Reviewerly/exHarmony huggingface dataset:
Unbalanced Big Data-Compatible Cloud Storage Method Based on Redundancy Elimination Technology

In order to meet the requirements of users in terms of speed, capacity, storage efficiency, and security, with the goal of improving data redundancy and reducing data storage space, an unbalanced big data compatible cloud storage method based on redundancy elimination technology is proposed. A new big data acquisition platform is designed based on Hadoop and NoSQL technologies. Through this platform, efficient unbalanced data acquisition is realized. The collected data are classified and processed by classifier. The classified unbalanced big data are compressed by Huffman algorithm, and the data security is improved by data encryption. Based on the data processing results, the big data redundancy processing is carried out by using the data deduplication algorithm. The cloud platform is designed to store redundant data in the cloud. The results show that the method in this paper has high data deduplication rate and data deduplication speed rate and low data storage space and effectively reduces the burden of data storage.
Vocabulary Enhancer

This study deals with creating a smart bot for improving and enhancing the English of an individual. Keeping in mind the importance of the English Language as a medium of expression across the globe and the lack of Indians to speak English proficiently such technologies have to be made and brought into the Indian Market. The model that we have proposed will basically help people of all ages to learn and improve their English-speaking Skills. The smart bot will further be adaptive with the person who is using it to improve his/her ability to speak English.
Resources and Evaluations for Danish Entity Resolution

Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations.This paper describes a conversion of its partial, yet well-documented, coreference relations into coreference clusters and the training and evaluation of coreference models on this data.To the best of our knowledge, these are the first publicly available neural coreference models for Danish.We also present a new entity linking annotation on the dataset using Wiki-Data identifiers, a named entity disambiguation (NED) dataset, and a larger automatically created NED dataset enabling wikily supervised NED models.The entity linking annotation is benchmarked using a state-of-the-art neural entity disambiguation model.
Effects of Botswana Cultural Values on Information Security

National culture has been proven to have an adverse impact on the quality of information security in a country. It influences the perceived norms, behaviours, and attitudes associated with cybersecurity like setting strong password adherence to privacy policies, training, and educational programs. To thaw the existing information security culture, its shortcomings must be identified and communicated, after which new cultural beliefs can be instilled through change management. This chapter aims to examine the effects of Botswana's cultural values on information security culture. Botswana is chosen as it is one of the fast-developing countries.
M-RS: A Systematic Literature Review on Multi-Objective Optimization along with Recommendation System

Recommendation systems (RS) are used for extracting data on the basis of rating and interest provided by the users on a particular item. In the current era, the world is relying more on RS to get the relevant data. To increase the efficiency of RS, Multi-Objective Optimization (MOO) is integrated with it. MOO is a branch of a multi-criteria decision-making scheme. MOO considers different conflicting objectives to optimize the results. The combination of MOO and RS will enhance the reliability of recommendations because the recommendations are provided by using different intentions of users with diverse requirements. This paper performs a Systematic Literature Review (SLR) on the articles that use MOO and RS with each other. The SLR is performed on 26 studies that are extracted from the study selection procedure. These 26 articles are then analyzed through different perspectives such as their domains, the method they have used, and evaluation techniques adopted, and the overall research trend of M-RS. The result shows that the application of MOO in field of RS is most popular among generic RS rather than specific RS. Furthermore, the results reflect that the MovieLens dataset has the highest popularity as 13 studies used it for the evaluation of their techniques. In the future, more research questions will be proposed to have a more in-depth insight into this field.
Morpheme Ordering Across Languages Reflects Optimization for Processing Efficiency

Abstract The ordering of morphemes in a word displays well-documented regularities across languages. Previous work has explained these in terms of notions such as semantic scope, relevance, and productivity. Here, we test a recently formulated processing theory of the ordering of linguistic units, the efficient tradeoff hypothesis (Hahn et al., 2021). The claim of the theory is that morpheme ordering can partly be explained by the optimization of a tradeoff between memory and surprisal. This claim has received initial empirical support from two languages. In this work, we test this idea more extensively using data from four additional agglutinative languages with significant amounts of morphology, and by considering nouns in addition to verbs. We find that the efficient tradeoff hypothesis predicts ordering in most cases with high accuracy, and accounts for cross-linguistic regularities in noun and verb inflection. Our work adds to a growing body of work suggesting that many ordering properties of language arise from a pressure for efficient language processing.
SMARKET - Shopping in Supercenters (Hypermarkets) with Augmented Reality

Not so long ago, online shopping for groceries, electronics, and furniture items seemed futuristic. But today, it has become a norm to order requisites through online platforms using smart devices and deliver them to customers' doorstep. With the emerge of technologies such as artificial intelligence, machine learning, deep learning, augmented reality, retail becomes progressively effortless. One such emerging futuristic technology involved recently in online shopping is Augmented Reality (AR) which is rapidly adopted by many industries. In multi-story supercenters, also known as “Hypermarkets”, the customer often feels lost due to difficulty in finding exactly what they looking for, and also in conventional online shopping, often customers are in two minds whether to purchase an item or not since it lacks the proper visualization, touch, and feel of the product. In this research study, we propose a mobile-based solution with augmented reality, which assists the customer when shopping in-store as well as when shopping online to mitigate the difficulties and hesitancies faced while shopping. The results are commendable with 96.21 % accuracy in suggesting visually similar items and 89.59% accuracy in detecting emotional implications of product reviews.
Generating Adversarial Examples by Distributed Upsampling

The development of neural networks provides state-of-the-art results for many tasks. However, much research has shown that deep neural networks are vulnerable to adversarial attacks that fool deep models by adding small perturbations into original images. The defense of the neural networks also benefits from a better understanding of the attacks. In consequence, generating adversarial examples with a high attack success rate is worth researching. Inspired by single image super-resolution, this paper treats adversarial attacks as an image generation task and designs a new model based on generative adversarial networks (GANs). The latent feature maps have been divided into low-level and high-level in this research. We exploit low-level features with noises to add perturbations during the upsampling process. To further generate perturbed images, we reconsider and make use of checkerboard artifacts caused by deconvolution. We illustrate the performance of our method using experiments conducted on MNIST and CIFAR-10. The experiment results prove that adversarial examples generated by our method achieve a higher attack success rate and better transferability.
Speaker Recognition based on Mel-Frequency Cepstral Coefficients and Vector Quantization

Speaker recognition is the process of recognizing the voice sample of a particular speaker from the set of speaker voice samples stored in the system. It can be done by using the parametric representation of speech signals to extract the various features using various methods. In this paper, we propose a voice recognition method which is implemented using Mel Frequency Cepstral Coefficients (MFCC) for feature extraction from voice samples and speaker modelling using Vector Quantization (VQ). LBG algorithm is used for the effective codebook generation in VQ. The system starts by storing the voice samples into the database, after purification of these samples the important features are extracted using the MFFC and by using the LBG algorithm in the VQ the centroid of the voice samples is calculated to recognize the speaker. The experimental results indicate that an average identification accuracy of 85-90% is achieved and hence this is one of the best methods for speaker recognition.
Reasearch on Cross Domain Few-shot Learning Method Based on Local Feature Association

Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learn based and metric based methods elaborately design various task-shared meta-knowledge or embeddings to solve such tasks, and have achieved promising peformance. Howere, when there exists domain shift between the test tasks and train tasks, the meta-knowledge and embeddings fails to generalize across domain, which causes the performance degradation. To address the problem, this paper employs an attention module upon a local-descriptor based model called DeepEMD to enable interaction between the local features. The experimental results show that proposed method improves 4.63% in 5-way 1-shot and 0.46% in 5-way 5-shot in the cross-domain setting compared to the origin DeepEMD, which verifies the effectiveness of the method.
E-MIMIC: Empowering Multilingual Inclusive Communication

Preserving diversity and inclusion is becoming a compelling need in both industry and academia. The ability to use appropriate forms of writing, speaking, and gestures is not widespread even in formal communications such as public calls, public announcements, official reports, and legal documents. The improper use of linguistic expressions can foment unacceptable forms of exclusion, stereotypes as well as forms of verbal violence against minorities, including women. Furthermore, existing machine translation tools are not designed to generate inclusive content.The present paper investigates a joint effort of the research communities of linguistics and Deep Learning Natural Language Understanding in fighting against non-inclusive, prejudiced language forms. It presents a methodology aimed at tackling the improper use of language in formal communication, with a particular attention paid to Romanic languages (Italian, in particular). State-of-the-art Deep Language Modeling architectures are exploited to automatically identify non-inclusive text snippets, suggest alternative forms, and produce inclusive text rephrasing. A preliminary evaluation conducted on a benchmark dataset shows promising results, i.e., 85% accuracy in predicting inclusive/non-inclusive communications.
Performance Comparison of Anomaly Detection Algorithms

In several studies, machine learning techniques that are repossessed in intrusion detection systems wangle wide-ranging acknowledgment by changing into a high-yielding domain and continues to be the main target of the researcher's vast significance. After several years of study, the intrusion detection community still faces difficult issues. During the process of detecting unexpected new attacks, reducing the high rate of false alarms remains an unanswered problem. Identification of anomalies is a key problem in malware detection in which the existence of planned or unintentional caused assaults, defects elsewhere is demonstrated by disturbances of normal conduct. This paper offers a top-level view of analysis directions for the utilization of tagged and untagged information to handle the difficulty of the identification of anomalies. By performance comparison of the available semi-supervised and unsupervised algorithms, you can select the best anomaly detection algorithm. The documented references would cowl the most theoretical issues, leading the researcher in new ways for study.
An Automated Abstract Approach for Investigating Bitcoin Balances and Wallet Addresses

While Bitcoin is legal, hackers, narcotics smugglers, and other dubious persons that have to be prosecuted are still utilizing it. Bitcoin is used in various industries due to its wide range of applications but it is also on the radar of malicious people and they are performing various types of cybercrimes in the dark web. Future conflicts will be cyber wars, with crimes combining cryptography and malware to manipulate information technology and compromise their security. Cyber-attacks are made easier by the rapid development of the Internet. Loss of private information and degradation of customer trust in e-commerce are two examples of web threats. In this paper, the authors have implemented an automated process for investigating the bitcoins balances and wallet addresses. The authors have also highlighted the use of bitcoin in various cybercrimes. The tool used in investigating the Bitcoin balances and the bitcoin wallets is SpiderFoot. The results are generated in our paper are the form of hashes of bitcoin balances and wallet addresses that are investigated properly to check for any cyber fraud in the dark web.
Analysis of Convolution Neural Network Technology on the Visual Effect of the Game

With the wide application of computer technology, the requirements of computer technology also have a high level of request. Convolutional neural network technology has also made many important achievements in various fields. Its powerful classification ability and learning ability also have a certain role and influence on the visual effect of the game in the game field.
FPGA implementation of high-fidelity hybrid reversible watermarking algorithm

In the digitally dominated world, to enhance the data security of digital images, this paper proposes a hybrid algorithm. The algorithm enhances quality metrics and simultaneously eliminates irreversibility and underflow-overflow errors. To enhance the important evaluation parameters, PSNR and embedding bit rate, distance between neighboring pixels is reduced by employing modified enhanced normalized mean interpolation (MENMI) in the first stage. In the second stage, improved reversible contrast mapping (IRCM) provides reversibility and error elimination by contrast improvement. The robustness of the proposed algorithm against different types of attacks is also checked and verified using MATLAB. To further validate the efficiency of the proposed algorithm in terms of power, hardware resources, and simulation time, the proposed architecture has been implemented and tested on Zynq SoC FPGA platforms. The findings demonstrate that the proposed algorithm provides an average of 20% and 18% improvements in PSNR over ENMI and ENMI-HM algorithms. The hardware implementation results signify that the proposed algorithm has low timing complexity and less power consumption at high simulation frequency than existing reversible watermarking (RW) algorithms.
Sentiment Analysis of Kaspi Product Reviews

Customer reviews are the key to the successful functionality of the companies. A producer will gain the true result of his products from the customer feedback. What should the seller do to achieve good quality for his product? Just by looking at the stars and reading the usual text, the seller will not be able to improve their product well enough, because different people can understand the same text in different ways. That’s why the seller can improve the wrong part of their product and make their product worse. Sentimental analysis can be applied here. He can make necessary changes to his product according to the feedback. However, it is common to see when the most clients may inadequately overestimate or underestimate the mark. To avoid confusion, it is better to analyze the text. This paper focuses on the sentiment feedback analysis and finding the relationship between reviews and ratings based on data collected from the “Kaspi.kz” marketplace. These data streams are cleaned, analyzed and reviews are got through opinion mining. After we performed data augmentation. We used several algorithms on both data to find the best one.
Evaluation Algorithm of Skilled Talents’ Quality Based on Deep Belief Network Model

In order to solve the problem that the evaluation algorithm is easy to fall into local extremum, which leads to slow convergence speed, a skilled talent quality evaluation algorithm based on a deep belief network model was designed. Establish an evaluation set with 4 first level indicators and 14 second level indicators, and calculate the corresponding weights to complete the construction of the evaluation index system. A DBN structure composed of several RBMs and a BP network is constructed. Based on the DBN, a quality evaluation algorithm is designed. The algorithm training is used to evaluate the test data and output the evaluation level. The experimental results show that the convergence speed of DBN based evaluation algorithm is significantly better than that of BP neural network and SVM based evaluation algorithm under the same number of iterations, which is suitable for the accurate evaluation of talent quality.
Forward-Secure Public Key Encryption Without Key Update from Proof-of-Stake Blockchain

A forward-secure public-key encryption (PKE) scheme prevents eavesdroppers from decrypting past ciphertexts in order to mitigate the damage caused by a potential secret key compromise. In prior works, forward security in a non-interactive setting, such as forward-secure PKE, is achieved by constantly updating (secret) keys. In this paper, we formalize the notion of blockchain-based forward-secure PKE and show the feasibility of constructing a forward-secure PKE scheme without key update (i.e. both the public key and the secret key are immutable), assuming the existence of a proof-of-stake blockchain with the distinguishable forking property introduced by Goyal et al. (TCC 2017). Our construction uses the proof-of-stake blockchain as an immutable decryption log and witness encryption by Garg et al. (STOC 2013) to ensure that the same ciphertext cannot be decrypted twice, thereby rendering a compromised secret key useless with respect to decryption of past ciphertext the legitimate user has already decrypted.
Increasing Performance of Plasmodium Detection Using Bottom-Hat and Adaptive Thresholding

Based on WHO data, in 2019 there were around 229 million cases of malaria worldwide, with a death rate of 409,000 people. This disease can be cured if diagnosis is done accurately and quickly. The gold standard for malaria diagnosis is microscopic examination. Despite the huge number of microscopic examinations, there are still some misdiagnose caused by human error. The previous research has developed a computer aided diagnosis (CAD) method for identify the phase of Plasmodium Falciparum. However, the study focused only on thin blood smear, meanwhile in cases of thick blood smear, a better technique is needed because plasmodium in thick blood smear is not well visualized and very small. The purpose of this research is to develop detection method based on digital image processing to support the malaria detection by paramedics. The result of this research will be able to differentiate Plasmodium parasites and artifacts. The data used is a digital microscopic image of thick blood smear. Data acquisition was held in Laboratory of Parasitology, Faculty of Medicine Universitas Gadjah Mada. 846 objects are obtained from 38 image in a time. There are nine features of the texture. The features are then selected by the Wrapper, CFS and the Gain Ratio feature selection method. There are three different classification comparisons in the classification stage, including naïve Bayes (NB), support vector machine (SVM) and multilayer perceptron (MLP). The best results in the classification of species were obtained from the NB classification with an accuracy of 82.05%.
APLIKASI KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN METODE NAÏVE BAYES BERBASIS ANDROID

Malnutrition is a condition that a person experience because the lack of nutrient intake or the amount of nutrients that exist is still below standard. nutritional problems are most commonly experienced by toddlers. Nutritional status in toddlers is a benchmark of the health condition of toddlers. The nutritional status of toddlers can currently be measured based on anthropometric index using WHO standards. Anthropometrics is an assessment of the nutritional status of toddlers determined through measurements of the human body In this study measurement of nutritional status using a technique in data mining namely Naïve Bayes Method that aims to facilitate health centers in measuring the nutritional status of toddlers quickly through android-based applications. Naïve Bayes method performs nutritional status classification of toddlers that will produce a classification very thin, thin, normal and fat. Based on data taken from banda sakti health center Lhokseumawe city which then carried out the classification process, obtained the results of nutritional status "skinny" with data testing male sex, weight 7.4 kg and height 64.6.
Security Analysis and Fraud Prevention in Cloud Computing

Cloud services nowadays became an essential requirement for the IT infrastructure to meet the demand for any organization’s growth and dynamic changes. Organizations are focusing more on the business goal than managing IT infrastructure as it was there in the earlier days. They are moving the workloads to cloud services and using IAAS, SAAS, or PaaS models as per the requirement. IT infrastructures are moving from an on-premises data center to a cloud data center. By doing so, organizations are leveraging the benefits in terms of cost as they go for services, easy management, and flexibility to meet the unexpected demands. There are many other key reasons which attract customers towards cloud services. Despite of having several benefits, cloud security is the major challenge that have been raised by most of the customers. Among all cloud security challenges, data breach is one of the topmost concerning points. There are many researchers have already been done in this area and different security frameworks have been implemented to make the cloud environment secured. Still we face cyber-attacks in every second.