Unicode Characters Research Articles

Performance of TF-IDF for text classification reviews on Google Play Store: Shopee

TF-IDF is a technique used to extract features in the field of text classification. The TF-IDF approach extracts feature by considering the frequencies of terms and their inverse document frequencies. The performance of various feature extraction methods varies, and it is necessary to determine the most appropriate approach for accurately classifying Shopee's application user reviews to enhance the user experience in Malaysia. This study aims to assess the efficacy of TF-IDF in text classification tasks, analyze their advantages and disadvantages, and identify the specific conditions in TF-IDF. The study employs a dataset of Shopee customer reviews acquired from the Google Play Store as the main data source. The methodology entails pre-processing the text data by applying a text normalization procedure that includes several processes, such as eliminating stop words, Unicode characters, and lemmatizing. The Logistic Regression, Support Vector Machine, and Decision Tree classifiers are trained and graded using both feature extraction approaches. The research notes that the efficacy of feature extraction approaches may differ based on the specific data set and task being considered. Subsequent studies might examine alternative methods of extracting features and assess their efficacy across various domains and datasets.

A novel approach to secure communication in mega events through Arabic text steganography utilizing invisible Unicode characters.

Mega events attract mega crowds, and many data exchange transactions are involved among organizers, stakeholders, and individuals, which increase the risk of covert eavesdropping. Data hiding is essential for safeguarding the security, confidentiality, and integrity of information during mega events. It plays a vital role in reducing cyber risks and ensuring the seamless execution of these extensive gatherings. In this paper, a steganographic approach suitable for mega events communication is proposed. The proposed method utilizes the characteristics of Arabic letters and invisible Unicode characters to hide secret data, where each Arabic letter can hide two secret bits. The secret messages hidden using the proposed technique can be exchanged via emails, text messages, and social media, as these are the main communication channels in mega events. The proposed technique demonstrated notable performance with a high-capacity ratio averaging 178% and a perfect imperceptibility ratio of 100%, outperforming most of the previous work. In addition, it proves a performance of security comparable to previous approaches, with an average ratio of72%. Furthermore, it is better in robustness than all related work, with a robustness against 70% of the possible attacks.

Robotic Writing of Arbitrary Unicode Characters Using Paintbrushes

Human handwriting is an everyday task performed regularly by most people. In the domain of robotic painting, multiple calligraphy machines exist which were built to replicate some aspects of human artistic writing; however, most projects are limited to a specific style of handwriting, often Chinese calligraphy. We propose a two-stage pipeline that allows industrial robots to write text in arbitrary typefaces and scripts using paintbrushes. In the first stage, we extract a set of strokes from character glyphs which are similar to how humans choose strokes during writing. In the second stage, we generate corresponding brush trajectories by applying a brush model to the extracted strokes. Our brush model computes the required brush pressure to achieve the given stroke width while also accounting for brush lag. We also present a method to automatically measure the parameters needed to predict brush lag by painting and recording calibration patterns. Our method generates trajectories for text in any given typeface, which, when executed by a robotic arm, results in legible written text. We can render most writing systems, excluding emoji and ligatures, in which arbitrary texts can be specified to write.

한자 표준 속성정보 DB 고도화의 필요성과 과제

This paper discussed the data information, the most basic information collection for the use of Han-characters in Korea, the necessity of upgrading, and finally, the method of upgrading.
 Han-characters are a collection of information that has a formal meaning. Han- are expressed in numerous visual signs for reasons that cannot deviate from the properties of the characters in the table. Currently, there are more than 80,000 characters developed by Unicode. These 80,000 characters not only have different shapes, but also have various information such as pitch and meaning information accordingly, as well as bookkeeping, stroke count, and components. We call this attribute information. According to this attribute information, Han-characters can be arranged, listed, searched, and used. In some cases, metadata may be included in the attribute information. Metadata is a source, its own type, child relationship, consonant relationship, and type relationship.
 Accurate Han-character attribute information is needed to organize and use Han-characters because the Han- character attribute information becomes the standard for searching and using characters. Nevertheless, it is often difficult to implement a standardized search system because there are still many errors in the dictionary, dictionary, and attribute information applied to the Unicode character search machine in Korea.
 This paper aims to clarify the necessity of upgrading by reviewing the problem of “music” and transfer problems included in the attribute information DB of Han-characters.

Homoglyph Attack Detection Model Using Machine Learning and Hash Function

Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This similarity in characters is generally known as homoglyphs. Malicious adversaries utilize homoglyphs in URLs and DNS domains to target organizations. To reduce the risks caused by phishing attacks, effective ways of detecting phishing websites are urgently required. This paper proposes a homoglyph attack detection model that combines a hash function and machine learning. There are two phases to the model approach. The machine was being trained during the development phase. The deployment phase involved deploying the model with a Java interface and testing the outcomes through actual user interaction. The results are more accurate when the URL is hashed, as any little changes to the URL can be recognized. The homoglyph detector can be developed as a stand-alone software that is used as the initial step in requesting a webpage as it enhances browser security and protects websites from phishing attempts. To verify the effectiveness, we compared the proposed model on several criteria to existing phishing detection methods. By using the hash function, the proposed security features increase the overall security of the homoglyph attack detection in terms of accuracy, integrity, and availability. The experiment results showed that the model can detect phishing sites with an accuracy of 99.8% using Random Forest, and the hash function improves the accuracy of homoglyph attack detection.

Invisible Unicode Programming

Invisible Unicode programming (IUP) can be defined as the art and science of hiding information in data that can be read by the computer. Invisible Unicode Programming (IUP) has the explicit goal of converting traditional character encodings to invisible encoding. This paper presents a new method to hide text. This systematic method uses the binary format of invisible character to hide and extract secret information. Creating a secret message involves four main steps, first using the binary format of the original letters in the message.nextcreating the appropriate Binary Masking Value (BMV)to cover the text, and subtracting the Binary Value (BV) from the Binary Masking Value (BMV)text to get Binary Invisible Unicode (BIU), and finally masking secret text using Binary Invisible Character (BIU). This Invisible Unicode Programming (IUP) Concept uses the 54 Invisible Unicode characters to make the text invisible. The results of the experiments show that this IUP method creates highly secured invisible information using the multi-level complexity algorithm to avoid the hackers.

АНАЛИЗ БЕЗОПАСНОСТИ ПОДСИСТЕМ ЛОКАЛЬНОЙ АУТЕНТИФИКАЦИИ ОС СЕМЕЙСТВ WINDOWS И LINUX

The paper is devoted to the security analysis of authority subsystem services for Windows and Linux operating systems. The paper provides security analysis for both local and network-based authentication in Windows. The Mimikatz (France) will be presented to demonstrate attacks on the authentication subsystem. Mimikatz is a software tool that can extract users’ credentials and password information from the memory of the LSASS process. To prevent such attacks on process memory Windows OS includes several security mechanisms: Security Reference Monitor, Protected Process Light, and Virtualization-Based Security. However, attackers can bypass these mechanisms to get illegal access to the process memory and steal users’ credentials. A similar analysis of the local authority subsystem for Linux OSes shows that gnome-keyring-daemon stores the users’ passwords in plain text. As a result, attackers can easily extract this sensitive information using memory forensics techniques via user-mode applications. Several modern Linux Distributions based on Red Hat Enterprise Linux (RHEL) still have this security issue: CentOS, Ubuntu, GNU/ Linux Rolling. Experts have developed software tools to locate and remove passwords from the memory to tackle this security challenge: MimiPenguin (USA) and Mimipy (USA). Comparison analysis of these tools reveals their drawbacks: these security tools cannot locate passwords with Unicode characters, and these tools have low speed. The proposed security solution called MimiDove is designed to solve both these issues. MimiDove expands features of MimiPenguin and Mimipy by locating and deleting passwords with ASCII and Unicode characters. MimiDove is faster than MimiPenguin and Mimipy.

New Text Steganography Technique based on Multilayer Encoding with Format-Preserving Encryption and Huffman Coding

Steganography is the process of hiding secret data inside other media or cover media. Balancing the requirements for capacity, security, and imperceptibility is the main challenge for any successful steganography system. In text steganography, the data hiding capacity is limited because of the lack of redundant data compared to other digital media, such as images, video, or audio. Other challenges in text steganography are imperceptibility and security. Poor imperceptibility results from the structure of the text file, which is more visually apparent in terms of syntax and grammar than in other media. Low level of security results from the sequential selection of positions for embedding secret data due to insufficient redundant data in a text file. Therefore, an attacker or a third party would notice slight changes in the text file. This paper proposes a new text steganography method that combines cryptography and compression techniques to deal with these issues. This technique is used to conceal secret data to achieve high data hiding capacity in the cover text while maintaining security and imperceptibility. Multilayer encoding and Format-Preserving Encryption (FPE) with Huffman Coding, are applied to secret data before embedding. Invisible Unicode characters are employed to embed secret data into English text files to generate stego files. Results show that the proposed method satisfies capacity and imperceptibility in the cover file by comparing it with previously developed methods.

Text-based Steganography using Huffman Compression and AES Encryption Algorithm

In every system of security, to keep important data confidential, we need a high degree of protection. Steganography can be defined as a way of sending confidential texts through a secure medium of communications as well as protecting the information during the process of transmission. Steganography is a technology that is used to protect users' security and privacy. Communication is majorly achieved using a network through SMS, e-mail, and so on. The presented work suggested a technology of text hiding for protecting secret texts with Unicode characters. The similarities of glyphs provided invisibility and increased the hiding capacity. In conclusion, the proposed method succeeded in securing confidential data and achieving high payload capacity by using the Huffman compression algorithm, which was implemented on an unlimited text length. In addition, this approach has the ability to hide a single bit in every digit or letter in the cover file. Also, the approach meets the cognitive transparency and does not make the modifications apparent on the original data. The method suggested in this work increases the security level through coding a secret message before embedding it within the cover text, with the use of the Advanced Encryption Standard (AES) algorithm.

Web-based text editing system for Nigerian major languages


 
 
 This study developed a web-based text editor to eliminate the incompatibility of computer keyboard with the three major indigenous languages in Nigeria. It also aims to reduce the time taken to produce characters with diacritical marks. The editors produced valid Unicode characters and require pressing less buttons to generating all the symbols of the alphabets for the three major indigenous languages in Nigeria. Client-side technologies were used to develop these applications. Three web pages, designated for Yorùbá, Igbo and Hausa language were generated with HTML. CSS was used to define the look and feel of the HTML elements on each page. Regular Expressions implemented in JavaScript functions were used to convert selected ASCII characters into desired Unicode characters. The editors are available at http://www.gazaliwakil.com.ng. The editors work well on latest version of browsers like (Google Chrome, Mozilla Firefox, Opera, and Internet Explorer). They are very light, consume minimal server resources and can work offline. The system was launched Fifty-one (51) times to extract data comprising the Loading, Scripting, Rendering, Painting, System, and Idle time. The obtained result showed that on the average, it takes about 13.77ms to load the HTML DOM elements, 42.83ms to load the javaScript, 13.10ms and 1.73ms for rendering and painting the page by CSS. Additional time taken are 43.91ms and 3,045.10ms for the system and idle time respectively. A total time of 3,160.43ms (3.16s) is required when any of the editors is launched before the page can accept inputs from the users. It also takes the editors 2.66ms to add diacritical marks on a letter. This would, in effect, not reduce the typing speed of users.
 
 

Transcoding billions of Unicode characters per second with SIMD instructions

AbstractIn software, text is often represented using Unicode formats (UTF‐8 and UTF‐16). We frequently have to convert text from one format to the other, a process called transcoding. Popular transcoding functions are slower than state‐of‐the‐art disks and networks. These transcoding functions make little use of the single‐instruction‐multiple‐data (SIMD) instructions available on commodity processors. By designing transcoding algorithms for SIMD instructions, we multiply the speed of transcoding on current systems (64 and ARM). To ensure reproducibility, we make our software freely available as an open source library.

Plain Text & Character Encoding: A Primer for Data Curators

Plain text data consists of a sequence of encoded characters or “code points” from a given standard such as the Unicode Standard. Some of the most common file formats for digital data used in eScience (CSV, XML, and JSON, for example) are built atop plain text standards. Plain text representations of digital data are often preferred because plain text formats are relatively stable, and they facilitate reuse and interoperability. Despite its ubiquity, plain text is not as plain as it may seem. The set of standards used in modern text encoding (principally, the Unicode Character Set and the related encoding format, UTF-8) have complex architectures when compared to historical standards like ASCII. Further, while the Unicode standard has gained in prominence, text encoding problems are not uncommon in research data curation. This primer provides conceptual foundations for modern text encoding and guidance for common curation and preservation actions related to textual data.

Convolutional Neural Network Based Ensemble Approach for Homoglyph Recognition

Homoglyphs are pairs of visual representations of Unicode characters that look similar to the human eye. Identifying homoglyphs is extremely useful for building a strong defence mechanism against many phishing and spoofing attacks, ID imitation, profanity abusing, etc. Although there is a list of discovered homoglyphs published by Unicode consortium, regular expansion of Unicode character scripts necessitates a robust and reliable algorithm that is capable of identifying all possible new homoglyphs. In this article, we first show that shallow Convolutional Neural Networks are capable of identifying homoglyphs. We propose two variations, both of which obtain very high accuracy (99.44%) on our benchmark dataset. We also report that adoption of transfer learning allows for another model to achieve 100% recall on our dataset. We ensemble these three methods to obtain 99.72% accuracy on our independent test dataset. These results illustrate the superiority of our ensembled model in detecting homoglyphs and suggest that our model can be used to detect new homoglyphs when increasing Unicode characters are added. As a by-product, we also prepare a benchmark dataset based on the currently available list of homoglyphs.

A Compression System for Unicode Files Using an Enhanced Lzw Method

Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file. This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process.

Combining Invisible Unicode Characters To Hide Information In A Text Document

Steganography develops tools and methods for hiding the fact of message transmission. The first traces of steganographic methods are lost in ancient times. For example, there is a known method of hiding a written message: the slave's head was shaved, a message was written on the scalp, and after the hair grew back, the slave was sent to the addressee. From detective works, various methods of secret writing between the lines of ordinary text are well known: from milk to complex chemical reagents with subsequent processing. Digital steganography is based on hiding or embedding additional information in digital objects while causing some distortion of these objects. In this case, text, images, audio, video, network packets, and so on can be used as objects or containers. To embed a secret message, steganographic methods rely on redundant container information or properties that the human perception system cannot distinguish. Recently, there has been a lot of research in the field of hiding information in a text container, since many organizations widely use text documents. Based on this, here the MS Word document is considered as a medium of information. MS Word documents have different parameters, and by changing these parameters or properties, you can achieve data embedding. In the same article, we present steganography using invisible Unicode characters of the Space type, but with a different encoding.

Multilingual emoji prediction using BERT for sentiment analysis

PurposeRecently, Unicode has been standardized with the penetration of social networking services, the use of emojis has become common. Emojis, as they are also known, are most effective in expressing emotions in sentences. Sentiment analysis in natural language processing manually labels emotions for sentences. The authors can predict sentiment using emoji of text posted on social media without labeling manually. The purpose of this paper is to propose a new model that learns from sentences using emojis as labels, collecting English and Japanese tweets from Twitter as the corpus. The authors verify and compare multiple models based on attention long short-term memory (LSTM) and convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT).Design/methodology/approachThe authors collected 2,661 kinds of emoji registered as Unicode characters from tweets using Twitter application programming interface. It is a total of 6,149,410 tweets in Japanese. First, the authors visualized a vector space produced by the emojis by Word2Vec. In addition, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. Second, it involves entering a line of tweets containing emojis, learning and testing with that emoji as a label. The authors compared the BERT model with the conventional models [CNN, FastText and Attention bidirectional long short-term memory (BiLSTM)] that were high scores in the previous study.FindingsVisualized the vector space of Word2Vec, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. The authors obtained a higher score with BERT models compared to the conventional model. Therefore, the sophisticated experiments demonstrate that they improved the score over the conventional model in two languages. General emoji prediction is greatly influenced by context. In addition, the score may be lowered due to a misunderstanding of meaning. By using BERT based on a bi-directional transformer, the authors can consider the context.Practical implicationsThe authors can find emoji in the output words by typing a word using an input method editor (IME). The current IME only considers the most latest inputted word, although it is possible to recommend emojis considering the context of the inputted sentence in this study. Therefore, the research can be used to improve IME performance in the future.Originality/valueIn the paper, the authors focus on multilingual emoji prediction. This is the first attempt of comparison at emoji prediction between Japanese and English. In addition, it is also the first attempt to use the BERT model based on the transformer for predicting limited emojis although the transformer is known to be effective for various NLP tasks. The authors found that a bidirectional transformer is suitable for emoji prediction.

A secure and size efficient algorithm to enhance data hiding capacity and security of cover text by using unicode

With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the detection of confidential information. It demands novelty in data hiding algorithms. In this regard, a novel algorithm is proposed by using steganography and cryptography together for the enhancement of capacity and security of confidential data. The recommended algorithm uses a linguistic steganography method to conceal data into the Arabic text carrier. In the described algorithm, the identification of secret information from text files is hard due to less redundant bits in the text as compared to the image, audio, and video steganographic mediums. The current solution uses Unicode characters such as Zero-Width-Character (ZWC) and Zero-Width-Joiner (ZWJ) to hide the secret information. Before hiding confidential information, secret data is encrypted by using bit inversion due to which algorithm achieved high security. It is observed from the simulation results that the proposed algorithm successfully achieved high cover medium capacity, security, and robustness.

Inclusion of Unicode Standard seamless characters to expand Arabic text steganography for secure individual uses

The need to protect confidential data stored on personal computers (PCs) or sent to other parties has increased significantly. Therefore, a secure, high-capacity strategy is needed to cover the data contained within media and make it difficult to detect. Due to the limited number of research studies that have achieved these ends through text steganography, we present an innovative approach for hiding secret bits in Arabic text with Unicode Standard seamless. Our method uses the contextual forms of Arabic characters to hide certain secret bits. Extra characters such as Zero-Width Joiners (ZWJ), Kashida, Medium Mathematical Spaces (MMSPs), and Zero-Width Non-Joiners (ZWNJ) are also used to further enhance the method’s capacity without diminishing the data’s security. Our experimental results show that this technique outperforms similar methods with an average improvement in capacity of more than 50%. This technique also has a higher security ratio than the other methods in this study, with the exception of Method 5, and is robust against electronic text modifications such as copying, pasting, and text formatting. Moreover, our algorithm can be widely adopted in related languages, such as Urdu and Farsi, due to its utilization of Unicode characters, which is the encoding standard used in most of the world’s writing systems.

The Role of Flag Emoji in Online Political Communication

Flags are important national symbols that have transcended into the digital world with inclusion in the Unicode character set. Despite their significance, there is little information about their role in online communication. This article examines the role of flag emoji in political communication online by analyzing 640,676 tweets by the most important political parties and Members of Parliament in Germany and the United States. We find that national flags are frequently used in political communication and are mostly used in-line with political ideology. As off-line, flag emoji usage in online communication is associated with external events of national importance. This association is stronger in the United States than in Germany. The results also reveal that the presence of the national flag emoji is associated with significantly higher engagement in Germany irrespective of party, whereas it is associated with slightly higher engagement for politicians of the Republican party and slightly lower engagement for Democrats in the United States. Implications of the results and future research directions are discussed.

Preprocessed Text Compression Method for Malayalam Text Files

The increasing importance of Unicode for text files implies an increase in storage space required for data and the time for the transmission of data, with a corresponding need for compression of data. Conventional compressors fair purely on UTF-8 texts, where each character can span multiple bytes. Malayalam which is one among the four major languages of the Dravidian family, is represented by using Unicode characters. The contribution of this paper is a reversible transformation mapping of the input to reduce the actual size of the input file before a general purpose compression method. After the preprocessing, LZW compression achieves more compression to Malayalam text files containing any characters including ASCII characters. This method can be extended to any native language files containing mostly the characters of only one script.