Reviewerly/exHarmony huggingface dataset:
Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

With the development of synthetic aperture radar (SAR) technology, more SAR datasets with high resolution and large scale have been obtained. Research using SAR images to detect and monitor marine targets has become one of the most important marine applications. In recent years, deep learning has been widely applied to target detection. However, it was difficult to use deep learning to train an SAR ship detection model in complex scenes. To resolve this problem, an SAR ship detection method combining YOLOv4 and the receptive field block (CY-RFB) was proposed in this paper. Extensive experimental results on the SAR-Ship-Dataset and SSDD datasets demonstrated that the proposed method had achieved supreme detection performance compared to the state-of-the-art ship detection methods in complex scenes, whether they were in offshore or inshore scenes of SAR images.
PARAMETER-DRIVEN GENERATION OF EVALUATION PROGRAM FOR A NEUROEVOLUTION ALGORITHM ON A BINARY MULTIPLEXER EXAMPLE

Context. The problem of automated development of evaluation programs for the neuroevolution of augmenting topologies. Neuroevolution algorithms apply mechanisms of mutation, recombination, and selection to find neural networks with behavior that satisfies the conditions of a certain formally defined problem. An example of such a problem is finding a neural network that implements a certain digital logic.&#x0D; Objective. The goal of the work is the automated design and generation of an evaluation program for a sample neuroevolution problem (binary multiplexer).&#x0D; Method. The methods and tools of Glushkov’s algebra of algorithms and hyperscheme algebra are applied for the parameterdriven generation of a neuroevolution evaluation program for a binary multiplexer. Glushkov’s algebra is the basis of the algorithmic language intended for multilevel structural design and documentation of sequential and parallel algorithms and programs in a form close to a natural language. Hyperschemes are high-level parameterized specifications intended for solving a certain class of problems. Setting parameter values and subsequent interpretation of hyperschemes allows obtaining algorithms adapted to specific conditions of their use.&#x0D; Results. The facilities of hyperschemes were implemented in the developed integrated toolkit for the automated design and synthesis of programs. Based on algorithm schemes, the system generates programs in a target programming language. The advantage of the system is the possibility of describing algorithm schemes in a natural-linguistic form. An experiment was conducted consisting in the execution of the generated program for the problem of evaluating a binary multiplexer on a distributed cloud platform. The multiplexer example is included in SharpNEAT, an open-source framework that implements the genetic neuroevolution algorithm NEAT for the .NET platform. The parallel distributed implementation of the SharpNEAT was proposed in the previous work of the authors.&#x0D; Conclusions. The conducted experiments demonstrated the possibility of the developed distributed system to perform evaluations on 64 cloud clients-executors and obtain an increase in 60–100% of the maximum capabilities of a single-processor local implementation.
A privacy‐preserving method for publishing data with multiple sensitive attributes

Abstract The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes (SAs) have not considered the personalised privacy requirement. Furthermore, sensitive information disclosure may also be caused by these personalised requirements. To address the matter, this article develops a personalised data publishing method for multiple SAs. According to the requirements of individuals, the new method partitions SAs values into two categories: private values and public values, and breaks the association between them for privacy guarantees. For the private values, this paper takes the process of anonymisation, while the public values are released without this process. An algorithm is designed to achieve the privacy mode, where the selectivity is determined by the sensitive value frequency and undesirable objects. The experimental results show that the proposed method can provide more information utility when compared with previous methods. The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary. The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.
Is JavaScript Call Graph Extraction Solved Yet? A Comparative Study of Static and Dynamic Tools

Code analysis is more important than ever because JavaScript is increasingly popular and actively used, both on the client and server sides.Most algorithms for analyzing vulnerabilities, finding coding issues, or inferring type depend on the call graph representation of the underlying program.Luckily, there are quite a few tools to get this job done already.However, their performance in vitro and especially in vivo has not yet been extensively compared and evaluated.In this paper, we compare several approaches for building JavaScript call graphs, namely five static and two dynamic approaches on 26 WebKit SunSpider programs, and two static and two dynamic approaches on 12 realworld Node.js programs.The tools under examination using static techniques were npm call graph, IBM WALA, Google Closure Compiler, Approximate Call Graph, and Type Analyzer for JavaScript.We performed dynamic analyzes relying on the nodejs-cg tool (a customized Node.js runtime) and the NodeProf instrumentation and profiling framework.We provide a quantitative evaluation of the results, and a result quality analysis based on 941 manually validated call edges.On the SunSpider programs, which do not take any inputs, so dynamic extraction could be complete, all the static tools also performed well.For example, TAJS found 93% of all edges while having a 97% precision compared to the precise dynamic call graph.When it comes to real-world Node.js modules, our evaluation shows that static tools struggle with parsing the code and fail to detect a significant amount of call edges that dynamic approaches can capture.Nonetheless, a significant number of edges not detected by dynamic approaches are also reported.Among these, however, there are also edges that are real, but for some reason the unit tests did not execute the branches in which these calls were included.INDEX TERMS call graph, comparative study, dynamic code analysis, JavaScript, static code analysis I. INTRODUCTIONJavaScript is the most popular programming language (and has been since 2014), according to GitHub statistics [15] (followed by Python and Java).The TIOBE Index chooses the fastest-growing programming language each year and honors it with the "Programming Language of the Year" title.
AsQM: Audio Streaming Quality Metric Based on Network Impairments and User Preferences

There are many users of audio streaming services because of the proliferation of cloud-based audio streaming services for different content. The complex networks that support these services do not always guarantee an acceptable quality on the end-user side. In this paper, the impact of temporal interruptions on the reproduction of audio streaming and the users preference in relation to audio contents are studied. In order to determine the key parameters in the audio streaming service, subjective tests were conducted, and their results show that users Quality-of-Experience (QoE) is highly correlated with the following application parameters, the number of temporal interruptions or stalls, its frequency and length, and the temporal location in which they occur. However, most important, experimental results demonstrated that users preference for audio content plays an important role in users QoE. Thus, a Preference Factor (PF) function is defined and considered in the formulation of the proposed metric named Audio streaming Quality Metric (AsQM). Considering that multimedia service providers are based on web servers, a framework to obtain user information is proposed. Furthermore, results show that the AsQM implemented in the audio player of an end users device presents a low impact on energy, processing and memory consumption.
CLASSIFICATION OF DISEASES AND PESTS OF MAIZE USING MULTINOMIAL LOGISTIC REGRESSION BASED ON RESAMPLING TECHNIQUE OF K-FOLD CROSS-VALIDATION

Some of the obstacles in the cultivation of maize that cause low productivity of maize yields are diseases and pests. Early detection of maize diseases and pests is expected to reduce farmer losses. A system for the early detection of diseases and pests can be created by classifying them based on digital images. This study aimed to classify maize diseases and pests using multinomial logistic regression. The model and testing resampling were based on resampling technique of k-fold cross-validation. The research data was obtained from the RGB color feature extraction process for each object in each class of diseases and pests of corn. The results showed that the classification into seven classes using five folds had an accuracy rate of 99.85%, macro precision of 98.59%, and macro recall of 98.15%.
Developing and improving competence profiles of project teams in engineering education

This concept paper reflects an ongoing research on designing students’ team projects in engineering education with a focus on soft skills development. The core idea is to relate project tasks with relevant team situations and team roles which require and train certain sets of soft skills. The paper proposes: a) a model for developing the relevant soft skills out of project tasks, and b) an approach to relate individual competence profiles of team members with an overall team competence profile. A core assumption is that if a team is formed, individual competences are aggregated in a certain way to form a single team competence profile. However, in the case of soft skills this aggregating is more complex than simply adding skill levels, e.g., soft skills in teams are a result of specific combinations of competences. Understanding these effects is relevant for project management and engineering education. The paper proposes a first draft of a systematic framework for investigating such effects and for making them usable for the design of student projects in engineering education. It also provides insight into an example of an agile cross-border project conducted fully online and using the scrum method. The paper is considered to be a contribution to the development of project-based learning.
An evolutionary game analysis of digital currency innovation and regulatory coordination

&lt;abstract&gt; &lt;p&gt;The innovation and regulatory coordination of digital currency is an important proposition in the new era of Fintech. There is increasing competition between traditional currencies and new digital currencies, so a spontaneous game model of currencies is analyzed. By introducing the role of financial coordination, this paper revises the evolutionary game model of digital currency innovation, and analyzes their competition strategies through case and simulation. The results show that: first, the dominant result of digital currency spontaneous game is that both parties tend to digital cooperation strategy. Second, with the introduction of financial regulation, the dominant result of digital currency tripartite evolutionary game is that financial institutions tend to participate in coordination and both currency parties tend to cooperate. Third, the choice strategy of currency is more sensitive to the changes of willingness to participate in cooperation, cooperation costs and cooperation benefits of financial coordination. The selection strategy of financial coordination institutions for digital currency is more influenced by changes in cooperation costs and incentive return in the process of participating in cooperation.&lt;/p&gt; &lt;/abstract&gt;
Handwritten Signature Verification using Deep Learning

Even though most areas, including land records, agreements between parties, legal certificates, identification cards, etc., are moving toward digital documents with digital signatures for authentication, uses only a signature written by hand. Verifying signatures is crucial because a false signature would have a significant impact on the actual owner. Therefore, it is essential to recognize genuine signatures in order to avoid such frauds. In this work, the deep learning technique is used to recognize the signature because it produces the highest accuracy and does not require excessive preprocessing. Image processing, classification, and segmentation are the most common applications for a deep learning model that is based on a convolutional neural network (CNN). The CNN algorithm learns more than KNN, SVM, and other algorithms. This work makes use of CNN to improve classification. Keywords: Signature, Convolutional Neural Networks (CNN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN).
Historical Isolated Forest for detecting and adaptation concept drifts in nonstationary data streaming

Concept drift refers to sudden changes in the fundamental structure of the streaming data distribution over time. The core objective of concept drift research is to develop techniques and strategies for detect, understand, and adapt data streaming drifts. Data research has shown that if concept drift is not handled properly, machine learning in such an environment would provide subpar learning outcomes. In this paper, a historical Isolated Forest (HIF) is presented that depends on a decision tree, which split the data streaming into chunks and each chuck considers a region in the tree. HIF is employed to detect concept drifts and adapt this region with current changes. Which HIF stores previously generated models and employs the most similar model of each concept drift distribution as the current model until generate the best performance model. HIF doesn’t stop the main system model when retraining a new model, which HIF is divided into three primary parallel blocks: detection block, similarity block (online block), and retraining block (offline block). For several authentic data sets (three data sets), our suggested algorithm was verified and contrasted. the accuracy and execution speed were specifically assessed, and memory usage. The experimental results demonstrate that our modifications use fewer resources and have comparable or greater detection accuracy than the original IForestASD.
Focusing on Pinocchio's Nose: A Gradients Scrutinizer to Thwart Split-Learning Hijacking Attacks Using Intrinsic Attributes

Split learning is privacy-preserving distributed learning that has gained momentum recently.It also faces new security challenges.FSHA [37] is a serious threat to split learning.In FSHA, a malicious server hijacks training to trick clients to train the encoder of an autoencoder instead of a classification model.Intermediate results sent to the server by a client are actually latent codes of private training samples, which can be reconstructed with high fidelity from the received codes with the decoder of the autoencoder.SplitGuard [10] is the only existing effective defense against hijacking attacks.It is an active method that injects falsely labeled data to incur abnormal behaviors to detect hijacking attacks.Such injection also incurs an adverse impact on honest training of intended models.
Intercept and Inject: DNS Response Manipulation in the Wild

DNS is a protocol responsible for translating human-readable domain names into IP addresses. Despite being essential for many Internet services to work properly, it is inherently vulnerable to manipulation. In November 2021, users from Mexico received bogus DNS responses when resolving whatsapp.net. It appeared that a BGP route leak diverged DNS queries to the local instance of the k-root located in China. Those queries, in turn, encountered middleboxes that injected fake DNS responses. In this paper, we analyze that event from the RIPE Atlas point of view and observe that its impact was more significant than initially thought—the Chinese root server instance was reachable from at least 15 countries several months before being reported. We then launch a nine-month longitudinal measurement campaign using RIPE Atlas probes and locate 11 probes outside China reaching the same instance, although this time over IPv6. More broadly, motivated by the November 2021 event, we study the extent of DNS response injection when contacting root servers. While only less than 1% of queries are impacted, they originate from 7% of RIPE Atlas probes in 66 countries. We conclude by discussing several countermeasures that limit the probability of DNS manipulation.
Skill Scanner

Skills are the common ground between employers, job seekers and educational institutions which can be analyzed with the help of artificial intelligence (AI), specifically natural language processing (NLP) techniques. In this paper we explore a state-of-the-art pipeline that extracts, vectorizes, clusters, and compares skills to provide recommendations for all three players—thereby bridging the gap between employers, job seekers and educational institutions. As companies hiring data scientists report that it is increasingly difficult to find a so-called "unicorn data scientist" [1], we conduct our experiments and analysis using companies’ job postings for a data scientist position, job seekers’ CVs for that position, and a curriculum from a master's program in data science. However, our investigated methods and our final recommendation system can be applied to other job positions as well. Our best system combines Sentence-BERT [2], UMAP [3], DBSCAN [4], and K-means clustering [5]. To also evaluate feedback from potential users, we conducted a survey, in which the majority of employers’, job seekers’ and educational institutions’ representatives state that with the help of our automatic recommendations, processes related to skills are more effective, faster, fairer, more explainable, more autonomous and more supported.
Evaluation of Transfer Learning for Handwritten Character Classification Using Small Training Samples

In pattern recognition fields, it is worthwhile to develop a pattern recognition system that hears one and knows ten. Recently, classification of printed characters that are the same fonts is almost possible, but classification of handwritten characters is still difficult. On the other hand, there are a large number of writing systems in the world, and there is a need for efficient character classification even with a small sample. Deep learning is one of the most effective approaches for image recognition. Despite this, deep learning causes overtrains easily, particularly when the number of training samples is small. For this reason, deep learning requires a large number of training samples. However, in a practical pattern recognition problem, the number of training samples is usually limited. One method for overcoming this situation is the use of transfer learning, which is pretrained by many samples. In this study, we evaluate the generalization performance of transfer learning for handwritten character classification using a small training sample size. We explore transfer learning using a fine-tuning to fit a small training sample. The experimental results show that transfer learning was more effective for handwritten character classification than convolution neural networks. Transfer learning is expected to be one method that can be used to design a pattern recognition system that works effectively even with a small sample.
Pendampingan AKM Kelas pada Sekolah Dasar Kelas V SDN Bandorasakulon 3 Kecamatan Cilimus Kabupaten Kuningan

The low ability of students in literacy and numeracy underlies the training on the application of the AKM application to students, in an effort to equip skills in terms of working on minimum competency assessments. These knowledge and skills can be used as capital in the work of assessing the minimum competency of students which can be used as ICT-based learning media. With this training as a learning medium, it is hoped that it can increase the knowledge and skills of students. Students can better understand what will be done during the implementation of the minimum competency assessment, especially things of a technical nature. This training can also provide provisions for our students about the use of this application which is a plus, where students can use it as a learning tool and a means of developing knowledge, especially in the field of using minimum competency assessment applications.
FPBFT: A Fast PBFT Protocol for Private Blockchains

Blockchain is an emerging technology that enables storing data and sharing it among many entities without the need for a central organization. Data is stored in an append-only ledger after the majority of participants agree on it. This mechanism is called the consensus, and many protocols are proposed in the literature (PoW, PoS, DPoS, RAFT, PBFT, etc.). In this paper, we focus on the Practical Byzantine Fault Tolerance (PBFT) protocol that has the advantages of being resilient to byzantine nodes, being faster than other protocols and energy saving in contrast to PoW. However, PBFT is not sufficiently scalable due to the important number of messages exchanged before consensus is reached which impacts the delays as well. In this paper, we propose FPBFT, a BFT protocol that is faster and more scalable than the traditional PBFT. We establish a scoring mechanism that eliminates slow and malicious nodes. In addition, PBFT steps are reduced following the SG-PBFT protocol and adapted to the blockchain use case. According to our experiments, our protocol performs better in terms of delays and communication traffic than PBFT and SG-PBFT.
American Sign Language fingerspelling Recognition Using RGB-D and DFANet

With the enrichment and improvement of gesture language, sign Language becomes more and more important for communication between hearing impaired and ordinary people, and American Sign Language(ASL) fingerspelling recognition using an uncalibrated visual camera is a meaningful attempt. This paper uses the low-cost Kinect depth camera to obtain RGBD images of sign language. Through the proposed image segmentation algorithm SD-Segment, the pixels of RGB and depth images are aligned when the camera’s internal parameters are unknown, and the image is accurately segmented at the same time. A dual-path feature blending attention network (DFANet) is developed to obtain the fine-grained distinguishing features related to gestures. In order to take advantage of the complementarity between depth and RGB image, a depth-pixel aware module (DPAM) is developed to utilize the pixel relationship in the RGBD feature maps. According to the experimental results, compared with the state-of-the-art methods on the publicly available ASL fingerspelling dataset, the accuracy rate(+2.40%) of the network is greatly improved, and the highest accuracy rate of 98.16% is obtained on the self-built 8-person dataset. DPAM helps the network highlight distinctive hand regions in RGBD images, and DPAM does not significantly increase the number of parameters and computational overhead of the network.