Reviewerly/exHarmony huggingface dataset:
Poster

Although not yet ubiquitous, censors and censorship-resistors have been eyeing QUIC as the next avenue for censorship-circumvention. In this poster, we highlight three QUIC features that are avenues for potential improvements to both Pluggable Transports and decoy routing: streams, connections identifiers, and congestion control. We also examine how traffic splitting can be used. We then discuss how these features can be integrated into Pluggable Transports and decoy routing. We close with providing guidance for future work on integrating QUIC into Pluggable Transports and decoy routing.
ASHES 2022 -- 6th Workshop on Attacks and Solutions in Hardware Security

The workshop on "Attacks and Solutions in HardwarE Security (ASHES)" welcomes any theoretical and practical works on hardware security, including attacks, solutions, countermeasures, proofs, classification, formalization, and implementations. Besides mainstream research, ASHES puts some focus on new and emerging scenarios: This includes the Internet of Things (IoT), nuclear weapons inspections, arms control, consumer and infrastructure security, or supply chain security, among others. ASHES also welcomes dedicated works on special purpose hardware, such as lightweight, low-cost, and energy-efficient devices, or non-electronic security systems. The workshop hosts four different paper categories: Apart from regular and short papers, this includes works that systematize and structure a certain (sub-)area (so-called "Systematization of Knowledge" (SoK) papers), and so-termed "Wild-and-Crazy" (WaC) papers, which distribute seminal ideas at an early conceptual stage. This summary gives a brief overview of the sixth edition of the workshop, which took place virtually on November 11, 2022 in Los Angeles, California, USA, as a post-conference satellite workshop of ACM CCS.
Development of an IoT-based Water Temperature Control and Monitoring System for Hydroponics

Hydroponics has gained popularity over the past several years because of its capacity to grow nutrient-rich crops with the least number of resources. This project concentrates on the development of an IoT-based water temperature control and monitoring system meant for a hydroponic system. Water temperature is a crucial element in hydroponics and indeed must be kept at a certain temperature range between 19 to 27 degrees Celsius. This is to enhance its effectiveness. To control the temperature, a cooling core made up of thermoelectric modules TEC1-12706 will be used in this water-cooling system. The basic function of thermoelectric modules TEC1-12706 is to heat or cool. However, this project only focused on the cooling part due to the fact that the temperature in Malaysia is normally between 25 to 30 degrees Celsius which is over the temperature ranges for hydroponics, thus a heating process is not necessary. This project involves both software and hardware implementation. A TEC1-12706, ESP32 Wi-Fi module, R385 water pump, and DS18B20 sensor are used in this cooling system. The circulating water system is proposed to be utilized in this project to keep the water temperature in the water tank evenly distributed. An experiment with various settings has been conducted in the presence of one to two TEC1-12706. The results show that when the two TEC1-12706 were connected in parallel, it will only take around 30 minutes to cool the water temperature down to 26 degrees Celsius as compared to the other configurations.
Towards a new design of ciphers to secure CPS: the role of control theory

Among several solutions to face the unprecedented increase of attacks towards Cyber Physical Systems, encryption plays a central role. In the form of a Proof of Concept, this contribution gives a new methodology for designing self-synchronizing automata, having in mind their use in symmetric cryptography, namely the Self-Synchronizing Stream Ciphers. The contribution of the paper is to recast the design as control theoretical issues. It calls for a graph-based approach and results borrowed from control theory and dynamical systems, in particular LPV systems. The design leads to not necessarily <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> -functions as state transition functions of the automata involved in the ciphering and deciphering sides. It is a consideration that is important for the sake of security. Another asset of the approach is that the resulting ciphers admit possibly vectorial inputs to enhance the throughput.
How to prioritize

Everyone has more things they want to do than time to accomplish them. The most productive people prioritize and accomplish the most important projects first, leaving time for medium-priority projects and, sometimes, low-priority projects. Developing an organizational system can help you better prioritize and be more productive overall. Create a comprehensive list. It may sound trivial, but a complete task list is necessary to compare options and decide what is most important and most urgent. It could be a lab-style notebook, a to-do list on your phone, file folders, or 3 × 5 cards on a bulletin board—find what works for you. Whatever the system, you need to be able to edit and rearrange the items as conditions change and delete items you have completed. When building your list, consider the size of the pieces included. A huge project may be better added as a series of smaller stages, or
Patch-Based Autoencoder for Document Image Denoising with Smoothening

Documents can be stored in digital formats permanently thanks to the ease of digitization. While scanning or capturing documents, noises such as low resolution, printing noise, and compression noise might be introduced. Noise has a negative impact on document analysis activities such as OCR and text identification. Denoising and restoration of noisy scanned document images are a classical image processing problem that aims to eliminate such type of artifacts from given noisy images. We propose a novel deep learning pipeline for denoising followed by smoothening, ultimately enhancing the quality of scanned documents images. We train an end-to-end patch-based convolutional autoencoder network along with ResNet skip connections. We introduce a noisy-clean paired dataset by synthetically adding noises to corresponding clean images. Our model is shown to effectively remove both physical noises like coffee stains and pencil scribbles, as well as digitally introduced noises like printer noise, compression noise, and low resolution, among others.
From SATD Recognition to an Interpretation Method Based on the Dataset

Technical debt describes a trade-off between short-term goals and long-term code quality during software development. Self-admitted technical debt (SATD), a type of technical debt, is intentionally introduced by developers. The existence of SATD is likely to leave hidden dangers for future changes in software systems, so identifying SATD is an essential task. Before this, many methods for recognizing SATD (such as pattern matching-based, natural language processing-based, text mining-based, etc.) have been proposed. This paper will present a pre-trained deep learning model to complete the SATD recognition task. An efficient deep learning model interpretation tool Captum can be used to understand the experimental results. At the same time, a new interpretation view is proposed for the matching-based model. Finally, combined with the research in this paper, reasonable suggestions are put forward for future SATD recognition tasks.
An Aspects Framework for Component-Based Requirements Prediction and Regression Testing

Component-based software development has become more popular in recent decades. Currently, component delivery only includes interface specifications, which complicates the selection and integration of suitable components to build a new system. The majority of the components are reused, after appropriate modifications in accordance with the new system, or new version of the system. After components integration, errors may occur during the interaction of their features due to incomplete, ambiguous, or mismatched terms used in requirement analysis and specification, affecting component validation. Therefore, there is a need for a study that identifies challenges and covert concepts into practice by providing solutions to these challenges. The objective of this study is to identify some attributes and information sources that are essential during component-based development. The proposed framework is based on these attributes and information sources. In this study, we provide a taxonomy of attributes and information sources among different activities of component development, and propose a framework to improve the component development process. To investigate the proposed framework, we performed an experimental study to get real-world scenario results from industrial practitioners. The results showed that the proposed framework improves the process of component specification and validation without ambiguity and component failures. Additionally, compared with other methods (random priority, clustering-based and execution rate), the proposed framework successfully outperforms other methods. As a result, the proposed framework’s accuracy, F-measures, and fault identification rate were higher (i.e., greater than 80%) than those of other methods (i.e., less than 80%). The proposed framework will provide a significant guideline for practitioners and researchers.
Retrospective Inspection for Research in Natural Language Processing of Hindi Language Using Fuzzy Logic

Natural Language Processing (NLP) and its related applications have come a long way since its initiation. A number of NLP-based applications can be spotted in our day-to-day lives. NLP has been widely discussed in the literature for various Indian languages as well. Hindi language-based NLP has gained popularity in recent years. This paper tries to focus on having a retrospective inspection of the trends of NLP research in Hindi using fuzzy logic, in terms of the paper publication. An in-depth study has been done of the concerned research papers in this field. The publication pattern is also identified along with the key terms.
Supervised Question Classification on SelQA Dataset Using Variational Quantum Classifiers

Machine learning and quantum computing fuse together to form quantum machine learning. Although the phenomenon is new, it has already proved its worth in various fields like finance and chemistry. The potential of quantum computing and its extraordinary properties enable us to process data in a way classical computer can never think of. When machine learning gets the power of quantum computing, information processing is enhanced significantly. In this paper, we have used variational quantum classifiers to classify questions from two domains of SelQA dataset. We keep the focus on the implications of circuit-depth in different experiments and analyze the results. VQC performs well with 11 features on lowest circuit depths and gives a testing accuracy of 58%.
Go Unsupervised via Artificial Intelligence

This chapter covers the role of artificial intelligence in the healthcare sector and supply chain management (SCM). Moreover, this chapter emphasises unsupervised learning part of artificial intelligence (AI). This work finds that the contribution of AI is in identification of diseases like diagnoses of diabetes, heart-related problems, breast cancer, and thyroid disorders. Various machine learning techniques and their algorithms will not only help in treatment of these diseases but also help the concerned persons in decision making. Unsupervised learning has major applications in the field of drug discovery and repurposing as well as performing clinical trials. Drug discovery includes computational design of drugs and clinical trial help in predicting the response of any particular drug to be injected into a patient’s body. Apart from this, AI has the capability to support some operations of SCM. These operations consist of managing inventory, stakeholder selection, vendor evolution, and risk mitigation production of customised products. It was found that inclusion of AI in SCM increases the efficiency and effectiveness of SCM.
Temporal Logic Planning and Receding Horizon Control for Signal Source Localization

This article copes with signal source localization by employing a receding horizon control approach with temporal logic planning in the light of a single mobile robot. First, a temporal logic planning approach is proposed such that the task requirements from the temporal logic specifications can be effectively dealt with based on the product automaton in an offline fashion. Second, in order to label the key nodes of the product automaton, a particle filter is utilized to predict the source positions as the key nodes. Third, on the basis of the product automaton, a receding horizon control approach with temporal logic planning is developed to produce the robot’s trajectory that satisfies a given linear temporal logic specification. Finally, the effectiveness of the proposed control approach is illustrated for signal source localization.
Making the computation of approximations of invariant measures and its attractors for IFS and GIFS, through the deterministic algorithm, tractable

We present algorithms to compute approximations of invariant measures and its attractors for IFS and GIFS, using the deterministic algorithm in a tractable way, with code optimization strategies and use of data structures and search algorithms. The results show that these algorithms allow the use of these (G)IFS in a reasonable running time.
Case Analysis of the Operation Status and Assignment Performance for the Students’ Assessment in Online Classes of the University

Objectives The study was to investigate the operating status and the assignment performance for the students’ assessment in the online classes of the university located in the metropolitan area and then identify the principles and categories of the assignment performance assessments in the online classes by analyzing the features of best cases that students are highly satisfied with. Methods For doing this, 1,062 students were surveyed to investigate the operating status for the assessments in their online classes in 2021 at the university. Also, the assignments of the best 9 online classes were analyzed and 4 instructors of the classes were interviewed to identify the key principles and characteristics of the performance assessment. Results First, according to the survey both the test type of examination and assignment performance assessments were implemented in the online classes for the past semester and the implementation of the assessments was recognized positively by most students. Second, most of online examinations were conducted by real-time video test through the online Learning Management System(LMS) and then the students were satisfied with the LMS. Third, the performance assessments of the online classes were conducted in many different ways but most of the assessments were long-term period assignments with the rubrics of the assessments. Fourth, the assignment performance assessments were effective to prevent the spread of COVID19 as well as the improvement of students’ learning by implementing the assignments. Fifth, the principles and types of assignments performance assessment were identified in several different categories based on the contents and methods of the performance assessments by analyzing the assignment from the 9 online classes. Conclusions It seems that the assessments in the online classes responding to the COVID-19 were implemented in effective ways through the online LMS. The students’ perception about the assessment in the online classes was quite positive and also many types of assignments performance assessments were developed by the instructors to enhance the competence of students during the semester of distance classes.
Analysis of Structural Relationships among High School Teachers' Learning Community Activities, ICT Utilization Ability, Teacher Burnout, and Teachers’ Job Satisfaction in the Period of Covid-19: Focusing on the differences by type of high school in Busan

본 연구에서는 부산교육종단연구 5차년도(2020) 교사 데이터를 활용하여 코로나19 이후 교사들의 교사학습공동체 활동, ICT 활용 능력, 교사소진 요인들을 중심으로 교직만족도에 영향을 미치는 요인들 간의 구조적 관계를 규명하고 재직하고 있는 고교 유형에 따른 차이를 파악하고자 수행되었다. 구조방정식 모형분석에 따른 주요 연구 결과는 다음과 같다. 첫째, 고등학교 교사 전체 표본을 대상으로 분석한 결과, 코로나19 시기에 교내 교사학습공동체 활동 정도가 강할수록, 교사소진이 적을수록, ICT활용능력이 높을수록 교직만족도가 높아지는 것으로 확인되었다. 또한 교사학습 공동체 활동 정도가 강할수록 교사들의 ICT활용능력이 더 높아지는 반면 교사소진은 낮아지는 것으로도 확인되었으며 ICT활용능력은 교사소진에 직접적인 영향력을 발휘하지는 않는 것으로 나타났다. 둘째, 교사학습공동체 활동 정도는 ICT활용능력 및 교사소진을 매개로 교직만족도에 간접효과도 발휘하는 것으로 확인되어 교사학습공동체 활동 정도가 강할수록 ICT활용능력이 좋아지고 이를 통해 교직만족도가 향상되는 한편, 교사학습공동체 활동 정도가 강할수록 교사소진은 낮아져 이를 매개로 교직만족도가 올라가는 것으로 판명되었다. 셋째, 재직하고 있는 고교 유형에 따른 다집단 분석 결과, 특성화고등학교에 재직하는 교사 집단에서 교사학습공동체 활동 정도가 교직만족도에 미치는 영향력이 일반고등학교에 재직하는 교사 집단에서보다도 더 큰 것으로 파악되었다. 이상의 연구 결과를 토대로 코로나19와 같은 교육 대혼란의 시기에 교사학습공동체 활동의 활성화를 중심으로 교직만족도를 제고할 수 있는 정책적 시사점을 모색하였다.Drawing on the fifth wave teacher data from Busan Education Longitudinal Study in 2020, this study investigates the structural relationships among high school teachers' learning community activities, ICT utilization ability, teacher burnout, and teachers’ job satisfaction in the period of COVID-19. It also finds out how the structural relationships vary by types of high school. The main results are as follows. First, it was confirmed that the stronger the teacher learning community activity in the school during the COVID-19 period, the less the teacher burnout and the higher the ICT use ability, the higher teachers’ job satisfaction as well. In addition, it was also revealed that the stronger the teacher learning community activity, the higher the teachers’ ICT use ability, while the teacher burnout decreased. Second, it was confirmed that teacher learning community activity exerts an indirect effect on teachers’ job satisfaction via ICT utilization ability and teacher burnout. Third, as a result of multi-group analysis according to high school type, it was found that the influence of teacher learning community activity on teachers’ job satisfaction was greater in the teacher group at vocational high schools than in the group of teachers at general high schools. Based on the results above, policy implications for improving teachers’ job satisfaction were suggested, highlighting the role of teacher learning community activities in the era of educational crisis such as COVID-19.
UTILIZING ROOTS AND PATTERNS TO IDENTIFY ARABIC NAMED ENTITIES

Named Entity Recognition NER is a subset of information extraction that seeks to recognize and categorize named things in text data into specified categories, such as people's names, organizations' names, geographic locations, and so on. This task has recently attracted a lot of attention due to the discovery it has the potential to boost the performance of a variety of NLP applications. In the domains of Question Answering and Summarization Systems, Information Retrieval and Extraction, Machine Translation, Video Annotation, Semantic Web Search, and Bioinformatics, the majority of difficulties require named entity recognition. Arabic is an inflectional language, which allows for non-concatenative morphological operations on the root. The purpose of this study is to extract and recognize entity names from Arabic articles. We proposed an algorithm for determining names from roots using patterns. We developed it in Python and leveraged the "pyqt5" visual package to see the results immediately, as well as modify and add patterns easily. To replicate the names, we used a random sample of 400 names and 45 different patterns. The algorithm correctly identified 370 names easily and quickly, yielding a success rate of 93%. All names with the same recognized names will be known in the same way by the method and do not need any manipulation in code or design. The names that are not recognized by our algorithm have no roots in the list of known Arabic roots. Our research shows that the approach can recognize names with roots with high speed and accuracy, but it is not possible to identify nouns that are not in the Arabic language using this method. As a result, we recommend using a hybrid method that incorporates multiple concepts.
Context of Digital Divide in the New Normal: Implication for the Schools of the Future

Digital divide becomes a vital and salient term in the field of education especially in this time of pandemic. Reviewed studies revealed a dearth of studies on the context of digital divide brought the paradigm shift in education during the new normal education. In such light, this case study was conducted to describe what it is like to be learning using the new learning modalities amidst the digital divide that the pandemic has brought to the learners and the teachers. In achieving this objective, the researchers interviewed six (6) students followed by member checking procedures. The cool and warm analyses of interview transcripts revealed their challenges, coping mechanisms, and views on the new learning modalities amidst the digital divide they are facing. The challenges are lack of digital skills, lack of devices, and lagging to poor internet connection. To cope with these challenges, they employ the two L’s: look of assistance and lean efforts in learning. With these challenges and coping, they view learning in the new normal as difficult and technology-driven. This study concludes that students remain steadfast and determined in learning despite being confronted with the challenges in the light of the digital divide during the new normal education. As such, studies should be conducted to formulate an intervention program that shall address the challenges of the students in the new normal learning and beyond.
IMPLEMENTASI NETWORK MONITORING SYSTEM ZABBIX UNTUK KEAMANAN JARINGAN KOMPUTER PADA STUDI KKASUS PT TRIDAYA SINERGI INDONESIA BANDUNG

PT Tridaya Sinergi Indonesia is a Direct Selling and Network Marketing Company that has an official license based on quality cigarette products and a support system that all partners can follow. A company in Bandung which is engaged in the distribution of goods, especially herbal cigarettes, the head office is located in the city of Bandung. There is no Network Monitoring System yet, so monitoring each branch is difficult and the impact on distribution is not well controlled. Attention to the condition of the performance of the gadget becomes an obstacle for a server head in surveying the condition of the gadget so that the transfer speed designation to be streamed must be successfully conveyed. Without the data transfer capacity of the executives, the host can take advantage of the largest data transfer capacity, rendering the data transfer capacity assignment of various hosts unusable. Checks are carried out by logging into the Zabbix Network Monitoring System dashboard and sending a message notification in case of an intrusion on the observed host. The Zabbix administration check framework is CPU, Memory, PING and Traffic Bandwidth in network, state and cycle units. The results of the Zabbix NMS setting can produce speed in indicating problems in the company's data center, especially Administrators and IT Managers with Zabbix being able to send notification messages via Telegram to administrator supervisors and Bandwidth Optimization with Network Development Life cycle (NDLC) techniques.