Reviewerly/exHarmony huggingface dataset:
Multi-triplet Feature Augmentation for Ponzi Scheme Detection in Ethereum

Blockchain technology revolutionizes the Internet, but also poses increasing risks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing scams, and a variety of other frauds emerge. Existing Ponzi scheme detection approaches based on heterogeneous transaction graph modeling leverages semantic information between node (account) pairs to establish connections, overlooking the semantic attributes inherent to the edges (interactions). To overcome this, we construct heterogeneous Ethereum interaction graphs with multiple triplet interaction patterns to better depict the real Ethereum environment. Based on this, we design a new framework named multi-triplet augmented heterogeneous graph neural network (MAHGNN) for Ponzi scheme detection. We introduce the Conditional Variational Auto Encoder (CVAE) to capture the semantic information of different triplet interaction patterns, which facilitates the characterization on account features. Extensive experiments demonstrate that MAHGNN is capable of addressing the problem of multi-edge interactions in heterogeneous Ethereum interaction graphs and achieving state-of-the-art performance in Ponzi scheme detection.
Distilling Influences to Mitigate Prediction Churn in Graph Neural Networks

Models with similar performances exhibit significant disagreement in the predictions of individual samples, referred to as prediction churn. Our work explores this phenomenon in graph neural networks by investigating differences between models differing only in their initializations in their utilized features for predictions. We propose a novel metric called Influence Difference (ID) to quantify the variation in reasons used by nodes across models by comparing their influence distribution. Additionally, we consider the differences between nodes with a stable and an unstable prediction, positing that both equally utilize different reasons and thus provide a meaningful gradient signal to closely match two models even when the predictions for nodes are similar. Based on our analysis, we propose to minimize this ID in Knowledge Distillation, a domain where a new model should closely match an established one. As an efficient approximation, we introduce DropDistillation (DD) that matches the output for a graph perturbed by edge deletions. Our empirical evaluation of six benchmark datasets for node classification validates the differences in utilized features. DD outperforms previous methods regarding prediction stability and overall performance in all considered Knowledge Distillation experiments.
Consultation with experts

The recommendations presented in this report were supported by a series of interviews and focus group discussions conducted by the OECD team with Korean exerts throughout 2021. Initial interview subjects were identified by HIRA personnel. These interview subjects recommended other experts for the OECD to consult. The OECD continued interviews until the information gathered from key informants began to share similar messages expanding our understanding of the data landscape and recent innovations in Korea.
Measuring transform invertibility with computational metrics

Modern face ID systems are often plagued with loss of privacy. To address this, some face ID systems incorporate image transformations in the detection pipeline. In particular, we consider transforms that convert human face images to non-face images (such as landscape images) to mask sensitive and bias-prone facial features and preserve privacy, while maintaining identifiability. We propose two metrics that study the effectiveness of face image transformations used in privacy-preserving face ID systems. These metrics measure the invertibility of the transformations to ensure the meta-data of the face (e.g. race, sex, age, etc.) cannot be inferred from the transformed image.
Bias-mitigated face ID using secure generative transforms

In this paper, we create mix-and-matched generative networks to address privacy and bias concerns in face recognition systems. There has been a rise in bias based on religion, gender, and race. To preserve the robustness of face ID systems while masking these bias-inducing facial features, we map the faces to neutral natural landscape images. This still leaves the possibility of estimating facial features from the landscape images. We address this issue through decorrelation shuffling functions between the latent spaces of the encoder and the generator networks, as a way of decorrelating facial and landscape features and preventing hacking.
Histopathological image classification with unsupervised approaches using deep convolutional autoencoder and k-nearest neighbors

Artificial intelligence (AI) based analysis accelerates clinical diagnosis from pathological images efficiently and accurately. Due to the high dimensionality of pathological images, extracting meaningful feature representations of the pixels from high-dimensional images is essential. This can be used for further analysis to obtain better insights. This study used Deep Convolutional Neural Networks (DCNN) and end-to-end Deep Convolutional auto-encoder outcomes (DCAE) models for feature extraction. K-means and K-Nearest Neighbors (KNN) methods were then used for clustering and classification and achieved 95% testing accuracy with these unsupervised classification methods. In addition, t-distributed Stochastic Non-linear Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) were applied for clustering and visualization of different tissue types and demonstrated promising representation for histopathological image clustering of 20 different tissue types. Within-Cluster and Square (WSS) errors were used to determine the optimal number of classes in cluster space with t-SNE and UMAP methods. Most importantly, the proposed system relies on class probability and the visual interpretation that provides the relationship among the 20 different pathological tissue types. The proposed pipeline is potentially applicable for understanding pathological image classification and clustering tasks to obtain better insight into digital pathology applications.
Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment

Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN). Initially, pre-processing is carried out with Intensive Mutual Disease Influence Rate (IMDIR) to identify the relational features. In addition, the Successive Disease Pattern Stimulus Rate (SDPSR) is carried out to create relative feature patterns. Based on the patterns, the features are selected and grouped into clustering. Inter-Class Sub-Space Clustering (ICSSC) is applied to split the features by class labels depending on the marginal rate. From the class labels, marginal features are obtained using spectral subset feature selection (SSFS). The selected features are then trained in a Multi-Layer Perception Neural Network (MLPNN) classifier to classify the patient features by risk. Its contribution is to exploit subset features to improve classification accuracy by clustering relational features. The proposed classifier yields higher classification accuracy than previous methods and observes cancer detection for early detection. Therefore, the proposed method achieved a risk analysis accuracy of 91.8% and an F-measure of 91.3% for early detection, which is recommended for early diagnosis.
Dynamic nanophotonics with conducting oxides and metal nitrides: from all-optical switching to new phenomena

Transparent Conducting Oxides (TCOs) and Transition Metal Nitrides (TMNs) are emerging platforms for applications in nonlinear optics such as all-optical switching, harmonic generation and photonic time crystals and more. We report on various methods to both dynamically tune their optical properties in ultrafast time scales, and also statitcally tailor their properties. We demonstrate great control in varying the epsilon-near-zero response of TCOs such as aluminum-doped zinc oxide (AZO) and yttrium doped cadmium oxide. We also investigate the strong thickness dependence of the TCOs and TMNs optical properties. Employing the Berreman modes of TiN and AZO films on the same platform, we demonstrate variable switching speeds of an optically-pumped metasurface. We also demonstrate the nonlinear capabilities of undoped ZnO for all optical switching6 and third harmonic generation. To explore the realization of photonic time crystals, we investigate the fastest material response to an optical pump in AZO, showing sub-10 femtosecond rise time. Our approach paves the way to novel phenomena and device design with ultrafast tunable and tailorable materials.
Qudit states in noisy quantum channels

Abstract In this work, we analyze a number of noisy quantum channels on a family of qudit states. The channels studied are the dit-flip noise, phase flip noise, dit-phase flip noise, depolarizing noise, non-Markovian Amplitude Damping Channel (ADC), dephasing noise, and depolarization noise. To gauge the effect of noise, the fidelity between the original and the final states is studied. The change of coherence under the action of noisy channels is also studied, both analytically and numerically. Our approach is advantageous as it has an explicit relation to the original approach to the multi-qubit hypergraph states.
Image encryption using infrared metamaterials

We explore the possibility of communicating encrypted information using infrared metamaterials. We first describe our algorithm for encrypting a plain image in multiple cipher images, each corresponding to a different wavelength channel. Our scheme is designed so that an interloper will not be able to recover the image from the summed intensity or the individual channels. However, the intended recipient can easily reconstruct the image by performing a mathematical operation over the channels. We implement this scheme in a 4-channel infrared metamaterial and demonstrate image encryption and decryption.
Energy-based neural network models with coherent laser networks

We propose utilizing coherently coupled laser networks for neural computing. The proposed scheme is built on harnessing the collective behavior of laser networks for storing phase patterns as stable fixed points of the governing dynamical equations and retrieving such patterns through proper excitation conditions, thus exhibiting an associative memory property. We further show that limitations on the number of images can be overcome by using nonreciprocal coupling between lasers, thus allowing for utilizing the large storage capacity inherent to the laser network. This work opens new possibilities for neural computation with coherent laser networks as a novel physical analog processor.
Colored digital Moire technique for self-occluding shading in fringe projection profilometry

In this work, we presented a phase demodulation technique for fringe analysis in fringe projection profilometry based on digital moiré and the co-phase technique. The approach involves the use of stereo illumination and the simultaneous projection and acquisition of two <i>&pi;</i>-shifted sinusoidal patterns from each projector. By taking advantage of color multiplexing, we code the sinusoidal patterns in the red and blue channels of the RGB images, so we need to project and acquire one image for each digital light projector. The phase demodulation consists of two steps: first, digitally generating the moiré pattern for each RGB image utilizing the spatial phase carrier; second, computing the analytic signals for the two moiré patterns via a third-order, low-pass Butterworth filter in the frequency domain; third, calculate the final phase by merging the two analytic signals using the co-phasing technique.
Detect Quakes with “Raspberry Shakes” &gt; A Raspberry Pi-Based Seismometer Can Detect Even Distant Earthquakes

I have only once felt an earthquake—in 1985, when a magnitude-4 temblor occurred just north of New York City. It wasn't until I heard the news reports later that I realized the vibration that had awakened me at 6 a.m. was, in fact, a small earthquake.
Message from the Chairman ICPCSN 2023

It is my great pleasure to announce that the “3rd International Conference on Pervasive Computing and Social Networking”, ICPCSN 2023, is to be held at our institution on 19-20, June 2023.
Mobile Training Context for In-Service Teachers

Mobile training is one of the modern training systems resulting from the integration of innovations in information and communication technology (ICT), which can be used to solve the problems of traditional training in educational institutions. Therefore, it is important to study the design variables of such training that enhance its effectiveness. Thus, the present study aims to design a mobile training context (MTC) and to investigate the effect of interaction between the training method, i.e., whole task (WT) versus part task (PT), and the task practice method, i.e., massed practice (MP) versus distributed practice (DP), on the skills of e-content production of 52 public education teachers in Jeddah, Saudi Arabia, through using the semi-experimental approach and the factorial design (2 × 2). Furthermore, an application-oriented knowledge test was used to measure the participant teachers’ skills through their application of 208 items related to e-content design, production, and publishing. Findings showed the effectiveness of part-task training (PTT) compared to whole-task training (WTT) and the effectiveness of the massed practice method (MPM) compared to the distributed practice method (DPM). In addition, there was a preference for the massed practice for part-task (MPPT) treatment in comparison to other treatments. This result encourages the possibility of providing a successful MTC for training in different skills. However, further research is still required to optimize the expected future potential of the mobile training context.
A Review of Latest Trends and Technologies in the Field of Facial Re-recognition in Surveillance Cameras

Deep Learning-driven advances in facial re-identification are transforming computer vision. The increasing need for precise identity identification in surveillance, law enforcement, and public safety is being met by this advancement. Complex face feature extraction is automated by Deep Learning, particularly with Convolutional Neural Networks (CNNs), improving recognition under difficult circumstances. This paper focuses on the most recent developments and the influence of Deep Learning on facial re-identification. For scholars, practitioners, politicians, and industry actors, it's a vital resource that emphasizes the need of staying current for the best possible system use. To sum up, the combination of Deep Learning with facial re-identification offers accurate, dependable, and effective identity recognition. It is essential for security, law enforcement, and public safety that technology advances.
An Alternating Treatment Design Comparing Small Group Reading Interventions Across Early Elementary Readers

Learning how to read accurately and fluently is a critical component for a student’s future academic success. Reading fluency is a skill that many students struggle to master. In addition, many students missed out on key skill development due to the loss of instruction from COVID-19. As schools begin to recover from these educational losses, small group reading interventions offer an efficient solution to service multiple students at once. Small group reading interventions such as Repeated Readings (RR), Listening Passage Preview (LPP) and LPP with RR (LPP+RR) have all been demonstrated to be effective methods for increasing reading fluency. Yet few studies have specifically examined the effectiveness of these interventions in comparison to each other in a group setting. The current study compared reading RR, LPP, and LPP+RR in a small group setting to determine which intervention yielded the largest gains in reading fluency.
Emotional Intelligence and Personality Development

Emotional intelligence is also one part of personality. We can not define Emotional intelligence from Personality. Emotional intelligence is a very important parts in personality development if we can develop personality first develop Emotional intelligence and then develop the other factors. For that development create some programs use teaching Aids, teaching models, Principles and New policies tools etc. "Intelligence is a foundation of personality and Emotional intelligence is a pillar of personality."
Digital education during pandemic and situation in Kazakhstan: current state and forecasts

The Internet has changed all areas of human activity and education is no exception. A new term “digital education” has appeared with the development of Internet technologies in education. This article describes the main benefits of digital education associated with the Covid-19 pandemic, when people were forced to adjust their lives to the current situation in the shortest possible time. The Covid-19 pandemic has disrupted the functioning of higher education around the world, including in Kazakhstan. The forced transformation of the educational process has become a big test for Kazakhstani universities both in terms of changing the entire format of education, and in terms of material support. At the same time, the activities of universities received a powerful incentive to reform the entire education system, when the digitalization of the educational process came to the fore. The article provides an overview of the platforms that have been developed during the described period (Zoom, Microsoft Teams, Google Classroom, Kundelik, etc.). Particular attention is paid to the analysis of the “univer.kaznu.kz” platform, which is a large-scale platform successfully tested by teachers, students and employees of al-Farabi KazNU before 2020 and developed during the quarantine period. Key words: virtual classrooms, online etiquette, digital transformation of education, distance learning, pandemic, quarantine, school online format, university online format
PELATIHAN PENINGKATAN SOFT SKILLS PENGGUNAAN MENDELEY PADA MAHASISWA PRODI PENDIDIKAN BIOLOGI FKIP UNIVERSITAS CENDERAWASIH SEBAGAI PENDUKUNG PENYUSUNAN TUGAS AKHIR

Mendeley is software that can help write citations and bibliography on scientific papers, with management and academic social networks to make it easier to arrange online research publications and find the most recent publication updates. It is important for students to be given training in using the Mendeley application. There are still many students, especially students who are entering their final year, who have not been able to maximize the features of the Mendeley software. The training was attended by 41 semester VI students of the Biology Education Study Program, the PMIPA FKIP UNCEN Department. The activity was carried out through several stages, namely: presentation of the introduction of Mendeley Software, installation of Mendeley Software on student software (laptops), application of Mendeley Software in Microsoft Word for citations in the preparation of the final project and evaluation on project activity. This service activity succeeded in providing an understanding of writing references/citations (Soft Skills) to semester VI students of the Biology Study Program in working on their final assignment in the form of a thesis. It is hoped that in the future, this training can be developed to the point where students can produce scientific papers that are published in international and national accredited journals.
Method for Calculating 3D Coordinates in Pose Estimation of a Stick Held by a Hand

Monocular human 3D pose estimation has become more practical with deep neural network (DNN) technology. One of the next challenges is pose estimation including stick held in the hand. In this study, we propose a method to compute the coordinates of the stick tip using a human 3D pose estimation network that measures the position where the stick is held in the hand. In particular, when the stick is orthogonal to the camera's line-of-sight to the tip point, the coordinates of the measurement point may be imaginary, and we present a solution using the method of Lagrange multiplier. Experiments confirmed the ability of the proposed method to modify the solution to a real number when it would result in an imaginary solution. The proposed method is expected to be applied to monocular motion capture of humans holding various stick-shaped objects, for example, in sword fighting techniques and baseball batting.
Sistem Informasi Manajemen Pendidikan Dalam Meningkatkan Pendaftaran Siswa Baru

This Study aims to determine the education management information system and the School’s efforts to increase the acceptance of new students at SD Muhammadiyah Parakan Temanggung. This Type of research uses qualitative data collection methods through observation, interviews, and documentation. The data analysis technique uses the Triangulation technique. The results showed that the education Information System was based on student management so it was easy to record. Students data input operators can be directly accessed. Through the internet and wifi so that anyone who will see it can be accessed easily and quickly. The school’s efforts to increase the acceptance of new student registration through an information system, namely using a good school image. Optimizing the advantages of the quality of school education. Through social media and printing, for example comparative study abroad and various national level Olympic Champions.
ANALISIS KESANTUNAN KALIMAT IMPERATIF DALAM TALKSHOW HOTROOM “MENAKAR KEADILAN DALAM TUNTUTAN”

Politeness is the ethics of communicating by using language as a medium of speech, which regulates polite language procedures. While the imperative is an utterance intending to order the interlocutor to do what the speaker wants. The method used in this research is descriptive method. This method applies the requirement that research must be based on existing facts so that the news or information provided is in accordance with the actual situation. The purpose of this study is to describe words that contain imperative meaning. The research subject is the Youtube Chanel, starring Hotman Paris with the theme of measuring justice in demands. The research object is imperative speech. The theory used to analyze imperative politeness is Chaer's theory. Data collection techniques use the YouTube video listening method. While the technique used is note-taking technique.
ANALISIS KEBUTUHAN PENGEMBANGAN BAHAN AJAR MENULIS CERITA FANTASI PADA KURIKULUM MERDEKA BELAJAR

The teacher has an important role in learning activities, one of which is evaluating learning activities. The purpose of this research is to analyze the needs of teachers in developing teaching materials for writing fantasy stories. This study uses a development research model that refers to the Thiagaragan 4D model with limitations in the first stage or the definition stage and the moted used is a descriptive qualitative method. The source of the research data was a junior high school Indonesian teacher in Jambi. Data collection techniques by distributing online questionnaires, interviews, and online FGDs were then analyzed using qualitative data analysis techniques. Based on the results of the needs analysis research on fantasy story material in the independent curriculum, it is less attractive, so material development is needed so that it can form students' writing competence by prioritizing local wisdom. This research is expected to be an evaluation material for teachers to develop teaching materials that are contextual with the conditions of students. Keywords: fantasy story text, Jambi folklore, Free Learning Curriculum