Reviewerly/exHarmony huggingface dataset:
Processes against tests: On defining contextual equivalences

In this position paper, we would like to offer and defend a template to study equivalences between programs-in the particular framework of process algebras for concurrent computation.We believe that our layered model of development will clarify the distinction that is too often left implicit between the tasks and duties of the programmer and of the tester.It will also enlighten pre-existing issues that have been running across process algebras such as the calculus of communicating systems, the π-calculus-also in its distributed version-or mobile ambients.Our distinction starts by subdividing the notion of process in three conceptually separated entities, that we call process terms, (completed) processes and tests, and by stressing the importance of formalizing the completion of process terms and the instrumentation that results from placing a (completed) processes into a test.While the role of what can be observed and the subtleties in the definitions of congruences have been intensively studied, the fact that not every term can be tested, and that the tester should have access to a different set of tools than the programmer is curiously left out, or at least not often formally discussed-in this respect, the theory of monitor is a counter-examples that we discuss and compare to our approach.We argue that this blind spot comes from the under-specification of contexts-environments in which comparisons occur-that play multiple distinct roles but are generally-at least, on the surface of it-given only one definition that fails to capture all of their aspects.
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation

In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study. We assume a strict setting with no access to parallel data or machine translation and find that common transfer learning approaches struggle in this setting, as a generative multilingual model fine-tuned purely on English catastrophically forgets how to generate non-English. Given the recent rise of parameter-efficient adaptation techniques, we conduct the first investigation into how one such method, prompt tuning (Lester et al., 2021), can overcome catastrophic forgetting to enable zero-shot cross-lingual generation. Our experiments show that parameter-efficient prompt tuning provides gains over standard fine-tuning when transferring between less-related languages, e.g., from English to Thai. However, a significant gap still remains between these methods and fully-supervised baselines. To improve cross-lingual transfer further, we explore several approaches, including: (1) mixing in unlabeled multilingual data, and (2) explicitly factoring prompts into recombinable language and task components. Our approaches can provide further quality gains, suggesting that robust zero-shot cross-lingual generation is within reach.
The dual influence of the reed resonance frequency and tonehole lattice cutoff frequency on sound production and radiation of a clarinet-like instrument

The internal and external spectra of woodwind reed instruments are partially determined by the tonehole lattice cutoff and reed resonance frequencies. Because they can impact the spectrum in similar ways, a study of one without accounting for the other risks incomplete or false conclusions. Here, the dual effects of the cutoff and reed resonance frequencies are investigated using digital synthesis with clarinet-like academic resonators. It is shown that the odd and even harmonics have similar amplitudes at and above the cutoff frequency or reed resonance frequency, whichever is lowest. However, because the resonators radiate efficiently at the cutoff, it has the additional role of reinforcing the amplitude of both the odd and even harmonics in the external spectrum. The spectra are analyzed using the single value descriptors playing frequency, spectral centroid (SC), odd/even ratio (OER), and brightness as a function of the musician mouth pressure. Higher reed resonances correspond to higher values for all descriptors. The OER and brightness increase with resonator cutoff frequency, whereas the SC exhibits more complicated trends. The reed resonance has a larger impact on the "playing condition oscillation threshold," implying that it may have a more important role in sustaining auto-oscillation.
Estimating Price and Exchange rate Hedging Elasticity of Cryptocurrency Demand in Nigeria

This study examined the price and exchange rate hedging elasticity of cryptocurrency demand in Nigeria. To achieve the objective, the study employed Autoregressive Distributed Lag (ADRL) model to estimate both the short-run and long-run response of demand to changes in crypto price, consumer income and exchange rate. The Study found among others that cryptocurrency is demanded as a financial asset (either as money for exchange purpose or asset for speculative purpose). It was discovered that cryptocurrency price changes have significant impact on cryptocurrency demand but price inelastic. Meaning that a percentage changes in crypto price leads to less than proportionate change in cryptocurrency demand. It was also discovered that Nigeria real exchange rate has negative relationship with cryptocurrency demand meaning that as Naira real exchange rate to dollar is falling, the demand for cryptocurrency is rising. Other factors that are influencing cryptocurrency demand in Nigeria include consumer income, cryptocurrency popularity, and inflation rate in the country. The study concludes that while price plays an important role in cryptocurrency demand, fluctuations in cryptocurrency demand is less than the fluctuation in its price. The implication is that there are several other factors (structural issues) including real exchange rate movement that influences demands for cryptocurrency other than its price that policy makers should explore.
Professional Subjectivity Formation Of Future Bachelors Of Pedagogical Education

The authors describe the study results of professional subjectivity formation of future bachelors of pedagogical education in the current educational conditions.The paper presents a theoretical analysis of the literature on the problem under consideration based on the research purpose and subject.The authors also specify the definition of the concept of "future teacher's professional subjectivity."The paper describes the research procedure and results of future teachers' professional subjectivity formation conducted in 2020-2021 in Amur State University of Humanities and Pedagogy (Khabarovsk Territory, Komsomolsk-on-Amur).The article presented the substantiation of the developed diagnostic techniques complex, essential to conducting research.They are the "Self-Attitude Research Technique", "Reflexivity Questionnaire", "Terminal Values Questionnaire", "Multidimensional Personality Self-Realization Questionnaire,".The authors put forward a hypothesis that the use of productive technology complex in the professional educational process of a pedagogical university will contribute to the professional subjectivity formation of future bachelors of pedagogical education.The paper substantiates the pedagogical technologies complex necessary for target quality formation.The latter includes the following technologies: critical thinking development, dialogue, gaming technologies, problem-based learning technologies, semantic reading, research, heuristic and project technologies, and contextual learning technology.The effectiveness of the pedagogical technology complex is proved.The authors also show the dynamics of future teachers' professional subjectivity formation.These affect the efficiency of the professional and educational process realized in a pedagogical university.
Analysis and Correction of Wrong Technical Actions in Juvenile Sports Training Based on Deep Learning

Scientific analysis of students’ incorrect actions in class, as well as timely and effective correction, is frequently an important link in the PE process. At the same time, it is an important symbol for assessing a teacher’s teaching level and quality. In this paper, the analysis and correction model of sports wrong technical movements is built using DCNN to address shortcomings in the process of detecting wrong movements in PE and training. This article is based on CNN and has been enhanced by DL. The model learns both manual and DL features; the manual features use an improved dense trajectory, the DL features use CNN based on motion information, and the generalization ability of the kernel support vector machine is used to fuse the two. The simulation results show that the accuracy of the wrong action judgment of this method can reach 92.16 percent, which is 4.6 percent higher than the method of combining NN with region prediction and 5.7 percent higher than the method of detecting image matching score. This method can accurately describe the characteristics of human motion and identify incorrect movements, improve the ability of judging and correcting incorrect movements in sports training, and help athletes improve their sports level.
DENSE RECONSTRUCTION FOR TUNNELS BASED ON THE INTEGRATION OF DOUBLE-LINE PARALLEL PHOTOGRAPHY AND DEEP LEARNING

Abstract. In scenes of tunnels and underground engineering projects, the 3D modeling based on the traditional horizontal baseline photography method is difficult to make a trade-off between the modeling efficiency and image overlap under narrow space and close photography constraint conditions. Parallel photography provides a better alternative. Furthermore, the undulating tunnel surfaces make occlusion unavoidable, and the pixel scales vary evidently in parallel photography, both of which make it difficult to obtain expected models neither by Semi-Global Matching nor by Patch-based Multi-view Stereo techniques. Comparatively, more accurate 3D reconstruction results can be achieved by using learning techniques, which consider the global semantic information such as specular prior and reflective prior to making the matching more robust. Besides, it is convenient for geologists to acquire photos with smartphones or cameras by a single-line parallel photography method at the tunnel sites. But enough image overlap for reconstruction is still difficult for the above photography way. Therefore, this paper proposes a dense reconstruction method for tunnels using deep learning and double-line parallel photography techniques, and respectively makes comparisons with single-line parallel photography and traditional modeling methods. Experimental results show the feasibility and robustness of the proposed method, especially for tunnel surfaces with complicated textures and occlusions. Moreover, both the completeness and accuracy of the tunnel model with double-line photography are better than with single-line photography.
XSS filter evasion using reinforcement learning to assist cross-site scripting testing

Machine learning and deep learning are widely utilized and highly effective in attack classifiers. Little research has been undertaken on detecting and protecting cross-site scripting, leaving artificial intelligence systems susceptible to adversarial assaults (XSS). It is crucial to develop a mechanism for increasing the algorithm's resilience to assault. This study intends to utilize reinforcement learning to enhance XSS detection and adversarial combat attacks. Before mining the detection model's hostile inputs, the model's information is extracted using a reinforcement learning framework. Second, the detection technique is simultaneously trained using an adversarial strategy. Every cycle, the classification method is educated with freshly discovered harmful data. The proposed XSS model effectively mines destructive inputs missed by either black-box or white-box detection systems during the experimental phase. It is possible to train assault and detection models to enhance their capacity to protect themselves, leading to a lower rate of escape due to this training.&#x0D;
Blockchain Based e-Voting System

Abstract: There may be no doubt that the progressive idea of the blockchain, that is the underlying era in the back of the famous cryptocurrency Bitcoin and its successors, is triggering the beginning of a brand new generation in the net and the web services. At the same time as most people focus simplest at cryptocurrencies; in truth, many administrative operations, fintech techniques, and regular services that may handiest be finished offline and/or in person, can now competently be moved to the net as on-line offerings. What makes it a powerful tool for digitizing everyday services is the creation of clever contracts, as in the Ethereum platform. Smart contracts are meaningful portions of codes, to be integrated in the blockchain and achieved as scheduled in each step of blockchain updates. E-voting on the other hand, is every other trending, yet vital, subject matter related to the online services. The blockchain with the smart contracts, emerges as an excellent candidate to apply in trends of more secure, less expensive, more comfortable, more obvious, and less complicated-to-use e vote casting systems. Ethereum and its community is one of the most suitable ones, due to its consistency, full-size use, and provision of smart contracts common sense. An e-balloting system should be comfy, as it has to now not allow duplicate votes and be completely obvious, at the same time as shielding the privacy of the attendees. In this work, we have carried out and tested a sample e-vote casting utility as a smart settlement for the Ethereum community, the use of the Ethereum wallets and the Solidity language. Android platform is likewise taken into consideration to permit vote casting for individuals who do not have an Ethereum wallet. After an election is held, eventually, the Ethereum blockchain will maintain the facts of ballots and votes. customers can submit their votes through an Android tool or immediately from their Ethereum wallets, and those transaction requests are treated with the consensus of each unmarried Ethereum node. This consensus creates a transparent environment for e-balloting. similarly to a large dialogue about reliability and performance of the blockchain-based e vote casting structures, our application and its take a look at consequences are presented on this paper, too.boundaries. Keywords: E-voting, Smart-contracts, Blockchain, Ethereum
Readability Analysis of Local Wisdom Non-Fiction Texts in Android-Based Interactive Multimedia for Elementary School Students With Fry Graph Formulas

The purpose of this study was to analyze the legibility of non-fiction texts containing Kediri Raya local wisdom in Android-based interactive multimedia intended for 4th grade elementary school students with the readability formula of the Fry Graph. The method in this research is descriptive quantitative. The objects of this research are ten non-fiction texts containing local wisdom in Kediri Raya. The research instrument uses documentation with data collected in the form of non-fiction texts of local wisdom of Kediri Raya in Android-based interactive multimedia. Analysis of the data in this study using the Fry Graph formula as a guide for analyzing the level of readability in non-fiction texts. The results showed that the readability level of ten non-fiction texts containing Kediri Raya local wisdom in Android-based interactive multimedia had the feasibility and suitability of the readability level for 4th grade elementary school students, because the readability levels of these ten nonfiction texts were in areas 2, 3, 4 and 6 which means the non-fiction text of Kediri Raya's local wisdom in interactive multimedia based on Android is suitable and appropriate for grades 2, 3, 4 and 6 of elementary school.
Pembelajaran Matematika Berbasis Aplikasi Google Classroom pada Materi Bangun Ruang Sisi Datar

Online learning using the Google Classroom application has no effect on student grades in learning mathematics. This study aims to determine the analysis of courage-based mathematics learning using the Google Classroom application on the material for Constructing Flat Sided Space at SMPN 1 Bungbulang. The type of research used in this research is qualitative research. The research method used is the descriptive analysis method. The subjects in this study were teachers and students of class VIII at SMPN 1 Bungbulang for the academic year 2020/2021 located in Tegallega Village, Bungbulang District, Garut Regency who found one teacher and 5 students. Data collection techniques used are interviews, open questionnaires, and documentation. Data analysis techniques in this study, namely data reduction, data presentation, and drawing conclusions. The results of the study showed that learning that dared to use the Google Classroom application for SMP class VIII students were effectively implemented, as indicated by the daily scores of students reaching the KKM set by the school with an average student score of 67.7 in the fairly good category.
Encoding Probabilistic Graphical Models into Stochastic Boolean Satisfiability

Statistical inference is a powerful technique in various applications. Although many statistical inference tools are available, answering inference queries involving complex quantification structures remains challenging. Recently, solvers for Stochastic Boolean Satisfiability (SSAT), a powerful formalism allowing concise encodings of PSPACE decision problems under uncertainty, are under active development and applied in more and more applications. In this work, we exploit SSAT solvers for the inference of Probabilistic Graphical Models (PGMs), an essential representation for probabilistic reasoning. Specifically, we develop encoding methods to systematically convert PGM inference problems into SSAT formulas for effective solving. Experimental results demonstrate that, by using our encoding, SSAT-based solving can complement existing PGM tools, especially in answering complex queries.
Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering

In this paper, we frame homogeneous-feature multi-task learning (MTL) as a hierarchical representation learning problem, with one task-agnostic and multiple task-specific latent representations. Drawing inspiration from the information bottleneck principle and assuming an additive independent noise model between the task-agnostic and task-specific latent representations, we limit the information contained in each task-specific representation. It is shown that our resulting representations yield competitive performance for several MTL benchmarks. Furthermore, for certain setups, we show that the trained parameters of the additive noise model are closely related to the similarity of different tasks. This indicates that our approach yields a task-agnostic representation that is disentangled in the sense that its individual dimensions may be interpretable from a task-specific perspective.
A Simplistic and Cost Effective Design for Real-World Development of Ambient Assisted Living Systems for Fall Detection and Indoor Localization: Proof of Concept

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (A.D.L.s), which are crucial for one's sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with A.D.L.s to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during A.D.L.s while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as &amp;ndash; lack of real-world testing, lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware or software for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a simplistic design paradigm for an Ambient Assisted Living system with functionalities to perform indoor localization and fall detection during A.D.L.s. The results from real-world experiments uphold the effectiveness of the system to capture multimodal components of the user interaction data during A.D.L.s that are necessary for performing fall detection and indoor localization. To add to the above, a comparison study is also presented, which shows that the cost for the development of this system is the least as compared to prior works in the fields of fall detection, indoor localization, and assisted living, which involved real-world development, thereby upholding the cost-effective nature of the proposed system.
(Quantum) Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher

Symmetric cryptosystems (i.e., stream ciphers and block ciphers) have always played an important part in securing the various generations of 3GPP (3rd Generation Partnership Project) mobile telephony systems. The SNOW-V stream cipher, published in September 2019, is the most recent member of the well-known SNOW family of ciphers. It is designed to provide confidentiality and integrity for 5G communications. There have been no time-memory-data tradeoff (TMDTO) attacks on the cipher published so far. By combining with the BSW sampling technique, we propose TMDTO attacks on SNOW-V. The results show that the attacker can mount a TMDTO attack, where none of the online time complexity, the memory complexity and the offline time complexity are bigger than 2256, if the keystream sequences generated by the secret key, together with different IVs, are provided to the attacker. Furthermore, we analyze the security of SNOW-V against quantum TMDTO attacks, and the results show that a quantum TMDTO attack offers, strictly, better online time complexity than Grover’s algorithm, when the available memory space is bigger than 2170.67. These results are helpful in evaluating the security of SNOW-V against (quantum) TMDTO attacks.
Best practices in renewable energy resourcing and integration

Renewable energy resourcing and forecasting are enabling technologies for low-cost integration of increasingly higher market penetration of low-carbon power generation into the grid. The “Best Practices in Renewable Energy Resourcing and Integration” Special Collection Issue in the Journal of Renewable and Sustainable Energy covers best practices in solar and wind forecasting for renewable energy integration and includes datasets for testing, development, and for the augmented reproducibility of methods and results. This Special Collection focuses on manuscripts containing methodologies that substantially advance the state-of-the-art in renewable resourcing and forecasting.
Segmentation-based Background-inference and Small-person Pose Estimation

&lt;p&gt;Our manuscript aims to tackle the &lt;b&gt;two tricky problems&lt;/b&gt; in pose estimation, confusion caused by the complex background interferences and small persons with low-resolution. Those two problems, even the state-of-the-art methods, such as MultiPoseNet, Detectron2, can’t solve them well. But we achieve superior performances than them.&lt;/p&gt; &lt;p&gt;Our method surpasses the state-of-the-art methods because our &lt;b&gt;novel&lt;/b&gt; auxiliary network, which has never appeared in all the previous networks.&lt;/p&gt;