Reviewerly/exHarmony huggingface dataset:
The Sixth International Workshop on Automation in Machine Learning

The Sixth International Workshop on Automation in Machine Learning aims to identify opportunities and challenges for automation in machine learning, to provide an opportunity for researchers to discuss best practices for automation in machine learning potentially leading to definition of standards, and to provide a forum for researchers to speak out and debate on different ideas in automation in machine learning. The workshop agenda includes four invited keynote speakers and four accepted paper presentations chosen from a peer review process. The workshop seeks to drive engaging and interactive exchange of thoughts and ideas on AutoML.
2nd Workshop on Online and Adaptive Recommender Systems (OARS)

Recommender systems (RecSys) play important roles in helping users navigate, discover, and consume large and highly dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. A RecSys should adapt in real time to meet user needs and be robust against distribution shifts. The online and adaptive recommender systems (OARS) workshop offers a focused discussion of the study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia. KDD, as the premier data science conference, is an ideal venue to gather leaders in the field to further research into OARS and promote its adoption. This workshop is complementary to several sessions of the main conference (e.g., recommendation, reinforcement learning, etc.) and brings them together using a practical and focused application.
OVERVIEW OF LEARNING ANALYTICS DASHBOARDS AND ELEMENTS OF GAMIFICATION IN INTELLIGENT SYSTEMS FOR SELF-REGULATED LEARNING

This article presents a systematic review of the research literature of the information panels of learning analytics, self-regulated learning and gamification, which report empirical results to assess the impact on learning and teaching. Also in this pape
THE CORRELATION BETWEEN SKIMMING SKILL AND READING COMPREHENSION AT THE ELEVENTH GRADE STUDENTS OF SMKN 2 KOTABUMI NORTH LAMPUNG IN ACADEMIC YEAR OF 2020 / 2021

Reading is one of factors who support into process learning sucessfully. However, students still get any difficulties when they will apply it. One of them, students reading speed still limited. Students often spend their times only to read all of the text and it was over before they finished their assignment. Based on the problem, researcher did an research to know whether there is correlation that significant toward students skimming skill and students ability in understanding reading teks. This research was implemented at eleventh grade students of SMKN 2 Kotabumi in academic year of 2020/2021. This research used correlational research. The population was all of students of eleventh grade at SMKN 2 Kotabumi in academic year of 2020/2021. The amount of population were 379 students. Meanwhile, the sample were taken 10% from the population, by using proportional random sampling. The sample in this research was 38 students. In this research, researcher did two tests to collect the data. Multiple choice test in 20 questions was given to measure students’ reading comprehension and questionnare with 10 statements to measure students skimming skill. Then, the data was tested by using t-test. The result of hypothetical test showed that tcount &gt; ttable , so was accepted and was rejected. In other words, it can get conclusion that there is a significant correlation of students’ skimming skill and students’ reading comprehension towards understanding a text at eleventh grade students of SMKN 2 Kotabumi in academic year of 2020/2021.
Implementation of Business Model Canvas Development (Case Study : Beauty Clinic dr.Reni.G)

As one of the business development strategies, dr. Reni. G made improvements by remodeling its business model. This study uses a descriptive analysis methodology on the case study of the Beauty Clinic of dr. Reni. G. After conducting the literature study, interview process, Focus Group Discussion, and observation, then a SWOT analysis was carried out to determine the uniqueness of dr. Reni. G to be in line with the capabilities of existing resources. The results of the SWOT analysis in quadrant I (1.662602; 1.777778), showed that the Beauty Clinic dr. Reni. G requires a progressive strategy. To be able to achieve the expected performance targets and profit, it is important to immediately implement improvements to the nine elements of the Business Model Canvas in the Beauty Clinic of dr. Reni. G
Peran Orang Tua Dalam Membimbing Anak Belajar Dari Rumah di Kelas IVb SDN Pakuhaji

This study aims to determine the role of parents in guiding children to learn from home at the Pakuhaji Parungkuda Public Elementary School (SDN). This research is a type of qualitative research with a case study method. Data collection techniques using observation, interviews, documentation and questionnaires. The data analysis procedure uses Miles and Huberman, through data reduction, data presentation, and drawing conclusions. Check the validity of the data using triangulation of sources and techniques. The respondents of this study were fourth grade teachers, parents and students. The results showed that parents prepared learning tools (tools) for children including cellphones, books, study tables and provided comfortable places for children such as in the room, in the tv room and in the terrace. Parents help children overcome learning difficulties, namely by helping explain material that they do not understand in learning and providing examples in working on the questions given by the teacher. The inhibiting factors faced by parents during learning from home are due to 1) the availability of parental time is inadequate in supervising children during learning such as dividing time by selling and taking care of the household by doing homework, 2) low economic conditions in meeting children's learning needs including quotas must always be available to facilitate learning and prepare cellphones to be able to take part in learning.
PEMBELAJARAN DARING KOTOBA (KOSAKATA) BAHASA JEPANG DI SMAN 8 PEKANBARU

Vocabulary is an important element in mastering a language. Because having a good quantity and quality of vocabulary affects the use of good language too. In learning vocabulary, learning strategies are needed. However, vocabulary learning that usually done in class (offline) has turned into online learning (online) due to the COVID-19 Pandemic. This study aims to describe (1) the vocabulary online learning strategies used by Japanese language teachers at SMAN 8 Pekanbaru, (2) the obstacles experienced by teachers in teaching Japanese vocabulary online at SMAN 8 Pekanbaru. The subjects of this study were Japanese language teachers at SMAN 8 Pekanbaru. The data were collected by means of observation, interviews, and documentation, then analyzed using descriptive qualitative methods. The results showed that (1) the learning strategies used in online learning of Japanese vocabulary at SMAN 8 Pekanbaru were drill, question and answer, lecture and assignment giving strategies; (2) The obstacles faced by the Japanese language teacher at SMAN 8 Pekanbaru were weak internet networks that made it difficult for teachers to teach and students had difficulty understanding the vocabulary material being taught, students who did not activate the camera made it more difficult for the teacher to monitor students while learning.
Multifaceted Analysis of Malicious Ethereum Accounts and Corresponding Activities

In recent years, Ethereum, one of the leading applications to realize the service of blockchain technology, has received a great deal of attention with the usability and functionality to execute smart contracts, arbitrary programmable calculations in addition to cryptocurrency trading. However, misconfigured Ethereum clients with application programming interface (API) enabled, JSON-RPC in particular, are targeted by cyberattacks. In this research, we propose a new framework to detect malicious and suspicious Ethereum accounts using 3 different data sources (honeypot, Internet-wide scanner and blockchain explorer). The honeypot, named Etherpot, utilizes a proxy server placed between a real Ethereum client and the Internet. It modifies responses from the Ethereum client to attract attackers, identifies malicious accounts and analyzes their behaviors. With the Internet-wide scan results from Shodan, we also detect suspicious Ethereum accounts that are registered on multiple nodes. Finally, we utilize Etherscan, a well-known blockchain explorer for Ethereum, to track and analyze the activities related to the detected accounts. Through the observation of 6 weeks, we observed 538 hosts trying to call JSON- RPC of our honeypots with 41 different types of methods, including 2 types of unreported attacks in the wild. We detected 16 malicious accounts from the honeypots and 64 suspicious accounts from Shodan scan results, 5 out of which are overlapped. Finally, from Etherscan, we collected records of activities related to the detected accounts, including transactions of 21.50 ETH and mining of 22.61 ETH (equivalent to 167,560 USS at the rate of 2021/10/14). To an end, we provide a much brighter view of malicious activities on Ethereum.
PENGGUNAAN BUKU PAKET UNTUK MEMPERMUDAH PROSES PEMBELAJARAN SISWA DI SMP NEGERI 2 KAMPAR

The textbook is a student learning resource with several uses that can simplify the learning process. With textbooks, students can learn more actively in class, students can also repeat lessons from books when they are brought home. Likewise, the teacher can expand the subject matter based on the information contained in it. This paper aims to find ideas on how to use textbooks to facilitate the learning process. To achieve the objective of the observation, the data were obtained using participatory techniques, in-depth interviews, documentation and Focus Group Discussions. Furthermore, the data obtained were analyzed descriptively, namely by providing an explanation of the findings obtained. The research yielded information on the use of textbooks as learning resources for students, textbooks as learning media and increasing students' learning motivation. At SMP Negeri 2 Kampar the use of textbooks was not optimal due to the lack of quotas for textbooks at school, and the lack of quality textbooks given to students. students like there are scribbles and tears.
Critical Information Literacy in the Post-Truth Era: A Strategy for Facing Information Flow in Indonesia

Information literacy in this post-truth era is an important thing that must be understood by the Indonesian society in dealing with the truth and lies of the information obtained. This paper examines through a literature approach about the conception of information literacy in the post-truth era and what strategies must be understood by the information community in Indonesia in dealing with the spread of fake news from various media. This study concludes that critical thinking is an important part of information literacy that must be understood by Indonesian people to face the dynamics of information in the post-truth era. Critical information literacy can help our ability to search, find and use quality information. Thus, the strategy that must be carried out in dealing with the dissemination of information in the post-truth era is to identify the source, check the author, read the entire contents, check the novelty of the writing, check the balance of the information, the authenticity of the information, and always ask the experts when checking the web source.
Pengembangan Perangkat Research-Based Learning dengan Pendekatan STEM dalam Meningkatkan Metaliterasi Peserta Didik Menyelesaikan Masalah Himpunan Pasangan Berurutan

The ability of metaliteracy is very important in the industrial era 4.0. Metaliteracy is a comprehensive framework of thinking that goes beyond other literacies with the main literacy being information literacy. It is indicated that the metaliteracy of students is still low, it is because this literacy is still relatively new. The use of the IoT in learning is also still uneven, including the learning model used by teachers has not been able to fully support the formation of this ability. Therefore, in this study, the Research-Based Learning model which is integrated with the STEM approach (Science, Technology, Engineering, Mathematics) will be applied to solve the STEM problem of flight scheduling. Students are introduced to the application in the form of a set of sequential pairs, then with the representation of the set of points and lines a specific schedule is drawn up and includes its generalizations. The next step is how to bring STEM problem solving in learning class, then the developed RBL and STEM tools in learning to improve students' metaliteracy. From the results of the study, it was found that the RBL device with the STEM approach developed met the valid, effective, and practice criteria.
AI-enabled Multi-modal Network Anomaly Association: A Deep Self/Semi-Supervised Learning Approach

In nowadays large-scale networks, it is challenging for network operation and maintenance systems to analyze the reported massive network anomaly information. To handle this problem, we proposed a deep multi-modal learning approach called multi-modal anomaly root cause analysis, which enables network operation and maintenance systems to automatically and effectively associate the related network anomalies that appear from different modalities or aspects, and then locate the root causes. As a self/semi-supervised approach, our proposal is capable of realizing self-learning, self-adapting, and does not rely on a large number of manual annotations. According to the experimental results in a real large-scale network, without any annotations, our approach achieves up to 14% accuracy improvement in terms of multi-modal network anomaly association and root cause locating compared to the classical association rule mining algorithm Apriori, while its performance turns even much better when a few of labeled training samples are provided. The experiment also well proves the versatility and self-adaptability of our approach, which means our learning-based approach is able to not only achieve fast convergence but also automatically adapt itself to network changes.
Active Attack Detection Based on Interpretable Channel Fingerprint and Adversarial Autoencoder

This paper investigates how to build an active attack detection framework that is driven by fundamental channel modeling and practical wireless datasets. Firstly, we propose the concept of interpretable channel fingerprints (ICFs), which correspond to the spatial-temporal parameters in real physical wireless signal propagation channels. Based on this, we design an adversarial autoencoder (AAE) with a semi-supervised learning network, which takes as inputs the power spectrum of quantized ICFs and enables small sample learning multiclassification tasks for different types of wireless channel active attacks. We have experimentally verified the performance of our AAE network using the Wireless InSite ray tracing software. Our results show that the proposed semi-supervised network outperforms the fully-supervised network especially in small sample conditions. We highlight the need for careful selection of the hyperparameters for learning rate and mini-batch size, and the system parameters for the ICF power spectrum resolution. We show that the detection accuracy of the proposed AAE model can reach more than 98% with only a small number of input samples.
IoT Traffic Obfuscation: Will it Guarantee the Privacy of Your Smart Home?

Recent research has shown the efficacy of machine learning-based IoT network traffic analysis to infer attributes such as IoT device type, IoT device activity state and even user behaviours in smart home environments. Therefore, various traffic obfuscation techniques have been proposed to reduce the classification performance of these machine learning algorithms. However, most of the proposed traffic obfuscation techniques can only alter traffic originating from the IoT device, with the incoming traffic from the communicating servers largely unaffected. We show that IoT device activity can still be successfully inferred by only using incoming network traffic for analysis. Therefore, this research emphasizes the need for obfuscation techniques, which can alter network traffic in both directions between the IoT devices and their communicating servers.
An efficient enhanced medicinal leaf image production using Adaptive Gamma Correction Weighting Distribution (AGCWD) algorithm with guided filter

The quality of an image is always degraded by the random variation called noise which is included in the contrast, intensity and other properties like brightness. Noise is added with an image during the digital image transmission. The image sensors are always affected by the normal physical environment factors like temperature, scanner dust particles and the parametric characteristics of the lens and the camera. Noise removal is important in the image transmission because any interference in the transmission channel can include noise into it. A medicinal plant leaf needs to be disease free for a harmless medicine production. It becomes a necessity for a method to produce a more contrast enhanced image with the efficient noise removal mechanism. The paper proposes a combinational approach of an Adaptive Gamma Correction Weighting Distribution (AGCWD) which mainly serves to enhance the contrast of the image and the Noise removal mechanism which is performed by a guided filter. The proposed method evaluated various performance metrics like Mean square error (MSE), Image enhancement factor (IEF), Peak Signal to noise Ratio (PSNR) And Mean Absolute Error (MAE). The evaluation of the proposed system gave more better performance results than while using the existent Bilateral filter.
Cloud and Electricity Portfolio Optimization for Internet Data Centers under Uncertainties

The introduction of cloud and electricity markets brings in promising economic potentials for cloud service providers (CSP) to increase profits. However, it is a challenging task to optimize the CSP&#x2019;s portfolio decisions in both markets in the face of market dynamics and uncertainties. The goal of this paper is to investigate the strategic behaviors of a CSP in the context of cloud and electricity markets under uncertainties. A two-stage portfolio optimization framework is proposed. The first stage optimizes the CSP&#x2019;s dynamic cloud pricing scheme, biddings in day-ahead wholesale electricity markets, and resource management. While the second stage considers the CSP&#x2019;s real-time resource management and trading decisions in a cloud spot market and real-time wholesale electricity markets after capturing the actual values of cloud demands, market prices and renewable output fluctuations. An opportunity profit due to server redundant provisioning in the first stage is proposed, which can reflect the potential economic benefits in the second stage. Numerical studies are conducted to show the effectiveness of the proposed model.
Phase-space quantum Wiener–Khintchine theorem

We derive a quantum version of the classical-optics Wiener-Khintchine theorem within the framework of detection of phase-space displacements with a suitably designed quantum ruler. A phase-space-based quantum mutual coherence function is introduced that includes the contribution of the detector. We obtain an universal equality linking resolution with coherence. This is illustrated with the case of Gaussian states and number states.
The presentation of noun class systems of Niger-Congo languages

The graphical presentation of Niger-Congo noun class systems shows considerable variation, which deserves a careful examination. The choices of presentation seem to depend on the mere fancy of authors: close languages may be presented in very different ways while different systems may be given very similar graphical choices. After consulting hundreds of descriptions of noun class languages of the Niger-Congo phylum, I could determine four major presentation types. This huge amount of information also allowed me to present new statistical data on the typology of the noun class systems of the phylum.
Using the Unity environment for learning programming

The article discusses the possibility of using computer game development environments when teaching programming. The advantages of using the Unity multi-platform environment over other game development environments have been identified. The historical prerequisites for the emergence and development of various game engines are also given.
Exploring Multivariate Event Sequences with an Interactive Similarity Builder

Abstract Similarity‐based exploration is an effective method in knowledge discovery. Faced with multivariate event sequence data (MVES), developing a satisfactory similarity measurement for a specific question is challenging because of the heterogeneity introduced by numerous attributes with different data formats, coupled with their associations. Additionally, the absence of effective validation feedback makes judging the goodness of a measurement scheme a time‐consuming and error‐prone procedure. To free analysts from tedious programming to concentrate on the exploration of MVES data, this paper introduces an interactive similarity builder, where analysts can use visual building blocks for assembling similarity measurements in a drag‐and‐drop and incremental fashion. Based on the builder, we further propose a visual analytics framework that provides multi‐granularity visual validations for measurement schemes and supports a recursive workflow for refining the focus set. We illustrate the power of our prototype through a case study and a user study with real‐world datasets. Results suggest that the system improves the efficiency of developing similarity measurements and the usefulness of exploring MVES data.