Reviewerly/exHarmony huggingface dataset:
Assessing Superhuman Speed as a Gamified Reward in a Virtual Reality Bike Exergame

This research presents a Virtual Reality (VR) bike exergame system consisting of a stationed road bike, four novel virtual steering methods, two in-game superhuman speed reward ideas, and a pedalling system simulating real-life resistance. A study is conducted to understand how virtual speed can be used as a reward to encourage enjoyment in physical activities. The result suggests that adding reward-based superhuman speed positively impacts user enjoyment. Adding steering control and increasing speed potentially do not inhibit greater motion sickness symptoms using well-aligned physical-to-virtual input and output representations.
Pop-up Books in The Snowball-Throwing Learning Model Compared to Fifth-Grade Students

The learning outcomes of social studies subjects obtained by students were not as expected. In order to solve this problem it is requires learning model and appropriate media to support social studies in grade V MI students. The purpose of the study was to analyses the efficacy of employing pop-up book medium in the snowball-throwing learning paradigm for fifth-grade MI students' social studies learning outcomes. This study utilizes quantitative research using a proper experimental design and a pre- and post-test control group design. 50 fifth-grade students participated in this study, 20 in the treatment group and 20 in the control group. In this study, the independent variable (X) is the pop-up book media and the snowball-throwing learning model, while the dependent variable (Y) is students of V grade MI learning results. This study's data analysis approach utilized a precondition test, specifically the initial and final normality tests, to determine whether or not the data were normally distributed, while the hypothesis test employed a one-sided T test. The results show that there are differences in student learning outcomes in the experimental class using pop-up book media in the snowball throwing learning model compared with the control class using conventional learning methods.
Gelati Ilmu: The Educational Game in Banten-Dialect Javanese Language for Learning Media of Fourth Grade Students

Banten-Dialect Javanese Language is one of the most difficult subjects, according to the 4th grade elementary school students. This situation is caused by several factors, namely motivation, environment, mother tongue, surrounding culture, as well as the boring and less innovative instructional media. The study aims to develop a quality educational game as an alternative learning media for the Banten-Dialect Javanese Language subject. This type of research is development. The study used the ADDIE model as a development method. This educational game had been validated by 1 material experts and 1 media expert. The experiment subjects were 4th grade students collected 30 students. Data collected methods are observation, interviews, and questionnaires. The technique of analysing the data is descriptive qualitative and quantitative analysis. The results of validity test by material experts were 94.44% (very good) and by media experts, namely 83.92% (very good), so it is said to be valid. Student responses in the small group test obtained a proportion of 77,25% (agree) so it is said to be practical. The results of this study indicated that the educational game, Gelati Ilmu, is feasible to be used as an alternative learning media for the Banten-Dialect Javanese Language subject.
From Multi-Hazard to Multi-Risk at Mount Etna: Approaches and Strategies of the PANACEA Project

Managing multiple hazards simultaneously affecting a territory is a challenge for effective risk mitigation. This is particularly true on active volcanoes like Mt. Etna, characterized by effusive and explosive eruptions, often coupled with intense seismic activity. This work presents the PANACEA project’s approach to treating multi-hazards in terms of risk, which requires a common definition of the exposed elements and their vulnerability. Another aspect emerging from the recent and historical volcanic crises at Etna is the cascading effects and the problem of assessing their short-term interactions. A risk model is presented considering a set of sequences of hazardous events that may result from volcano unrest to possible impacts on some infrastructural elements. The project’s outcomes are intended to be a significant step toward a more comprehensive resilience to volcanic disasters, leading to a safer society.
Infrasonic Long-Range Observations at IS42: Study Cases of Grimsvötn (Iceland), Mt. Etna and Stromboli Volcanoes (Italy)

The collaborative research UNIFI–University of the Azores (IVAR) focused on the detection and characterization of events injecting energy into the atmosphere and allowed to analyze of the Grímsvötn volcano eruptive activity (Iceland) and Mt. Etna and Stromboli volcanoes (Italy) by several Stations (among which IS42) of the CTBTO's International Monitoring System (IMS). Grímsvötn volcano, located under the Vatnajökull glacier, is the most active Icelandic volcano. The May 2011 explosive eruption broke the ice-covering and turned into sub-aerial, ejecting ash into the atmosphere and restricting flights across Northwestern Europe and the North Atlantic region. Mt. Etna, located in Sicily Island (Italy), is Europe's largest and most active volcano. It is typically effusive with explosive episodes, and lava fountaining activity has recently produced high eruptive plumes causing an impact on local air traffic and nearby airports and cities. Finally, Stromboli volcano (Italy), located in one of the Aeolian Islands (Italy) with the same name, is one of the most active volcanoes on Earth, with sustained explosive activity and permanent degassing. A strong explosive event characterized by two distinct explosions occurred on July 3rd, 2019. In this study, we describe a multiple-detections analysis of (a) the May 2011 Grímsvötn eruption as observed at IS42 and other infrasound stations and its correlations with on-site observations; (b) the Mt. Etna eruptive activity of the May–September 2011 and the 2016 eruptions, as observed at IS42 and other stations, compared with near-source observations; and (c) the Stromboli July 3rd, 2019 event detections from IS42 and other IMS infrasound stations. We relate those results with events listed in the CTBTO Reviewed Event Bulletin (REB) to evaluate the potential of the IMS network in detecting and identifying sources of volcanic activity. In the framework of earth-based volcanic monitoring techniques, infrasound is a unique technology with the potential to detect explosive eruptions at great distances. Furthermore, simultaneous infrasound recording in near- and far-field represents a substantial improvement in the characterization of explosive sources.
A Stronger Secure Ciphertext Fingerprint-Based Commitment Scheme for Robuster Verifiable OD-CP-ABE in IMCC

Outsourced decryption attribute-based encryption (OD-ABE) is emerging as a promising cryptographic tool to provide efficient fine-grained access control for data accessing and sharing in cloud-assisted Intelligent Internet of Mobile Things (IIoMT). Decryption verification is an essential property of OD-ABE to enable the mobile user to verify the precision of the decryption data. Unfortunately, the most representative verification (commitment) algorithms have various security flaws. In this article, we first indicate that the two state-of-art key-based commitment schemes are vulnerable to “Commitment Extract(Decrypt)-then-Reuse Attack” and “Commitment Impersonation Attack” which demolish the unforgeability of the commitment. Then to cover all the existing attacks to commitment algorithms, we redefine a robuster verifiable security model for verifiable OD-ABE. Subsequently, we invent a ciphertext fingerprint (CTfp)-based commitment scheme and give rigorous proof to the proposed commitment scheme, including binding, hiding, unforgeability, and nonrepudiation (traceability) in the random oracle. Next, we apply our CTfp-based commitment to the widely used OD-ABE schemes to provide them robuster verifiability. Finally, the theoretical comparison and simulation experiments are presented to show our new type of commitment algorithm is more secure and practical.
Deciding Contextual Equivalence of $$\nu $$-Calculus with Effectful Contexts

Abstract We prove decidability for contextual equivalence of the $$\lambda \mu \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>λ</mml:mi> <mml:mi>μ</mml:mi> <mml:mi>ν</mml:mi> </mml:mrow> </mml:math> -calculus, that is the simply-typed call-by-value $$\lambda \mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>λ</mml:mi> <mml:mi>μ</mml:mi> </mml:mrow> </mml:math> -calculus equipped with booleans and fresh name creation, with contexts taken in $$\lambda \mu _{\texttt{ref}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>λ</mml:mi> <mml:msub> <mml:mi>μ</mml:mi> <mml:mi>ref</mml:mi> </mml:msub> </mml:mrow> </mml:math> , that is $$\lambda \mu \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>λ</mml:mi> <mml:mi>μ</mml:mi> <mml:mi>ν</mml:mi> </mml:mrow> </mml:math> -calculus extended with higher-order references. The proof exploits a labelled transition system capturing the interactions between $$\lambda \mu \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>λ</mml:mi> <mml:mi>μ</mml:mi> <mml:mi>ν</mml:mi> </mml:mrow> </mml:math> programs and $$\lambda \mu _{\texttt{ref}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>λ</mml:mi> <mml:msub> <mml:mi>μ</mml:mi> <mml:mi>ref</mml:mi> </mml:msub> </mml:mrow> </mml:math> contexts. The induced bisimulation equivalence is characterized as equality of certain trees, inspired by the work of Lassen. Since these trees are computable and finite, decidability follows. Bisimulation coincides also with trace equivalence, which in turn coincides with contextual equivalence.
Light Field Reflection and Background Separation Network Based on Adaptive Focus Selection

Separating the background and the reflection layer is an ill-posed but meaningful task. Since the two layers often have different depths, Light Fields (LFs), which contain the depth information, show great potential to deal with this task. In this paper, we propose to fully exploit the difference between background and reflection layers from different kinds of images in LFs. Specifically, we fuse the complementary features from sub-aperture images, in which the overlaps of the two layers vary a lot. Then we propose the adaptive focus selection strategy and use the dynamic filter to select the appropriate focus. The refocused image is then obtained from the focal stack images, where the clearness of the two layers is different. Finally, the clean images are restored using a dual attention reconstruction module. Our model is the first one that successfully addresses the LF reflection separation task with an end-to-end deep neural network. The experiments show that the background and reflection layers are well separated, regardless of the relative intensity difference between the two layers on both synthetic and real-world datasets. The quantitative and qualitative comparison results show that our method achieves better performance than other state-of-the-art methods.
ADfM-Net: An Adversarial Depth-From-Motion Network Based on Cross Attention and Motion Enhanced

The temporal consistent and accurate depth estimation for consecutive images is essential for many downstream applications. However, most existing methods only infer depth from a single image, ignoring the temporal information and important depth cues from motion in the sequence. Additionally, the depths of adjacent frames are estimated separately without any constraint. In this paper, we promote the temporal consistency and accuracy of depth results from the aforementioned two aspects: multi-frame framework and consistency constraint. Firstly, a framework with cross-frame attention and motion enhancement module is proposed for better temporal consistency and depth precision. Secondly, an adversarial metric learning strategy is introduced to further constrain the consistency of adjacent depth results, without any additional computation and memory cost. The experiments on KITTI and Cityscapes datasets demonstrate the effectiveness of our framework. Furthermore, noting that the traditional metrics can not reveal the consistency of the depth results, a new temporal consistency metric is proposed, which would facilitate further research.
The Impact of Image Resolution in the training of Generative Adversarial Networks for Violence Detection

Since time immemorial, violence has been a problem that the world has been facing. The rise of technology has presented an opportunity to help in this matter. Violence detection solutions have been created for this cause. The problem with existing solutions is that they are not appropriate for settings in a developing country. Factors such as the place, objects seen, people involved, among others, are different from those models who are trained with datasets from developed countries, which might prove ineffective for developing countries. That is why the researchers aim to create a Generative Adversarial Networks Model trained with data that are location-specific to the country of Philippines. In this study, the researchers will gauge the effects and impact that resolution brings in the training of the GAN Model, named V.GAN, to help with improving its performance and implementation.
An Intelligent Search System Based on Knowledge Graph

This paper studies the intelligent search method based on knowledge graph, constructing knowledge graph; rationally designing word segmentation; inputting retrieval keywords; keyword processing; outputting search results; using NLP semantic understanding; using ES for search, this article is building a retrieval data model At the same time, the established data model can be divided into modularized processing according to the content of the data, the type of the data and the characteristics of the data, and the divided data model can be composed of multiple modular sets, and in the data model The sub-collections are established in each corresponding collection after the internal division, so that the internal data structure of the data model can be established in the form of a collection frame; at the same time, the collection is used to form a data model, which is convenient for the frame data structure formed inside the data model. Classification can greatly improve the actual search speed of this search method, and it is convenient to deal with huge data information.
Energy Management - Remote Operation and Maintenance Service System

Sustainability is an even bigger topic, and 5G is also involved. In the future, the trend of massive growth in data centers will continue and even accelerate. Therefore, providers of cloud computing and managed data center services have to face more and more inspections and supervision on electricity consumption. The current momentum in the climate change discussion, as well as shifting policy winds in the U.S. and around the world, will only further heighten the focus on the data center industry. After all, the data center industry already accounts for 1% of global energy consumption. Therefore, there will be a wave of innovation in the coming year, aimed at optimizing the energy efficiency performance of the entire data center. The benefits brought to data center operators are self-evident: From cost reduction to compliance (including existing and future regulations), and then to a quality corporate image that establishes leadership in the global sustainability movement. All parties in the industry are actively seeking important innovations in the field of data center infrastructure, especially in the field of energy management. This product is mainly responsible for improving the energy consumption of computers, intelligently adjusting the fan speed, so as not to affect the performance of computing, but also to reduce power consumption. Connect to the server program of this product by scanning the QR code, and use the mobile phone's own WIFI, 2G\3G\4G\5G network to connect to the 8085-port provided by the server program. The data display is refreshed in a multi-threaded manner in order to solve the above-mentioned remote viewing problems. You can also view the computer's historical temperature curve through the software for better monitoring and understanding.
Consumer Transaction Problems under the Supervision Mode of Blockchain Technology Construction

In the CCA report of 2021, we can see that the prepaid consumption mode has been widely used in recent years, and many problems have been exposed, which makes it difficult to protect the rights and interests of consumers. This paper first explains the operation principle of consumer prepayment mode, then systematically analyses the exploration and practice of various regulatory modes in recent years, further puts forward the problems still existing in the construction of regulatory mechanism integrated with block chain technology, and tentatively puts forward a preliminary construction plan, the combination of public chain and private chain in block chain technology. It solves the problem of automatic settlement of intelligent contracts and avoids the leakage of privacy information.
Performance Evaluation of Basic Education Informatization Based on Improved BP Algorithm

In recent years, with the development of educational informatization, the problem of educational informatization has become increasingly prominent, which makes educators have to pay attention to the performance evaluation of educational informatization. The education work focuses more on the integration of various forms of education. In order to solve the limitations of basic education informatization, this paper analyzes the BP algorithm function and basic education informatization performance evaluation index, designs and discusses the network parameter setting and performance evaluation index data collection of applied research, and analyzes basic education information. The BP calculation flow chart of the performance evaluation is designed, and finally the experimental data analysis is carried out for the expected evaluation value, actual evaluation value and training evaluation value of the designed algorithm in the performance evaluation of education. The experimental data shows that the expected evaluation value and the training evaluation value are analyzed. The difference of the actual evaluation value fluctuates in the range of 0.1 to 0.5 on average, thus verifying the feasibility of applying the BP algorithm in the performance evaluation of various schools in and out of elementary education.
Modular Design of English Pronunciation Level Evaluation System Based on Deep Learning Algorithm

The problem that modern foreign language teaching technology brings to teachers is that the calculation amount of lecture rating and identification is relatively huge, and higher computer hardware materials and software systems are needed to see fast preparing of monumental lecture symbols. The algorithm mechanic learning is an effective algorithm widely adopted in many walks at present. BP neural network, support vector machine and other methods in machine education algorithm have lofty preparing function. This article focuses on the present situation of contemporary language education and the factual usage strategies of applying machine deep learning algorithm to the level evaluation system of language pronunciation to improve the performance of the pronunciation level rating, which has a positive reference implication for the progress of foreign language teaching theory under the context of the continuous maturity of AI.
Henry David Thoreau (1817–1862)

Abstract Henry Thoreau is celebrated as a vegetarian, a champion of all nature’s creatures. But he held complex, contradictory views of hunting. He expressed revulsion and guilt at eating flesh but recorded dietary lapses followed by promises to pursue a “higher law.” He disdained religious orthodoxy, yet his life was a religious quest. Thoreau was well versed in the Bible and Asian sacred literature, which he employed to skewer moral and political hypocrisy and to convey numinous encounters with animals. Craving the feeling of his “own limits transgressed,” he found sublimity not in rarefied transcendence but in material animality he shared with “brute neighbors.” Disaffected with collecting, killing, and classifying living things, Thoreau criticized science’s failure to go beyond the “surface of things.” He sought to capture the “vital spirit” of animals. Thoreau’s intuitive sense of “anima” is an electric link between empathy toward individual creatures and wider moral community with all animals.
Risk-Based Authentication for OpenStack: A Fully Functional Implementation and Guiding Example

Online services have difficulties to replace passwords with more secure user authentication mechanisms, such as Two-Factor Authentication (2FA). This is partly due to the fact that users tend to reject such mechanisms in use cases outside of online banking. Relying on password authentication alone, however, is not an option in light of recent attack patterns such as credential stuffing. Risk-Based Authentication (RBA) can serve as an interim solution to increase password-based account security until better methods are in place. Unfortunately, RBA is currently used by only a few major online services, even though it is recommended by various standards and has been shown to be effective in scientific studies. This paper contributes to the hypothesis that the low adoption of RBA in practice can be due to the complexity of implementing it. We provide an RBA implementation for the open source cloud management software OpenStack, which is the first fully functional open source RBA implementation based on the Freeman et al. algorithm, along with initial reference tests that can serve as a guiding example and blueprint for developers.
Construction of systematic (k + 1,k) memory MDS sliding window codes over embedded fields

In this paper, based on the structure of embedded fields, we investigate explicit construction of systematic (k+1,k) mMDS sliding window codes with memory m=2,3. First, over GF(2lh) with l≥1 and h≥2, we propose an algorithm to construct (2lh−1+1,2lh−1) mMDS codes with memory 2, which are optimal in the sense that 2lh−1 is the maximum possible value of k for a (k+1,k) sliding window code with memory 2 over GF(2lh) to be mMDS. When l≥2, every constructed code has the extra property that it contains a (2h−1+1,2h−1) mMDS sliding window code with memory 2 as a subcode over the subfield GF(2h). Next, over GF(22lh) with l≥1 and h≥2, we introduce a method to construct (k+1,k) mMDS codes memory 3, and a few new codes have been obtained consequently. When l≥2, every code constructed by the new approach also has the property that it contains an mMDS subcode over the subfield GF(22h). The embedding subfield-subcode property enhances the flexibility and efficiency of the designed codes.
MDS and AMDS symbol-pair codes constructed from repeated-root cyclic codes

Symbol-pair codes introduced by Cassuto and Blaum in 2010 are designed to protect against the pair errors in symbol-pair read channels. One of the central themes in symbol-error correction is the construction of maximal distance separable (MDS) symbol-pair codes that possess the largest possible pair-error correcting performance. Based on repeated-root cyclic codes, we construct two classes of MDS symbol-pair codes for more general generator polynomials and also give a new class of almost MDS (AMDS) symbol-pair codes with the length lp. In addition, we derive all MDS and AMDS symbol-pair codes with length 3p, when the degree of the generator polynomials is no more than 10. The main results are obtained by determining the solutions of certain equations over finite fields.