Reviewerly/exHarmony huggingface dataset:
TEGDetector: A Phishing Detector That Knows Evolving Transaction Behaviors

Recently, phishing scams have posed a significant threat to blockchains. Phishing detectors direct their efforts in hunting phishing addresses. Most of the detectors extract target addresses’ transaction behavior features by random walking or constructing static subgraphs. The random walking methods, unfortunately, usually miss structural information due to limited sampling sequence length, while the static subgraph methods tend to ignore temporal features lying in the evolving transaction behaviors. More importantly, their performance undergoes severe degradation when the malicious users intentionally hide phishing behaviors. To address these challenges, we propose TEGDetector, a dynamic graph classifier that learns the evolving behavior features from transaction evolution graphs (TEGs). First, we cast the transaction series into multiple time slices, capturing the target address’s transaction behaviors in different periods. Then, we provide a fast nonparametric phishing detector (FD) to narrow down the search space of suspicious addresses. Finally, TEGDetector considers both the spatial and temporal evolutions toward a complete characterization of the evolving transaction behaviors. Moreover, TEGDetector utilizes adaptively learned time coefficient to pay distinct attention to different periods, which provides several novel insights. Extensive experiments on the large-scale Ethereum transaction dataset demonstrate that the proposed method achieves state-of-the-art (SOTA) detection performance. The code of TEGDetector is open sourced at https://github.com/Seaocn/TEGDetector.
Explanation of clustering result based on multi-objective optimization

Clustering is an unsupervised machine learning technique whose goal is to cluster unlabeled data. But traditional clustering methods only output a set of results and do not provide any explanations of the results. Although in the literature a number of methods based on decision tree have been proposed to explain the clustering results, most of them have some disadvantages, such as too many branches and too deep leaves, which lead to complex explanations and make it difficult for users to understand. In this paper, a hypercube overlay model based on multi-objective optimization is proposed to achieve succinct explanations of clustering results. The model designs two objective functions based on the number of hypercubes and the compactness of instances and then uses multi-objective optimization to find a set of nondominated solutions. Finally, an Utopia point is defined to determine the most suitable solution, in which each cluster can be covered by as few hypercubes as possible. Based on these hypercubes, an explanations of each cluster is provided. Upon verification on synthetic and real datasets respectively, it shows that the model can provide a concise and understandable explanations to users.
An Extensive Analysis of Digital Image Compression Techniques Using Different Image Files and Color Formats

Data storage on the device can affect the access speed of the device used; for example, files, images, and data will affect the performance of the device, become slow to access, difficult to open, download, and save images, files, because the available storage capacity is limited, with the problems that arise, an image compression technique is needed to minimize storage space and speed up the access. The compression technique can reduce a file, image, and data size but does not reduce the existing image's quality or lower the threshold during the sending or receiving. This research aims to reduce the size, speed up the process of accessing data on devices, and, more importantly, minimize memory space. It can also affect the bandwidth used when sending and receiving files and can speed up the process of sending from source to destination. The method used in this study is Lossy Compression, lose less Compression by comparing RLE, Huffman, and LZW using different image file types. For the Lossless Technique, the best quality reduction ratio is in binary image types, whether using a background or not using a background. The best results obtained are 99.10% (PNG Compression). Using the BMP file extension type, the recommended reduced ratio is lossy compression with format image BMP (JPG compression) for binary image using Lossless Compression has a good reduced ratio compression with an average of 99%.
A Multitask Framework for Graffiti-to-Image Translation

Recently, image-to-image translation models have achieved great success in terms of content consistency and visual fidelity. However, in most of these tasks, the inaccuracy of sketches and the high cost of fine semantic masks acquisition limit the large-scale use of image translation models. Therefore, we propose to use graffiti that combines the advantages of sketches and semantic masks as model input. Graffiti reflects the general content of an image using lines and color distinctions, with some unlabeled regions. However, due to the large number of unknown areas in the graffiti, the generated results may be blurred, resulting in poor visual effects. To address these challenges, this paper proposes a multi-task framework that can predict unknown regions by learning semantic mask from graffiti, thereby improving the quality of generated real scene images. Furthermore, by introducing an edge activation module, which utilizes semantic and edge information to optimize the object boundaries of the generated images, the details of the generated images can be improved. Experiments on the Cityscapes dataset demonstrate that our multi-task framework achieves competitive performance on graffiti-based image generation task.
Who is Speaking Actually? Robust and Versatile Speaker Traceability for Voice Conversion

Voice conversion (VC), as a voice style transfer technology, is becoming increasingly prevalent while raising serious concerns about its illegal use. Proactively tracing the origins of VC-generated speeches, i.e., speaker traceability, can prevent the misuse of VC, but unfortunately has not been extensively studied. In this paper, we are the first to investigate the speaker traceability for VC and propose a traceable VC framework named VoxTracer. Our VoxTracer is similar to but beyond the paradigm of audio watermarking. We first use unique speaker embedding to represent speaker identity. Then we design a VAE-Glow structure, in which the hiding process imperceptibly integrates the source speaker identity into the VC, and the tracing process accurately recovers the source speaker identity and even the source speech in spite of severe speech quality degradation. To address the speech mismatch between the hiding and tracing processes affected by different distortions, we also adopt an asynchronous training strategy to optimize the VAE-Glow models. The VoxTracer is versatile enough to be applied to arbitrary VC methods and popular audio coding standards. Extensive experiments demonstrate that the VoxTracer achieves not only high imperceptibility in hiding, but also nearly 100% tracing accuracy against various types of audio lossy compressions (AAC, MP3, Opus and SILK) with a broad range of bitrates (16 kbps - 128 kbps) even in a very short time duration (0.74s). Our source code is available at https://github.com/hongchengzhu/VoxTracer.
Performance analysis of improvemental LOD technology under VR headsets

In recent years, with advancements in technology and improvements in hardware performance, virtual reality (VR) technology has been widely applied. optimizing elements within VR scenes can help ensure stable operation and enhance the user experience. Through the use of the QEM algorithm, this article optimizes the models in the scene, reducing the number of polygons and further improving the smoothness of rendering and user experience. Research results prove that the QEM-based optimization LOD technique is a highly applicable model optimization method in virtual reality application development. This method not only reduces device resource consumption but also enhances application performance and user experience, effectively lowering development costs.
Is one brick enough to break the wall of spoken dialogue state tracking?

In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs (a.k.a dialogue state tracking) is key to a smooth interaction. Traditionally, TOD systems perform this update in three steps: transcription of the user's utterance, semantic extraction of the key concepts, and contextualization with the previously identified concepts. Such cascade approaches suffer from cascading errors and separate optimization. End-to-End approaches have been proved helpful up to the semantic extraction step. This paper goes one step further paving the path towards completely neural spoken dialogue state tracking by comparing three approaches: (1) a state of the art cascade approach, (2) a locally E2E approach with rule-based contextualization and (3) a completely neural approach.
ANALISIS TINGKAT KERAWANAN BANJIR SERTA UPAYA PENGENDALIANNYA DI KECAMATAN TINANGGEA KABUPATEN KONAWE SELATAN

This study aims to determine the flood-prone class and the efforts that need to be made to minimize the occurrence of flooding in Tinanggea District, South Konawe Regency. The research method used is attribute analysis (scoring and weighting) of flood-prone variables (land use, rainfall, slope, soil type, land height), and spatial analysis (overlay) using Geographic Information Systems (GIS). The results showed that the level of flood vulnerability in Tinanggea District, South Konawe Regency was classified into 4 classes, namely the very flood-prone class which occupied the highest percentage of 179,94 ha or 50,72% of the total area of Tinanggea District, the vulnerable class was 123,69 ha or 34,86%, slightly prone area of 3,84 ha or 3,588% and safe class 1,27 ha or 3,588%. Efforts are being made to minimize the occurrence of flooding: drainage construction, vegetation planting or reforestation measures to control or reduce surface runoff and soil erosion, natural vegetation maintenance, or appropriate planting of waterproof vegetation, along drainage embankments, channels and other areas to control excessive flow or soil erosion, and specifically regulate flood control structures such as check dams.
Understanding Grokking Through A Robustness Viewpoint

Recently, an unusual phenomenon called grokking has gained much attention, where sometimes a neural network generalizes long after it perfectly fits the training data. We try to understand this seemingly strange phenomenon using the robustness of the neural network. Using a robustness viewpoint, we show that the popular $l_2$ weight norm (metric) of the neural network is actually a sufficient condition for grokking. As we also empirically find that $l_2$ norm correlates with grokking on the test data not in a timely way, we propose new metrics based on robustness and information theory and find that our new metrics correlate well with the grokking phenomenon. Based on the previous observations, we propose methods to speed up the generalization process. In addition, we examine the standard training process on modulo addition dataset and find that it hardly learns other basic group operations before grokking, including the commutative law. Interestingly, the speed up of generalization when using our proposed method can be partially explained by learning the commutative law, a necessary condition when the model groks on test dataset.
Research on the Application of Ultimate Frisbee in College Teaching

In recent years, college students generally have a low level of physical health, which is affected by online games, mobile game and so on to a certain extent, resulting in the gradual decrease of the time students spend in sports. Therefore, in the important period for college students to develop physical exercise habits, more fresh, interesting, and diverse sports are needed to stimulate them to participate. Ultimate Frisbee is a relatively new sports event, with the characteristics of simple skills, high spectator, high safety, co-participation of men and women, and so on. As an important supplement to the college PE curriculum, it meets the needs of college students and college PE, very suitable for application in college teaching.
MODIFIKASI REAKTOR DRUM UNTUK PRETREATMENT PENGOLAHAN LIMBAH CAIR PADA HOME INDUSTRY TAHU DAN TEMPE

Liquid waste in the case of tofu industrial waste has a variety of very high organic matter content, if it is not processed properly it will pollute the environment. Based on the results of research on the waste from the tempe-tofu factory, it is known that the pH is 4.3; TSS 140 mg/L; COD 4506.6 mg/L; and BOD 1285.59 mg/L. To meet quality standards, it must be processed first with the right technology before being disposed of so as not to pollute the environment. The problems faced by Partners 1 and Partners 2 are that the factory does not provide funds specifically for processing, has limited land and does not understand the proper processing of liquid waste, is not aware of the impact on the environment, and lacks reuse of waste for other benefits. The purpose of this Community Service Community Partnership Program is to educate and apply appropriate technology for home industrial wastewater treatment (case study of Mitra 1 (tempe) and Mitra 2 (tofu) factories in Banjarbaru City) in an effort to reduce pollutant content. The service methods carried out include data collection, dissemination of research results; tool application, technical guidance, monitoring and evaluation. The results of the community service activities that have been carried out are carrying out outreach activities and coordination with Partners 1 (tempe factory) and Partner 2 (tofu factory), sharing and exposing research results involving relevant agencies, making drum reactors and technical guidance on equipment installation, monitoring and evaluation of community service activities. Through Community Service Activities, the Community Partnership Program is expected to provide benefits for increasing the understanding and skills of the tofu-tempe home industry as partners regarding the application of appropriate technology for liquid waste management. In addition, it is also hoped that a better network of cooperation will be established.&#x0D;
Multi-dimensional Data Visualization for Analyzing Materials

High-throughput chemical synthesis is extensively used for analyzing materials since it allows a systematic probing of a large span of three space parameters: initial conditions, final products, and the characteristics of the obtained products. High-dimensional data visualization is required to fully understand the relations between these three spaces. However, most methods are limited to up to three dimensions (3D) at the time per graph. Here, we show how correlation analysis and parallel coordinate plots reveal the relations in a multidimensional space. This representation technique is general and may serve many synthetic and fabrication related processes. We demonstrate the power of our approach on a specific chemical colloidal synthesis of CsPbBr3 nanocrystals, which results in highly emissive semiconductor nanocrystals, relevant for photonic applications. The colloidal synthesis of these nanocrystals is a multi-parameter process, with complex inter-relationships between the parameters, such as precursors and organic ligands concentrations and reaction temperature. The resulting nanocrystals present distinct morphological differences, resulting in a detectable shift of their emission spectrum. We use a dataset of 1351 samples to investigate the relations between and within these three spaces. In our case study, we have identified trends and anomalies in the data that provide directions for further research and illustrated thereby the potential of correlation analysis and parallel coordinates to explore patterns and relationships.
Visual State Estimation for False Data Injection Detection of Solar Power Generation

As the penetration level of solar power generation increases in smart cities and microgrids, an automatic energy management system (EMS) without human supervision is most communly deployed. Therefore, assuring safe and reliable data against cyber attacks such as false data injection attacks (FDIAs) has become of utmost importance. To address the aforementioned problem, this paper proposes detecting FDIAs considering visual data. The aim of visual state estimation is to enhance the resilience and security of renewable energy systems. This approach provides an additional layer of defense against cyber attacks, ensuring the integrity and reliability of solar power generation data and facilitating the efficient and secure operation of EMS. The proposed approach uses a modified VGG-16 neural network model to obtain an intermediate representation that provides textual and numerical explanations about the visual weather conditions from sky images. Numerical results and simulations corroborate the validity of our proposed approach. The performance of the modified VGG-16 neural network model is also compared with previous state-of-the-art machine learning models in terms of accuracy.
Students’ Multiple Perceptions of English Teaching Model Reform in Colleges and Universities under Cognitive Coupling Networks

Abstract On the basis of linear multiple regression methods such as PLSR and ICR, this paper introduces the kernel function to kernelize these linear multiple statistical regression methods, derives the KPLSR and KICR algorithms, and gives the specific implementation process. Then kernel-based multiple regression methods are applied to analyze the English teaching mode using a cognitive coupling network. According to the students’ learning data under the English teaching mode of cognitive coupling network, then the students’ learning situation and other precise calculations and statistics, so as to truly reflect the student’s learning status, presenting the students’ learning process in need of an urgent solution. Finally, the correlation between students’ multi-sensory learning styles and English performance is analyzed, and the results show that visual, auditory, kinesthetic, experiential, and independent styles all show a significant positive correlation to the total English performance, with correlation coefficients of 0.416, 0.158, 0.349, 0.339, and 0.327, respectively, among which the correlation coefficient of the visual type of perception is the largest.
A Quantum Adaptation to Roll Truly Random Dice in Role Playing Games

In role-playing games (RPGs), players are called upon to assume the role of a character moving in an imaginary environment and facing several challenges. Their success or failure often depends on randomizers like cards or dice. Regarding the latter, the most commonly used in RPGs are the Platonic solids with the addition of the ten-sided die. They are commonly simulated through classical computers, however, since true randomness is not in their nature, they can only generate pseudorandom numbers. On the contrary, quantum computers exploit the nondeterministic nature of quantum mechanics, so they are perfect candidates for truly random simulations in games of chance. For this reason, this paper proposes and tests various quantum circuits for sampling uniformly distributed discrete values within a fixed range, corresponding to the number of faces of the dice. The simulations reveal the pure randomness of the output of the implemented circuits. They were then used to generate random numbers within a three-dimensional dice-rolling game.
A Systematic Mapping of Approaches and Techniques in Object Navigation

Object navigation (ObjectNav) is a widely used method for visual perception tasks in 3D environments. ObjectNav is based on the use of a variety of technologies and approaches, with a particular focus on semantic navigation. This systematic literature mapping (SLM) study aims to provide a comprehensive overview of the state of the art in this area, identify trends and gaps in current research, and recommend direction for future work. We systematically searched for five key scientific databases: ACM Digital Library, IEEE Digital Library (IEC), Google Scholar, CAPES Journals, and Web of Science. Our inclusion and exclusion criteria led to the identification of 28 relevant articles. The results of our systematic mapping are instrumental in understanding the current landscape of object navigation and in identifying promising avenues for future research.
A fast 3D-HEVC video encoding algorithm based on Bayesian decision

In order to improve the encoding performance of 3D videos, the 3D-HEVC encoding standard has also added new stereo video encoding technologies such as interview prediction and depth map encoding. However, excellent encoding performance is achieved through significant computational complexity. In order to reduce the computational complexity of the 3D-HEVC encoding process, this paper proposes a 3D-HEVC fast encoding algorithm based on Bayesian decision-making. This algorithm uses machine learning methods to extract features that meet specific relationships and uses these features to learn and establish classification and prediction models. The experimental results show that compared with traditional 3D-HEVC encoding algorithms, the machine learning method based on Bayesian decision-making can effectively improve the video coding performance of 3D-HEVC, with a reduction of 23.28% in total encoding time and 39.09% in deep mapping encoding time, respectively.
Reinforcement Learning for Combating Cyberbullying in Online Social Networks

In recent decades, social network holds a pivotal role for people’s communication, but it is also a particularly susceptible to cyberbullying due to their rapid information dissemination capabilities. The state-of-art research in cyberbullying mainly focus on cyberbullying detection and literature discover, which encounters significant challenges in aspects like theoretical assurances, time effectiveness, and adaptability to broad contexts. In this paper, we present a resilient framework leveraging deep reinforcement learning (DRL) to tackle the problem of cyberbullying in online social networks. Our approach leverages dynamic graph neural networks to perform network embedding and the double deep Q-network (DDQN) for the parameter learning. To evaluate the effectiveness of our proposed approach, we conducted a comprehensive set of experiments using realistic datasets. The experimental findings demonstrate that our approach outperforms the comparison methods, even we train our model with small randomly generated ER graphs. This shows the strong generalization ability of our proposed model.
Speech recognition and enhancement in underground mines for the use of smart voice assistants

Smart voice assistants are being used progressively in new fields and environments. This trend has also reached Polish underground mines, where the first attempts to implement solutions of this type are already being executed. Unfortunately, the underground mine environment places great demands on the operation of such assistants. This problem is even greater when we take into account less popular and more difficult languages like Polish. In this article, we approach the problem of noise in the underground mine environment and the correct transmission of speech to text by a smart voice assistant. In particular, we researched the possibility of using various algorithms for speech enhancement. This was achieved by taking a series of recordings in an underground environment, during which a voice assistant was used in a standardized way by different people. This voice material was then denoised using 10 different algorithms, converted to text, and evaluated. Such a comparison showed that only one of the chosen methods influenced the speech-to-text solution in a positive way, showing that current algorithms are not sufficient for harsh industrial environments similar to underground mines.