Reviewerly/exHarmony huggingface dataset:
Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation

Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality microscopic image i.e. either blood smear or fluorescence based, thus missing the potential of a better learning from multimodal images. In this work, we develop an efficient multimodal architecture based on a first of its kind multimodal WBC dataset for the task of WBC classification. Specifically, our proposed idea is developed in two steps - 1) First, we learn modality specific independent subnetworks inside a single network only; 2) We further enhance the learning capability of the independent subnetworks by distilling knowledge from high complexity independent teacher networks. With this, our proposed framework can achieve a high performance while maintaining low complexity for a multimodal dataset. Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.
The training response law explains how deep neural networks learn

Deep neural network is the widely applied technology in this decade. In spite of the fruitful applications, the mechanism behind that is still to be elucidated. We study the learning process with a very simple supervised learning encoding problem. As a result, we found a simple law, in the training response, which describes neural tangent kernel. The response consists of a power law like decay multiplied by a simple response kernel. We can construct a simple mean-field dynamical model with the law, which explains how the network learns. In the learning, the input space is split into sub-spaces along competition between the kernels. With the iterated splits and the aging, the network gets more complexity, but finally loses its plasticity.
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task

This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition. Our system's key contributions are as follows: 1) For multilingual NER tasks, we offer an unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource code-mixed NER task, one can easily enhance his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we propose a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, our system achieves macro-f1 scores of 77.66, 84.35, and 74.00 on subtasks 11, 12, and 9, respectively, during the testing phase.
Understanding Game-Playing Agents with Natural Language Annotations

We present a new dataset containing 10K human-annotated games of Go and show how these natural language annotations can be used as a tool for model interpretability. Given a board state and its associated comment, our approach uses linear probing to predict mentions of domain-specific terms (e.g., ko, atari) from the intermediate state representations of game-playing agents like AlphaGo Zero. We find these game concepts are nontrivially encoded in two distinct policy networks, one trained via imitation learning and another trained via reinforcement learning. Furthermore, mentions of domain-specific terms are most easily predicted from the later layers of both models, suggesting that these policy networks encode high-level abstractions similar to those used in the natural language annotations.
Developing Reading E-Module Based on Langkat Sultanate History for English Department Study of STKIP Al-Maksum Langkat

Providing English learning material with developing reading E-Module Based on Langkat Sultanate History is important as English Department Study of STKIP Al-Maksum Langkat. This research aims to develop Reading e-Module Based on Langkat Sultanate History, to increase students’ awareness about Langkat Sultanate History and to promote local wisdom at Langkat District. This research is concerned with the development of an English reading e-Module for students of the English department study of STKIP Al-Maksum by applying the four phase Research and Development (R &amp; D) proposed by Gall, Gall &amp; Borg (2003). At the define stage, first, the author decides to create the requirements to create the e-Module following the need analysis. Furthermore, at the design stage, the author creates the initial design (prototype) e-Module. The results of the study show that the module that has been developed is valid, practical and effective. The validity of the module reaches a valid category based on the expert’s and validator’s evaluation. It is hoped that they will be able to continue to raise local culture or local wisdom in Indonesia as a learning module to preserve Indonesian culture for students.
A PROPOSED MODEL FOR THE AUDIT PROCEDURES EXAMINATION FORM IN THE LIGHT OF CLOUD COMPUTING CONTROL STRATEGIES

Cloud computing is a new technology that is widely used in various units today as well as in society and it is the result of revolutionary changes in the world of information and communication technology because of its benefits such as low cost, speed, and can be accessed anywhere at any time as well as Finding automatic solutions for various operations and financial transactions and Oversight procedures on cloud computing are one of those developments that Transforming accounting software from products to services As a result of the rapid dynamic developments and changes in the business environment surrounding this profession. In the context of cloud censor ship the aim of the study Learn about cloud computing and its types and the possibility of benefiting from the benefits that they add to the economic units and their use in the field of accounting and auditing and obstacles to use This study will seek to demonstrate the integrity of cloud data, by designing a test form to show these procedures. The research reached results, including that cloud computing has characteristics, including speed and cost reduction, and immediate access without the need for prior installation of accounting programs on computers, and these characteristics are useless in the absence of control measures and audit methods.
A multi-task learning framework for efficient grammatical error correction of textual messages in mobile communications

Abstract In mobile communications, plenty of textual messages need to be transmitted and processed rapidly. However, messages usually contain noise, which will affect the performance of related applications. Thus, we investigate grammatical error correction (GEC) to correct errors in messages. Unlike recent works, we focus on improving the efficiency of GEC because low time delay is significant in mobile communications. We propose a novel multi-task learning approach to GEC by detecting errors first and then making corrections. Two classifiers are used to serially detect sentence-level and token-level errors, so the correct content can be free from correction operations. We adapt a non-autoregressive decoder to parallelly generate corrected tokens, making the correction stage efficient. Experiments show that our approach is ten times faster than the traditional approach and can achieve a comparable GEC performance.
Model Development of Universal Hardware and Software Module for Medical Information System

Medical information systems (MIS) are the object of the research presented. They allow information processing throughout the patient's interaction with medical services - from contacting such institutions to monitoring the patient after rehabilitation. However, for various reasons, during the information process exchange within the framework of MIS, distortions of collected or transmitted data are possible, which leads to wrong medical decisions. Currently, new approaches are being implemented using Software-Defined Networks, but our work proposes an alternative solution designed to work conservatively with existing equipment using established protocols. To solve the stated problem, this study has developed a universal hardware-software module (UHSM) model, which ensures the effective transmission of sophisticated medical information through networks. A distinctive feature of such UHSM is the connection of different medical equipment in various related to MIS information networks, as well as automatic configuration under all current standards. The study demonstrates that for the most effective UHSM functioning, it is essential to consider the existing features arising during the interaction process of devices in the context of modern networks created for information exchange, based on the established communication model of open-system interconnection (OSI). Keywords—classification methods, information coding methods, information networks, knowledge base, medical information system, multilevel approach, structure synthesis of large systems, telemedicine technologies
Students’ Perception and Motivation toward English E-Learning during Covid-19 Pandemic of Eleventh Grade Students at SMAN 1 Monta

This research aimed at knowing the students' perception towards English E-Learning during Covid-19 pandemic at SMA N 1 Monta. This research generally answered two questions as follows: (1) What are students’ perceptions towards English E-Learning during Covid-19 pandemic? (2) How are students’ motivation towards English E-Learning during Covid-19 pandemic? The data was collected through a questionnaire and interviews. There were 31 students of MIPA-2 in eleventh graders at SMA N 1 Monta participated in this research. Since this research explored the students' perception and motivation which coming from the students' experiences during Covid-19 pandemic, descriptive research by using qualitative techniques was considered as an appropriate approach as a type of research. The result of this research was presented descriptively in order to reveal the students' perception and motivation comprehensively. The researcher found E- learning gets positive perception because it is flexible and effective. Meanwhile, its flexibility makes some students motivated but some are being lazy in doing it and choose to procrastinate the assignments.
Data Mining Based Soft Computing Methods For Web Intelligence

Data mining is the procedure of extracting interesting knowledge from enormous amounts of data contained in databases, including such patterns, associations, changes, deviations, and prominent structures. Soft Computing Methods such as fuzzy logic, artificial neural network, etc. aims to uncover the potential for error and inaccuracy in order to accomplish scalability, durability, and reduced methods. In today's information age, the Web is the most common distribution medium. Due to its popularity on the Internet, it is widely used in commercial, entertainment, and educational purposes. Web Intelligence (WI) is engaged with the scientific study of the Web's new areas. It is a new area of computer science that integrates artificial intelligence with sophisticated information technology in the framework of the Web, expanding well outside each one of them. In online applications, data mining gives a plethora of possibilities. The biggest concern is figuring out how to identify relevant hidden patterns for improved application. Soft computing techniques such as neural networks, fuzzy logic, support vector machines, and genetic algorithms are used in evolutionary computation to solve this problem. We look at how soft computing approaches may be used to build web intelligences in this research.
Identifying the Language Requirements of English for Tourism at Toraja Christian University of Indonesia

The purpose of this study is to identify the language requirements associated with English for Tourism. The respondent of the current research were Students in the sixth semester of the English Department at UKI Toraja who have recently completed the English for Tourism course. The researcher employed accidental sampling. Data were collected through interviews. The results of this research show that the language requirements needed in learning English for Tourism pertain to: Tourism knowledge, Accommodation, Culinary Tour [ pa’piong (meat cooked in bamboo), Torajan Culture [tradition, music, and art] tradition: rambutuka’ (wedding party) and rambu solo’ (funeral ceremony)., art: Ma’badong ( Traditional dance), pompang ( Traditional music). This suggests that the topic for the English for Tourism course should be developed through Torajan culture.
Data Immutability Challenges: A Security Analysis of Digital Archiving Systems

This study examines how well ‘cloud computing’ and ‘digital archive resource management’ work in the present time and what advantages they provide. Additionally, it explains a novel approach to using cloud computing by means of blockchain with an appropriate technique for sharing and distributing digital archive contents. The combination of virtualization and cloud storage technologies results in a widely accessible dataset. Specifically, it demonstrates its high security, excellent scalability, and ideality for standardization. This article also investigates the feasibility of implementing the archival bond in a blockchain-based record-keeping infrastructure. To achieve this, it suggests a database backend that preserves the immutability of the blockchain while facilitating metadata updates and improved search capabilities.
PENERAPAN MEDIA PEMBELAJARAN COMPUTER BASED INSTRUCTION (CBI) DALAM PENINGKATAN AKTIFITAS DAN PRESTASI BELAJAR SISWA PADA MATA PELAJARAN EKONOMI DI SMAN 1 KOTA JAMBI

This study aims to apply Computer Based Instruction (CBI) learning media in an effort to improve student achievement in economics subjects for class X students at SMA Negeri 1 Jambi City in the 2021/2022 academic year. This research method is CAR (Classroom Action Research) with four stages, namely planning, implementation, observation and reflection. The study population used all students of class X at SMA Negeri 1 Jambi City in the academic year 2021/2022 and the subjects of this study were 34 students who were taken randomly (random sampling). The use of CBI (Computer Based Instruction) learning media in Economics learning can improve student learning achievement by looking at the results of tests carried out in two cycles, where the pre-action student achievement results with an average of 68 and student learning completeness of 67% experiencing an increase in the cycle I with an average score of 71 and student learning completeness of 83%. Further refined with the results of student achievement in the second cycle with the acquisition of an average score of 87 and student learning completeness that reaches 100%. So it can be concluded that the use of CBI (Computer Based Instruction) learning media is an alternative in an effort to improve student achievement in economics subjects for class X MIPA 3 at SMA Negeri 1 Jambi City in the 2021/2022 academic year. ABSTRAKPenelitian ini bertujuan untuk menerapkan media pembelajaran Computer Based Instruction (CBI) dalam upaya meningkatkan prestasi belajar siswa pada mata pelajaran ekonomi siswa kelas X di SMA Negeri 1 Kota Jambi tahun pelajaran 2021/2022. Metode penelitian ini adalah PTK (Penelitian Tindakan Kelas) dengan empat tahapan yaitu perencanaan, pelaksanaan, observasi dan refleksi. Populasi penelitian menggunakan seluruh siswa kelas X di SMA Negeri 1 Kota Jambi tahun pelajaran 2021/2022 dan subjek penelitian ini yaitu 34 siswa yang diambil secara acak (random sampling). Penggunaan media pembelajaran CBI (Computer Based Intruction) dalam pembelajaran Ekonomi dapat meningkatkan prestasi belajar siswa dengan melihat hasil tes yang dilaksanakan dalam dua siklus, dimana pra tindakan hasil prestasi belajar siswa dengan rata–rata 68 dan ketuntasan belajar siswa sebesar 67% mengalami peningkatan pada siklus I dengan perolehan nilai rata–rata 71 dan ketuntasan belajar siswa sebesar 83%. Selanjutnya disempurnakan dengan hasil prestasi belajar siswa pada siklus II dengan perolehan nilai rata–rata 87 dan ketuntasan belajar siswa yang mencapai 100%. Maka dapat disimpulkan penggunaan media pembelajaran CBI (Computer Based Intruction) merupakan salah satu alternative dalam upaya meningkatkan prestasi belajar siswa pada mata pelajaran ekonomi kelas X MIPA 3 di SMA Negeri 1 Kota Jambi tahun pelajaran 2021/2022.
Analysis indicators of the networks of collaboration for learning in environments virtual

This paper presents the importance of collaborative learning in current education, mainly in higher education, which is where the least research has been done on the use of ICT in the classroom. The experience and use of networks in virtual learning spaces was analyzed in an exploratory way, in order to verify the real advantages of their use and how they favor learning. The research was considered from the training framework of the graduate programs of the Virtual University System. The methodological procedure carried out was to analyze collaboration networks in virtual environments and how they influence the learning of postgraduate students, dimensions and categories were determined. Its approach was both qualitative and qualitative, an intentional sampling was chosen, the dimensions of analysis included collaboration for learning as a central axis.
Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control

Autonomous agents situated in real-world environments must be able to master large repertoires of skills. While a single short skill can be learned quickly, it would be impractical to learn every task independently. Instead, the agent should share knowledge across behaviors such that each task can be learned efficiently, and such that the resulting model can generalize to new tasks, especially ones that are compositions or subsets of tasks seen previously. A policy conditioned on a goal or demonstration has the potential to share knowledge between tasks if it sees enough diversity of inputs. However, these methods may not generalize to a more complex task at test time. We introduce compositional plan vectors (CPVs) to enable a policy to perform compositions of tasks without additional supervision. CPVs represent trajectories as the sum of the subtasks within them. We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training. Analogously to embeddings such as word2vec in NLP, CPVs can also support simple arithmetic operations -- for example, we can add the CPVs for two different tasks to command an agent to compose both tasks, without any additional training.
A Recent Systematic Review of Cervical Cancer Diagnosis: Detection and Classification

Women around the world are frequently diagnosed with cervical cancer. In the beginning, there were no symptoms for the fourth most common cause of fatality in women. Cells of cervical cancer develop gradually at the cervix. Several studies have mentioned that the initial detection of cervical tumours is essential for cancer to be properly treated and to make sure cancer can be successfully treated while minimizing deaths due to cervical cancer. The diagnosis of such cancer before it spreads fast is currently a pressing issue for healthcare professionals. This also provides an extensive understanding with respect to the physical characteristics of the healthy and unhealthy cervix and aids in early treatment planning by giving detailed information about one another. Utilizing image segmentation, several techniques are employed to find malignancy. The dataset contains four distinct pathological pictures, including normal, malignancy, and high-grade squamous intraepithelial lesions (HSIL). While pap tests are the most popular way to diagnose cervical cancer, their accuracy depends a lot on how well cytotechnicians can use brightfield microscopy to spot abnormal cells on smears.
CyberSecurity Challenges for Software Developer Awareness Training in Industrial Environments

Awareness of cybersecurity topics facilitates software developers to produce secure code. This awareness is especially important in industrial environments for the products and services in critical infrastructures. In this work, we address how to raise awareness of software developers on the topic of secure coding. We propose the "CyberSecurity Challenges", a serious game designed to be used in an industrial environment and address software developers' needs. Our work distils the experience gained in conducting these CyberSecurity Challenges in an industrial setting. The main contributions are the design of the CyberSecurity Challenges events, the analysis of the perceived benefits, and practical advice for practitioners who wish to design or refine these games.
Time-Dilation Attacks on the Lightning Network

Lightning Network (LN) is a widely-used network of payment channels enabling faster and cheaper Bitcoin transactions. In this paper, we outline three ways an attacker can steal funds from honest LN users. The attacks require dilating the time for victims to become aware of new blocks by eclipsing (isolating) victims from the network and delaying block delivery. While our focus is on the LN, time-dilation attacks may be relevant to any second-layer protocol that relies on a timely reaction. According to our measurements, it is currently possible to steal the total channel capacity by keeping a node eclipsed for as little as 2 hours. Since trust-minimized Bitcoin light clients currently connect to a very limited number of random nodes, running just 500 Sybil nodes allows an attacker to Eclipse 47\% of newly deployed light clients (and hence prime them for an attack). As for the victims running a full node, since they are often used by large hubs or service providers, an attacker may justify the higher Eclipse attack cost by stealing all their available liquidity. In addition, time-dilation attacks neither require access to hashrate nor purchasing from a victim. Thus, this class of attacks is a more practical way of stealing funds via Eclipse attacks than previously anticipated double-spending. We argue that simple detection techniques based on the slow block arrival alone are not effective, and implementing more sophisticated detection is not trivial. We suggest that a combination of anti-Eclipse/anti-Sybil measures are crucial for mitigating time-dilation attacks.
A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality

In this paper, we propose two contributions to neural network based denoising. First, we propose applying separate convolutional layers to each sub-band of discrete wavelet transform (DWT) as opposed to the common usage of DWT which concatenates all sub-bands and applies a single convolution layer. We show that our approach to using DWT in neural networks improves the accuracy notably, due to keeping the sub-band order uncorrupted prior to inverse DWT. Our second contribution is a denoising loss based on top k-percent of errors in frequency domain. A neural network trained with this loss, adaptively focuses on frequencies that it fails to recover the most in each iteration. We show that this loss results into better perceptual quality by providing an image that is more balanced in terms of the errors in frequency components.