Reviewerly/exHarmony huggingface dataset:
Efficient Accreditation Document Classification Using Naïve Bayes Classifier

Objectives: To develop a desktop application that automatically classifies a document as to which area of accreditation documents it should belong to.Specifically, it aims to: a) To create a predictive model that addresses document classification tasks.b) To design and develop an application that classifies documents according to document classification.c) To evaluate the performance measures of the automatic document classification.Methods: We introduce an innovative approach for the automatic classification of accreditation documents.Specifically, an approach of including scanned or captured documents in classification task using Optical Character Recognition (OCR); use TF-IDF (Term-frequency Inverse Document Frequency) with stopwords removal, ngram of 1-2 in preprocessing of the text documents; and Naive Bayes algorithm with additive (Laplace/Lidstone) smoothing as a classifier in building our model.Results: Performance measures such as accuracy, precision, recall, and f-score were conducted to evaluate the efficiency of the study.The results showed 82% accuracy, 84% precision, 82% recall, and 82% F-1 score.As we explore the use of OCR for text extraction, TF-IDF for text preprocessing, and Naive Bayes classifier, the results indicate that the proposed approach is efficient.Conclusions: Classification of input documents in whatever forms, may it be captured image, scanned or simple text documents were obtained using OCR, TF-IDF, and Naive Bayes classifier.It provides an efficient way of automatic classification of accreditation documents and it gives an avenue to address limiting factors of the previous works, i.e classifying documents based on one's opinion and time-consuming classification.
Elemental analysis of rice husk using X-ray fluorescence techniques – a case study of Jigawa State, Nigeria

The rice husk or hard protective shell are woody, siliceous, and not edible part of the rice plant. Here in, the elemental composition of rice husk was determined using the energy dispersive X-ray Fluorescence Technique. The rice husks were obtained from four different Local Governments namely, Hadejia, Dutse, Auyo and Ringim of Jigawa State, Nigeria. The samples were collected during dry season and ground into a fine powder using mortar and pestle The results of the analyses revealed the presence of eleven elements which are; Silicon (Si), Rubidium (Rb), Potassium (K), Calcium (Ca), Manganese (Mn), Iron (Fe), Nickel (Ni), Copper (Cu), Zinc (Zn), Bromine (Br) and Strontium (Sr) for Hadejia and Ringim while Dutse and Auyo are having ten with the absence of Br. The elements detected and their concentration (in %) for Hadejia, Dutse, Auyo and Ringim respectively are as follows Si (69.05, 70.52, 73.18, 69.33), Rb (0.321, 0.153, 0.164, 0.219), K (16.52, 16.03, 14.29, 16.66), Ca (8.45, 8.064, 8.101, 8.85), Mn (1.104, 1.026, 0.99, 1.077), Fe (3.739, 3.564, 2.719, 3.162), Ni (0.27, 0.291, 0.291, 0.249, 0.26), Cu (0.021, 0.018, 0.017, 0.015), Zn (0.36, 0.266, 0.226, 0.307), Br (0.065, 0.00, 0.00, 0.046) and Sr (0.087, 0.056, 0.05, 0.063). The results of the analysis in % shows Si with high percentage concentration followed by K and the rest are in the following ascending order Ca &gt; Fe &gt; Zn &gt; Rb &gt; Sr &gt; Br &gt; Ni &gt; Cu. Four new elements were discovered namely; Rb, Cu, Sr and Br in rice husk which were not common in other existing studies. The composition can also be affected by factors such as soil chemistry, climatic conditions, and the type of fertilizer used.
Digital Learning Environment Values of Pre-Service Teachers as a Basis for Successful Professional Self-Realisation: A Case Study

The paper analyses the value mindsets of pre-service teachers in terms of the digital learning environment (DLE). DLE is considered to be both a system of new tools and methods for teaching activity and a prospective educational eco-system for learning. The DLE value model is based on a psychodidactic approach, which concerns three main constructs—digital content values, communication values, and management values. The research involved 200 pre-service teachers who were asked to complete a questionnaire, evaluating 27 DLE values and opportunities. The results show that pre-service teachers value digital learning content but are not sufficiently ready for its creation; they develop tolerance in communication yet do not have enough skills to show empathy; they appreciate independence in learning management and still need training in professional digital self-presentation. Consequently, professional training and especially internships for pre-service teachers should include a synthesis of traditional (face-to-face) and virtual communication and interaction with children to facilitate mastering an expanded set of competencies for successful professional self-realisation. The findings can be beneficial in terms of e-learning practices, DLE design, and the organisation of internships.
Comprehensive security analysis of CRAFT

CRAFT is a lightweight block cipher, designed to provide efficient protection against differential fault attacks. It is a tweakable cipher that includes 32 rounds to produce a ciphertext from a 64-bit plaintext using a 128-bit key and 64-bit public tweak. In this paper, compared to the designers’ analysis, we provide a more detailed analysis of CRAFT against differential and zero-correlation cryptanalysis, aiming to provide better distinguishers for the reduced rounds of the cipher. Our distinguishers for reduced-round CRAFT cover a higher number of rounds compared to the designers’ analysis. In our analysis, we observed that, for any number of rounds, the differential effect of CRAFT has an extremely higher probability compared to any differential trail. As an example, while the best trail for 11 rounds of the cipher has a probability of at least 2−80, we present a differential with probability 2−49.79, containing 229.66 optimal trails, all with the same optimum probability of 2−80. Next, we use a partitioning technique, based on optimal expandable truncated trails to provide a better estimation of the differential effect on CRAFT. Thanks to this technique, we are able to find differential distinguishers for 9, 10, 11, 12, 13, and 14 rounds of the cipher in single tweak model with the probabilities of at least 2−40.20, 2−45.12, 2−49.79, 2−54.49, 2−59.13, and 2−63.80, respectively. These probabilities should be compared with the best distinguishers provided by the designers in the same model for 9 and 10 rounds of the cipher with the probabilities of at least 2−54.67 and 2−62.61, respectively. In addition, we consider the security of CRAFT against the new concept of related tweak zero-correlation (ZC) linear cryptanalysis and present a new distinguisher which covers 14 rounds of the cipher, while the best previous ZC distinguisher covered 13 rounds. Thanks to the related tweak ZC distinguisher for 14 rounds of the cipher, we also present 14 rounds integral distinguishers in related tweak mode of the cipher. Although the provided analysis does not compromise the cipher, we think it provides a better insight into the designing of CRAFT.
Embedding Pose Information for Multiview Vehicle Model Recognition

Vehicle model recognition is a typical fine-grained classification task that has a wide range of application prospects in safe cities and constitutes a research hotspot in the field of computer vision. Vehicles in images can appear at various angles, resulting in large differences in appearance. The existence of “multiviews” renders vehicle model recognition challenging. Recent research on vehicle model recognition has not fully explored the pose information of vehicles in different images, resulting in low model performance. In this study, we use vehicle pose information to solve the multiview vehicle model recognition (MV-VMR) problem and design a convolutional neural network (CNN) model with embedded vehicle pose information, known as the embedding pose CNN (EP-CNN). The proposed model includes two subnetworks: the pose estimation subnetwork (PE-SubNet) and vehicle model classification subnetwork (VMC-SubNet). PE-SubNet extracts the vehicle pose information, including the pose features and vehicle viewpoint. In VMC-SubNet, considering the scale variation of vehicles, an improved squeeze-and-excitation (SE) block, named the MultiSE block is implemented. We embed the vehicle viewpoint into the MultiSE block, which reweighs each channel such that the extracted features elicit different responses to different viewpoints. Subsequently, the pose features and classification features are integrated for classification. Experiments are conducted on the benchmark CompCars web-nature and Stanford Cars datasets. The results demonstrate that the proposed EP-CNN method can achieve higher recognition accuracy than most classic CNN models and several state-of-the-art fine-grained vehicle model classification algorithms. Code has been made available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HFUT-CV/EP-CNN</uri> .
A Metaheuristic Approach for Encrypting Blockchain Data Attributes Using Ciphertext Policy Technique

Unlike public chains, the Alliance Blockchain Hyperledger Fabric has a member management service mechanism that may provide data isolation security at the channel level. However, because this data isolation security technique synchronizes plaintext data inside the channel, data leakage is still a possibility. Furthermore, in some fine-grained privacy protection circumstances, channel-based data access restriction is ineffective. In order to solve the data privacy security problems in the above-mentioned consortium chain superledger, a blockchain data attribute encryption scheme based on ciphertext policy is proposed. Combining the original Fabric Certificate Authority module in the Hyperledger, the proposed scheme can realize the user-level fine-grained security access to control blockchain data while also realizing the secure distribution of user attribute keys in the blockchain data attribute encryption scheme based on the ciphertext policy scheme. The security analysis of the scheme shows that the scheme achieves the security goals of attribute-based encryption user attribute private key secure distribution and data privacy protection. The scope of this research is that this study confirms that the solution’s architecture achieves fine-grained access control of private data on the Hyperledger Blockchain network and also the security objectives of secure transmission of user characteristic secret keys and data privacy protection. The performance analysis part also shows that the proposed scheme has good usability.
Improving Augmentation Efficiency for Few-Shot Learning

While human intelligence can easily recognize some characteristics of classes with one or few examples, learning from few examples is a challenging task in machine learning.Recently emerging deep learning generally requires hundreds of thousands of samples to achieve generalization ability.Despite recent advances in deep learning, it is not easy to generalize new classes with little supervision.Few-shot learning (FSL) aims to learn how to recognize new classes with few examples per class.However, learning with few examples makes the model difficult to generalize and is susceptible to overfitting.To overcome the difficulty, data augmentation techniques have been applied to FSL.It is well-known that existing data augmentation approaches rely heavily on human experts with prior knowledge to find effective augmentation strategies manually.In this work, we propose an efficient data augmentation network, called EDANet, to automatically select the most effective augmentation approaches to achieve optimal performance of FSL without human intervention.Our method overcomes the disadvantages of relying on domain knowledge and requiring expensive labor to design data augmentation rules manually.We demonstrate the proposed approach on widely used FSL benchmarks (Omniglot and mini-ImageNet).The experimental results using three popular FSL networks indicate that the proposed approach improves performance over existing baselines through an optimal combination of candidate augmentation strategies.INDEX TERMS Few-shot learning, automatic search, efficient augmentation. I. INTRODUCTIONOver the past decade, we have witnessed remarkable performance gain with deep learning in many tasks, such as classification [2], [3], detection [4], [5], and segmentation [6], [7].The extensive calculation of deep learning through artificial neural networks, advances in computing power, and numerous labeled examples have allowed deep learning models to achieve performance improvement.However, many real-world scenarios do not allow us to access sufficient labeled data due to some reasons, including privacy, security, high labeling costs, and difficulty managing data.Therefore, many researchers have attempted to learn deep learning algorithms with few examples, and the field of few-shot learning (FSL) [17], [18] has been recently emerged.Few-shot learning recognizes patterns in data with few examples.In general, FSL approaches can be divided into
Developing Digital Literacy Practices in Yogyakarta Elementary Schools

The expansion of digital technology presents both obstacles and opportunities, particularly for young people. Consequently, educational institutions have been developing digital literacy curriculums. Since digital literacy is not yet included in the national curriculum as a compulsory subject, private schools have constructed digital literacy programmes based on local resources. This study investigates how digital literacy practices are carried out in selected private elementary schools in Yogyakarta, Indonesia, based on ICT learning, an information and media literacy approach, educators' role, and learning points. The data were collected in interviews with: (1) principals, (2) Information and Communication Technology (ICT) teachers, (3) librarians and (4) classroom teachers because, according to previous research, they are the main actors of digital literacy in school. The structured interviews use research instruments derived from two earlier studies and are tailored to the study's needs. This study demonstrates that (a) the digital literacy approach is implemented mainly through an ICT learning and media literacy approach while the information literacy approach is undermined; and (b) principals, classroom teachers, and ICT teachers are the three actors who play the most important roles in digital literacy, while librarians play a minor one. The majority of digital literacy training takes place in computer laboratories, while training in classrooms and libraries is less frequent. The study recommends the school blend ICT learning, information literacy, and media literacy pedagogy more comprehensively, with the library serving as the primary locus. In parallel, school principals should encourage school librarians to collaborate with the computer and class teachers to enhance the inclusive digital literacy curriculum.
A Method for Identifying Tor Users Visiting Websites Based on Frequency Domain Fingerprinting of Network Traffic

Although the anonymous communication network Tor can protect the security of users’ data and privacy during their visits to the Internet, it also facilitates illegal users to access illegal websites. Website fingerprinting attacks can identify the websites that users are visiting to discern whether they are performing illegal operations. Existing methods tend to manually extract the traffic features of users visiting websites and construct machine learning or deep learning models to classify the features. While these methods can be effective in classifying unknown website traffic, the effect of classification in the use of defensive measures or onion service scenarios is not yet ideal. This paper proposes a method to identify Tor users visiting websites based on frequency domain fingerprinting of network traffic (FDF). We extract the direction and length features of circuit sequences in access traffic and combine and transform them into the frequency domain. The classification of access traffic is accomplished by using a deep learning classification model combining CNN, FC, and Self-Attention. In this paper, the proposed FDF method is experimentally validated in common scenarios of Tor networks. The results show that FDF outperforms the existing methods for classification in different Tor scenarios. It can achieve 98.8% and 94.3% classification accuracy in undefended and WTF-PAD defense scenarios, respectively. In the onion service scenario, the accuracy is improved by 4.7% over the current state-of-the-art Tik-Tok method.
Sonar Image Target Detection Based on Deep Learning

Due to the nonlinearity of the imaging of sonar equipment and the complexity of the underwater sound field environment, the gray level of the target area of the acquired underwater sonar image is relatively small. These characteristics are the target of the subsequent sonar image. Work such as detection and location tracking has brought great challenges. It has brought great challenges to solving the work of positioning and tracking, which makes the research of sonar image target detection based on deep learning very important. This article aims at studying the use of sonar to detect image targets based on deep learning technology. This article proposes a variety of sound image denoising methods based on multiresolution tools. The purpose of this article is to divide the natural image into blocks at an appropriate rate according to the change of the sampling matrix and measure the underwater natural image. The sound image defines an information model. These methods have greatly changed the image and period of using remote and temporary information. The translation results of these methods are all valid. The sharpening separation method based on filtered image and bidirectional detection should be published through a solution algorithm and different frames, and the expected algorithm can be reused and extracted as an action to improve the similarity of the image and should be saved and separated in detail. The result is correct. This article studies the application of deep learning methods in sonar image target detection and designs corresponding algorithms for improvement and functional realization in view of the current deficiencies and needs in this field. The experimental results show that the improved scheme and applied algorithm proposed in this article can achieve good results, the verification sample set includes 184 remote-sensing aircraft targets, and the resolution of remote-sensing images is unified to 1644 × 971 size. The accuracy of the target detection algorithm has been significantly improved, reaching 74.6%, and the detection speed has also been greatly improved. Compared with the RNN algorithm, the speed has been increased by 7 times. The detection results confirmed that the improved algorithm has higher positioning accuracy and faster detection speed.
Bratter: An Instruction Set Extension for Forward Control-Flow Integrity in RISC-V

In recent decades, there has been an increasing number of studies on control flow integrity (CFI), particularly those implementing hardware-assisted CFI solutions that utilize a special instruction set extension. More recently, ARM and Intel, which are prominent processor architectures, also announced instruction set extensions for CFI called branch target identification (BTI) and control-flow enhancement technology (CET), respectively. However, according to our preliminary analysis, they do not support various CFI solutions in an efficient and scalable manner. In this study, we propose Bratter, a new instruction set extension for forward CFI solutions on RISC-V. At the center of Bratter, there are Branch Tag Registers and dedicated instructions for these registers. We implemented well-known CFI solutions (i.e., branch regulation and function signature check) using Bratter to evaluate its performance. Our experimental results show that, by using Bratter, even when these two solutions work together, they impose only 1.20% and 5.99% overhead for code size and execution time, respectively.
Distributed Attack Deployment Capability for Modern Automated Penetration Testing

Cybersecurity is an ever-changing landscape. The threats of the future are hard to predict and even harder to prepare for. This paper presents work designed to prepare for the cybersecurity landscape of tomorrow by creating a key support capability for an autonomous cybersecurity testing system. This system is designed to test and prepare critical infrastructure for what the future of cyberattacks looks like. It proposes a new type of attack framework that provides precise and granular attack control and higher perception within a set of infected infrastructure. The proposed attack framework is intelligent, supports the fetching and execution of arbitrary attacks, and has a small memory and network footprint. This framework facilitates autonomous rapid penetration testing as well as the evaluation of where detection systems and procedures are underdeveloped and require further improvement in preparation for rapid autonomous cyber-attacks.
Automation of The Palembang Semage Fabric Yarn Spinner

Songket weaving craft of Palembang City has been the center of the easy ikat weaving craft since ancient times and has become the hallmark of the city of Palembang.Currently the production process used by the songket weaving craft is still very simple, most of the work process in making this woven fabric is done manually.This causes the production process to take a relatively long time compared to the manufacture of fabrics in general.The yarn spinning device is modified in such a way that the yarn spinner can work with the help of electronic components.The yarn spinner is given a DC motor which has previously been programmed using the L298N Motor Driver so that it can rotate as needed and is connected to the Node MCU ESP8266 module so that it can be connected to an Android Smartphone which is used as a remote control to run the yarn spinning device.The ESP8266 Node MCU functions as a command provider for each component, the Gearbox DC Motor functions as the main driver for the coils, the L298N Motor Driver functions as a DC Motor controller, and the MG966R Servo Motor functions as a thread guide to the left and right so that the results of the thread roll look neat.The results of testing how many spools of yarn are in a fast IoT state in a period of 5-60 minutes at a time of 60 minutes get very fast results compared to manual.
A Secure E-Coupon Service Based on Blockchain Systems

As the popularity of e-commerce grows, an electronic coupon (e-coupon) is widely used due to its convenience and portability. In most e-coupon services, the information of e-coupons is managed on a centralized server. However, e-coupon services are often vulnerable to security issues because of centralization. For example, when the e-coupon information which is stored in a centralized e-coupon server is forged, it becomes difficult to match the user and the e-coupon’s owner, and an expired e-coupon can be used repetitively (i.e., double-spending). To handle this issue, we propose a new e-coupon service by exploiting a blockchain system to improve the security of the service. To do this, we first design a server to enable the e-coupon service and communicate with the blockchain system. Second, we devise a smart contract on the blockchain system to provide integrity of the e-coupon business logic and the e-coupon’s information. We implemented the proposed service on an Ethereum-based blockchain system. The experimental results show that our proposed service improves higher security with a minor performance overhead compared with an existing e-coupon service.