Reviewerly/exHarmony huggingface dataset:
Why Cyber Threat Modeling Needs Human Factors Expansion: A Position Paper

Current threat modeling methods focus on understanding the protected network from the perspective of the owners of those networks rather than on comprehensively understanding and integrating the methodology and intent of the threat. We argue that layering the human factors of the adversary over the existing threat models increases the ability of cybersecurity practitioners to truly understand possible threats. Therefore, we need to expand existing adversary and threat modeling approaches in cyberspace to include the representation of human factors of threats, specifically motivations, biases, and perceptions. This additional layer of modeling should be informed by an analysis of cyber threat intelligence reporting. By creating and adopting this expanded modeling, cybersecurity practitioners would have an understanding of how an adversary views their network, which would expand their ability to understand how their network is most likely to be attacked.
Flexible and Efficient Implementation of CRYSTALS-KYBER SIMD RISC-V Coprocessor Based on Customized Vector Instruction-Set Extension

With the development of quantum computers in recent years, the security of traditional public-key encryption algorithms is facing serious threats, and post-quantum cryptography (PQC) algorithms that can resist quantum computer attacks are urgently needed. CRYSTALS-KYBER as the finalized NIST key-encapsulation scheme, is continuously advancing the standardization process. The existing hardware implementations of Kyber mostly use compact architectures to pursue high speed and high performance with the cost of programmability, while most hardware-software co-designs suffer from low parallelism and performance. Aiming at flexibly and efficiently implementing the key encapsulation mechanism (KEM) of Kyber, this work presents a single instruction multiple data (SIMD) Kyber coprocessor that supports the RISC-V instruction-set. A reconfigurable polynomial and logic unit (PLU) is designed, which can accelerate all types of polynomial vector instruction operations, and a dynamic hardware scheduling strategy is proposed to enable different types of instructions to be executed parallelly, improving the coprocessor pipeline throughput. Implemented on the Ultrascale+ FPGA platform and evaluated under SMIC 40nm technology, the proposed coprocessor achieves the fastest computing speed with the lowest power consumption and 3.5×/6.2× improvement in FPGA/ASIC AT product efficiency.
Heuristics to reduce linear combinations of activation functions to improve image classification

Image classification is one of the classical problems in computer vision, and CNNs (Convolutional Neural Networks) are widely used for this task. However, the choice of a CNN can vary depending on the chosen dataset. In this context, we have trainable activation functions that are crucial in CNNs and adapt to the data. One technique for constructing these functions is to write them as a linear combination of other activation functions, where the coefficients of this combination are learned during training. However, if we have a large number of activation functions to combine, the computational cost can be very high, and manually testing and choosing these functions may be impractical, depending on the number of available activation functions. To alleviate the difficulty of choosing which activation functions should be part of the linear combination, we propose two heuristics: Linear Combination Approximator by Coefficients (LCAC) and Major and Uniform Coefficient Extractor (MUCE). Our heuristics provide an efficient selection of a subset of activation functions so that their results are better or equivalent to the linear combination that uses all 34 available activation functions in our experiments (C34), considering the image classification problem. Compared to the C34 function, the LCAC function was better or equivalent in 62.5%, and the MUCE function in 87.5% of the conducted experiments.
Sistem Pendataan Peserta PKH Lolos Jalur Seleksi Perguruan Tinggi Berbasis Website Pada Lembaga Mitra Gerakan Ayo Kuliah Lampung

The Bakrie Center Foundation is a charitable organization established in 2010, dedicated to nurturing emerging leaders in Indonesia. Its vision is to identify and cultivate leaders across all strata of society throughout the Indonesian archipelago, with a mission to bolster their capabilities and foster a talent pool that will ultimately stimulate national development and economic growth. In the 6th edition of the Campus Leader Program, the Bakrie Center Foundation collaborated with partner institutions as part of the "Let's Go to Lampung" initiative. The Foundation recognized the effectiveness of the "Let's Go to Lampung" movement, which is geared towards providing education, motivation, and support to KPM PKH children. During the operational phase, there were challenges in data collection, prompting the founder of GAK Lampung to propose the development of a website for a Data Collection System designed to assist PKH children in navigating the university selection process, to be implemented at the partner institution of the "Let's Go to College" Movement (GAK) in Lampung.
Enhancing IIoT Vision Data Transmission and Processing via Spatial Difference Attention-Guided Saliency Detection

The swift expansion of the Industrial Internet of Things (IIoT) presents formidable challenges for both data transmission networks and processing units. In this paper, we delve into the issue of accelerating visual data transmission and processing within IIoT systems. Our approach aims to deploy visual saliency detection models in edge data collection devices to empower them with data pre-processing capabilities. To address the challenge of model lightweight deployment on edge devices, we have designed a lightweight saliency detection model. It utilizes our proposed Spatial Difference Attention-based Dynamic Fusion (SADF) module for adaptive detection of salient objects of varying scales. As our model outputs retain only task-relevant information from the images, it maximizes compression on the raw data, significantly reducing the volume of data subsequent tasks and transmission networks need to process. This enhances the speed of data processing. Finally, extensive experimentation across multiple datasets demonstrates that our proposed model achieves a balance between performance and efficiency. Ultimately, this paper presents an effective avenue for efficiently handling data within IIoT systems by combining edge computing nodes with intelligent models.
Developing EFL Students’ Writing Ability Through Contextual Teaching and Learning (A Classroom Action Research Study)

Writing ability is an ability that refers to the notion of knowledge and understanding of writing. Judging from language skills, writing is an active productive activity, namely the activity of producing language and writing activity is also a form of realization of abilities and skills in the most recent language mastered by language learners after listening, speaking, and reading skills. Seeing the importance of this writing skill, the purpose of this study is to see the effectiveness of the CLT method in improving students' descriptive text writing skills. In its application, the researcher used CAR. In this study there were 20 students of SMA PGRI Kalabahi in the academic year 2022/2023. The data of this study were obtained from students' writing tests. As a result of the research, the CLT method has a significant impact on students' writing ability. In addition, the supporting factors of learning success include the use of props to write descriptive text and the teacher's proficiency in applying the contextual approach. Meanwhile, the inhibiting factors were students' lack of knowledge about descriptive text, their lack of ability and enthusiasm in stringing words to create descriptive text, students' low interest in descriptive text writing activities, as well as students' enthusiasm and unsupportive play environment. The application of contextual approach was able to develop students' descriptive text writing ability seen from the results of students' descriptive text writing, students' interest and response in following the learning of descriptive text writing by using contextual teaching and learning method.
New Geochemical and Age Data on the Bajgan Complex (Makran Accretionary Prism, SE Iran): Implications for the Redefinition of Its Tectonic Setting of Formation from a Paleozoic Continental Basement to a Cretaceous Oceanic Domain

The Bajgan Complex (BC) in the North Makran Domain (Makran Accretionary Prism, SE Iran) has been considered a remnant of a Paleozoic metamorphic continental basement detached from the southernmost edge of the Lut Block. We present here new geochronological and petrological data on metamorphic rocks from this Complex, intending to assess the age and tectonic–magmatic setting of the formation of their magmatic protoliths. The BC consists of meta-ophiolitic tectonic slices, including meta-peridotites, meta-gabbros, meta-plagiogranites, and meta-basaltic lavas. Whole rock geochemical data show that their magmatic protoliths include: (1) magmatic rocks showing both normal (N-) and enriched (E-) MORB affinities, as those formed at mid-ocean ridge settings; (2) basalts showing alkaline ocean island (OIB) chemistry similar to those formed in seamounts. Zircon U-Pb dating indicates magmatic ages of the protoliths from 161–114 Ma (Late Jurassic–Early Cretaceous). Our new data suggest that the interpretation of the BC as a Paleozoic continental ribbon should be abandoned. Instead, it represents a long-lived (at least ~50 My) Cretaceous oceanic lithosphere whose composite chemistry and petrogenesis indicate that it was characterized by mantle plume activity and plume–ridge interaction processes. Similar rock associations and procedures have been documented in many other Cretaceous ophiolites of the North Makran, suggesting that the magmatic protoliths of the Bajgan meta-ophiolites were most likely formed in the same oceanic basin.
The Effectiveness of E-Learning Madrasah in Teaching Reading toward Students' Reading Achievement in MTsN 2 Kota Kediri

The purpose of this research was to describe the effectiveness of using E-learning Madrasah in teaching reading. This research was conducted using a descriptive quantitative approach, precisely the experimental method and the technique was used in data collection was a posttest-only control design. This research conducted to find out is there any significant different in student’s reading achievement between the experimental class and the control class. This research was conducted on 7th grade students of MTsN 2 Kota Kediri. The researcher used two classes, namely class 7I as an experimental class with a total of 35 students, and class 7J as a control class with a total of 36 students. The instrument that used was posttest and the data were analyzed by T-Test. The result of this research indicates that the student's English reading achievements in the experimental class has the mean score 80,00 which is categorized as GOOD based on the categorization table, the student's English reading achievements in the control class has the mean score 69,17 which is categorized as GOOD based on the categorization table, and the result of T-Test show that t = 3,740, df = N-2 (71-2 = 69), Sig.(2-tailed) = 0,000, and 95% confidence interval ranging from 16,612 to 5,054. The Sig.(2-tailed) is lower than 5% (0.000&lt; 0.05), it can be concluded that there is significant difference between the experimental class and the control class.&#x0D;
Investigating Writing Instrument and Descriptive Paragraph Web-blog based

This qualitative research was conducted to determine the benefits, reasons, experiences, knowledge of students in the development of writing instruments, students' understanding of descriptive paragraphs and Malay culture as local content in the classroom. This research is a survey research conducted in October 2020 and involved 50 respondents in grades VI, VII, IX of SMP Negeri 3 Kabun. To take the sample, the researcher used random sampling technique. The questionnaire is used as an instrument consisting of 17 questions which are then analyzed descriptively with an opinion that has been surveyed by researchers. To answer the researcher's question, the technique used is observation to analyze the completed questionnaire. The findings reveal that stationery is still used in modern times, students can also understand descriptive paragraphs. Students experience an increase in the value of writing descriptive paragraphs while some students are still unable to write descriptive paragraphs, students' understanding of using blog media is very good as a medium of learning and make students more interested so that it fosters motivation, teaching materials will be clearer and can be understood by students and allow students to master teaching objectives better, learning methods will be more varied, not boring students and students doing more learning activities. Riau Malay culture has not yet been applied to local content. If it is applied, it will make students understand the culture will increasingly develop among students.
Fiat-Shamir Security of FRI and Related SNARKs

We establish new results on the Fiat-Shamir (FS) security of several protocols that are widely used in practice, and we provide general tools for establishing similar results for others. More precisely, we: (1) prove the FS security of the FRI and batched FRI protocols; (2) analyze a general class of protocols, which we call $$\delta $$ -correlated, that use low-degree proximity testing as a subroutine (this includes many “Plonk-like” protocols (e.g., Plonky2 and Redshift), ethSTARK, RISC Zero, etc.); and (3) prove FS security of the aforementioned “Plonk-like” protocols, and sketch how to prove the same for the others. We obtain our first result by analyzing the round-by-round (RBR) soundness and RBR knowledge soundness of FRI. For the second result, we prove that if a $$\delta $$ -correlated protocol is RBR (knowledge) sound under the assumption that adversaries always send low-degree polynomials, then it is RBR (knowledge) sound in general. Equipped with this tool, we prove our third result by formally showing that “Plonk-like” protocols are RBR (knowledge) sound under the assumption that adversaries always send low-degree polynomials. We then outline analogous arguments for the remainder of the aforementioned protocols. To the best of our knowledge, ours is the first formal analysis of the Fiat-Shamir security of FRI and widely deployed protocols that invoke it.
STUDENTS’ PERCEPTION TOWARD THE TRANSITION OF ONLINE TO OFFLINE LEARNING ENGLISH AFTER COVID-19 PANDEMIC AT A SENIOR HIGH SCHOOL IN BATANGHARI REGENCY JAMBI PROVINCE

This research aims to find out students' perceptions and the obstacles experienced by students towards the transition of English learning from online to offline after the Covid-19 pandemic in a high school in Batanghari district, Jambi province. This research is qualitative research with a case study approach. The informants in this research were eleventh grade students and English teachers who taught eleventh-grade. The research location is a school in Batanghari district, Jambi province. Data collection techniques through interview.&#x0D; The results of the data analysis showed that all students responded well to the transition from online to offline learning. This perception is shown to emphasize that online learning is not always effective due to the lack of supporting equipment at school. In addition, the students were not fully engaged during online learning and they could not participate as effectively as offline learning. This research shows, all students have felt bored to do online learning at home. Because what students get during online learning is only receiving materials, after that, students do the tasks given by the teacher. When offline students get a real learning atmosphere in class, and can interact directly with friends and teachers. In addition, students still need time to adapt when switching from online to offline learning. Students show the same attitude when studying online even though they have studied offline at school. In this case, the role of the teacher is needed by students at school because it helps to generate learning motivation, direct students in class during the learning process, and improve new students’ activities.
Intellectual analysis of large data stores

An intelligent system for processing big data. The paper classified and considered the further development of the PHI accelerator in modern processors for computing artificial intelligence in games. Based on the approach, an intelligent system was developed that increases efficiency and speed in game projects using artificial intelligence. The practical significance of the obtained results is that the obtained results can be used to improve the efficiency of using machine learning in the development of computer games.
Peran Teacher Librarian dalam Meningkatkan Minat Baca Siswa di YP Raudhatul Faizin Desa Tanjung Rejo

This study aims to find out how the role of the teacher librariansnncreasing students’nteresetn YP Raudhatul Faizin village Tanjung Rejo and factors and constraintsn the role of teacher librariansnncreasing thentereset of students at YP Raudhatul Faizin Village Tanjung Rejo. The type of research useds a qualitative approach. The techniques used for data collection are observation,nterviews and documentation. While the data analysis technique useds data reduction, presentation of data and conclusiion drawings. The results showed that the role carried out by the teachernncreasing thenterest of students was to dominate to the role ofnformation providers, motivators, facilitators, showtles and supervisors. Librarians have carried out their roles wellnncreasing thenterest of reading students by becoming an educator, manager and administrator. Factor and obstacles the role of the teacher librariannncreasing thenterest of students at YP Raudhatul Faizin Village Tanjung Rejos a lack of funds and facilitiesnadequate to manage and add library facilities such as bookshelves, tablles, chairs and so on.
Development of banks of test items as a type of modern certification pedagogical measuring materials

In connection with the transition of higher education to the Federal State Educational Standards of Higher Education of the third generation in terms of evaluating learning outcomes, a new task has arisen to create such an assessment system that would allow determining the formation of competencies and parts of competencies of students and graduates. To do this, universities should create a qualitatively new system for evaluating educational outcomes. At the same time, it is certainly desirable that the new system make maximum use of already well-tested, repeatedly tested and familiar tools and techniques. Testing is one of these tools that meet modern requirements and at the same time are already familiar to users. Of particular note is the value of testing for current and widely used distance learning methods. The article is devoted to some aspects of modern testing, namely, the creation of databases of test tasks and the design of the test tasks themselves as a type of certification and pedagogical measuring materials based on the provisions of the theory of testing and international standards.&#x0D; The article may be useful to all teachers who use or intend to use modern testing tools in their professional activities, teachers who form funds of assessment tools for disciplines at departments of educational institutions of higher education, as well as to the organizers and performers of diagnostic work of Rosobrnadzor – heads of departments and heads of the main professional educational programs.
The Extraction of Linguistic Knowledge and Construction of Linguistic Resources

In recent years, our research team has completed a series of studies and established a cognition-based, computation-oriented linguistic approach and practical technology roadmap. In one of these studies, a paraphrasing template library for Chinese noun-noun compounds (NNCs) was constructed to reveal implicit predicates, and an automatic paraphrasing system was developed to explain the meaning of the whole construction. Another study focused on the automatic paraphrasing of ba-constructions in Chinese, classified into different types based on the sentence patterns they can be transformed into. A pattern recognition-based program was then developed to automatically paraphrase the construction and transform the syntactic formation of ba-constructions. This chapter will introduce the structure of a lexical knowledge base and the construction of a Chinese verbal and adjectival valency dictionary, as well as a nominal qualia dictionary and a semantic role system for Chinese. Finally, a discussion on how the lexical knowledge base can be employed to make inferences and facilitate the computing of content in natural language processing will be presented.
Emotion Recognition in Reddit Comments Using Recurrent Neural Networks

Background: Reddit comments are a valuable source of natural language data where emotion plays a key role in human communication. However, emotion recognition is a difficult task that requires understanding the context and sentiment of the texts. In this paper, we aim to compare the effectiveness of four recurrent neural network (RNN) models for classifying the emotions of Reddit comments. Methods: We use a small dataset of 4,922 comments labeled with four emotions: approval, disapproval, love, and annoyance. We also use pre-trained Glove.840B.300d embeddings as the input representation for all models. The models we compare are SimpleRNN, Long ShortTerm Memory (LSTM), bidirectional LSTM, and Gated Recurrent Unit (GRU). We experiment with different text preprocessing steps, such as removing stopwords and applying stemming, removing negation from stopwords, and the effect of setting the embedding layer as trainable on the models. Results: We find that GRU outperforms all other models, achieving an accuracy of 74%. Bidirectional LSTM and LSTM are close behind, while SimpleRNN performs the worst. We observe that the low accuracy is likely due to the presence of sarcasm, irony, and complexity in the texts. We also notice that setting the embedding layer as trainable improves the performance of LSTM but increases the computational cost and training time significantly. We analyze some examples of misclassified texts by GRU and identify the challenges and limitations of the dataset and the models Conclusion: In our study GRU was found to be the best model for emotion classification of Reddit comments among the four RNN models we compared. We also discuss some future directions for research to improve the emotion recognition task on Reddit comments. Furthermore, we provide an extensive discussion of the applications and methods behind each technique in the context of the paper.