Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of Lattice-Based Blind Signature and Blind Ring Signature Schemes

A blind signature enables a user to obtain signatures on any message from an authority who cannot acquire any information on the message being signed. A blind ring signature scheme is designed as a ring signature scheme with the blindness property. The scheme allows any member of a group anonymously sign a message on behalf of the group. Also, the user with the message can blind it before transmitting to the group. At Asiacrypt 2010, Rückert constructed the first blind signature scheme using ideal lattices. Recently, Zhang, Jiang and Zheng, and Alkadri, Bansarkhani and Buchmann proposed two improved blind signature schemes based on the SIS problem and the Ring SIS problem in 2018 and 2020, respectively. At WISA 2019, motivated by these blind signature schemes, Le, Duong and Susilo constructed the first lattice-based blind ring signature scheme provably secure under the hardness assumption of the SIS problem in random oracle model. In this paper, we show that Rückert’s scheme, Alkadri-Bansarkhani-Buchmann scheme and Zhang-Jiang-Zheng scheme, and Le-Duong-Susilo scheme do not achieve blindness, i.e. the signer can link a valid message-signature pair after interacting with various users.We show that the cause of vulnerabilities of the blind schemes is that the blinding factors to hide real messages being signed are exposed by specific algebraic relations in the underlying rings. To hide the blinding factors, we use homomorphic encryption schemes. Finally, we propose a generic construction from a semantically secure homomorphic encryption scheme and a one-more unforgeable blind signature scheme that does not achieve blindness to a new blind signature scheme that achieves blindness as well as one-more unforgeability.
Software-intensive Systems-of-Systems

The growing trend to use multi-core processors to get more performance is increasingly present in safety-critical systems. Synchronous dataflow programming is naturally well-suited to parallel execution, thanks to the fact that all data dependencies are always explicit. MiniSIGNAL is a multi-task code generation tool for the synchronous dataflow language SIGNAL. The existing MiniSIGNAL code generation strategies mainly consider coarse-grained parallelism based on Ada multi-task model. However, when we applied it to industrial case studies, this code generation scheme has revealed inefficient: architecture aspects of the target platform have to be taken into account to achieve fine-grained parallelism. To generate more efficient target code from industrial cases, this paper presents a new multi-task code generation method for MiniSIGNAL. Starting at the level of synchronous clocked guarded actions (S-CGA) which is an intermediate language for the compilation process of MiniSIGNAL, the transformation consists of two parts: at the platform-independent level, transforming the S-CGA representation to an abstract multi-task structure (called Virtual Multi-Tasks, VMT); at the platform-dependent level, adopting the thread pool pattern concurrent JobQueue to support fine-grained parallel Ada code generation from the VMT structure. Moreover, the formal syntax and the operational semantics of VMT are mechanized in the proof assistant Coq. Finally, the effectiveness of our approach is illustrated by an application of the real-world Guidance, Navigation and Control system.
Digital Agenda, New Technologies and Education for the Integration of Europe: an Economic Study

New technologies are a key factor for integration in Europe by enabling citizens to solve their daily problems and interact with people everywhere. The European Commission established the Digital Agenda for Europe with the aim of improving the speed of navigation, establishing a single market for telecommunications and achieving a digital single market. We highlight the importance of applying new technologies in the field of education, since teaching and learning through these means will contribute to increasing creativity and reasoning capacity, allowing for more interactive and participative learning. The Horizon Report and the new emerging technologies proposed by that report are analysed, and are aimed not only at facilitating learning, but also at enabling a more integrated Europe. It also presents some technological ideas to be developed in the educational field that will enhance the development and integration of the EU Member States. Furthermore, in order to analyse the influence of the use of the Internet and new technologies on other socio-economic variables, a regression analysis has been carried out. The objective is to study whether in any way the fact that citizens use the Internet affects variables such as Gross Domestic Product, unemployment or Human Development Index which may affect the integration of Europe.
MOVER: Mask, Over-generate and Rank for Hyperbole Generation

Despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing.In this paper, we tackle the challenging task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase.To address the lack of available hyperbolic sentences, we construct HYPO-XL, the first large-scale English hyperbole corpus containing 17,862 hyperbolic sentences in a non-trivial way.Based on our corpus, we propose an unsupervised method for hyperbole generation that does not require parallel literalhyperbole pairs.During training, we fine-tune BART (Lewis et al., 2020) to infill masked hyperbolic spans of sentences from HYPO-XL.During inference, we mask part of an input literal sentence and over-generate multiple possible hyperbolic versions.Then a BERT-based ranker selects the best candidate by hyperbolicity and paraphrase quality.Automatic and human evaluation results show that our model is effective at generating hyperbolic paraphrase sentences and outperforms several baseline systems.
Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter

Twitter is one of the social media that is currently in great demand by internet users. The number of tweets circulating on Twitter is not yet known whether these tweets contain more positive, negative, and neutral opinions. For that we need a system that can process data by applying sentiment analysis. This study uses the Naive Bayes Classifier (NBC) method to analyze the level of sentiment towards data carried out by crawling on Twitter. The data studied as a simple case study uses only 8 tweet data which is divided into 5 training data and 3 test data. The data is processed using the preprocessing stage, then classified using the NBC method, the calculation of performance uses confusion matrix techniques. This study resulted in a structured exposure to the process and results of NBC implementation and performance testing using the confusion matrix which obtained 82% accuracy, 93% precision, and 52% recall. However, these results are more focused on ease explaining for each stage and process in more detail, not on the numbers obtained. Research with larger data will be carried out later by developing a computer-based application system.
Comparison of Single Linkage, Complete Linkage, and Average Linkage Methods on Community Welfare Analysis in Cities and Regencies in East Java

Community welfare is one of the important points for a region and is also the essence of national development. The welfare of the people in Indonesia is fairly unequal, especially in East Java. To be able to map an area to the welfare of its people in East Java, one way that can be used is to use clustering. The hierarchical clustering method is one of the clustering methods for grouping data. In hierarchical clustering, single linkage, complete linkage, and average linkage methods are suitable methods for grouping data, which will compare the best method to use. The results of the calculation show that the average linkage method with three clusters is the best calculation with a silhouette index value of 0.6054, with the 1st cluster there are 23 regions, namely the city/district with the highest community welfare, the 2nd cluster there are 11 regions, namely cities/districts with moderate social welfare, and in the third cluster there are 4 regions, namely cities/districts with the lowest community welfare.
Head tracker using webcam for auralization

Binaural rendering is a technique that seeks to generate virtual auditory environments that replicate the natural listening experience, including the three-dimensional perception of spatialized sound sources. As such, real-time knowledge of the listener's position, or more specifically, their head and ear orientations allow the transfer of movement from the real world to virtual spaces, which consequently enables a richer immersion and interaction with the virtual scene. This study presents the use of a simple laptop integrated camera (webcam) as a head tracker sensor, disregarding the necessity to mount any hardware to the listener's head. The software was built on top of a state-of-the-art face landmark detection model, from Google's MediaPipe library for Python. Manipulations to the coordinate system are performed, in order to translate the origin from the camera to the center of the subject's head and adequately extract rotation matrices and Euler angles. Low-latency communication is enabled via User Datagram Protocol (UDP), allowing the head tracker to run in parallel and asynchronous with the main application. Empirical experiments have demonstrated reasonable accuracy and quick response, indicating suitability to real-time applications that do not necessarily require methodical precision.
Development and Feasibility Test on Android-Based Interactive Multimedia Applications for Mathematics Learning

This study aims to produce and test the feasibility of interactive multimedia application products for learning mathematics for elementary schools. This research was conducted at SD Negeri 07 Padang City using the Research and Development method of the APPED development model. In the process, the APPED development model consists of 5 steps, namely the analysis and initial research, Design, Production, Evaluation and Dissemination stages. The results of this study indicate that the resulting interactive multimedia application product is suitable for use by elementary students. The conclusion of this study is that this interactive multimedia application can create an effective and integrated online learning process between teachers and students. Make it easier for elementary students to learn mathematics anywhere and anytime. This interactive multimedia product is feasible as a learning medium based on the results of trials and evaluations of media or application experts and teachers with a score of 3.85 (high).
skillsChain: A Decentralized Application That Uses Educational Robotics and Blockchain to Disrupt the Educational Process

Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial Revolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students’ skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills’ records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final results present its efficacy.
THE DEEP TIME OF BITCOIN: EXCAVATING THE "WORK" IN PROOF-OF-WORK CRYPTOCURRENCY SYSTEMS

Estimates place Bitcoin’s current energy consumption at 141.83 terawatt-hours/year, an amount comparable to Ukraine. While Bitcoin’s energy problem has become increasingly visible in both academic and popular discourse (see Lally et al. 2019), the computational mechanisms through which the Bitcoin network generates coins, proof-of-work, has gone under-examined. This paper interrogates the “work” in proof-of-work systems. What is this work? How can we access its material history? I trace this history through a media archaeology of computational heat, in an attempt to better situate the intimate relationship between information and energy in proof-of-work systems. I argue the “work” in these systems is principally heat-work, and trace its ideological constructions back to nineteenth-century thermodynamic science, and the reframing of doing work as something exhaustible, directional, and irreversible (Prigogine &amp; Stengers 2017; Daggett 2019). I then follow thermodynamic discourse through Cybernetics debates in the 1940s, illustrating how, early in the formation of Information Theory, the heat-work undergirding the functioning of a “bit” was obscured and compartmentalized, allowing information to be productively abstracted apart from its energetic infrastructures (Hayles 1999; Kline 2015). I conclude with a discussion of the heat-work within the Application Specific Integrated Circuit (ASIC), Bitcoin’s principal mining tool, arguing that proof-of-work mining is not a radical exception to the computing status quo, but rather a lens through which to think more broadly about computing’s complex relationship to energy, and ultimately, how this relationship can be different.
ANÁLISE DE MÉTODOS DE DETECÇÃO E RECONHECIMENTO DE FACES UTILIZANDO VISÃO COMPUTACIONAL E ALGORITMOS DE APRENDIZADO DE MÁQUINA

The advancement in technology in recent decades has provided many facilities for humanity in various applications, and facial recognition technology is one of them. There are several problemsto be solved to perform face recognition from digital images, such as varying ambient lighting, changing the face physical characteristics and resolution of the images used. This work aimed to perform a comparative analysis between some of thedetection and facial recognition methods, as well as their execution time. We use the Eigenface, Fisherface and LBPH facial recognition algorithms in conjunction with the Haar Cascade facedetection algorithm, all from the OpenCV library. We also explored the use of CNN neural network for facial recognition in conjunction with the HOG facial detection algorithm, these from the Dlib library. The work aimed, besides analyzing the algorithms in relation to hit rates, factors such as reliability and execution time were also considered
Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments

In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since current basket-market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high-utility itemsets) in a large-scale database especially with IoT situations. First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases. The <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3-tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR-Miner compared to the well-known and state-of-the-art CLS-Miner. We present our in-depth results to show that the developed GMR-Miner outperforms CLS-Miner in many criteria, i.e., memory usage, scalability, and runtime.
MAPPING AND POSITIONING SYSTEM ON OMNIDIRECTIONAL ROBOT USING SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) METHOD BASED ON LIDAR

The main problem from autonomous robot for navigation is how the robot able to recognize the surrounding environment and know this position. These problems make this research weakness and become a challenge for further research. Therefore, this research focuses on designing a mapping and positioning system using Simultaneous Localization and Mapping (SLAM) method which is implemented on an omnidirectional robot using a LiDAR sensor. The proposes of this research are mapping system using the google cartographer algorithm combined with the eulerdometry method, eulerdometry is a combination of odometry and euler orientation from IMU sensor, while the positioning system uses the Adaptive Monte Carlo Localization (AMCL) method combined with the eulerdometry method. Testing is carried out by testing the system five times from each system, besides that testing is also carried out at each stage, testing on each sensor used such as the IMU and LiDAR sensors, and testing on system integration, including the eulerdometry method, mapping system and positioning system. The results on the mapping system showed optimal results, even though there was still noise in the results of the maps created, while the positioning system test got an average RMSE value from each map created of 278.55 mm on the x-axis, 207.37 mm on the y-axis, and 4.28o on the orientation robot.
To the 210&lt;sup&gt;th&lt;/sup&gt; anniversary of the Library of the General Staff of Armed Forces of the Russian Federation (St. Petersburg)

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Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach

Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks.Existing works usually formulate the method as a zero-sum game, which is solved by alternating gradient descent/ascent algorithms.Such a formulation treats the adversarial and the defending players equally, which is undesirable because only the defending player contributes to the generalization performance.To address this issue, we propose Stackelberg Adversarial Regularization (SALT), which formulates adversarial regularization as a Stackelberg game.This formulation induces a competition between a leader and a follower, where the follower generates perturbations, and the leader trains the model subject to the perturbations.Different from conventional approaches, in SALT, the leader is in an advantageous position.When the leader moves, it recognizes the strategy of the follower and takes the anticipated follower's outcomes into consideration.Such a leader's advantage enables us to improve the model fitting to the unperturbed data.The leader's strategic information is captured by the Stackelberg gradient, which is obtained using an unrolling algorithm.Our experimental results on a set of machine translation and natural language understanding tasks show that SALT outperforms existing adversarial regularization baselines across all tasks.Our code is publicly available.
The Students’ Strategies in Online Learning Interaction: Exploring Politeness in Google Classroom during Covid-19 Pandemic

Teacher-student politeness has been well-explored over the years, but mostly in direct or face-to-face communication. This study aims to fill the gap of politeness between students and educators in online learning, specifically in the sessions of one of the staple and most used educational platforms during the Covid-19 pandemic, namely Google Classroom. This study applies qualitative research method by describing the phenomenon of the language. This study observed the interaction of 36 English Literature Department students during their Google Classroom sessions from April to July 2021. The researchers analysed the students’ interaction with their lecturer according to Brown and Levinson’s politeness theory. Five politeness strategies were identified, namely the positive politeness strategies (i.e., greeting, gratitude and address term), negative politeness (i.e., apology) and vernacular language. The gratitude and address term expression conveyed by “Thank you, Mam” is dominantly used by students to minimize threat when giving comments for every instruction created by lecturer. Results of this study showed that the students use politeness strategies to ensure the effectiveness of online learning, yet they also limit their text-based interaction with the short expressions of politeness such as “Hi, mam” or “Yes, mam”. This finding contributes to shedding light on the aspect of politeness in online interaction in Google Classroom and directs future studies to explore politeness in other contexts.
Application of Single Identification Number on an Identity Card (E-KTP) in the Era of the Industrial Revolution 4.0

This research aims to analyze the efficiency of the Single Identification Number on the identity card (e-KTP). The method used in this study was descriptive with the survey method, and to determine the efficiency of this study, we conducted questionnaires. This research results in several factors that determine the success of the Single Identification Number's success on e-KTP, including technical and economic aspects. From some of these factors, technological factors are obtained. Meanwhile, economic factors can help people to simplify administration anywhere without having to carry a different card. Moreover, it also anticipates the scarcity of blanks. Therefore, the Single Identification Number of e-KTP can be efficient in carrying out its function as a multi-purpose single identification number in a digital card (e-KTP) that makes it easier for the public. E-KTP becomes a solution for the community problem that can use as a multifunctional card, which will make it easier for the community to carry out administrative activities. Not only that, but the community is also facilitated by only doing one organizational activity
Analysis of Moodle as E-Learning in the Online Learning Implementation in Aviation Polytechnic of Surabaya

&lt;em&gt;&lt;span&gt;This study aims to analyze the implementation of Moodle as e-learning in the online learning application at the Aviation Polytechnic of Surabaya. The research method employed the descriptive qualitative method. The sample in this study consisted of 46 cadets from the Aviation Communication Study Program Batch V Alpha and Bravo at Aviation Polytechnic of Surabaya. This research was carried out from March 2020 to June 2020 at the Aviation Polytechnic of Surabaya. Data were collected using observation, questionnaires, interviews, and literature studies. The data obtained through questionnaires were then analyzed utilizing descriptive analysis techniques with percentages. The results of the optimal level of Moodle as e-learning in the online learning application from users, namely cadets, reached 79.56% for the user satisfaction aspect, with a good category. From the usability aspect, it got 73.61%, with enough category. From the graphic design aspect, it attained 56.74%, with enough category. The navigation aspect reached 76.95% in the good category, while the content aspect obtained 84.34% in the good category. Overall, the response of the VA and VB Aviation Communication cadets obtained 74.24%, with enough category. Thus, it can be concluded that Moodle as e-learning in the online-based learning application at the Aviation Polytechnic of Surabaya was not fully optimal since it was still in enough category. For this reason, it needs to be further developed from the usability aspect, graphic design, and additional features, such as video on demand and video streaming&lt;strong&gt;&lt;span&gt;.&lt;/span&gt;&lt;/strong&gt;&lt;/span&gt;&lt;/em&gt;