Reviewerly/exHarmony huggingface dataset:
Context Normalization Layer with Applications

Deep neural networks (DNNs) have gained prominence in many areas such as computer vision (CV), natural language processing (NLP), robotics, and bioinformatics. While their deep and complex structure enables powerful representation and hierarchical learning, it poses serious challenges (e.g., internal covariate shift, vanishing/exploding gradients, overfitting, and computational complexity), during their training phase. Neuron activity normalization is an effective strategy that lives up to these challenges. This procedure consists in promoting stability, creating a balanced learning, improving performance generalization and gradient flow efficiency. Traditional normalization methods often overlook inherent dataset relationships. For example, batch normalization (BN) estimates mean and standard deviation from randomly constructed mini-batches (composed of unrelated samples), leading to performance dependence solely on the size of mini-batches, without accounting for data correlation within these batches. Conventional techniques such as Layer Normalization, Instance Normalization, and Group Normalization estimate normalization parameters per instance, addressing mini-batch size issues. Mixture Normalization (MN) utilizes a two-step process: (i) training a Gaussian mixture model (GMM) to determine components parameters, and (ii) normalizing activations accordingly. MN outperforms BN but incurs computational overhead due to GMM usage. To overcome these limitations, we propose a novel methodology that we named "Context Normalization" (CN). Our approach assumes that the data distribution can be represented as a mixture of Gaussian components. However, unlike MN that assumes a-priori that data are partitioned with respect to a set of Gaussian distributions, CN introduces the notion of concept that accounts for data relationship via a neural network classification scheme. Samples that are gathered within a cluster define a context. The estimation of the Gaussian components parameters is conducted through a supervised neural network-based concept classification. CN is more precise when clusters are thick and not sparse. Extensive comparative experiments conducted on various datasets demonstrates the superiority of CN over BN and MN in terms of convergence speed and performance generalization. In fact, CN outperforms BN and MN with a convergence speed margin of 5% and a performance margin of 10%. These results reveal the importance and the need of capturing inherent data context to learn the Gaussian component parameters. Our proposed approach harnesses data relationships, and therefore enhances deep learning models in various applications.
E2VQ: Enabling Expressive, Efficient, and Verifiable Queries over Blockchain Databases

With the extensive application of blockchain technology, there is an increasing demand for querying the blockchain data. Verifiable query processing for blockchain (VQPBC) assures query integrity without the need to store the entire blockchain database. To the best of our knowledge, prior work Vchain+ serves as the state of the art in VQPBC. However, it still suffers from the following limitations: (i) fewer SQL-like queries; (ii) low query efficiency; and (iii) impractical public key management. To this end, we propose a novel solution, which additionally supports aggregate queries such as sum, count, and average. To support these expressive queries, we propose two new authenticated data structures. To improve query efficiency, we utilize the inverted index table to directly locate the desired block. The size of the public key depends on the universe size of transaction values rather than the number of transactions like vChain+. Furthermore, the detailed performance evaluation and simulation show that our solution is more practical.
MaxProb: Controllable Story Generation from Storyline

Controllable story generation towards keywords or key phrases is one of the purposes of using language models. Recent work has shown that various decoding strategies prove to be effective in achieving a high level of language control. Such strategies require less computational resources compared to approaches based on fine-tuning pre-trained language models. The paper proposes and investigates the method MaxProb of controllable story generation in Russian, which works at the decoding stage in the process of text generation. The method uses a generative language model to estimate the probability of its tokens in order to shift the content of the text towards the guide phrase. The idea of the method is to generate a set of different small sequences of tokens from the language model vocabulary, estimate the probability of following the guide phrase after each sequence, and choose the most probable sequence. The method allows evaluating the consistency of the token sequence for the transition from the prompt to the guide phrase. The study was carried out using the Russian-language corpus of stories with extracted events that make up the plot of the story. Experiments have shown the effectiveness of the proposed method for automatically creating stories from a set of plot phrases.
DIGITALIZATION OF ISLAMIC ACCOUNTING LEARNING AT IAIN BONE ACCOUNTING LABORATORY: PERSPECTIVE ON UTILITARIANISM THEORY

This Research aims to develop Islamic accounting learning media through the perspective of digitalization-based utilitarianism theory as part of digital transformation. Media developed by utilizing social media accounting laboratory IAIN Bone. This research is a Research and Development (R&amp;D) research with an orientation to develop a research product. Research starts from needs analysis, planning, product development, expert validation, and revision. To measure its effectiveness, limited field tests are used. The results of this study conclude the availability of learning media for the subject of Islamic accounting material that has been tested for effectiveness based on utilitarianism theory which is useful as celebrity digital content and received a positive response from students. The implication of this research is to be an input to educators and laboratories to be sensitive and able to optimize digitalization of learning as an accountable and useful digital transformation effort.
IT Maturity Level Analysis Using Framework COBIT 5 Work for Management Cyber Incident: Case Study of Company Z in ICT Field

Based on the report security year 2022 from company Z in the ICT sector, it was found that on the report firewall security, 96% of level threat is level critical, and 4% is level high. Report email security is session domain 27.3%; Session limits 15.35 %; Forti Guard AntiSpam-IP 3.11%, and the rest is receipt verification; directory filter and access control relay denied. With the challenge of current cyber-attacks, precious data assets need analysis to measure the the level of IT maturity that can ensure stakeholders' interest and maximize benefits and opportunities through technology information. We overcome limitations by analyzing IT maturity using COBIT 5. Research focused only on the APO13 and DSS05 process domains. A study was done to identify the problems based on results observation, checking, and use of a questionnaire in a direct manner. Measurement is done through method evaluation self and interviews deep with the IT team and all power expert who has COBIT certification 5. Analysis results show that the measurement level for the APO13 domain is 3, and for DSS05 is level 2. These results still need to be below the set level 4 target management; therefore, building a framework to monitor, track, and record security data in real time is necessary. With the build framework, Work can help lower the threat level from a critical level to a high level and increase the COBIT Maturity Level to APO13 and DSS05 according to organizational targets.
Multimodal Sentimental Classification using Long-Short Term Memory

The increasing popularity of mobile devices and social networks has leads people to share images and text to express their emotions and opinions. This paper proposed a deep learning-based technique for multimodal sentimental analysis. For this work, the CMU-MOSI and CMU-MOSEI dataset is utilized which contains video, audio and text clips. The inputs are in the form of video, audio and text, so individual preprocessing and feature extraction techniques are performed with separate techniques. Then, the extracted features from separate techniques are concatenated and given as input for the classification of sentimental analysis. The Long-Short Term Memory (LSTM) is utilized in this research for classification. The performance metrics like 7-class accuracy (Acc-7), binary accuracy (Acc-2), F1-score, Mean Absolute Error (MAE) and Correlation Coefficient (CORR) are utilized to evaluate the LSTM model. The attained result shows that the LSTM model obtains better Acc-2 of 91.57% on CMU-MOSI dataset and 91.28% of CMU-MOSEI dataset when compared to existing techniques like Multi-Tensor Fusion Network with Cross-Modal Modeling (MTFN-CMM), Sparse and Cross-Attention Network (SCANET) and Sparse and Cross-Attention Network (SCANET).
Toxic Comment Analyzer using BERT: A Deep Learning Approach for Toxicity Detection

With the good hike of social and communication platforms, a significant increase in the volume of user-generated content is produced. Unfortunately, this surge in online interactions has also led to an increase in toxic and offensive comments. Toxic comments not only contribute to a negative online environment but also have the potential to harm individuals and communities. Consequently, the development of automated methods for detecting toxic comments has become imperative. In this research paper, we propose a toxic comment analyzer using BERT to address this issue. Our approach demonstrates the effectiveness of leveraging pre-trained language models for identifying toxic language, with promising results of 97% accuracy on benchmark datasets.
Study of the effectiveness of Generative Adversarial Networks towards Music Generation

Machine learning-based music generation has the potential to significantly change the music business and creative processes. The field of artificial intelligence offers new opportunities for creativity, artistic inquiry, and individualized musical experiences. Future music composition and generation will continue to be shaped by the research and development carried out in this area. Generative Adversarial Networks and reinforcement learning can be aptly used to improve the caliber and variety of music being created. The presented work discusses about the difficulties faced when creating music, whilst preserving musical coherence and preventing copying. A workflow is proposed for generating music using the LSTMGAN model. Loss graphs were used as the determinant of music quality. The results obtained show that LSTMGAN model performs remarkably well when used for music generation.
Optimizing Deep Learning for Facemask Detection: A Comparative Study of Optimizers

During the COVID era, a significant influence on our well-being was the choice to don face masks, which played a crucial role in curbing viral transmission and promoting overall hygiene. The current study centers around the utilization of Deep Neural Networks to accomplish the task of detecting facemasks. However, this endeavor demands a considerable investment of time and substantial computational resources for effective training. Our investigation is particularly concerned with the synergy between the chosen optimizer and the pooling layer, aiming to yield a precise outcome that discerns the presence or absence of a facemask on an individual. Among the various configurations, the combination of Adam optimizer and Average pooling layer is the most effective, attaining an impressive accuracy rate of 96.25%.
Fraud Detection and Analysis System for Car Insurance Claim Using Random Forest Classifier

In recent years, commercial insurers have faced many cases of fraud in all types of claims. Fraud claims have been huge in amount and can cause serious problems. As a result, various organizations that are private or public, including the government, work to identify and prevent fraudulent activities. One of the most prevalent forms of fake insurance claims is related to the auto industry, which are often made through false accident claims. This project aims to solve this problem by developing a machine learning model that uses insurance claim datasets to detect and classify fraud and false claims from the legitimate ones. The project will use the Python PySpark library and the Random Forest Classifying algorithm to label and rank claims and compare their performance using metrics such as soft accuracy, precision, recall and confusion matrix.
Hand gesture recognition using multi-objective optimization-based segmentation technique

Hand Gesture Recognition (HGR) software is winding up progressively open with the advances in depth cameras and sensors, however, these sensors are as yet costly and not uninhibitedly accessible. A continuous HGR programming is intended to work with a minimal effort monocular web camera. Skin discovery and skin extraction is a typical type of image handling utilized for motion acknowledgment. The hand gesture image is gone through three phases, preprocessing, feature extraction, and characterization or segmentation. In the preprocessing stage, a few tasks are connected to separate the hand gesture from its experience and set up the hand gesture image for the feature extraction stage. In this paper, Multi-Target Optimization Based Segmentation (MTOBS) has been proposed for HGR. The performance has been analyzed for gesture recognition with and without optimization technique. The outcomes demonstrate that the recognition method without optimization has an exhibition of 85% recognition, while the proposed technique with optimization, has a superior execution of 96% recognition rate.
Archaeological Investigations at the Trader's Cave,Niah National Park: Reports on the Second and Third (2018 &amp; 2019) Field Seasons

In November 2017, 50 years after Tom and Barbara Harrisson completed their excavations in the West Mouth of the Niah Great Cave Complex (NGCC), we began archaeological excavations in the Trader's Cave (Fig.1). While the area containing the Niah Caves was privately owned land at the time the Harrissons undertook their research it was gazetted as a National Park in 1974 and today comprises 3,138 hectares of forest and karst areas including many largely unexplored caves. Our research project, underway since 2011, aims to assess the archaeological and palaeoenvironmental potential of the caves in Niah National Park beyond the areas subject to research previously by the Harrissons. The Trader's Cave (3°49’1.16”N, 113°46'54.42”E) is the second largest cavern in Nia National Park and is located approximately 150 m north of the West Mouth within the same karst tower (Bukit Bekajang) as the NGCC. As noted previously (Curnoe et al. 2017), the Harrissons excavated a small trial excavation in the northern tourist entrance of the cave during 1956, but never published the results or returned to the locality for further archaeological research. Until our project began, there had been no archaeological activities within the main cavern of the Trader's Cave despite its size: the cave being ~200 m in maximum length, ~30 m at its widest point and with a maximum height &gt;15 m. Similarly, while Hazelbroek and Morshidi (2000) provided a brief description of the geology of the cave, the only detailed speleological investigation of the Trader's Cave was published in 2017 (Dodge-Wan 2017). Since our first field campaign (Curnoe et al. 2017), we have completed a further two field seasons with a total duration of 16 weeks of field research 2017 - 2019 in this cave, with further excavations planned for 2020 - 2022.
The Effect of Herringbone Technique on The Students’ Achievement in Reading Narrative Text at Smas Budisatrya in Academic Years Of 2021/2022

The objective of this research is finding out whether or not there is significant The Effect of Using Herringbone Technique on Students’ Achievemenet in Reading Narrative Text at the second semester of the grade of SMAS Budisatrya Medan in academic year 2021/2022.Reading comprehension is the ability to get comprehension, including to recognize the unfamiliar words or sentence in the text. Comic series is one of media that uses some comics related to the story given. These comics has function to describe the story. It is an appropriate media to be taught in Senior High School level to open up their mind widely by reading passage.&#x0D; There were two variables in this research. The independent variable is the word herringbone technique (X) and dependent variable is the students’ reading comprehension (Y). The research methodology in this research is Quasi experimental design with experimental and control class. The population of this research is the eight grade of SMAS Budisatrya Medan. In determining the sampling technique. The samples of research were 2 classes VIII A consisted of 15 students while in VIII B consisted 15 students. In collecting the data, there were pre-test and post-test and instrument. After giving the post-test ,the researcher analyzed the data of research .&#x0D; From the result, it was obtained that t-count is higher than t-critical (46 &gt;39,33). Therefore, there is significant influence of using series pictures towards students’ reading comprehension in narrative text at the second semester of the eleven grade of SMAS Budisatrya Medan in academic year 2021/2-2022.
9. The Input Measure Problem for MM-CCS Chance

Abstract The previous chapter introduced basic ideas of measure-map complex causal structure (MM-CCS) chance and outlined my strategy for applying these ideas to population-environment systems. At this point, the account is very incomplete, though. Probabilities of outcomes have so far only been defined by a potentially arbitrary, purely mathematical input measure. This chapter is devoted to clarifying the main challenge that the idea of MM-CCS faces (§9.2)—what I call “the input measure problem”—and to investigating a potential response to the challenge in the case of population-environment systems (§9.3).
Enhancing Image Clarity in Real Time: An Automated Gamma Correction Approach for Dehazing

Haze is a natural occurrence resulting from the scattering of unwanted particles in the atmosphere, leading to issues such as color distortion, decreased contrast, and diminished visual appeal in outdoor images. However, computer vision applications necessitate clear images for effective processing. This study proposes a simple and fast image dehazing technique that reduces time complexity by employing gamma correction with a dark channel prior-based technique. This reduction in time complexity enables real-time application. The proposed method enhances image contrast through automatically adjusts brightness levels using gamma correction method where the gamma value is calculated based on the average pixel value of the gray scale image and contrast modification technique to enhance the image quality to suit the image requirements. Experimental results demonstrate that the proposed method produces higher-quality images compared to existing technologies. The combination of gamma correction and contrast limited Adaptive Histogram Equalization proves to be an effective approach for image enhancement and dehazing tasks, resulting in improved contrast and finer details across various image types. Comparative analysis based on parameters such as SSIM, MSE, PSNR, and Correlation confirms the competitive performance of the proposed method.
An Abnormal Event Classification Model for Big Data Platforms Based on Semi-supervised Learning

With the increasing diversity of network attacks, the security of big data platforms is receiving more and more attention. To solve the problem of detecting and classifying attack events on unlabeled, multi-source heterogeneous big data platform log data, this paper proposes a semi-supervised security event detection and classification identification model based on a time-series detection algorithm and UEBA. First, based on data analysis and processing and security event knowledge base construction, a time series detection algorithm is used to detect anomalies in some log data. Based on the anomaly identification results, a fine-grained analysis guide rule encoding module is conducted to initially label the anomaly results. Then, semi-supervised learning is performed on a small amount of labeled data by the Pu Learning algorithm to train an optimized detection model to achieve anomaly identification of unlabeled data. Finally, based on the classification results, the XGBoost algorithm is further used to train the recognition results for multi-classification to enhance the real-time detection and prediction capability of subsequent related attacks. The experimental results show that the proposed model can effectively identify anomalous intrusion detection sequences and obtain better classification results.
Spoken Language Identification and Translation using Deep Learning

Identifying the language spoken in an audio clip by a speaker using a model is practiced here as Spoken language identification. Here the tedious task is to identify the important features that differentiate languages. The proposed system collects numerous audios and converts to images of spectrogram using convolutional neural network algorithms to detect the language. The main objective of the proposed system is to detect the languages spoken by the speaker along with the gender identification of the speaker. Experiments will be applied on diverse audio archives dataset “spoken language identification” from Kaggle. Here, the audio files are encompassed with utterances for 10 seconds. The dataset is divided into training set and testing sets. The proposed system also identifies the gender of the speaker and translation of the identified language into English. It consists of three convolutions followed by a fully-connected layer. The first two convolutions are for feature extraction, and the last one is for classification. RNN is applied to classify the gender of the speaker in an audio recording and Seq2Seq model is trained to translate the identified language into English.
Cloud data Management, a challenge to security

With its unmatched benefits for scalability, flexibility, and cost-effectiveness, cloud data management has recently experienced significant growth in the field of information technology. Cloud data management does come with advantages, but there are also issues that need to be resolved, such as worries about data privacy, security, and recovery. It looks at various models and frameworks that provide insights into best practices, enabling businesses to fully utilize cloud data management and advance their data-driven strategies. Numerous frameworks and models have been put forward in the literature to address these issues, guiding organizations on how to manage data in the cloud in an efficient manner. In order to identify common trends and themes and best practices for cloud data management, this article carefully analyzes the frame of existing research. Organizations can transform their approaches to data management and open up fresh avenues for innovation and expansion by utilizing the power of cloud data.
STRATEGIES TO BUILD SUCCESSFUL MOBILE BASED SERVICES FOR HEALTH CARE DELIVERY: AN INNOVATIVE THINKING OF YOUNG PROFESSIONALS

Ashish Agrawal; Dr. Vinod Kumar SV