bidirectional encoder representations from transformer (BERT). Our findings show that among different models, fine-tuning a pre-trained Persian BERT language model with a linear network as the classifier yields the best results. Furthermore, we analysed this model to find out which layer of the model contributes most to this superior accuracy. We also proposed an alternative transformer-based model that yields competitive results to the BERT model despite its lower size and faster inference time. The proposed model can be used as a tool for analysing moral sentiment and framing in Persian texts for downstream social and psychological studies. We also hope our work provides some resources for further enhancing the methods for computing moral sentiment in Persian text.
An Improved Lightweight Framework for Handwritten Chinese Text Recognition Based on CRNN

Robots with computer vision and text recognition functions are widely used in industrial production, especially in highly automated factories. However, most robots have an excellent ability to recognize printed characters and show low accuracy in recognition of handwritten characters. Therefore, this paper considers recognizing handwritten text in the intelligent processing of handwritten documents. Its high accuracy prediction results are closely related to the effectiveness of manuscript input, intelligent translation, and intelligent scoring. Handwritten text is more difficult to recognize because it contains sequential information, and the images are more complex than single-character images. This paper proposes a new handwritten Chinese text recognition (HCTR) framework based on existing classical convolutional neural network (CNN) and recurrent neural network (RNN) algorithms. We use a handwritten Chinese text dataset from CASIA-HWDB containing numbers and symbols close to real application scenarios to train the model and compare the performance of various models, such as MobileNetV1 and MobileNetV2, with the proposed model. From the analysis of experimental results, it can be found that the proposed method can achieve higher performance with fewer parameters. In addition, we optimize the dropout rates of input blocks and obtain the best CER of our method is 6.11%.
Lightweight super-resolution networks with global and local residual characteristics

At present, there is a problem of complexity and calculation of the image super-resolution algorithm network. To improve this problem, we propose a lightweight super-resolution network that blends global and local features. First, shallow image features are extracted using a convolutional block, and secondly, deep features are extracted by multiple cascading residual feature distillation blocks GLRB, where in order to achieve a good trade-off between model performance and network parameter quantity, local features are learned by enhancing the feature selection module ESA and the balanced dual-attention module to improve model performance. Then, the extracted residual features are fused, and the reconstructed image is obtained by sub-pixel convolution sampling. The experimental results under multiple standard test data sets show that the reconstructed image performance PSNR is improved by 0.25 dB to 32.23, and the number of model parameters is 470.21 K. Compared with DRCN, CARN, IMDN, RFDN and other algorithms, the proposed algorithm has better model lightweight and image reconstruction quality.
MDSNet: a lightweight network for real-time vision task on the unmanned mobile robot

To makes a trade-off between accuracy and inference speed for semantic segmentation, Multi-Scale Depthwise Separation network (MDSNet) is designed to be effective both in terms of accuracy and inference speed. This network extract local information and contextual information jointly and has feature maps with high spatial resolution. Compared with state-of-the-art algorithms, MDSNet achieves 66.57 MIoU on Camvid with only 0.5M parameters and 79.4 FPS inference speed on a single GTX 1070Ti card. Besides, MDS is deployed on the unmanned platform to test performance under different conditions. The results show that the proposed algorithm performs well on real-time applications in the real world.