Reviewerly/exHarmony huggingface dataset:
Design of a Website-Based Arabic Typing Application for Students of Arabic Language Education Program at University

Today's internet technology has advanced extremely quickly and is still expanding. The virtual world is a place where everyone and any group of individuals are free to engage in their activities. In this all-IT era, typing skills are very important for students. Because almost all student activities are carried out by typing such as compiling papers, reports, theses and others. However, the results of observations made at the Arabic Language Education Study Program at the University of Muhammadiyah Malang show that the skills of typing Arabic texts for Arabic Language Education Study Program students are still minimal. The aim of the research is to design a website-based Arabic Typing application and test the feasibility of using the application. The research subjects were students of the Arabic Language Education Study Program. The development model used is the ADDIE model. Through research and development, researchers are tasked with designing applicationsArabic Typingwebsite-based and test the feasibility of using the application for Arabic Language Education Study Program students. Based on media expert validation obtained an average percentage of eligibility of 97.11% (Very Feasible), whereas based on material expert validation obtained an average percentage of eligibility of 85% (Very Feasible). The results of the feasibility test by students obtained an average percentage of eligibility of 83.9% (Very Feasible). On this basis the Arabic Typing application based on this website is very feasible to use as a medium to improve Arabic text Keyboarding skills specifically for students of Arabic Language Education Study Program Faculty of Islamic Stadies at University of Muhammadiyah Malang
Comparative study on object detection in visual scenes using deep learning

Object detection is a crucial aspect of computer vision, enabling machines to identify and locate various objects within images or videos. This paper provides an in-depth review of the subject, discussing its importance and applications across diverse fields, including autonomous vehicles, surveillance, augmented reality, healthcare, retail, and environmental monitoring. The object detection framework is outlined, highlighting key steps such as image acquisition, preprocessing, feature extraction, object detection models, and post-processing. Deep learning techniques have significantly improved object detection, making it more accurate and faster. Various state-of-the-art models, such as YOLOv4, YOLOv5, and MobileNetV3, are presented with their respective performance metrics. The paper also explores recent developments in object detection, including novel loss functions, neural architecture search (NAS), and advancements in handling challenging conditions like occlusions and low lighting. Despite the progress, there remain challenges in the field, such as improving object detection in complex environments. Looking to the future, the paper predicts that object detection models will become more accurate and versatile, capable of handling challenging conditions and detecting a wider range of objects. Deep learning will continue to play a vital role in advancing object detection, leading to further breakthroughs in the field. The provided references offer a comprehensive overview of the literature on object detection, making this paper a valuable resource for researchers and practitioners in the field.
Hate Speech Detection based on Word Embedding and Linguistic Features

Objectives: To develop an improved hate speech detection method based on word embedding and linguistic features. Methods: Many machine-learning classifiers like Logistic Regression (LR), Gaussian Naive Bayes (GNB), Random Forest (RF), K-Nearest Neighbor (KNN) and Linear Support Vector Classifier (SVC) are trained on linguistic data for identifying hated speech. For this research two datasets has been used with the size of 24783 tweets and 6977 tweets for Tweet hate speech detection dataset and Hasoc19 dataset respectively. We have taken the size of training and testing dataset is 67/33 for both the dataset, in which size of training dataset is 67 and size of testing dataset is 33. Findings: On Tweet hate speech detection dataset we target the highest accuracy 0.90 and highest precision, recall and f1-score like 0.87, 0.85 and 0.90 respectively for label 0 and 0.98, 0.98 and 0.93 respectively for label 1 and 0.86, 0.85 and 0.74 for class 2 after applying random forest classifier. On Hasoc2019 dataset we achieve the highest accuracy 0.99 and highest precision, recall and f1-score values like 1.00, 0.99 and 1.00 for class 0 and 1.0, 0.99 and 0.99 for class 1 after applying Random Forest classifier with linguistic features TF-IDF word embedding technique. Novelty: Twenty linguistic features with term frequency-inverse document frequency (TF-IDF) word embedding technique make this research unique. Twenty linguistic characteristics have been chosen for detecting the despised information based on three groups of attributes which is complexity attributes, stylometric attributes and psycho-linguistic attributes have been chosen. Keywords: Machine Learning Classifiers, Linguistic Features, Accuracy, TF­IDF, Random Forest Classifier
Fashion Recommendation System using Collaborative Filtering

Collaborative Filtering is an method used to build a recommendation system with the concept that conclusions from different clients are used to anticipate things that may be of interest to users. This research uses data from Rent the Runway and the method used is Item-based Collaborative filtering, where the system will look for similarities in products that have been purchased by customers and then look for predictive values. Fashion requires recommendations because it plays a crucial role in helping individuals express their identity, personal style, and personality through clothing choices, accessories, and dressing styles.The recommendation system uses the item method based on analyzing the number of purchases or sales and grouping according to each product category so that it can help consumers in choosing fashion products. It was found that the use of Adjusted Cosine Similarity produces better recommendations with an average MAE value of 0.2750, while Cosine Similarity with an average MAE difference of 0.3989. This proves that the use of adjusted cosine similarity can produce better recommendations because the adjustment algorithm not only considers user behavior, but also produces lower performance errors.
Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm

Vitamin C-rich fruits not only taste fresh and delicious but also have the potential to increase the body's resistance to various diseases and maintain a proper nutritional balance. Information about fruits high in vitamin C is very important in order to increase public knowledge about which fruits contain high levels of vitamin C. However, to classify fruits high in vitamin C based on their image, a model is needed that is able to analyze the characteristics present in the image of the fruit. The purpose of this study is to build a classification model for high-vitamin C fruits with a combination of the Self-Organizing Map (SOM) artificial neural network algorithm and K-Means Clustering. Prior to classification, an image segmentation process is carried out using the K-Means Clustering algorithm, which will separate the image into parts that have similar visual characteristics. After the segmented image, the features of the object are extracted based on shape and texture. After the features of the image have been obtained, proceed with classifying images using the SOM algorithm by mapping multidimensional data into a lower-dimensional spatial representation to obtain the appropriate group or class. The accuracy test results for the built model produce an accuracy value of 93.33% and are included in the good category
Topic Detection on Twitter using GloVe with Convolutional Neural Network and Gated Recurrent Unit

Twitter is a social media platform that allows users to share thoughts or information with others for all to see. However, twitters often use abbreviations, slang, and incorrect grammar because tweets are limited to 280 characters. Topic detection often has problems with low accuracy, one method that can be used to overcome this problem is feature expansion. Feature expansion on Twitter is a semantic addition to the process of expanding the original text syllables to make it look like a large Document. That way, feature expansion is used to reduce word mismatches. This study uses the expansion of the GloVe feature with the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) classification methods. The results show that the topic detection system with the GloVe feature extension and CNN-GRU hybrid classification has an accuracy of 94.41%
Pengaruh Periklanan, Publisitas dan Personal Selling Terhadap Strategi Kampus

The role of education is a form of education of a country as a manifestation of love for its nation to create educated human beings and become noble people. This study aims to determine the effect of advertising, publicity and personal selling on students' decisions to choose to study at STAI Darul Ulum Banyuwangi with a quantitative descriptive research approach, STAI Darul Ulum students who were taken as samples in this study were 66 respondents with solvin techniques probability sampling The data analysis method to test the hypothesis that has been compiled uses a stepwise test through validity testing, reliability testing, classical assumption testing and multiple linear regression testing. Based on the results of multiple linear regression analysis by considering the hypothesis test consisting of the F test and T test, it can be concluded that based on the t test results, the advertising variable has a positive and significant effect on student decisions, indicated by the tcount value greater than ttable or 2,617 &gt; 1,670 with a value significant 0.011 &lt; 0.05. while the publicity variable has no significant effect on student decisions as indicated by the tcount value smaller than ttable 0.809 &lt; 1.670 and a significant value 0.422 &gt; 0.05. And the personal selling variable has a significant effect on student decisions. It is shown that the tcount value is greater than ttable 3,369 &gt; 1,670 and a significant value of 0.001 &lt; 0.05. Then from the results of the F test simultaneously the independent variables of advertising, publicity, and personal selling have a significant influence on the student's decision to choose to study at STAI Darul Ulum Banyuwangi indicated by the Fcount 32.249 &gt; Ftable 2.753 and the significant value 0.000 &lt;0.05. For the results of the R squer test (R2) of 0.60.9, it means that overall the variables are able to influence 60.9% on the student's decision to choose to study at STAI Darul Ulum Banyuwangi
Derivational Morpheme Errors in English: A Study of Libyan Students at Al-Mergib University

Proficiency in a language entail mastering components such as lexical nuances and grammatical rules. However, morphology often gets overlooked in university programs, underscoring the need for students to heighten their morphological awareness to refine their English writing. This research delves into the errors in using derivational morphemes by third- and fourth-year English major students from Libya. The objective is to identify, categorize, and understand the root causes of these errors. The study, involving ten participants from Al-Mergib University, used a qualitative research approach with essay writing as the primary data collection tool. The analysis uncovers various errors in derivational morpheme usage. Specifically, the study recorded 36 omission errors, 16 order errors, 6 incorrect formations, and 3 other errors. A striking observation was that errors predominantly centered on suffixes over prefixes. The most frequent mistakes arose in the transformation of adjectives to adverbs and verbs to nouns, encompassing omissions, additions, misordering, and misformations. For educators, this study underscores the importance of understanding student challenges with derivational morphemes, especially related to verbs and nouns. It advocates for a pedagogical approach that includes group discussions analyzing relevant texts. By highlighting these errors, the study offers valuable insights into areas where Libyan students require focused attention in English morphology.
Enhanced Color QR Codes with Resilient Error Correction for Dirt-Prone Surfaces

This study focuses on overcoming the limitations of traditional QR codes, specifically their vulnerability to damage and insufficient error correction capabilities. We introduce an innovative approach, the Enhanced Color QR (CQR) code, which strengthens the error correction ability of QR codes by employing red, green, and blue color channels. This pioneering technology removes critical zones, enabling damage tolerance anywhere on the code and allowing for up to 50% damage to the code area, considerably surpassing the performance of existing QR code systems. Importantly, our CQR code maintains backward compatibility, ensuring readability by current QR code scanners. This state-of-the-art improvement is especially useful in scenarios where conventional 2D barcodes face challenges, such as on non-flat or reflective surfaces frequently encountered on fruits, cans, bottles, and medical equipment like blood test sample tubes and syringes. Furthermore, our CQR code’s four corners and boundary can be estimated without requiring corner visibility, offering potential advantages for augmented reality applications. By addressing the key issues associated with traditional QR codes, our research presents a significant advancement in the field of computer vision and provides a more resilient and versatile solution for a wide range of real-world applications.
Research on standardization of information security of renewable energy power generation

Under the context of national energy security and strategies for carbon emissions peak and carbon neutralization, the importance of information security in the construction of new power systems for renewable energy cannot be overlooked. This paper aims to analyze the current state of information security standardization in the renewable energy power generation field. By doing so, it identifies the existing risks and proposes development plans for the relevant standards.
QnAs with David M. Knipe

The noninteractive zero-knowledge proof (NIZKP) is underlying a broad spectrum of applications. The major categories of NIZKP are instantiated with deterministic hash functions in the real world. However, the outputs of deterministic hash ...Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, noninteractive zero-knowledge proof (...
Application and Prospect of Deep Learning and Machine Learning Technology

This paper systematically expounds the relationship between the current hot concepts such as artificial intelligence, machine learning and deep learning, and makes it clear that the technical category covered by them is a descending hierarchical relationship. In addition, deep learning and machine learning technologies are compared and analysed from five aspects: data volume, data feature analysis, hardware dependency, model training time and learning algorithm types. Finally, the application fields and prospects of deep learning and machine learning are summarized.
Short Text Classification of Invoices Based on BERT-TextCNN

Traditional invoice text classification methods are labor-intensive and inefficient. In order to effectively identify the types of invoices, a Chinese text classification model based on deep learning BERT-TextCNN is designed, and a short text classification dataset of invoices is obtained from a municipal tax bureau to train and test the model, and to compare and analyze the performance of BERT-TextCNN model, BERT model, and TextCNN model. As a result, compared to traditional neural network models, the BERT + TextCNN model can accurately classify Chinese text, effectively prevent excessive fitting, and have good generalization ability. The performance of text classification is improved compared to both BERT model and TextCNN model alone. Draw a conclusion through experiments which show that the BERT-TextCNN model has good classification effect and good stability.
LSDedup: Layered Secure Deduplication for Cloud Storage

To implement encrypted data deduplication in a cloud storage system, users must encrypt files using special encryption algorithms (e.g., convergent encryption (CE)), which cannot provide strong protection. The confidential level of an outsourced file is determined by the user himself/herself subjectively or by the owner number of the file objectively. These files owned by a few users are considered strictly confidential and require strong protection. In this paper, we design, analyze and implement LSDedup, which attains a high storage efficiency while providing strictly confidential files (SCFiles) with strong protection. LSDedup allows cloud users to securely interact with cloud servers to check the confidential level of an outsourced file. Users encrypt the SCFiles using standard symmetric encryption algorithms to achieve a high security level, whereas encrypting the less confidential files (LSFiles) using CE such that cloud servers can perform deduplication. LSDedup is designed to prevent cloud servers reporting fake confidential level and a fake file user claiming the ownership of the file. Formal analysis is provided to justify its security. Besides, we implement an LSDedup prototype using Alibaba Cloud as backend storage. Our evaluations demonstrate that LSDedup can work with existing cloud service providers’ APIs and achieves modest performance overhead.
Instance-Level Semantic Maps for Vision Language Navigation

Humans have a natural ability to perform semantic associations with the surrounding objects in the environment. This allows them to create a mental map of the environment, allowing them to navigate on-demand when given linguistic instructions. A natural goal in Vision Language Navigation (VLN) research is to impart autonomous agents with similar capabilities. Recent works take a step towards this goal by creating a semantic spatial map representation of the environment without any labeled data. However, their representations are limited for practical applicability as they do not distinguish between different instances of the same object. In this work, we address this limitation by integrating instance-level information into spatial map representation using a community detection algorithm and utilizing word ontology learned by large language models (LLMs) to perform open-set semantic associations in the mapping representation. The resulting map representation improves the navigation performance by two-fold (233%) on realistic language commands with instance-specific descriptions compared to the baseline. We validate the practicality and effectiveness of our approach through extensive qualitative and quantitative experiments.
KextFuzz: A Practical Fuzzer for macOS Kernel EXTensions on Apple Silicon

macOS drivers, i.e., Kernel EXTensions (kexts), are attractive attack targets for adversaries. However, automatically discovering vulnerabilities in kexts is extremely challenging because kexts are mostly closed-source, and the latest macOS running on customized Apple Silicon has limited tool-chain support. Most existing static analysis and dynamic testing solutions cannot be applied to the latest macOS. In this paper, we present the first end-to-end fuzzing solution KextFuzz to detect bugs in the latest macOS kexts running on Apple Silicon. Unlike existing driver fuzzing solutions, KextFuzz does not require source code, execution traces, hypervisors, or hardware features (e.g., coverage tracing) and thus is universal and practical. We note that macOS has deployed many mitigations, including pointer authentication, code signature, and userspace kernel layer wrappers, to thwart potential attacks. These mitigations can provide extra knowledge and resources for us to enable kernel fuzzing. KextFuzz exploits these mitigation schemes to instrument the binary for coverage tracking, infer the type and semantic information of kext interfaces, and generate multi-dimension inputs. KextFuzz has found 49 unique kernel bugs in the macOS kexts and got five CVEs. Some bugs could cause severe consequences like running arbitrary code with kernel privilege.
Polarization-insensitive silicon optic switch based on mode manipulated power splitters and phase shifters

A polarization-insensitive thermo-optic switch is proposed and demonstrated on the silicon-on-insulator platform with a 220 nm silicon core layer. The present device is based on the Mach–Zehnder interferometer structure, consisting of polarization-insensitive power splitters and polarization-insensitive phase shifters (PIPSs). The polarization-insensitive power splitter has been realized by employing an adiabatic directional coupler, which utilizes the fast adiabatic mode evolution by introducing cubic Bézier curves on outer contours, providing broadband 3-dB power splitting for TE and TM polarization modes with only 70 µm coupling length. For the novel PIPSs, the ridge waveguide with large aspect ratio, based on the mode hybridness property, could obtain the same power consumption (Pπ) for an optical switch working at TE and TM polarizations. Experimental results indicate that the measured insertion losses are less than 2 dB and the extinction ratios are larger than 15 dB over a 40 nm wavelength band (covering the C-band).
Active Disturbance Rejection Predictive Control for Local Trajectory Planning of Unmanned Ground Vehicles

In this paper, an active disturbance rejection predictive control strategy is proposed for trajectory planning task of unmanned ground vehicles. Rather than error estimation of accurate system modeling, internal error and environment disturbance are processed via single extended state observer. Nonlinear feedback control law is applied to reduce steady-state error significantly. Then motion planning and nonholonomic constraints could be handled via nonlinear model predictive control. Simulation and experiment results show that the proposed algorithm is able to accomplish trajectory planning task with internal error and environment disturbance.