Reviewerly/exHarmony huggingface dataset:
Practical OSINT investigation in Twitter utilizing AI-based aggressiveness analysis

Open-source intelligence is gaining popularity due to the rapid development of social networks. There is more and more information in the public domain. One of the most popular social networks is Twitter. It was chosen to analyze the dependence of changes in the number of likes, reposts, quotes and retweets on the aggressiveness of the post text for a separate profile, as this information can be important not only for the owner of the channel in the social network, but also for other studies that in some way influence user accounts and their behavior in the social network. Furthermore, this work includes a detailed analysis and evaluation of the Tweety library capabilities and situations in which it can be effectively applied. Lastly, this work includes the creation and description of a compiled neural network whose purpose is to predict changes in the number of likes, reposts, quotes, and retweets from the aggressiveness of the post text for a separate profile.
Practical OSINT investigation - Similarity calculation using Reddit user profile data

This paper presents a practical Open Source Intelligence (OSINT) use case for user similarity measurements with the use of open profile data from the Reddit social network. This PoC work combines the open data from Reddit and the part of the state-of-the-art BERT model. Using the PRAW Python library, the project fetches comments and posts of users. Then these texts are converted into a feature vector - representation of all user posts and comments. The main idea here is to create a comparable user's pair similarity score based on their comments and posts. For example, if we fix one user and calculate scores of all mutual pairs with other users, we will produce a total order on the set of all mutual pairs with that user. This total order can be described as a degree of written similarity with this chosen user. A set of "similar" users for one particular user can be used to recommend to the user interesting for him people. The similarity score also has a "transitive property": if $user_1$ is "similar" to $user_2$ and $user_2$ is similar to $user_3$ then inner properties of our model guarantees that $user_1$ and $user_3$ are pretty "similar" too. In this way, this score can be used to cluster a set of users into sets of "similar" users. It could be used in some recommendation algorithms or tune already existing algorithms to consider a cluster's peculiarities. Also, we can extend our model and calculate feature vectors for subreddits. In that way, we can find similar to the user's subreddits and recommend them to him.
SUMOPE: Enhanced Hierarchical Summarization Model for Long Texts

With the progress of the times, the ever-advancing and improving Internet technology and the ever-generating social media network platforms have made the amount of information in the network explode, which contains a massive scale of redundant content. Then how to quickly extract the key information from the huge amount of data becomes crucial. In this paper, we propose a novel enhanced hierarchical summarization model SUMOPE for long texts, which combines both extractive and abstractive methods to deal with long texts. Our model first uses an extractive method called SUMO to select key sentences from the long text and form a bridging document. Then, our model uses an abstractive method based on PEGASUS with a copy mechanism to generate the final summary from the bridging document. Our model can effectively capture the important information and relations in the long text and produce coherent and concise summaries. We evaluate our model on two datasets and show that it outperforms the state-of-the-art methods in terms of ROUGE scores and human evaluation.
Multi-Domain Rapid Enhancement Networks for Underwater Images

Images captured during marine engineering operations suffer from color distortion and low contrast. Underwater image enhancement helps to alleviate these problems. Many deep learning models can infer multi-source data, where images with different perspectives exist from multiple sources. To this end, we propose a multichannel deep convolutional neural network (MDCNN) linked to a VGG that can target multi-source (multi-domain) underwater image enhancement. The designed MDCNN feeds data from different domains into separate channels and implements parameters by linking VGGs, which improves the domain adaptation of the model. In addition, to optimize performance, multi-domain image perception loss functions, multilabel soft edge loss for specific image enhancement tasks, pixel-level loss, and external monitoring loss for edge sharpness preprocessing are proposed. These loss functions are set to effectively enhance the structural and textural similarity of underwater images. A series of qualitative and quantitative experiments demonstrate that our model is superior to the state-of-the-art Shallow UWnet in terms of UIQM, and the performance evaluation conducted on different datasets increased by 0.11 on average.
Recent Trends in Cryptanalysis Techniques for White-box Block Ciphers

Black box cryptography is a cryptographic scheme based on a hardware encryption device, operating under the assumption that the device and the user can be trusted. However, with the increasing use of cryptographic algorithms on unreliable open platforms, the threats to black box cryptography systems have become even more significant. As a consequence, white box cryptography have been proposed to securely operate cryptographic algorithms on open platforms by hiding encryption keys during the encryption process, making it difficult for attackers to extract the keys. However, unlike traditional cryptography, white box-based encryption lacks established specifications, making it challenging to verify its structural security. To promote the safer utilization of white box cryptography, CHES organizes The WhibOx Contest periodically, which conducts safety analyses of various white box cryptographic techniques. Among these, the Differential Computation Analysis (DCA) attack proposed by Bos in 2016 is widely utilized in safety analyses and represents a powerful attack technique against robust white box block ciphers. Therefore, this paper analyzes the research trends in white box block ciphers and provides a summary of DCA attacks and relevant countermeasures, adhering to the format of a research paper..
Adversarial learning based intra-modal classifier for cross-modal hashing retrieval

Cross-modal hashing is popular for its high efficiency, low dimensionality, low storage overhead in information retrieval, and its ability to effectively characterize the consistency of high-level features of multi-source heterogeneous data. In this article, we present a fresh method that utilizes an intra-modal classifier that employs discrimination loss to learn intra-modal discriminations based on classification targets of image-text pairs. Meanwhile, the modality classifier serves as the discriminator in a GAN framework, using adversarial loss to reduce heterogeneity between different modality representations. To measure the efficiency of our method, we evaluate our proposed approaches against 8 other methods on three public benchmark datasets and show that our approaches outperform them in both I2T and T2I query.
A new CADConform Method Matchs As-Built Geometry with Trimmed NURBS to Facilitate NDE Data Fusion

Manufactured parts rarely match their computer-aided design (CAD) precisely, and sometimes the difference can be significant. As industry moves toward capturing, recording, and fusing NDE data registered to physical geometry, differences between as-built and as-designed geometries will become problematic. Using the CAD model as a reference configuration, this work proposes a CADConform method for distorting the CAD model to match an as-manufactured condition, so that NDE data acquired in the context of an as-built part can be mapped onto the original CAD model for fusion or inference. Continuing our previous work, this framework fills the research gap of retaining the CAD topology while handling large-scale deformation and arbitrarily trimmed NURBS. This project will facilitate a new generation of research and applications in NDE data fusion, including over time and over different serial numbers that will allow better quantitative evaluation, detection, and prognostics.
Course Correlation Analysis using MLP

In the era of burgeoning AI technology and progressive educational systems, AI’s influence on education has become increasingly apparent. The intricacies between courses continue to intensify, heightening the need for researchers to conduct precise correlation analyses. Traditional practices, which rely solely on correlation coefficients, tend to focus on linear relationships between two courses, leaving much to be desired. To delve deeper into the relationships between multiple courses, a growing number of AI-assisted methodologies have emerged in the education arena. Consequently, we introduce the Multilayer Perceptron (MLP) course relation approach, which scrutinizes the direct relationships embedded within grade data. Empirical evidence highlights the proposed MLP course correlation method’s capacity to reveal nonlinear and profound associations across multiple courses, showcasing its potential to revolutionize course correlation analysis.
RHCP: A Reconfigurable High-efficient Cryptographic Processor for Decentralized IoT Platforms

In decentralized IoT platforms, high flexibility, per-formance, and hardware efficiency are the requirements for cryptographic hardware for maintaining data security. This paper proposes a reconfigurable high-efficient cryptographic processor (RHCP), incorporating cutting-edge optimization techniques, cryptographic arithmetic logic unit (C-ALU), RHCP host management, and two-level pipeline scheduling to reach outstanding throughput and energy efficiency. Evaluation results on the Xilinx FPGA ALVEO U280 System on Chip exhibit a maximum frequency of 251.06 MHz, throughput of 4.02 Gbps, and power consumption of 14.98 W. Moreover, the ex-perimental outcomes conducted on the 45nm ASIC technology demonstrate the remarkable flexibility of RHCP when evaluating the throughput and energy efficiency of various cryptographic algorithms, such as BLAKE-256, BLAKE2s, MD5, RIPEMD-160, Salsa20/12, SHA-256, and Chacha20. Additionally, the results obtained from the ASIC analysis results highlight the superior performance of RHCP compared to existing approaches. Notably, RHCP showcases significant enhancements, with throughput improvements ranging from 2.54 to 10.68 times, energy efficiency enhancements ranging from 1.71 to 8.46 times, and energy-delay product improvements ranging from 1.81 to 21.5 times.
Reinforcement Learning Algorithms for Autonomous Mission Accomplishment by Unmanned Aerial Vehicles: A Comparative View with DQN, SARSA and A2C

Unmanned aerial vehicles (UAV) can be controlled in diverse ways. One of the most common is through artificial intelligence (AI), which comprises different methods, such as reinforcement learning (RL). The article aims to provide a comparison of three RL algorithms—DQN as the benchmark, SARSA as a same-family algorithm, and A2C as a different-structure one—to address the problem of a UAV navigating from departure point A to endpoint B while avoiding obstacles and, simultaneously, using the least possible time and flying the shortest distance. Under fixed premises, this investigation provides the results of the performances obtained for this activity. A neighborhood environment was selected because it is likely one of the most common areas of use for commercial drones. Taking DQN as the benchmark and not having previous knowledge of the behavior of SARSA or A2C in the employed environment, the comparison outcomes showed that DQN was the only one achieving the target. At the same time, SARSA and A2C did not. However, a deeper analysis of the results led to the conclusion that a fine-tuning of A2C could overcome the performance of DQN under certain conditions, demonstrating a greater speed at maximum finding with a more straightforward structure.
Barriers for the performance of graph neural networks (GNN) in discrete random structures

Recently, graph neural network (GNN)-based algorithms were proposed to solve a variety of combinatorial optimization problems [M. J. Schuetz, J. K. Brubaker, H. G. Katzgraber, Nat. Mach. Intell. 4 , 367–377 (2022)]. GNN was tested in particular on randomly generated instances of these problems. The publication [M. J. Schuetz, J. K. Brubaker, H. G. Katzgraber, Nat. Mach. Intell. 4 , 367–377 (2022)] stirred a debate whether the GNN-based method was adequately benchmarked against best prior methods. In particular, critical commentaries [M. C. Angelini, F. Ricci-Tersenghi, Nat. Mach. Intell. 5 , 29–31 (2023)] and [S. Boettcher, Nat. Mach. Intell. 5 , 24–25 (2023)] point out that a simple greedy algorithm performs better than the GNN. We do not intend to discuss the merits of arguments and counterarguments in these papers. Rather, in this note, we establish a fundamental limitation for running GNN on random instances considered in these references, for a broad range of choices of GNN architecture. Specifically, these barriers hold when the depth of GNN does not scale with graph size (we note that depth 2 was used in experiments in [M. J. Schuetz, J. K. Brubaker, H. G. Katzgraber, Nat. Mach. Intell. 4 , 367–377 (2022)]), and importantly, these barriers hold regardless of any other parameters of GNN architecture. These limitations arise from the presence of the overlap gap property (OGP) phase transition, which is a barrier for many algorithms, including importantly local algorithms, of which GNN is an example. At the same time, some algorithms known prior to the introduction of GNN provide best results for these problems up to the OGP phase transition. This leaves very little space for GNN to outperform the known algorithms, and based on this, we side with the conclusions made in [M. C. Angelini, F. Ricci-Tersenghi, Nat. Mach. Intell. 5 , 29–31 (2023)] and [S. Boettcher, Nat. Mach. Intell. 5 , 24–25 (2023)].
Effective method of eliminating scan error in structured light measurement

The structured light measurement method uses the principle that the fringe modulation information is sensitive to the object height information to realize the mapping from the fringe modulation information to the height information, which can be utilized to restore the three-dimensional shape of the object, smooth and rough surfaces. This method has a simple measurement system and belongs to coaxial measurement, which is considered a highly promising measurement method and has good application prospects in the field of aircraft surface shape measurement, surface defects detecting, etc. In most SIM, two essential parameters need to be accurately obtained to restore the 3D shape of the object which are the focus position of each pixel and the scan step. For years, researchers have constantly improved the structure of SIM system to obtain more accurate focus position to realize optimized measurement results. Meanwhile, the preset value of the scan step is directly substituted into calculation. However, the actual value of the scan step is very likely inconsistent with the preset value due to various factors in practical such as nonlinear motion of scan actuator, which will introduce great error into the measurement result. However, in the current structured light measurement methods, there is no solution to the problem of errors caused by the scanning system, which limits the further improvement of measurement accuracy and repeatability. In this paper, a random scanning error suppression method is proposed to solve this problem. The principle of mutual calibration of the second order slope of the theoretical modulation curve and the second order slope of the actual modulation curve of the grating fringe is used to achieve the accurate value of the scan step. Theoretical analysis and simulation are carried out to verify the feasibility of the proposed method.
NAS-assisted single-shot real-time 3D measurement

Due to the advantages of non-contact, low cost and high accuracy, Fringe Projection Profilometry (FPP) has been widely used in different kinds of fields for robust three-dimensional measurement. However, limited by the filter operations in Fourier transform profilometry and multiple fringe patterns requirement in phase shifting profilometry, it’s difficult for traditional methods to achieve high-accuracy 3D reconstruction for moving objects. Meanwhile, because of the large amount parameters of neural networks and the complexity of network structures, it is time-consuming to perform one interface and hard to achieve real-time processing for current deep learning based FPP methods. In this paper, we proposed a single-shot method based on Neural Architecture Search (NAS) technique for the lightweight network processing. The experimental results demonstrate that the proposed method can achieve the reconstruction rate of up to 32 fps in high-accuracy and realize real-time 3D measurement for dynamic scenes.
PENGEMBANGAN MEDIA PEMBELAJARAN MINIATUR PATAYA (PETA BUDAYA) PADA PEMBELAJARAN IPS KELAS IV SD NEGERI 96 PALEMBANG

Based on the problems at SD Negeri 96 Palembang, the lack of use of social studies learning media makes it difficult for students to understand the material of theme 7 subtheme 2 Indanhya ethnic and religious diversity in my country. This study aims to produce learning media miniature pataya (cultural map) that is valid, practical and potential effects. The type of research method used is Research and Development (R&amp;D) with the ADDIE model. Based on the analysis of the needs of student learning outcomes, it is known that out of 26 students only 3 students have not reached the KKM score while the other 3 students are still below the kkm so that there are students who still do not understand the Beautiful Material of ethnic and religious diversity in my country. Development of Learning Media Miniature Pataya (Cultural Map) in Social Studies class IV SD Negeri 96 Palembang has produced learning media products that have been declared valid by media, material and language experts. The results of the Miniature Pataya media research were declared valid with an average result of 86.45% with the category "very valid" student response questionnaire of 97.72% in the one to one test and 96.14% in the small group, each of these tests got the category "very practical". The test results obtained by students have an average of 8.15% with the category "very effective". So it can be concluded that in the development research of Miniature pataya in social studies learning class IV SD Negeri 96 Palembang there is validity, practicality and effectiveness.
UTILIZING THE 'SEGMENT ANYTHING MODEL' FOR MAMMOGRAPHY IMAGE ANALYSIS

Mammography, considered the "gold standard" for breast mass detection, boasts approximately 90% sensitivity. However, the process of mammography reading and interpretation is time-consuming and susceptible to a significant number of false negatives and false positives. Compounding these issues is a notable shortage of radiologists specializing in breast imaging, highlighting a need for innovative solutions. This paper proposes the use of an artificial intelligence (AI) system, the Segment Anything Model (SAM), to address these challenges. SAM, a versatile image segmentation model, can generate segmentation masks in response to various prompts such as points, bounding boxes, masks, or textual descriptions. It employs a transformer-based architecture and utilizes a large-scale dataset for training. Within mammography, SAM could be deployed to automatically segment suspicious areas in the breast, thereby providing invaluable support to radiologists during diagnostic decision-making. Furthermore, SAM can be fine-tuned and trained on specific medical image datasets to enhance its performance. Our results underscore SAM's efficacy in producing accurate segmentation masks, demonstrating its potential for integration into human-in-theloop systems for annotation and decision support in medical image analysis. Nevertheless, further research and training are essential before implementing SAM in real-world medical scenarios.
A Study on Underwater Image Processing Techniques

Abstract: In recent years, a potential interest has been developed in exploring and enhancing the quality of underwater images. The captured underwater images often experience scattering and high levels of noise due to the properties of light transit within the water and the biotic movement that occurs inside the ocean bottom. Underwater cameras or devices were fitted inside the unmanned underwater vehicles (UUVs), autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), ocean detector networks, etc to see the ocean bottom. Despite being a crucial detector for keeping an eye on underwater landscapes, modern underwater camera sensors or detectors contain a number of problems. Numerous solutions to the issues with conventional underwater photography have been put out during the last five years. With the help of the number of contributions and difficulties reported by more than 40 researchers in their research article, this study focused to mention the current underwater image processing methods. This article represents an overview of numerous underwater image-processing techniques utilized, including underwater picture de-scattering, underwater image colour refurbishment, and underwater image aspect evaluations. Also, the latest trends, problems, and challenges for creating and processing underwater image sensors were discussed.
IMPLEMENTATION AND EVALUATION OF INTERACTIVE EDUCATIONAL GAME OF WADAI BANJAR AS AN EFFORT TO PRESERVE TRADITIONAL CAKES OF SOUTHERN KALIMANTAN

When tourists visit an area, they will definitely look for food and cakes typical of the area to taste or as souvenirs. Including when tourists visit South Kalimantan, tourists will also look for special cakes often called Wadai Banjar, there are various types of Wadai Banjar and must be preserved. Among the younger generation now, Wadai Banjar must also be introduced and strived to be preserved. Based on the information, the purpose of this research is to design, developing, and evaluating the Wadai Banjar educational game. The method used is the Game Development Life Cycle (GDLC) which consists of initiation, pre-production, production, testing, beta, and release, followed by an evaluation of the use of the game application. A total of 100 users have been involved in evaluating the use of the application. The result of this study is that the educational game that has been designed and built can function according to the test features provided and can be released to users. The evaluation of use provides results that the Wadai Banjar educational game application can provide fun, comfort, a fitting and attractive user interface, can find out and learn in recognizing the types of Wadai Banjar, useful and as an effort to introduce and preserve traditional cakes typical of South Kalimantan. Then as many as 70 users are more interested if this Wadai Banjar educational game is desktop-based, because interaction is more flexible and a mouse is needed as the main input media in performing actions.
Social Portals

As you remember from Chapter 3 , Java Portlets were an early approach to mashups; therefore, popular portal servers also support social media integration like social login, mashups, or social sharing.