Reviewerly/exHarmony huggingface dataset:
Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump

With the proliferation of pump-and-dump schemes (P&amp;Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors. In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&amp;D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&amp;Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&amp;D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&amp;D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&amp;D dataset on GitHub https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.
A New Sparse Data Clustering Method Based On Frequent Items

Large, sparse categorical data is a natural way to represent complex data like sequences, trees, and graphs. Such data is prevalent in many applications, e.g., Criteo released a terabyte size click log data of 4 billion records with millions of dimensions. While most existing clustering algorithms like k-Means work well on dense, numerical data, there exist relatively few algorithms that can cluster sets of sparse categorical features. In this paper, we propose a new method called k-FreqItems that performs scalable clustering over high-dimensional, sparse data. To make clustering results easily interpretable, k-FreqItems is built upon a novel sparse center representation called FreqItem which will choose a set of high-frequency, non-zero dimensions to represent the cluster. Unlike most existing clustering algorithms, which adopt Euclidean distance as the similarity measure, k-FreqItems uses the popular Jaccard distance for comparing sets. Since the efficiency and effectiveness of k-FreqItems are highly dependent on an initial set of representative seeds, we introduce a new randomized initialization method, SILK, to deal with the seeding problem of k-FreqItems. SILK uses locality-sensitive hash (LSH) functions for oversampling and identifies frequently co-occurred data in LSH buckets to determine a set of promising seeds, allowing k-FreqItems to converge swiftly in an iterative process. Experimental results over seven real-world sparse data sets show that the SILK seeding is around 1.1\sim3.2× faster yet more effective than the state-of-the-art seeding methods. Notably, SILK scales up well to a billion data objects on a commodity machine with 4 GPUs. The code is available at https://github.com/HuangQiang/k-FreqItems.
Foreign Keys Open the Door for Faster Incremental View Maintenance

Serverless cloud-based warehousing systems enable users to create materialized views in order to speed up predictable and repeated query workloads. Incremental view maintenance (IVM) minimizes the time needed to bring a materialized view up-to-date. It allows the refresh of a materialized view solely based on the base table changes since the last refresh. In serverless cloud-based warehouses, IVM uses computations defined as SQL scripts that update the materialized view based on updates to its base tables. However, the scripts set up for materialized views with inner joins are not optimal in the presence of foreign key constraints. For instance, for a join of two tables, the state of the art IVM computations use a UNION ALL operator of two joins - one computing the contributions to the join from updates to the first table and the other one computing the remaining contributions from the second table. Knowing that one of the join keys is a foreign-key would allow us to prune all but one of the UNION ALL branches and obtain a more efficient IVM script. In this work, we explore ways of incorporating knowledge about foreign key into IVM in order to speed up its performance. Experiments in Redshift showed that the proposed technique improved the execution times of the whole refresh process up to 2 times, and up to 2.7 times the process of calculating the necessary changes that will be applied into the materialized view.
AutoOD: Automatic Outlier Detection

Outlier detection is critical in real world. Due to the existence of many outlier detection techniques which often return different results for the same data set, the users have to address the problem of determining which among these techniques is the best suited for their task and tune its parameters. This is particularly challenging in the unsupervised setting, where no labels are available for cross-validation needed for such method and parameter optimization. In this work, we propose AutoOD which uses the existing unsupervised detection techniques to automatically produce high quality outliers without any human tuning. AutoOD's fundamentally new strategy unifies the merits of unsupervised outlier detection and supervised classification within one integrated solution. It automatically tests a diverse set of unsupervised outlier detectors on a target data set, extracts useful signals from their combined detection results to reliably capture key differences between outliers and inliers. It then uses these signals to produce a "custom outlier classifier" to classify outliers, with its accuracy comparable to supervised outlier classification models trained with ground truth labels - without having access to the much needed labels. On a diverse set of benchmark outlier detection datasets, AutoOD consistently outperforms the best unsupervised outlier detector selected from hundreds of detectors. It also outperforms other tuning-free approaches from 12 to 97 points (out of 100) in the F-1 score.
University students’ perceptions of the use of Know-Want-Learned (KWL) strategy in reading comprehension

This study investigates the English learners’ perceptions of the application of Know-Want-Learned (KWL) strategy in reading comprehension class. The study used the quantitative method, employing a questionnaire to collect the data needed. The respondents of the study were 30 students majoring in English Education at a public university in Banda Aceh, Aceh Province, Indonesia. The respondents had already experienced the use of KWL strategy in the Reading Comprehension class. The findings showed that and the students’ perception of KWL strategy was generally positive that most of them felt that KWL is suitable to use to help them in their reading comprehension on English texts.
An iLauncher plugin for PostgreSQL with extensions for various backend HPC resources

The Interface Launcher (iLauncher) technology automates the submission of HPC jobs and provides a mechanism for rapidly prototyping web interfaces from the user’s desktop to powerful capabilities running on back-end high performance computing (HPC) resources, including Amazon Web Services (AWS) GovCloud, distributed clusters of heterogeneous nodes with multiple graphics processing units (GPUs) per node running the Slurm batch queuing software, and Department of Defense (DoD) supercomputers running the Portable Batch Scheduling (PBS) software. We present some of the latest advancements in iLauncher plugin development, particularly in the use of channels to make deployment of plugins easier for groups of users. We also describe our latest plugin for using PostgreSQL with the TimescaleDB and PostGIS extensions in a Singularity container with the pgAdmin and Jupyter Notebook web interfaces for use on these HPC resources.
A.L. Zakharenko – Researcher of Siberia and Kazakhstan in the second half of the 19th century

The article analyzes the contribution of the Kazakh historian A.L. Zakharenko to the study of the history of Siberia in the second half of the 19th century and the socio-historical connections between Kazakhstan, Siberia, the Volga region, and the Urals through the use of the method of intellectual biography.Siberia in the second half of the 19th -early 20th centuries underwent socio-political changes in which a new category of people -"raznochintsy" -played a noticeable role.They united in organizations such as "Siberian community" and formed the socio-political movement of «oblastnichesvo».Kazakhstan in this period, due to its geographical and historical circumstances, was closely linked to Siberia.In the second half of the 19th century, the formation of the national Kazakh intelligentsia was taking place, which had close cultural and educational ties with the Siberian oblastnichesvo, especially with its outstanding representatives such as G.N. Potanin and N.M.Yadrintsev.The study of Siberian oblastnichesvo, the life and activities of individual oblastniki, and the cultural and socio-political interconnection between Siberia, Kazakhstan, the Urals, and the Volga region is particularly significant for the historical science of Kazakhstan.Zakharenko is one of the first Kazakh scientists who has achieved significant results in the field of Siberian studies.
Correction to: QuantumRNG, A Random Number Generator Using One Qubit

In the original version of the book, the following belated correction has been incorporated in chapter “QuantumRNG, A Random Number Generator Using One Qubit”: The chapter author name “Bhemmanathy Saketh Chandra” has been changed to “Bheemanathy Saketh Chandra” in Chapter 10 and Table of Contents. The correction chapter and the book has been updated with the changes.
Theory on the Role of Artificial Moral Agent (AMA) according to the Level of Development of Artificial Intelligence Teacher

지금은 인공지능 시대라 부를 수 있을 정도로 인공지능 중심의 기술사회가 형성되고 있다. 이러한 상황은 교육에 있어서도 무관하지 않으며 인공지능 교사에 대한 기대가 이를 나타내고 있다. 인공지능 교사가 앞으로 어떻게 발전할지를 예측해보는 것은 인공지능 교사의 발전 방향성을 제공하는 역할을 한다. 그러므로 본 논문은 인공지능 교사의 발전 수준을 1수준부터 5수준까지 5가지 기능항목에 따라 5가지 단계로 제시하였다. 또한 인공적 도덕행위자로서의 인공지능 교사가 어떠한 역할을 할 수 있는가를 논의하였다.Now, a technological society centered on artificial intelligence is being formed to the extent that it can be called the age of artificial intelligence. This situation is not irrelevant in education, and expectations for artificial intelligence teachers are showing this. Predicting how AI teachers will develop in the future serves to provide a direction for AI teachers' development. Therefore, this paper presents the development level of AI teachers in 5 stages according to 5 functional items from level 1 to level 5. In addition, the role of artificial intelligence teachers as artificial moral actors was discussed.
Connected spectrogram graph fitting and random optimization combined time frequency analysis

Because of its simple approximate representation to nonlinear time variant frequency modulations, polynomial chirplet has been used in radar, gravity analysis, electronic warfare and acoustic signal processing. However, due to its high dimension parameter spaces, direct polynomial chirplet transform has extremely high computational cost. In addition, the discrete implementation of polynomial chirplet transform causes a limited parameter estimation accuracy which may not satisfy the requirement in real applications. In this paper, by combining a connected spectrogram graph fitting with random optimization, we develop a new technique to address these computational cost and parameter estimation accuracy issues. We first convert a high dimensional polynomial chirplet transform into a low dimensional spectrogram implementation which significantly reduces computational cost. We then introduce interpolation and random optimization methods to improve the parameter estimation accuracy.
Quantum radar: a brief review of current progress and new methods of understanding and signal processing, validated by experimental results

Modern approaches to quantum radar implementation utilize the intrinsic correlations of two-mode squeezed vacuum photon pairs emerging from a nonlinear interaction. The most popular approach has been the use of delay lines for the idler and performing joint measurements on the idler and returning signal together. In this paper, it is argued and shown that this sort of implementation is not necessary to extract the quantum cross correlation terms. Immediate detection of the idler and later cross correlation on a large enough data set will yield identical covariance terms. Moreover, immediate idler detection facilitates the use of conventional radar signal processing which allows existing waveform toolboxes of classical radar to be utilized for quantum radar. This allows a much more relaxed set of constraints on the implementation of quantum radar techniques. This paper discusses these concepts, including new detection techniques from the author, and validates the framework with some preliminary experimental data. The presented data, as well as the recent work of others allows for the possibility of a much larger quantum advantage than previously thought, particularly when comparing to real-world practical classical sensors.
Images cipher based on convolution with chaotic maps and retrieving using the alternating direction method of multipliers

Keeping information hidden has been of paramount importance in all civilizations. Cryptography has accompanied humanity for millennia and many encryption methods and models have been proposed. One of the main challenges for information encryption is the encryption method, which implies that the invulnerability of the encryption system is directly related to how this process is conducted. Herein, we propose an encryption model based on a convolution process, and because the convolution lacks a direct inversion process (such as the Fourier transform), restoring encrypted data to its original state is not a simple task. The results of this work show that the proposed model is highly resistant to external attacks, which makes it a good candidate for information encryption applications.
Camera measurement techniques using virtual prototyping

Scene generators and high-resolution displays carry the potential for creating adaptive scenes for camera characterization. We demonstrate a camera modulation transfer function (MTF) measurement technique using a commercial computer display. Once the proper conditions for MTF measurements are satisfied, other measurements like the instantaneous field of view (IFOV) and the distortion of a visible camera can be characterized using stimuli from the same display. We developed the measurement techniques using a high-fidelity imaging simulation tool that provides control of the scene and camera parameters. The high-fidelity simulation allowed replication of the laboratory setup and fast iteration of parameters which drastically reduced the method development time. Virtual prototyping of measurements in this way allows for sensitivity analysis to be conducted over measurement parameters. In this work we describe the method development for measurements with a commercial computer display.
Parental difficulties during online learning process

This study aimed to investigate the problems that parents faced and how they dealt with them during the online learning process. The study used the qualitative research method. The study enlisted 10 people (parents) as the participants to describe the type of issues that arose during the online learning process. The data were then examined through data reduction, data display, and conclusion drawing. The findings revealed that parents faced a variety of issues that hampered the learning process; the issues were primarily caused by a lack of a good phone, internet data, and poor connection. The study suggests that in order to fix the problems, parents should contact the teachers and seek help from others.
Peningkatan Keaktifan dan Hasil Belajar Pelajaran Komputer Jaringan Dasar Menggunakan Model Pembelajaran Problem Based Learning

The level of activity and learning outcomes of students in Basic Network Computer learning for class X TKJ SMK Negeri 1 Pleret has not been as expected. This study aims to determine the increase in the activity and learning outcomes of Computers and Basic Networks through problem-based learning models which include psychomotor aspects or skills, cognitive or knowledge, and affective or attitude aspects. Class action research will be carried out in two cycles where each cycle consists of four stages, namely planning, action, observation and reflection. The subjects of this study were students of class X TKJ especially class X TKJ A for the 2019-2020 academic year which consisted of 37 students, consisting of 33 boys and 4 girls. Data collection techniques were carried out using written tests and observations, as well as analysis of quantitative and qualitative descriptive data. The results of this class action research showed that the learning outcomes of students in pre-cycle obtained a complete percentage of 40.5%, cycle I obtained a complete percentage of 43.24%, and in cycle II obtained a complete percentage of 81.08%. Increased activity and learning outcomes are indicated by an increase in the percentage of pre-cycle completeness to cycle I by 6.7% and from cycle I to cycle II an increase in the percentage of completeness by 87.5% Based on research results show that learning with the problem based learning model can increase activity and results learn computer network basic learners.
Sigmoid function generator using stochastic adiabatic superconductor logic

Stochastic-computing-based artificial neural networks (SC-ANNs) can be used to perform hardware- and energy-efficient neuromorphic computing. We have been developing SC-ANNs using an energy-efficient superconductor logic family, namely, adiabatic quantum-flux-parametron (AQFP) logic. AQFP logic is suitable as a building block for SC-ANNs since it can perform stochastic operations with extremely small energy dissipation. In this Letter, we propose and demonstrate a sigmoid function generator (SFG) for AQFP SC-ANNs, which we refer to as the AQFP SFG. An SFG is an important circuit in neural networks that generates outputs from the sum of weighted inputs in accordance with the sigmoid function. The AQFP SFG performs the sigmoid function using a finite state machine based on an AQFP buffer coupled to a flux storage loop. We experimentally demonstrate that the AQFP SFG generates output signals from stochastic bitstreams in accordance with the sigmoid function and that the characteristics of the sigmoid function can be controlled by a bias current. Furthermore, we show that the AQFP SFG operates with small power dissipation due to its simple structure.
ISVABI: In-Storage Video Analytics Engine with Block Interface

The wide use of cameras in the past decade has increased the need to process video data significantly. Due to the large volume of video data, analyzing videos to extract useful information has become a critical challenge. Several prior works have tried to accelerate video analytics workloads by offloading some operations to embedded processors within storage devices.
Penerapan Video Pembelajaran Daring dan Gamifikasi Untuk Penguatan Kompetensi Guru Dalam Di SDN 3 Kabila Bone

school teachers on the implementation of ICT-based learning models. In accordance with the Ministerial Regulation number 37 of 2018. The target of this activity is for primary school teachers to create TIK-based learning tools that are easy and simple to use to improve the knowledge and skills of the training participants, which will have a positive impact on improving student skills and learning outcomes. In addition, through this activity, the creation of TIK-based learning tools by participating teachers is targeted by applying TIK-based learning models in learning for each subject in primary school. The method is by conducting training on the implementation of ICT-based learning models for primary school teachers in the coastal areas of Bone Bolango Regency, Gorontalo Province, such as the use of learning videos using Canva, and also related to gamification for learning. This activity begins with the provision of basic material related to learning media, followed by practice in creating Canva-based learning videos, and ends with practice in creating gamification-based learning media using the Wordwall application. This community service activity ends with the signing of an Implementation Agreement between the Electrical Engineering study program and SDN 3 Kabila Bone, Bone Bolango Regency
A Subgoal-based Hierarchical Path Planning Framework for a Mobile Robot in Unstructured Environments

Navigating unstructured environments poses challenges for mobile robots. Because such environments are partially observable, ground mobile robots must consider both global traversability and local obstacles. In this paper, we proposed a subgoal-based hierarchical motion planning framework for mobile robots to navigate unstructured environments. This framework comprises a global path planner, a subgoal planner, and a local path planner. This framework enables the mobile robot to effectively avoid local obstacles that cannot be represented on the global map. The hierarchical framework plans subgoals in local coordinates to avoid obstacles when following the planned global path. The local planner utilizes subgoals to generate local paths. To evaluate the proposed framework, we constructed a simulated unstructured terrain environment with diverse obstacles. The hierarchical framework exhibited better and safer quantitative performance than a conventional global path-based method, and the mobile robot successfully navigated unstructured environments by using the hierarchical framework while avoiding obstacles in local coordinates in real time.
TRANSFORMASI PERILAKU KOMUNIKASI PENGGUNA GAME ONLINE MOBILE LEGEND: BANG BANG (STUDI KASUS PADA PELAJAR SMA NEGERI 1 RANTAU SELATAN)

This research is entitled "Transformation of Communication Behavior of Users of Online Game Mobile Legend: Bang-Bang". The purpose of this study was to find out how students' communication behavior changes after using the online game Mobile Legend: Bang-Bang and to find out how the activities of students after using the online game Mobile Legend: Bang-Bang. The theory related to this research is the uses and effects theory. This study uses a qualitative research methodology with a case study approach. The results of this study are changes in students' communication behavior towards people around them depending on their mood when they play the game. If they lose in the game, they will feel annoyed and angry which will carry over to the people around them, but when they win, they will feel happy and carry out activities as usual. Then there are no negative behaviors such as fighting that students follow from the game; students only tend to be carried away by harsh words in the game. Although the game has nothing to do with special subjects in the students' schools, there is knowledge that they get, namely knowing and understanding new foreign language vocabulary that can be practiced in the real world.