Reviewerly/exHarmony huggingface dataset:
Integrating reflection into a mobile-assisted reading program for learning English as a second language in China

The application of mobile technology in language education is gaining increasing momentum for its potential benefits, and scholars cast attention to issues such as learner motivation, learning effects and learner behaviors in the mobile learning process. Reflection is an essential part in learning as it can record learner behaviors, cultivate self-awareness of knowledge construction, facilitate cognitive growth, and promote academic achievement. Despite of the wide approval of reflection, not much study has been done concerning the application of reflection in mobile language learning process. Therefore, this study aims to investigate students’ perception of a mobile-assisted reading program facilitated with reflective activities as well as their preferences for reflection modes adopted. The participants were 60 students from two classes in a Chinese college. Students read passages on mobile applications and completed a reflection in one mode every two weeks. Four modes (paper journal reflection, e-journal reflection, audio reflection, and collaborative reflection) were adopted in the study. The study lasted approximately nine weeks. At the end of the program, all students were required to complete an anonymous questionnaire concerning their learning perception. In addition, ten students were selected randomly to attend a semi-structured interview. A pretest and a posttest were conducted to observe students’ language gains. A combination of quantitative and qualitative analysis was conducted with the data obtained. Results showed students generally approved of the effect of this mobile-assisted reading and their reading proficiency improved significantly after the program. In addition, most students favored reflective practices as a good way to stimulate interest, deepen understanding and promote reflective and summarizing abilities, but they didn’t consider it a good method to monitor the learning process in the mobile-assisted reading program. As for the preference for reflection mode, most students favored traditional paper reflection and audio reflection, while collaborative reflection and e-journal reflection received the least support. The findings provided implications for educators and app designers. For educators, based on the understanding of students’ age, learning experience, and possible preferences, they may create a good reflective environment with technical and instructional support, and then provide two or three popular modes for students to reflect on whatever they read. For app developers, some preferable reflection modes facilitated with stimulative measures may be offered to cater to more learners to conduct reflective activities.
Learning Mediated by Social Network for Education in K-12: Levels of Interaction, Strategies, and Difficulties

This study aims to capture evidence on the effectiveness of emergency remote learning mediated by educational technology according to the interaction levels of K-12 students. The study involved students from a public institution that adopted emergency remote learning during the COVID-19 pandemic. From a mixed approach that used quantitative and qualitative methods, data from 963 students were collected and analyzed on the domain and use of the virtual learning environment Redu. Data were collected through student interactions and classified according to eleven classes that were used to identify the level and characteristics of those interactions between students and educators. We also performed interviews with 13 students based on their interaction patterns (highly interact, sporadically interact, rarely interact) to characterize the students’ interaction strategies within the virtual learning environment and strategies without the use of the virtual learning environment. It was found that students seek other means to interact and to send requests for help, regularly interact about school content, participate in discussions, and contact peers and teachers. The results can serve as a basis for proposing new functionalities for virtual learning environments.
Implementasi Gerakan Literasi Sekolah (GLS) Sebagai Upaya Meningkatkan Keterampilan Membaca Dan Menulis Di SMA Negeri 3 Batusangkar

This study aims to describe the form of implementation of the school literacy movement, the supporting and inhibiting factors and to determine the role of the library in the implementation of the GLS as an effort to improve reading and writing skills at SMA Negeri 3 Batusangkar. The research method in this research is descriptive qualitative. Data collection techniques in this study by observation, interviews, and documentation. Data analysis techniques are data reduction, data display and conclusion drawing. The validity of the data in this study used triangulation techniques, sources and lengthened observations. Results: The form of GLS implementation at SMA Negeri 3 Batusangkar is in the form of programs related to reading, writing and speaking literacy for students. Supporting factors: active role of school community, program, students, support from parents and school committee, conducive school environment, facilities. inhibiting factors: the consistency of the organizers, the condition of books that are starting to deteriorate due to age, and the limited number of collections of fiction books to support the School Literacy Movement in the literacy corner, not all students have the same interest and love for literacy, time allocation, and socialization of the GLS program. The role of the library in the implementation of the GLS is that the library acts as a source of information, the library plays a role in collecting student work and preserving it, the library acts as a non-formal educational institution.
A Convolutional Attention Based Deep Learning Solution for 5G UAV Network Attack Recognition over Fading Channels and Interference

When users exchange data with Unmanned Aerial Vehicles - (UAVs) over Air-to-Ground - (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e., data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a Deep Learning (DL) approach for detecting attacks on UAVs equipped with Orthogonal Frequency Division Multiplexing - (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The developed algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks shows that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. The developed algorithm also detects moving attackers from a distance of 500 m.
A recognition method of learning behaviour in English online classroom based on feature data mining

This paper proposes a recognition method of learning behaviour in English online classroom based on feature data mining. Firstly, with the support of fractal theory, the adjacent search method is used to extract the edge of learning behaviour image, and then the data clustering method is used to reduce the dynamic change range of data caused by edge extraction and improve the degree of data standardisation. Finally, the optimal characteristics of learning behaviour are obtained by Drosophila optimisation algorithm, then the learning behaviour recognition of English online classroom is realised by mining characteristic data. Simulation results show that this method has the highest accuracy of 98% and the comprehensiveness of recognition of different types of learning behaviour can reach 0.95. This method retains the details of behaviour image as much as possible to make it more practical.
Archaeological Practice and Digital Automation

This chapter examines the extent to which archaeological tasks can be devolved to software or software-driven machines. It identifies three variants of automation: augmentation, automatization, and heteromation, and argues that each is visible within current and developing archaeological practice. Although far removed from a fully automated archaeologist, efforts in fields such as artificial intelligence and machine learning are increasingly complementing and supporting archaeological practice, and their developing use within ceramic classification and identification systems is discussed as an example. The implications of these kinds of systems are identified in terms of transparency, explainability, authority, and the need for ethics development. Ultimately, a digital participatory turn for archaeology is proposed, which seeks to ensure that the human practitioner retains critical influential and strategic oversight, rather than delegating responsibility to automated devices.
Becoming of the Methodology of Complex-semiotic Thinking in Dialogue with Quantum Mechanics

It is stated that the process of human evolution on planet Earth has entered a permanent crisis mode. The Earth’s civilization is facing a growing set of challenges, threats of uncertainty, even in the very near future. These chal­lenges are associated with the onset of the epoch of the Anthropocene, when human intervention in the bio-geo-sphere leads to its irreversible transforma­tions, which with a high degree of probability may not be compatible with his own existence. On the other hand, on the horizon of the crisis evolution of mankind, a qualitatively new but still ghostly virtual entity appears, called artificial intelligence, which carries a potential threat of its enslavement, turn­ing it into a new pet at best. In this context, a request is rapidly being formed for a new way (or mode) of integratively oriented polyocular thinking, a new conceptual optics that allows us to comprehend the current situation here and now, in order to build a variety of possible scenarios for future human co­evolution, technology and the environment endowed with semiotic agency. Complexity thinking is evolutionary thinking together with complexity, think­ing quantum-relativistic, mediating, procedural-temporal, thinking immersed in a non-equilibrium flow of becoming distinctions, along with the retention of the distinguishable and indistinguishable, as a kind of figure and background, text and context, self-organizing autopoietic system and structurally related en­vironment. Thus, this mode of thinking becomes a recursive semiotically loaded contingent process in which the dialectic of necessity and randomness generates its qualitatively new states. Summing up, we can say that complexity acts as a kind of potentially active background, not fully manifested process, an infinite-dimensional quantum mechanical function, contact-meeting with which brings to life the appearance of a figure, a sign and its observer, referred to by us as a cross-border observer of network complexity.
Model compression optimized neural network controller for nonlinear systems

Neural network-based controllers are widely used within the domain of robotic control systems. A network controller with more neurons typically achieves better performance, but an excessive number of neurons may make the model computationally intensive, resulting in slow dynamic responses in real-world environments. This paper reports a network compression method that integrates knowledge distillation technology for the development of concise neural network-based controllers to achieve a balance between the control performance and computational costs. The method first trains a full-size teacher model, which is then pruned, leading to a concise network with a minimum compromise of performance. From in this study, the resulting concise network is considered to be the prototype of a student model, which is further trained by a knowledge distillation process. The proposed compression method was applied to three classical networks, and the resultant compact controllers were tested on a robot manipulator for efficacy and potential demonstration. The experimental results from a comparative study confirm that the student models with fewer neurons resulting from the proposed model compression approach can achieve similar performance to that of the teacher models for intelligent dynamic control but with faster convergence speed.
Model-based clustering for multidimensional social networks

Abstract Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterised by multiple social relations, captured by a multidimensional network. A common situation is that of colleagues working in the same institution, whose social interactions can be defined on professional and personal levels. In addition, individuals in a network tend to interact more frequently with similar others, naturally creating communities. Latent space models for network data are useful to recover clustering of the actors, as they allow to represent similarities between them by their positions and relative distances in an interpretable low-dimensional social space. We propose the infinite mixture latent position cluster model for multidimensional network data, which enables model-based clustering of actors interacting across multiple social dimensions. The model is based on a Bayesian non-parametric framework that allows to perform automatic inference on the clustering allocations, the number of clusters, and the latent social space. The method is tested on extensive simulated data experiments. It is also employed to investigate the presence of communities in two multidimensional workplace social networks recording relations of different types among colleagues.
Analysis of temporal structure of laser chaos by Allan variance

Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multiscale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers- not just for conventional phase stability analysis. In the present study, we demonstrated Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics. While the detection of LFFs is difficult with the conventional power spectrum analysis method in the low-frequency regime, the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, such as LFFs. This study demonstrates that Allan variance can help in understanding and characterizing diverse laser dynamics, including LFFs, spanning a wide range of timescales.
Metaverse as a Platform for Event Management: The Sample of the Metaverse Türkiye E-Magazine

In this study, the events in Metaverse are discussed. For this purpose, Metaverse-themed digital media content was analyzed. The research universe is Metaverse-themed magazines, and the sample is Metaverse Türkiye E-Magazine. The study is critical because it is the first study on the activities in the Metaverse and contributes to the literature. The magazine was analyzed by applying the content and discourse analysis method in the MAXQDA program. The findings obtained are as follows: Content and discourse on Metaverse phenomena and concepts, meetings/promotions/conferences, event dimensions, and festivals are produced in the magazine. The most frequent discourse in the magazine is related to macro Metaverse concepts and phenomena. The least frequent is about meetings/ promotions/ conferences.Quality and signature events constitute the digital event dimension in the Metaverse universe. When the discourses of the magazine are analyzed on a micro-scale, it is striking that proper names containing Metaverse concepts are used as subjects. As a result, considering the growing number and size of events held in Metaverse, it is crucial to research planning and implementing event management in Metaverse.
Matching entropy based disparity estimation from light field data

A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions. An effective matching window satisfying three characteristics: texture richness, disparity consistency and anti-occlusion should be able to prevent mismatches to some extent. According to these characteristics, we propose matching entropy in the spatial domain of light field to measure the amount of correct information in a matching window, which provides the criterion for matching window selection. Based on matching entropy regularization, we establish an optimization model for depth estimation with a matching cost fidelity term. To find the optimum, we propose a two-step adaptive matching algorithm. First, the region type is adaptively determined to identify occluding, occluded, smooth and textured regions. Then, the matching entropy criterion is used to adaptively select the size and shape of matching windows, as well as the visible viewpoints. The two-step process can reduce mismatches and redundant calculations by selecting effective matching windows. The experimental results on synthetic and real data show that the proposed method can effectively improve the accuracy of depth estimation in occlusion and smooth regions and has strong robustness for different noise levels. Therefore, high-precision depth estimation from 4D light field data is achieved.
Nonmalleable Digital Lockers and Robust Fuzzy Extractors in the Plain Model

We give the first constructions in the plain model of 1) nonmalleable digital lockers (Canetti and Varia, TCC 2009) and 2) robust fuzzy extractors (Boyen et al., Eurocrypt 2005) that secure sources with entropy below 1/2 of their length. Constructions were previously only known for both primitives assuming random oracles or a common reference string (CRS). Along the way, we define a new primitive called a nonmalleable point function obfuscation with associated data. The associated data is public but protected from all tampering. We use the same paradigm to then extend this to digital lockers. Our constructions achieve nonmalleability over the output point by placing a CRS into the associated data and using an appropriate non-interactive zero-knowledge proof. Tampering is protected against the input point over low-degree polynomials and over any tampering to the output point and associated data. Our constructions achieve virtual black box security. These constructions are then used to create robust fuzzy extractors that can support low-entropy sources in the plain model. By using the geometric structure of a syndrome secure sketch (Dodis et al., SIAM Journal on Computing 2008), the adversary’s tampering function can always be expressed as a low-degree polynomial; thus, the protection provided by the constructed nonmalleable objects suffices.
VISIT: A Content Management and Exploration System for Mobile Augmented Reality in the Context of Digital Humanities and Cultural Heritage

We present in this article an augmented reality system called VISIT designed to deliver content about artworks to visitors. This system aims to simplify the use of augmented reality by allowing (non-computer) users to define content and place it in 3D around an artwork. VISIT is composed of two main generic elements with respect to historical or artistic content: a Web interface allows to represent in a cloud all the necessary information (sites, areas, artworks, media) to define the augmented reality content that visitors will see. Its use is simple and accessible to non-computer specialists and does not require additional software development or special hardware. A mobile augmented reality application under Android will use this content and deliver it to the visitor on his/her smartphone. The application automatically detects the presence of the artworks and then places the 3D content as specified in the Web interface. The visitors can choose the elements that interest them. The user’s actions are recorded and allow site managers to analyze their behavior. VISIT is operational and we tested it in the context of a large-scale exhibition at the Château d’Azay-Le-Rideau in France. We conclude with the progress achieved as well as the limits and perspectives of our work.
A Critical Discourse Analysis of Hate Speech Comments on Nigerian News Websites

Though hate speech has become a global phenomenon, organisations and individuals find it difficult to come up with a universal definition, or with detection techniques and classifications. This challenge has created a lacuna which has made legislation against hate speech difficult in many countries, especially Nigeria. In this study, the researcher analysed and categorised hate speech comments from two Nigerian news websites with a view to describing, categorising and identifying prevalent hate speech themes. Words with the highest frequency of occurrence in the corpus are identified. Observation and descriptive methods were used for data collection while both qualitative and quantitative approaches were adopted for analyses. The findings of the study showed that ethnic affiliation is the most common trigger of hate speech comments in Nigeria while the second person pronoun “you” used to emphasise others’ negative (EON) is the word with the highest frequency.
Reform of English Writing Teaching Method Under the Background of Big Data and Artificial Intelligence

In the new era of informatization, big data, and artificial intelligence, the education field has also set off a wave of informatization development of English education. Teaching writing has always been the focus and challenge of English teaching, but there are various problems in English writing teaching in China. Problems such as difficulty in writing, vague expression, unclear main points, simple sentence structure, low vocabulary, etc. How to effectively carry out English writing teaching courses is worthy of our serious consideration. Based on this, this article studies the reform of English writing teaching methods under the background of big data and artificial intelligence. Survey data shows that only 10.53% of teachers often combine online teaching resources with English writing teaching, 25% of teachers have hardly used them, and 64.47% of teachers occasionally use online teaching platform resources. This shows that the reform of English writing teaching methods needs to start from the aspect of education informatization.