Reviewerly/exHarmony huggingface dataset:
Vision Question Answering System Based on Roberta and Vit Model

With the advent of the information age, VQA has become a key research direction cross the field of CV and NLP. Based on a large number of real scene images on the KAGGLE platform, this paper applies the Transformers model to the CV field, and combines the Transformers based NLP algorithm to establish a VQA system. The experimental results show that our model can give accurate answers in a simple and orderly scenario, and there is a certain deviation between the generated results and the real answers in a chaotic scenario, which verifies the effectiveness of our model. Wup similarity is selected as a metric in this paper. The results show that the RoBERTa-ViT model has the most outstanding performance in VQA task, with the metric value of 0.351. Surprisingly, the BERT-SwinT model performs relatively poorly. which may be because the SwinT model is more complex than the VIT model and does not show its own advantages in small data set task.
Estimation of Accuracy and Recall Values for the Different Age Groups Based on Voice Signals using Different Classifiers

Voice signals are the most common and essential medium to exchange information. The generation of the voice signal can be possible due to the synchronized movement of the vocal tract. The voice signals contain extensive information about the speakers, such as age, gender, emotions, etc. The gender identification of the speaker can be done by the analysis of the voice signals. The voice samples are classified into the teenage, middle, and old age groups. This paper presents the recall values of the genders for the three age groups using different classification algorithms. This paper also concentrates on the accuracy of the gender identification system for the different types of classifiers. Mel Frequency Cepstral Coefficients (MFCCs) are used as an extracted feature. Linear Discriminant Analysis (LDA), Recurrent Neural Network Bidirectional Long Short-Term Memory (RNN-BiLSTM), and Support Vector Machine (SVM) are used as the classification algorithms to identify the gender of speakers. The male recall values in the teenage group have the highest value in the RNN classification algorithm. Similarly, the female recall values in the old age group have the highest in the RNN classification algorithm. The LDA shows the lowest recall values for each gender. It also shows the lowest gender identification accuracy. Further, the recall values of the genders and the accuracy of the proposed system of the RNN are higher than SVM and LDA.
Hierarchical Water Wave Optimization

Water wave optimization algorithm (WWO) draws inspiration from the natural summary of the shallow water wave theory. It benefits from a modest population size and straightforward parameter design. However, WWO still has some performance problems that need to be solved, e.g., the convergence speed is too slow, and it cannot find the optimal point efficiently and accurately. This paper proposes a strategy of multi-level population structure for it, namely DWWO. The multi-level population structure strategy further enhances the balance between exploitation performance and exploration performance of the WWO algorithm. It makes the algorithm performance more stable, which leads to the DWWO algorithm can be used in more practical problems. DWWO algorithm is compared with the classical WWO algorithm, cuckoo search algorithm, sparrow search algorithm, and sine cosine algorithm on the basis of IEEE CEC2017 problem set. Comprehensive experimental results show that DWWO algorithm has better optimization ability and relatively fast convergence speed in comparison with other algorithms.
Minimum Cost Adaptive Submodular Cover

We consider the problem of minimum cost cover of adaptive-submodular functions, and provide a 4(ln Q+1)- approximation algorithm, where Q is the goal value. This bound is nearly the best possible as the problem does not admit any approximation ratio better than ln Q (unless P = NP). Our result is the first O(ln Q)-approximation algorithm for this problem. Previously, O(ln Q)-approximation algorithms were only known assuming either independent items or unit-cost items. Furthermore, our result easily extends to the setting where one wants to simultaneously cover multiple adaptive-submodular functions: we obtain the first approximation algorithm for this generalization.
A Novel Blockchain-MEC-Based Near-Domain Medical Resource Sharing Model

Current medical information systems face important security issues in the transmission and storage of medical data, especially since there is currently no research related to the security of medical resource sharing in near-domain networks. Most of the medical data is transmitted through an insecure environment without encryption, and there is a risk of interception and tampering. In addition, lots of medical data is uploaded to the cloud platform for storage without processing, which is inefficient. Therefore, this paper proposes a near-domain medical resource sharing scheme based on block-chain and Multi-access Edge Computing (MEC). The approach makes use of device-to-device (D2D) communication technologies to make data sharing between medical devices safer and more efficient. The MEC is used to process medical data, which is encrypted and decrypted using the ShangMi cryptographic algorithms. Finally, the blockchain is used to ensure data security and tamper resistance. Through the simulation experiment, the performance test results show that the scheme meets the performance index, and the security analysis results show that the scheme can provide security. It offers a novel approach to the secure and trustworthy sharing of medical and health data and intelligent processing of that data.
Governance Audit Using COBIT 5 in CV. XYZ on Accounting Information System

This study aims to determine the results of audits and governance in accounting information systems using COBIT 5 in CV. XYZ. Data collection in this research process uses interview, observation and documentation methods. This research is carried out in stages, starting from determining the needs of the company, determining the scope of identification, the data collection process, determining and assessing the capability level of previously selected IT processes, and providing recommendations based on selected IT processes. This research focuses on the domains EDM02 (Ensuring Achievement of Benefits), APO01 (Managing IT Management Framework), APO04 (Managing Innovation) and APO07 (Managing Human Resources). Based on the results of the evaluation and analysis carried out, it is currently CV. XYZ has an average maturity level of 2.69 or is at level 3 (Established) with an expected maturity level of level 4 (Predictable), so it has an average gap of 1.31 in each of its domains.
A New Relaxed Total Generalized Variation (RTGV) Technique for Image Denoising

In this paper, a new relaxed second-order total generalized variational model is proposed for image denoising.In this model, the regularization term is a combination of a gradient operator and weighted divergence operator, while the data fidelity terms we used is l 2 -norm.The weighted divergence operator in the regularization is used to adjust the higher-order smoothing term, which can reduce the computational complexity and guarantee the discrete accuracy.Moreover, the Augmented Lagrangian algorithm, which produced several closed form solutions is used to solve the proposed RTGV method.Then we applied the denoising model to both gray and color image denoising, and performed numerous experiments.Our algorithm is discussed from several aspects, including influence of parameters, numerical discretization and comparisons with other methods.Numerical experimental results demonstrate that this technique has the significant advantage in preserving image features and can effectively prevent the staircase artifacts when compared to several other classical existing based methods.
Research on the Application of Cloud Computing Network Technology and Computer Big Data Analysis Technology

Along with the development of computer network technology, various fields have ushered in the era of big data.The reasonable exploitation of computer information resources and the adoption of cloud computing network technology and computer big data analysis technology for information data processing can promote the stable development of society.However, cloud computing and big data require high professionalism, and how to apply them scientifically and reasonably to give full play to the maximum value of computer technology is the main research direction.This paper discusses the necessity and shortage of data processing at first, then analyzes the advantages and disadvantages of cloud computing network technology and big data analysis technology, and finally discusses the specific application of cloud computing network technology and big data analysis technology, so that they can complement each other and play a greater role in the future.
Classification of graphic office information in intelligent office automation system

Due to the low F-Measure value of traditional methods in practical application, the classification effect of graphic office information is not good, so a classification method of graphic office information in intelligent office automation system is proposed.Through the semantic association of images and texts, the features of image and text office information are extracted, the weight of information features is determined, and the similarity between image and text office information is calculated according to the features, so as to achieve information classification.The experiment proves that the F-Measure value of the design method is high, and it has a good application prospect in the field of image and text office information classification.
Person Following Robot Based on Real Time Single Object Tracking and RGB-D Image

The demand for intelligent robots is increasing rapidly with the advancement in technology. This paper presents an end-to-end approach for a person following robot. It would be a great help if robots could behave as a helping hand for humans in critical environments like industries, markets as well as in hospitals. Because of balanced accuracy and speed, Siamese trackers got great attention. Here we are using a single object tracker for a person following and Robot Operating System (ROS) for communication between various nodes. The Robot is equipped with a Microsoft Kinect sensor which will provide RGBD (Red Green Blue image and Depth image) image. Initially, this system utilizes a deep learning algorithm for single object tracking to track a person. Further, the system can calculate the centroid of the target person as well as the depth information. Finally, this data is given to the navigation module of the robot. This will control the robot's motion
Comparative Analysis of Deep Fake Detection Techniques

Deep learning and artificial intelligence are important knowledge areas that have provided solutions allowing the successful resolution of complex problems. Some of these problems include, but are not limited to, human-level control, data analytics and other digitisation challenges. One of the offshoots of deep learning is a concept termed ‘deepfake’, which can be described as the imposition of video of a face image from a source to video of the face image of a target individual in order to make the targeted person appear to express the content of the source video [2]. It is important to establish the fact that deepfakes have been used for malicious purposes, becoming a threat to national security, privacy, democracy, and society at large. It is, therefore, fundamental to review the science behind the method, and the available detection techniques to curtail this digital innovation, so as to reduce its level of threat; that is the focus of this paper.
Integration of ROS Navigation Stack with Dynamic Environment Information in Gazebo Simulation

Sensing the environment is a crucial task that robots have to perform to navigate autonomously. Furthermore, it must be well executed to make navigation safer and collision-free. As autonomous mobile robots are being deployed in several applications, they often encounter dynamic habitats, where sensing and perceiving the environment becomes harder. This work proposes integrating a wireless sensor network with the Robotic Operating System to incorporate data into layered costmaps used by the robot to navigate, feeding the algorithms with advanced information about the territory. The architecture was tested in simulation, where we could validate the structure and collect data showing improved paths calculated and reduced computational load through better parametrization. Thus, this strategy ensures that the advanced information about the environment has improved the navigation process.
Transferring From Textual Entailment to Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BioNER) aims at identifying biomedical entities such as genes, proteins, diseases, and chemical compounds in the given textual data. However, due to the issues of ethics, privacy, and high specialization of biomedical data, BioNER suffers from the more severe problem of lacking in quality labeled data than the general domain especially for the token-level. Facing the extremely limited labeled biomedical data, this work studies the problem of gazetteer-based BioNER, which aims at building a BioNER system from scratch. It needs to identify the entities in the given sentences when we have zero token-level annotations for training. Previous works usually use sequential labeling models to solve the NER or BioNER task and obtain weakly labeled data from gazetteers when we don't have full annotations. However, these labeled data are quite noisy since we need the labels for each token and the entity coverage of the gazetteers is limited. Here we propose to formulate the BioNER task as a Textual Entailment problem and solve the task via Textual Entailment with Dynamic Contrastive learning (TEDC). TEDC not only alleviates the noisy labeling issue, but also transfers the knowledge from pre-trained textual entailment models. Additionally, the dynamic contrastive learning framework contrasts the entities and non-entities in the same sentence and improves the model's discrimination ability. Experiments on two real-world biomedical datasets show that TEDC can achieve state-of-the-art performance for gazetteer-based BioNER.
A Generative-Based Chatbot for Daily Conversation: A Preliminary Study

Currently AI has been integrated in our daily life. One of basic example of AI in our daily life is chatbot. The common chatbot that is developed is a rule-based chatbot. Rule-based chatbot have several drawbacks that is easy to predict, repetitive, an unnatural conversation might happened. In addition rule-based chatbot would rely on fixed pair question and answer, developed in a closed domain conversation, and has limited self-learning. Due to this problem, there is a need to develop a chatbot that is able to dynamically response to the question. The chatbot model in this study is based on Simple Dialog and Daily Dialog dataset. The dataset are then merged into single dataset. In this research, we proposed the Seq2seq model architecture with LSTM and GRU cell to create a Generative-Based chatbot. The Seq2seq model consists of two parts, encoder and decoder. The proposed model is evaluated using Cross Entropy Loss and BLEU score. The result shows that the LSTM model has better performance than the GRU model. The LSTM model resulted in 0.7064 training loss, 8.9740 validation loss and 0.0588 BLEU. Meanwhile, the GRU model resulted in 2.1125 training loss, 7.1840 validation loss and 0,0028 BLEU. Compared to GRU, LTSM model have the ability to generate a more acceptable response to the given questions.
Estimating 3D Human Pose using Point Based Pose Estimation and Single Stage Method

Graphically estimating a person's pose is a computer vision task called human pose estimation. Predicting the location of a person's body parts or joints is an everyday use case for this method. The vast number of potential uses for this kind of technology has made it one of the most intriguing areas of study in computer vision. To estimate a human's 3D stance from 2D photos, the location of the important body joints in 3D space shall be determined. Despite extensive research in the field of computer vision, this issue continues to pose significant difficulties. This is because the depth information is lost when obtaining 2D images of 3D objects, leading to ambiguity. While using a 2D Image as input, only a portion of a person's body is exhibited; estimating their 3D position is challenging. In this study, we offer methods for recovering a human's 3D position from a 2D photograph when just a portion of the body is visible. Any situation where some of the body's major joints are outside the image’s bounds is considered a partial body presence. The presence of these joints in the input image is also detected by a CNN network that we propose. We propose integrating the two networks to determine the full human posture in 3 Dimensional Space and the partial 3D human pose calculated by the detection network. Comparing our experimental results to those of the state-of-the-art demonstrates the efficacy of our methods in the presence of a partially-occluded body. Furthermore, the experimental results show that the 3D human posture may be estimated more accurately from 2D pictures using a linear regression rather than an intermediary stage of 2D pose estimation.
Penggunaan Media Pembelajaran terhadap Keterampilan Berpikir Kritis IPS Siswa Sekolah Dasar

This study aims to determine the use of learning media in improving students' critical thinking skills in social studies learning in the Higher Class. This type of research is a literature study with a descriptive qualitative research type with library research from research journals in previous years to be used as research subjects. This research is sourced from several journals that are relevant to the topic of the discussion on Learning Media on Social Studies Critical Thinking Skills for Elementary School Students. The results obtained are the use of learning media is very effective and efficient in its use, depending on what learning media is used and the selection of learning media in accordance with the provisions of the social studies material being taught adds to the usefulness of the use of learning media.
MARKETING OF LIBRARY IN THE AGE OF SOCIAL MEDIA AND THE DIGITAL WORLD

We all are on Live in the Digital world. And this digital world everything runs from the strategy of item's marketing. And that's why we can say that in the digital world, any of the items if marketing is strong then we can cell any of the items and the economy will boost automatically. Marketing increases the value of information in the Marketplace ( Today we will see Marketplace is Mart). Marketing is not only increasing business but also promoting business also and into the field of library, marketing perspective, it is important for people to be aware of library service. Library marketing is effective not only for the regular user but also for a new user to know our library service, and as a result of this marketing, a new user will be coming to the library. Library Marketing serves a chain between library users and library services. Today's digital world, due to the marketing of the library. Academic as well as public library making good relations with users. In this paper, we will look at how university libraries should adopt a marketing approach. With the help of this approach provide information on what changes should be made to the library service. This paper discusses how to market a library in the digital world. In this paper, we also focus on how can we use our library with a social media platform like Facebook, Twitter, Instagram, Youtube, etc. With a good library marketing method we were able to provide a good library service and with this marketing library service and study will be increased.
Exploring the Role of Digital Transformation in Higher Education Institutions

Delivery of knowledge by using many known digital tools, such as massive online open course (MOOC), is one of the well-known digital transformations in higher education. This chapter explores digital transformation’s role in higher education institutions. The chapter starts by presenting the role of digital transformation in the online learning process, where the shift of the higher education system in China moved from the traditional knowledge delivery system to digital platforms. The impact of digital transformation in higher education institutions is also discussed. Then, the chapter considers the theoretical perspective (activity theory and social interactions) used for digital transformation in educational institutions. Finally, the benefits and challenges of using the online learning process in higher education institutions are presented, and recommendations for dealing with challenges are highlighted.
Lung Melanoma Recognition through Platelet Count Composition Using Supervised ML Technique

Blood samples are used to diagnose the health illness in the human body. Complete blood routine test is used as whole-body screening to detect illness in human body. Highly sensitive diagnosis can be detected by blood count such as by using RBC, RNA [12], WBC, DNA, platelet count or even protein biomarkers. Biopsies of liquid blood sample are effectively used nowadays even to detect diagnosis of cancer. By determining the routine indices of blood samples, lung carcinoma can be detected. Rather than using PET scan which is high in cost, Lung carcinoma can be detected by blood indices samples. Machine Learning can be used to decipher the multiple ranges of statistical data by using svm (support vector machine) technique from supervised learning framework. This technique can be used for identification purpose both in clinical research and by patients respectively. From the detailed report, physicians can aid the treatment cycle for the patient. Each patient's report is sent to their registered phone number via SMS using Sim900 with Arduino Uno sensor. The generated detailed report is sent to the registered mobile number as short messaging service. In this approach, advanced way of using blood indices to identify the lung carcinoma is been developed.
Reinforced Hybrid Wiener Deconvolutional - Convolutional Autoencoders Based Image Deblurring

This paper is addressing the issue of image deblurring by employing a novel reinforced hybrid Wiener deconvolutional-convolutional neural network (HWDCNN) in the context of images affected by Gaussian blur. The proposed method is set to explore the capabilities of a custom NN layer that performs the classical Wiener deconvolution operator in a trainable manner, hence, leading to a blind deblurring method where the weights represent the point spread functions (PSF) and signal-to-noise ratios (SNR) of the deconvolution layer which are optimized in a traditional deep learning paradigm. Additionally, the custom layer is set as a first layer on two of the branches of the model, followed by layers of transposed convolution to explore possible synergies. The results indicate that this method outperforms known state-of-the-art methods for the presented dataset setup in terms of both structural similarity index measure (SSIM) and peak SNR (PSNR) metrics.