Reviewerly/exHarmony huggingface dataset:
Privacy, personal data protection, and freedom of expression under quarantine? The Peruvian experience

In this article, the authors make a quantitative and qualitative study of all the Executive regulations issued during the State of Emergency in Peru, that may have impacted the fundamental rights of privacy, personal data protection, and freedom of expression. Peru represents an emblematic case study. It adopted one of the earliest, lengthiest and most severe lockdowns in the world, together with numerous regulations that restricted their citizens' civil liberties. Unlike other jurisdictions, the implemented measures in Peru did not focus solely on more controversial and technological activities such as mass surveillance, face recognition, and contact tracing mobile applications. Rather, massive collection and use of personal data, geolocation, and mandatory registration as a pre-requisite for mobilization during lockdown are among the most important hazards identified by this research. The research aims to illustrate some of the perils that similar countries may be facing during and after the fight against COVID-19.
Echoic Communications and Pragmatic Relevance of COVID 19 Memes in Nigeria’s Social Media Space

There are greater possibilities for a language utterance to have more meanings than what is stated. It is also possible for the hearers/audience to interpret what the speaker has in mind beyond what the speaker has put out in words. These meanings derived in this form are echoic and they are arrived at by choosing meanings that contextually relevant involving the least effort. How echoic relevant definitions are achieved has been studied using different language instances. Still, none in the researchers' knowledge has investigated how the COVID 19 social media memes, as used by Nigerians, communicate deeper meanings that are contextually relevant and how the echoic interpretations reveal the users' attitude. On this basis, therefore, the present study investigated the echoic nature of COVID 19 related social media memes as used by Nigerians. The study used Sperber and Wilson (1986) relevance theory as the theoretical framework and qualitatively analyzed Ten (10) purposefully selected memes. In the end, the study found out that interpretations of memes are significantly linked with the shared cognitive background knowledge which the speaker/meme creator and the hearer/audience have. Also, memes could echo the attitude of concern, disdain and fear while also harboring deeper meanings that contextually relevant and implicit.
Pengukuran dan Pelatihan Kesiapsiagaan Komunitas Sekolah Dasar Muhammadiyah Banyuraden terhadap Bencana Gempa Bumi

The earthquake that occurred during school hours requires the school community to have a high level of preparedness so as not to fall victim and loss a lot. This activity aims to measure the level of preparedness against the threat of earthquake disasters and provide earthquake disaster preparedness training to the Muhammadiyah Banyuraden Elementary School community. The method used was a survey using a questionnaire, analysis of survey results based on the UNSESCO framework, namely knowledge of earthquake disasters, policies and guidelines, emergency response plans, and resource mobilization. The analysis showed that the community preparedness level of SD Muhammadiyah Banyuraden was at a moderate level with a total preparedness index of 55.8. The lowest preparedness index is in the policy and guideline parameters. The results of the survey and analysis carried out resulted in preparedness strengthening activities carried out to increase the preparedness index, namely the formation of disaster preparedness groups, compiling evacuation route maps, adapting earthquake simulations, training on first aid and evacuation, and simulating earthquake evacuation. The activities to strengthen preparedness have an impact on the preparedness index
The Complex Model of the Semantic Space of Music: Structure and Features

The article examines the complex model of the semantic space of music which developed in music scholarship in the second half of the 20th century as the result of a generalization of theoretic developments connected with the study of the fundamental categories of musical space and time. On the basis of a number of systemological research works relying on the application of an apparatus of present-day mathematics, the authors of the article examine substantial elements which characterize the structural foundation of the model, the unity of its constituent parts and the open character of the entire model as a system. These include the functional dependence between the model’s components, the presence of elements of indeterminacy in its construction and the concrete figurative content, as well as the reflection of the temporal parameters of music on the basis of the processes of simultaneous registry occurring in musical perception. The authors examine in detail such elements of the structure of the complex model of the semantic space of music as the association of dimensions of time, the functional dependence between the associations of dimensions, the variability of the structure of the dimensions and their associations, as well as computer music technologies as an instrument of research of the semantical space of music. The most important tendencies of applying the given apparatus with the participation of computer musical technologies are marked to be the following: modeling of the regularities of the musical creative process, systematization of the musical traditions of the peoples of Russia and the world, research of the synesthetic laws in music, a comprehensive study of the process of music history and its components, as well as the problem range of the gateway of the concrete results of music into other fields of knowledge and into their practical application. Keywords: music and mathematics, computer musical technologies, musical education, semantic space of music, synesthesia, synthesis of the arts, music theory.
OB-MECC: An Efficient Confidentiality and Security Enhancement for Cloud Storage System

Cloud computing (CC) is one amongst the developing technologies, which get more attention from academia as well as industries. It offers diverse benefits like sharing computing resources, service flexibility, reducing costs, etc. The Cloud Services Provider (CSP) is accountable for the data that are delivered to the cloud. The threat of seeing the stored data and using sensitive raw data by strangers is the main barrier in the utilization of cloud services. So, Data Security (DS) along with privacy is the chief issue, which is an obstacle while adopting the CC. Countless techniques are existent for ensuring data confidentiality, but they do not completely give protection to the data. To trounce these drawbacks, this paper introduces the Obfuscation (OB) based Modified Elliptical Curve Cryptography (MECC) algorithm for protecting data as of malicious attacks, which is termed as OB-MECC. Primarily, the proposed method obfuscates the data before they are uploaded to the cloud. For the OB of the data, the proposed work employs methods like substitution cipher (SC), position update, Ceaser cipher, binary conversion, 8-bit binary conversion, decimal(), two complex(), and ASCII(). Then, encryption of the obfuscated data is done with the utilization of the MECC algorithm. After encryption, the data on the cloud is retrieved. The retrieved data is then decrypted by reversing the OB and encryption process to get the actual data. The outcomes corroborate that the confidentiality and security level are maximum for the proposed OB-MECC when contrasted to the existing approaches.
Adaptive Weighted Multi-Level Fusion of Multi-Scale Features: A New Approach to Pedestrian Detection

Great achievements have been made in pedestrian detection through deep learning. For detectors based on deep learning, making better use of features has become the key to their detection effect. While current pedestrian detectors have made efforts in feature utilization to improve their detection performance, the feature utilization is still inadequate. To solve the problem of inadequate feature utilization, we proposed the Multi-Level Feature Fusion Module (MFFM) and its Multi-Scale Feature Fusion Unit (MFFU) sub-module, which connect feature maps of the same scale and different scales by using horizontal and vertical connections and shortcut structures. All of these connections are accompanied by weights that can be learned; thus, they can be used as adaptive multi-level and multi-scale feature fusion modules to fuse the best features. Then, we built a complete pedestrian detector, the Adaptive Feature Fusion Detector (AFFDet), which is an anchor-free one-stage pedestrian detector that can make full use of features for detection. As a result, compared with other methods, our method has better performance on the challenging Caltech Pedestrian Detection Benchmark (Caltech) and has quite competitive speed. It is the current state-of-the-art one-stage pedestrian detection method.
Comment on “A novel chaos based optical image encryption using fractional Fourier transform and DNA sequence operation”

We numerically demonstrate that the optical image scheme using fractional Fourier transform and DNA sequence operation proposed recently by Farah et al. [Opt & Lasers Tech, 2020, 121: 105777] has the problem of using DNA xor operation and optical implementation. The original image cannot be decrypted completely accordingly to the inverse encryption process.
Virtual Reality Tourism

The impact of Covid-19 in Indonesia has led to information technology innovations such as virtual reality tourism.By making a digital application, it can provide education and benefits caused by boredom when having to stay at home and tourist objects that are still closed.In this study using data collection techniques and SWOT analysis.While the results of this study are to see the strengths and opportunities if the application of Virtual Reality Tourism as a means of supporting tourism education while at home.
Detecting ongoing events using contextual word and sentence embeddings

This paper introduces the Ongoing Event Detection (OED) task, which is a specific Event Detection task where the goal is to detect ongoing event mentions only, as opposed to historical, future, hypothetical, or other forms or events that are neither fresh nor current. Any application that needs to extract structured information about ongoing events from unstructured texts can take advantage of an OED system. The main contribution of this paper are the following: (1) it introduces the OED task along with a dataset manually labeled for the task; (2) it presents the design and implementation of an RNN model for the task that uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for detecting ongoing events in news; (3) it presents an extensive empirical evaluation that includes (i) the exploration of different architectures and hyperparameters, (ii) an ablation test to study the impact of each attribute, and (iii) a comparison with a replication of a state-of-the-art model. The results offer several insights into the importance of contextual embeddings and indicate that the proposed approach is effective in the OED task, outperforming the baseline models.
Part 3: Technical Workflows, Tools, and Platforms for Experimental Publishing, Interaction, and Reuse of Books

For this third part of the scoping report, we will be looking at the technical developments around experimental book publishing.We will be doing so in a three-fold manner in these subsequent sections.First, instead of conducting a landscape study ourselves, we will be reviewing a number of studies and resources that have recently been released and that have tried to categorise, analyse, and map the open source publishing tools and platforms currently available to support open access (book) publishing.Our focus in this analysis will predominantly be on those tools and technologies that can support the kinds of experimental publications that we have identified in the first two parts of this scoping report.With the current version of the report updated in 2022, we have substantially expanded this third part and included segments that had previously been covered in our Promoting and Nurturing Interactions with Open Access Books: Strategies for Publishers and Authors report (Adema et al., 2021), while also adding new examples and information that further research has revealed over the past year.Secondly, in section 2, we will outline a proposed methodology to analyse and categorise the currently available tools and technologies to support the creation of an online resource or Compendium for publishers and authors in year 3 of the COPIM project.This Compendium will include the technological support and workflows available to enable more experimental forms of book publishing, whilst showcasing examples and best practices for different levels of technical know-how.Thirdly, in section 3, we will make an initial attempt at categorising a selection of tools following this proposed methodology, where we will be focusing on collaborative writing tools and on annotation tools-and the software, platforms, and workflows that support these-in first instance.The choice for these tools is driven by the pilot projects we are supporting as part of the COPIM Experimental Publishing and Reuse Work Package, which focus on several experimental practices, including collaborative writing, annotation, remix, versioning, open peer review, and computational publishing.
Verb-second in spoken and written Estonian

This paper investigates clausal constituent order in Estonian, a language often described in the literature as exhibiting a verb-second “tendency”. We present a corpusbased study of ordering in independent affirmative declarative clauses, drawing data from both written and spoken corpora. Our results show that, while written Estonian is robustly a verb-second language along the same lines as the modern Germanic standard languages, spoken Estonian exhibits much more variation. Our findings lead us to suggest that spoken Estonian patterns with the recently-established class of “verb-third” languages, and that syntactic analyses developed to account for these languages can also account for our spoken Estonian data.
A New Measurement Method for High Voltages Applied to an Ion Trap Generated by an RF Resonator

A new method is proposed to measure unknown amplitudes of radio frequency (RF) voltages applied to ion traps, using a pre-calibrated voltage divider with RF shielding. In contrast to previous approaches that estimate the applied voltage by comparing the measured secular frequencies with a numerical simulation, we propose using a pre-calibrated voltage divider to determine the absolute amplitude of large RF voltages amplified by a helical resonator. The proposed method does not require measurement of secular frequencies and completely removes uncertainty caused by limitations of numerical simulations. To experimentally demonstrate our method, we first obtained a functional relation between measured secular frequencies and large amplitudes of RF voltages using the calibrated voltage divider. A comparison of measured relations and simulation results without any fitting parameters confirmed the validity of the proposed method. Our method can be applied to most ion trap experiments. In particular, it will be an essential tool for surface ion traps which are extremely vulnerable to unknown large RF voltages and for improving the accuracy of numerical simulations for ion trap experiments.
Applying the Blog in an Information Literacy Program in School Library Setting

For a long time library services of the elementary schools developed slowly due to the deficiency of funds and human resources. The phenomenon is also easily ignored or neglected by the public. The emergence of the blog brings new opportunities for the elementary school libraries with its attributes of low cost, low technique and easy interactions. The purpose of this study is to probe into the blog applied in an information literacy program in elementary school libraries from different perspectives: students’ uses of the blogs ,students’ learning behavior during the program, and opinions of students and the librarian-teacher toward the program.
KNN METHOD FOR INTELLIGENT OBSERVATIONAL CLASSIFICATION OF ROCK MASS QUALITY IN TUNNEL

During the construction of tunnel, the observational classification of rock mass quality is the most direct method to evaluate the quality of the tunnel face surrounding rock. It is also an important basis for preventing geological disasters in tunnel construction, deciding construction excavation method and supporting measures. The traditional Q method and BQ method of national standard rock mass quality grading evaluation method require on-site and indoor laboratory tests and analysis, which results in the lag time of rock mass quality evaluation, often reduces the construction efficiency, or misses the window time to prevent sudden construction geological disasters. Rapidly and accurately to rock mass quality classification of tunnel face is the primary problem to be solved in observational rock mass classification of highway tunnel during construction period. The artificial intelligence algorithm provides a method to solve the real-time and accurate evaluation of the rock mass of the tunnel. Taking Yanqing-Chongli highway for Beijing Winter Olympic Games as an example, we adopt a method of using photogrammetry and artificial intelligence rock structure parameter to identify working face. In this method, seven index parameters system are established. We use the KNN intelligent algorithm to evaluate the quality of the tunnel rock and select 150 samples from 40 working faces in 8 tunnels for the training and learning. Another 50 samples are selected for rock mass quality evaluation and verification. Compared with the rock mass quality evaluation results of BQ, the accuracy reachs 90%. Conclusions are as follows. (1)KNN method for observational classification of rock mass quality of highway tunnel is a fast and efficient method for observational classification of rock mass quality by using artificial intelligence technology, which can obtain real-time rock mass evaluation results on site. (2)KNN classification method selects seven discriminant indicators, considering the occurrence of rock environment, geological structure, rock structure and other characteristics, and reflecting the operability and applicability of these indicators in the actual engineering. (3)KNN classification method misjudgment rate is very low. It has strong discriminant ability when marking discriminant classification by excluding the influence of artificial factors. This method provides a new way to solve the rock mass grade determination and classification.
Sparsifying Transformer Models with Trainable Representation Pooling

We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on the task-specific parts of an input.A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-k operator.Our experiments on a challenging long document summarization task show that even our simple baseline performs comparably to the current SOTA, and with trainable pooling we can retain its top quality, while being 1.8× faster during training, 4.5× faster during inference and up to 13× more computationally efficient in the decoder. 1
Maximum violation of monogamy of entanglement for indistinguishable particles by measures that are monogamous for distinguishable particles

Two important results of quantum physics are the no-cloning theorem and the monogamy of entanglement. The former forbids the creation of an independent and identical copy of an arbitrary unknown quantum state and the latter restricts the shareability of quantum entanglement among multiple quantum systems. For distinguishable particles, one of these results implies the other. In this Letter, we show that in qubit systems with indistinguishable particles (where each particle cannot be addressed individually) a maximum violation of the monogamy of entanglement is possible by the measures that are monogamous for distinguishable particles. To derive this result, we formulate the degree of freedom trace-out rule for indistinguishable particles corresponding to a spatial location where each degree of freedom might be entangled with the other degrees of freedom. Our result removes the restriction on the shareability of quantum entanglement for indistinguishable particles, without contradicting the no-cloning theorem.
Injective Domain Knowledge in Neural Networks for Transprecision Computing

Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets. Nevertheless, there are learning problems that cannot be easily solved relying on pure data, e.g. scarce data or very complex functions to be approximated. Fortunately, in many contexts domain knowledge is explicitly available and can be used to train better ML models. This paper studies the improvements that can be obtained by integrating prior knowledge when dealing with a context-specific, non-trivial learning task, namely precision tuning of transprecision computing applications. The domain information is injected in the ML models in different ways: I) additional features, II) ad-hoc graph-based network topology, III) regularization schemes. The results clearly show that ML models exploiting problem-specific information outperform the data-driven ones, with an average improvement around 38%.
A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS

The current miniaturization trend in the market of inertial microsystems is leading to movable device parts with sizes comparable to the characteristic length-scale of the polycrystalline silicon film morphology. The relevant output of micro electro-mechanical systems (MEMS) is thus more and more affected by a scattering, induced by features resulting from the micro-fabrication process. We recently proposed an on-chip testing device, specifically designed to enhance the aforementioned scattering in compliance with fabrication constraints. We proved that the experimentally measured scattering cannot be described by allowing only for the morphology-affected mechanical properties of the silicon films, and etch defects must be properly accounted for too. In this work, we discuss a fully stochastic framework allowing for the local fluctuations of the stiffness and of the etch-affected geometry of the silicon film. The provided semi-analytical solution is shown to catch efficiently the measured scattering in the C-V plots collected through the test structure. This approach opens up the possibility to learn on-line specific features of the devices, and to reduce the time required for their calibration.