Reviewerly/exHarmony huggingface dataset:
New Cryptanalysis of ZUC-256 Initialization Using Modular Differences

ZUC-256 is a stream cipher designed for 5G applications by the ZUC team. Together with AES-256 and SNOW-V, it is currently being under evaluation for standardized algorithms in 5G mobile telecommunications by Security Algorithms Group of Experts (SAGE). A notable feature of the round update function of ZUC-256 is that many operations are defined over different fields, which significantly increases the difficulty to analyze the algorithm.As a main contribution, with the tools of the modular difference, signed difference and XOR difference, we develop new techniques to carefully control the interactions between these operations defined over different fields. At first glance, our techniques are somewhat similar to those developed by Wang et al. for the MD-SHA hash family. However, as ZUC-256 is quite different from the MD-SHA hash family and its round function is much more complex, we are indeed dealing with different problems and overcoming new obstacles.As main results, by utilizing complex input differences, we can present the first distinguishing attacks on 31 out of 33 rounds of ZUC-256 and 30 out of 33 rounds of the new version of ZUC-256 called ZUC-256-v2 with low time and data complexities, respectively. These attacks target the initialization phase and work in the related-key model with weak keys. Moreover, with a novel IV-correcting technique, we show how to efficiently recover at least 16 key bits for 15-round ZUC-256 and 14-round ZUC-256-v2 in the related-key setting, respectively. It is unpredictable whether our attacks can be further extended to more rounds with more advanced techniques. Based on the current attacks, we believe that the full 33 initialization rounds provide marginal security.
Attacks on the Firekite Cipher

Firekite is a synchronous stream cipher using a pseudo-random number generator (PRNG) whose security is conjectured to rely on the hardness of the Learning Parity with Noise (LPN) problem. It is one of a few LPN-based symmetric encryption schemes, and it can be very efficiently implemented on a low-end SoC FPGA. The designers, Bogos, Korolija, Locher and Vaudenay, demonstrated appealing properties of Firekite, such as requiring only one source of cryptographically strong bits, small key size, high attainable throughput, and an estimate for the bit level security depending on the selected practical parameters.We propose distinguishing and key-recovery attacks on Firekite by exploiting the structural properties of its PRNG. We adopt several birthday-paradox techniques to show that a particular sum of Firekite’s output has a low Hamming weight with higher probability than the random case. We achieve the best distinguishing attacks with complexities 266.75 and 2106.75 for Firekite’s parameters corresponding to 80-bit and 128-bit security, respectively. By applying the distinguishing attacks and an additional algorithm we describe, one can also recover the secret matrix used in the Firekite PRNG, which is built from the secret key bits. This key recovery attack works on most large instances of Firekite parameters and has slightly larger complexity, for instance, 269.87 on the 80-bit security parameters n = 16,384, m = 216, k = 216.
Divorce Fact Detection Based on Internet User Behavior Using Hybrid Systems with Combination of Apriori Algorithm and K-Means Method

An ideal couple should sustain the family's ark till the end of their life without divorce. This study aims at seeking the association between divorce and internet behavior of searching negative keywords. The study observes four keywords, which are porn, sex, gay, and lesbian. We collected keyword usage data from google trend reports and obtained divorce court figures from the Religion Court of Padang. We used the apriori algorithm to reveal the association between divorce and internet behavior observing individual keyword searches and in groups. We used the K-Means algorithm in classifying negative word searches and divorce trial numbers from a group of existing data. We also investigate the combination of the apriori algorithm and the K-Means method to detect divorce facts and the behavior of internet users. The combined method has been successful in revealing the positive association between divorce facts and the behavior of internet users.
Optimalisasi dalam Seleksi Bibit Kelapa Sawit Unggul Menggunakan Metode TOPSIS

The quality of palm oil that will be produced from oil palm has a great influence on the type of oil palm seeds planted. The process from the maintenance of oil palm, to the process of processing palm oil into palm oil. The purpose of this research is to build a decision support system that can assist in the selection of superior oil palm seeds based on the website. The method used is Technique for Others Preference by Similarity to Ideal Solution (TOPSIS) which is a multi-criteria decision-making method. The principle of this method is that the chosen alternative must have the closest distance to the positive ideal solution and the farthest distance to the negative ideal solution. In this method, you can choose or make decisions by determining the weighting value for each attribute and then proceed with a ranking process that will provide the best alternative from one with the alternative with the highest priority value. This method provides an ideal solution for farmers in making decisions about which superior oil palm seeds to plant. The processed data is divided into 3 varieties, namely Dura, Pisifera, and Tenera. Each variety has different characteristics. The results of the research can be ranked correctly as a reference in determining decision making in the selection of the best seeds. This research can be used as a recommendation in the calculation for the selection of superior oil palm seeds.
Information System on Mapping and Geolocation of COVID-19 in the City of Sukabumi

Coronavirus Disease (COVID-19) has made Indonesia's health condition critical. Therefore, the President of the Republic of Indonesia disclosed Presidential Decree No. 7 in 2020 regarding the Task Force for the Acceleration of Coronavirus Disease 2019 (COVID-19) Handling. The decree relates to Act No. 14 in 2008 regarding Public Information Disclosure, Presidential Regulation No. 95 in 2018 concerning Electronic-Based Government Systems, and Presidential Instruction No. 3 in 2003 concerning National Policies and Strategies for E-Government Development. The decree demands information system development, similar to https://covid19.go .id, which describes COVID-19 nationwide. The site explains what COVID-19 and data of the COVID-19 outspread with geolocation and digital map, which may attract public attention. The presidential instruction forces local governments to build an information system, which is in line with the site by the central government. This paper describes the development of the system using a spiral model. It involves a variety of free and open-source software such as CodeIgniter, Mapbox, Morris Chart, MySQL, and WordPress. The site has been operational, and it attracts 150 visitors a day with 200 visits per day. As of January 6, 2021, the website has recorded 89,852 views.
Developing programming language with compilers using JFlex in NetBean: Expanding and testing simple operators by implementing a calculator

Abstract: JFlex is a lexical analyzer generator and takes input requirements with a set of regular expressions and corresponding actions. It creates a program (a lexer) that reads input, matches the input against the regular expressions, and runs the matching action. This paper shows how Programming Language can be developed. This work was done to develop a simple programming language with compilers using JFlex in NetBean so that it can support assignment statements, if then else, while do and type checking and its execution. The data type included are int, real, char, and Boolean/String. The key concept used in this work was the execution of the grammar or rules in the cup file. The parse tree records a sequence of rules the parser applies to recognize the input. The tool used for the development of lexical analyzers was JFlex. JFlex Lexers was based on deterministic finite automata (DFAs). To show the implementation and working of operators, a simple calculator was designed that supports addition and multiplication operations. Further, the key compiler concepts like lexical analyzers, semantic analysis, and parse trees are discussed. This paper will help understand the syntax and way to develop simple language. For the programming language developed, the evaluations of the expressions and statements are recursively done. Type checking and Error checking are also done where two operands are checked for their compatibility with the operator and are shown if incompatible expressions are found.
Tracer Study Model in User Satisfaction Graduates Postgraduate Iain Purwokerto Juncto Uin Prof. K.H. Saifudin Zuhri Purwokerto

Tracer study is a study that traces graduates about their work in the world of work and society. The results of the tracer study describe the quality of higher education and are used as feedback to improve the quality of education. However, the tracer study encountered various obstacles due to the presence of graduates who spread across various agencies and regions. Therefore, a tracer study system is needed that can minimize these obstacles. In this study, a web-based tracer study conducted at UIN SAIZU will be studied with a waterfall model. The method used in this research is field research. Data collection techniques were carried out by observation, in-depth interviews with graduate informants and graduate users, documentation, and training. The data analysis technique used the Miles and Huberman method with the stages of data reduction, data presentation, and verification. The results of this study found that the tracer study using the web-based waterfall model became more effective and efficient. The information collected is systematic and tiered with real time, structured, and integrated data. Other results are the views of graduates who express their satisfaction with the implementation of postgraduate education and services at UIN SAIZU and the satisfaction of graduate users on the performance of UIN SAIZU graduates.
Pengembangan Modul Digital Menggunakan Pendekatan Collaborative Learning Pada Mata Kuliah Pengembangan Bahan Belajar

This Research and Development aims to produce products in the form of digital module development media using a collaborative learning approach in the Study Material Development Course. Research and Development (R&amp;D) is a research method used to produce multimedia products. The development method refers to the Lee &amp; Owens model in the form of stages (analysis, design, development, implementation). With the application of digital module media as an addition to learning courses, the development of learning materials can provide a sense of motivation, interest and ease in learning. The results of the product feasibility test that have been tested on media experts, material experts and students received a positive and proper response.
Pengembangan Kuis Interaktif Untuk Metode Drill And Practice Dengan Memanfaatkan Fitur Media Sosial

The aim of this research and development is to create a product for implement sosial media into drill and practice learning method. This quiz allows use of augmented reality and motion sensors technology. Taking advantage of features on Instagram sosial media, namely filters on Instagram stories, make this product accessible to anyone. This product also allows it to be used on smartphones of various types. The subject matter of “Things in The Classroom and Our Bag” in English for Class VII SMP. The ADDIE development paradigm was used to guide this project through the stages of analysis, design, development, implementation, and assessment. Products are validated by media experts and English subject matter experts. Student from SMP Muhammadiyah 1 in Malang City who are in seventh grade are the focus of this interactive quiz. It performed well in the product validation test, so that the product developed is suitable for use with the aim of increasing attractiveness during learning, as well as student motivation.
Pengembangan Media Video Podcast Pada Pembelajaran Sejarah Kelas XI Materi Pendudukan Bangsa Jepang Di Indonesia

This development and research is aimed at producing video podcast media products with the latest innovations for the learning process of Indonesian history, material on the Japanese population in Indonesia, which these media products are a complement to the learning process. By using Research and Development (R&amp;D), this research and development refers to the Lee &amp; Owen model. By using the stages, namely analysis, design, development and implementation. And the results of the due diligence have been tested by media experts, material experts, and students with good positive responses. So it can be concluded that the video podcast media product is suitable for use in the learning process in class XI SMA 2 Muhammadiyah Singaraja. As high as this video podcast media is the latest innovation in class and improves student learning.
Tes Berbasis Web Tool Quiz Pada Mata Pelajaran Bahasa Indonesia SMA Di Masa Pandemi

This study aims to develop a product in the form of a web-based test tool quiz from the provider, namely quizizz with explanatory text material for Indonesian high school subjects in the third semester of a 2-year program. The method used is Research and Development (R&amp;D) which is widely used to develop, and produce, a particular product, especially in the field of education. The development model used refers to the model with the steps of Analysis, Design, Development, Implementation, Evaluation). The trial application in the field was hybrid by dividing students into two groups due to restrictions on school mobility in the COVID-19 era. The results showed that this product had met the criteria for use by material experts and media experts and students responded positively even though some things were responded to negatively. However, the web-based quiz tool test is generally interesting and suitable for use in learning during the pandemic, because it can be used both inside and outside the classroom
Recursion in Abstract Argumentation is Hard --- On the Complexity of Semantics Based on Weak Admissibility

We study the computational complexity of abstract argumentation semantics based on weak admissibility, a recently introduced concept to deal with arguments of self-defeating nature. Our results reveal that semantics based on weak admissibility are of much higher complexity (under typical assumptions) compared to all argumentation semantics which have been analysed in terms of complexity so far. In fact, we show PSPACE-completeness of all non-trivial standard decision problems for weak-admissible based semantics. We then investigate potential tractable fragments and show that restricting the frameworks under consideration to certain graph-classes significantly reduces the complexity. As a strategy for implementation we also provide a polynomial-time reduction to DATALOG with stratified negation.
Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics

Classical regular path queries (RPQs) can be too restrictive for some applications and answering such queries under approximate semantics to relax the query is desirable. While for answering regular path queries over graph databases under approximate semantics algorithms are available, such algorithms are scarce for the ontology-mediated setting. In this paper we extend an approach for answering RPQs over graph databases that uses weighted transducers to approximate paths from the query in two ways. The first extension is to answering approximate conjunctive 2-way regular path queries (C2RPQs) over graph databases and the second is to answering C2RPQs over ELH and DL-Lite_R ontologies. We provide results on the computational complexity of the underlying reasoning problems and devise approximate query answering algorithms.
Development of an Integrated Healthcare Analytics Using Blockchain Tools and Techniques

Medical care is information escalated space where a massive amount of data is made, got to, and put away today. The report collected in the paper design is challenging to detect in the system, costly to document, and accessible when required. These difficulties might prompt health choices not being made with complete data; because of missing information, there is a requirement for rehashed tests or information put away in an alternate medical clinic in another state or country. The proposed approach can utilize Blockchain innovation to safeguard medical services information facilitated inside the cloud. Electronic Medical Records (EMRs) contain clinical and clinical information connected with a given patient and are put away by the capable medical services supplier using the proposed framework. The individual or clinic authority works with recovering and examining medical services information. The proposed strategy is an electronic application where clients can enlist and get their one-of-a-type keys. Engaging essential patients can also transfer patient information to the cloud. By allowing medical clinic specialists, they can again see patients' data. By associating at least two clinics, medical clinics can share their patient's information for additional treatment. They can share patients' information provided they have been given the right when they need to move their clinical history to another specialist. Will reflect changes in communication in all medical clinics' in-network. For this electronic connection point, clients need to utilize their Aadhar card number for enrollment and login purposes, with no requirement for emergency clinic participation.
PENGARUH METODE PEMBELAJARAN HOME VISIT DAN PEMBERIAN TUGAS TERHADAP HASIL BELAJAR MATEMATIKA DI KELAS VII SMP PADAMU NEGERI T.A 2020/2021

Abstract: The Effect of Home Visit Learning Methods and Giving Assignments on Mathematics Learning Outcomes in Class VII SMP Padamu Negeri 2020/2021. The purpose of this study was to determine the effect of student learning outcomes who received learning using the home visit method and learning with the assignment method on the eye. Mathematics lessons in class VII SMP Padamu Negeri in Medan city FY 2020/2021. This type of research is a quantitative descriptive research. The population used in this study were all students of class VII SMP Padamu Negeri totaling 60 students consisting of 2 classes. Sampling in this study was using a sampling technique, where the sample amounted to 60 divided into two groups, the experimental group I with a total of 30 students and the experimental group II with a total of 30 students. The tool used in data collection is a multiple choice test consisting of 20 questions to measure student learning outcomes. The data analysis technique in this study used normality, homogeneity, and hypothesis testing using the t-test formula. Based on data analysis, the average value of learning outcomes for the Home Visit learning method is 80.93 and the value of learning outcomes for the assignment-giving learning method is 74.68. From the results of the data analysis requirements test, the population is normally distributed and has a uniform variance (homogeneous) and the results of hypothesis testing show Ho is rejected and Ha is accepted with the results of tcount &gt; ttable, namely 2.46 &gt; 1.87. The researcher concluded that there was an effect using the Home Visit and Assignment learning methods on mathematics learning outcomes in Class VII SMP Padamu Negeri T.A 2020/2021. Keywords: Learning Outcomes, Home Visit Method, Assignment Method
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution

Evolutionary Computation (EC) has been shown to be able to quickly train Deep Artificial Neural Networks (DNNs) to solve Reinforcement Learning (RL) problems. While a Genetic Algorithm (GA) is well-suited for exploiting reward functions that are neither deceptive nor sparse, it struggles when the reward function is either of those. To that end, Novelty Search (NS) has been shown to be able to outperform gradient-following optimizers in some cases, while under-performing in others. We propose a new algorithm: Explore-Exploit $\gamma$-Adaptive Learner ($E^2\gamma AL$, or EyAL). By preserving a dynamically-sized niche of novelty-seeking agents, the algorithm manages to maintain population diversity, exploiting the reward signal when possible and exploring otherwise. The algorithm combines both the exploitation power of a GA and the exploration power of NS, while maintaining their simplicity and elegance. Our experiments show that EyAL outperforms NS in most scenarios, while being on par with a GA -- and in some scenarios it can outperform both. EyAL also allows the substitution of the exploiting component (GA) and the exploring component (NS) with other algorithms, e.g., Evolution Strategy and Surprise Search, thus opening the door for future research.
BQA: A High-performance Quantum Circuits Scheduling Strategy Based on Heuristic Search

Currently, quantum computing is developing at a high speed because its high parallelism and high computing power bring new solutions to many fields. However, due to chip process technology, it is difficult to achieve full coupling of all qubits on a quantum chip, so when compiling a quantum circuit onto a physical chip, it is necessary to ensure that the two-qubit gate acts on a pair of coupled qubits by inserting swap gates. It will cause great additional cost when a large number of swap gates are inserted, leading to the execution time of quantum circuits longer. In this paper, we designed a way based on the business to insert swap gates BQA(Busy Qubits Avoid). We exploit the imbalance of the number of gates on qubits, trying to hide the overhead of swap gates. At the same time, we also expect swap gates to make as little negative impact on subsequent two-qubit gates as possible. We have designed a heuristic function that can take into account both of these points. Compared with qiskit, the execution time of the circuit optimized by our proposed method is only 0.5 times that of the qiskit compiled circuit. And when the number of two-qubit gates is large, it will achieve higher level than general conditions. This implies higher execution efficiency and lower decoherence error rate.
Video Vision Transformers for Violence Detection

Law enforcement and city safety are significantly impacted by detecting violent incidents in surveillance systems. Although modern (smart) cameras are widely available and affordable, such technological solutions are impotent in most instances. Furthermore, personnel monitoring CCTV recordings frequently show a belated reaction, resulting in the potential cause of catastrophe to people and property. Thus automated detection of violence for swift actions is very crucial. The proposed solution uses a novel end-to-end deep learning-based video vision transformer (ViViT) that can proficiently discern fights, hostile movements, and violent events in video sequences. The study presents utilizing a data augmentation strategy to overcome the downside of weaker inductive biasness while training vision transformers on a smaller training datasets. The evaluated results can be subsequently sent to local concerned authority, and the captured video can be analyzed. In comparison to state-of-theart (SOTA) approaches the proposed method achieved auspicious performance on some of the challenging benchmark datasets.
Suspicious and Anomaly Detection

In this project we propose a CNN architecture to detect anomaly and suspicious activities; the activities chosen for the project are running, jumping and kicking in public places and carrying gun, bat and knife in public places. With the trained model we compare it with the pre-existing models like Yolo, vgg16, vgg19. The trained Model is then implemented for real time detection and also used the. tflite format of the trained .h5 model to build an android classification.