Reviewerly/exHarmony huggingface dataset:
A Critical Review of Mobile Learning: Phoenix, Fossil, Zombie or …..?

The established mobile learning paradigm is now two decades old; it grew out of the visions and resources of e-learning research communities in universities in the world’s more economically developed regions. Whilst it has clearly been able to demonstrate many practical, pedagogic and conceptual achievements, it is now running out of steam. It has failed to adapt to a world where mobile technologies are pervasive, ubiquitous and intrusive and where people and communities can now own their own learning. This paper looks at the evolution of the established mobile learning paradigm and explores the current global, demographic, social and technical environment in order to develop a new paradigm more suited to the changed and changing realities and priorities. This is mobile learning2.0. The paper looks at the axioms and values of this paradigm and its possible tools and techniques. The treatment is discursive and critical. The paper reimagines the concepts and practices of learning with mobiles. It embraces many significant themes at a high level, including inclusive and equitable education; learning theories and design; pedagogical frameworks and methodologies; digital and media literacies; social media and learning environments; online collaboration and communities; Informal and formal learning.
Phrase Break Prediction with Bidirectional Encoder Representations in Japanese Text-to-Speech Synthesis

We propose a novel phrase break prediction method that combines implicit features extracted from a pre-trained large language model, a.k.a BERT, and explicit features extracted from BiLSTM with linguistic features. In conventional BiLSTM based methods, word representations and/or sentence representations are used as independent components. The proposed method takes account of both representations to extract the latent semantics, which cannot be captured by previous methods. The objective evaluation results show that the proposed method obtains an absolute improvement of 3.2 points for the F1 score compared with BiLSTM-based conventional methods using linguistic features. Moreover, the perceptual listening test results verify that a TTS system that applied our proposed method achieved a mean opinion score of 4.39 in prosody naturalness, which is highly competitive with the score of 4.37 for synthesized speech with ground-truth phrase breaks.
Appraising discrepancies and similarities in semantic networks using concept-centered subnetworks

Abstract This article proposes an approach to compare semantic networks using concept-centered sub-networks. A concept-centered sub-network is defined as an induced network whose vertex set consists of the given concept (ego) and all its adjacent concepts (alters) and whose link set consists of all the links between the ego and alters (including alter-alter links). By looking at the vertex and link overlap indices of concept-centered networks we infer semantic similarity of the underlying concepts. We cross-evaluate the semantic similarity by close-reading textual contexts from which networks are derived. We illustrate the approach on written and interview texts from an ethnographic study of flood management practice in England.
Language Identification Research Based on Dual Attention Mechanism

Language identification(LID) is an important branch of speech technology. A key problem of language identification is how to extract effective speech segment representation from a given speech and improve the model performance. In recent years, deep learning has made significant progress in the application of language identification. Neural networks can be used to extract relevant features and effectively improve system performance. In order to solve the problem of poor feature extraction ability and low recognition rate, this paper considers both features and models, through the comparison of features such as MFCC, Fbank to determine spectrogram as the best input feature, and proposes a language identification method based on dual attention mechanism. This method first takes the spectrogram of the speech spectrogram, and converts it into a gray-scale spectrogram as input, uses a multi-level convolutional neural network to capture local features, extracts dual attention in channel and spatial dimension of the feature map through the CBAM module, catches temporal characteristics with bidirectional gated recurrent units, then transfers the local characteristics and timing characteristics jointly to a fully connected layer, and uses the fully connected layer to output language classes. This paper conducts experiments on the Common voice dataset and AP17-OLR dataset, it demonstrates that dual attention mechanism’s language identification method can achieve good results, increase the feature extraction ability and improve the performance of language identification.
Rainfall-Runoff Simulation Using HEC-HMS Model in the Benanain Watershed, Timor Island

Floods in a watershed area are caused by reduced water recharge due to changes in land use, increasing their discharge volume. Benanain watershed is an extensive area with many tributaries. Watershed morphometrics provides initial information about the hydrological behavior and the hydrograph shape of flooding in these areas. Furthermore, rainfall-runoff modeling uses as a unit to approach the hydrological values of the flooding process. This study determines the physical characteristics of the Benanain watershed based on curve number (CN) values, land cover, peak discharge, and peak time. It was conducted on the Benanain watershed with 29 sub-watersheds covering 3,181.521 km2. Data were collected on the rainfall experienced for 13 years from 1996 to 2008 and analyzed using the Log Pearson Type III method, while the HEC HMS model was used for flood discharge analysis. HEC-HMS model must calibrate by adjusting the model parameter values until the model results match historical data such as initial abstraction, lag time, recession, baseflow values, and curve number. The results show that the curve number values range from 56.55 - 73.90, comprising secondary dryland forest and shrubs. Moreover, the rock lithology in the Benanain watershed is dominated by scaly clay and other rock blocks. This means the area has low to very low permeability, which affects the volume of runoff. The return period of a 1000-year flood discharge obtained a peak of 5,794.50 m3/s, with a peak time of ± 14 hours. Morphometry of the Temef watershed with large catchment, radial shape pattern, an average of steep slope river, and meandering affects the peak of flood discharge hydrograph and the peak time of the flood.
Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model

Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest. While general quantum simulation algorithms likely require error-corrected qubits, there may be applications of scientific interest prior to the practical implementation of quantum error correction. The variational quantum eigensolver (VQE) is a promising approach to finding energy eigenvalues on noisy quantum computers. Lattice models are of broad interest for use on near-term quantum hardware due to the sparsity of the number of Hamiltonian terms and the possibility of matching the lattice geometry to the hardware geometry. Here, we consider the Kitaev spin model on a hardware-native square-octagon qubit connectivity map, and examine the possibility of efficiently probing its rich phase diagram with VQE approaches. By benchmarking different choices of variational Ansatz states and classical optimizers, we illustrate the advantage of a mixed optimization approach using the Hamiltonian variational Ansatz (HVA) and the potential of probing the system's phase diagram using VQE. We further demonstrate the implementation of HVA circuits on Rigetti's Aspen-9 chip with error mitigation.
Flood Rate Assessment of the Woyla River Watershed, Aceh Province, Indonesia

This study aims to assess Flood susceptibility and flood hazard levels and obtain the distribution of hazard levels and flood hazards in the Woyla watershed, Aceh Province. This research design generally uses a descriptive survey method and divide into several stages, such as data collection, data processing, data presentation, and delivery of research results. Each determinant of flood-prone areas includes a land slope, altitude, soil texture, drainage, land cover, and rainfall, analyzed spatially utilizing a map. Furthermore, based on the map, the regions are described based on the values that have been divided into classes. The results showed that the level of flood vulnerability in the Woyla watershed was divided into five classes, namely; the non-vulnerable class with an area of 14.88 Ha / 0.01%, low prone with an area of 90,731.62 Ha / 35.45%, medium with an area of 57,120.35 Ha / 22.32%, high with an area of 44,918.15 Ha / 17.55%, and very high with an area of 63,151.72 Ha / 24.67%. Also obtained a map of the distribution of flood hazard areas, the Woyla watershed area is divided into five classes, namely; the very light class with an area of 179,146.15 Ha / 70.00%, mild with an area of 32,868.84 Ha / 12.84%, moderate with an area of 20,129.93 Ha / 7.87%, danger with an area of 6.007. 29 Ha / 2, 35%, and very dangerous with an area of 17,784.51 Ha / 6.95%. The level of flood vulnerability in the Woyla watershed is dominant in West Aceh Regency, which is in the very high and high category classes with a total area of 56,876.65 Ha and 23,527.40 Ha. Meanwhile, the level of flood hazard in the Woyla watershed is also more dominant in West Aceh Regency than falls into the most dangerous and dangerous class category with a total area of 17,784.51 Ha and 6,007.29 Ha. With the largest part in the very light class at the flood hazard level of 179,146.15 ha.
A new affinity matrix weighted k-nearest neighbors graph to improve spectral clustering accuracy

Researchers have thought about clustering approaches that incorporate traditional clustering methods and deep learning techniques. These approaches normally boost the performance of clustering. Getting knowledge from large data-sets is quite an interesting task. In this case, we use some dimensionality reduction and clustering techniques. Spectral clustering is gaining popularity recently because of its performance. Lately, numerous techniques have been introduced to boost spectral clustering performance. One of the most significant part of these techniques is to construct a similarity graph. We introduced weighted k-nearest neighbors technique for the construction of similarity graph. Using this new metric for the construction of affinity matrix, we achieved good results as we tested it both on real and artificial data-sets.
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation

Despite constant improvements in machine translation quality, automatic poetry translation remains a challenging problem due to the lack of open-sourced parallel poetic corpora, and to the intrinsic complexities involved in preserving the semantics, style and figurative nature of poetry.We present an empirical investigation for poetry translation along several dimensions: 1) size and style of training data (poetic vs. non-poetic), including a zeroshot setup; 2) bilingual vs. multilingual learning; and 3) language-family-specific models vs. mixed-language-family models.To accomplish this, we contribute a parallel dataset of poetry translations for several language pairs.Our results show that multilingual fine-tuning on poetic text significantly outperforms multilingual fine-tuning on non-poetic text that is 35X larger in size, both in terms of automatic metrics (BLEU, BERTScore, COMET) and human evaluation metrics such as faithfulness (meaning and poetic style).Moreover, multilingual fine-tuning on poetic data outperforms bilingual fine-tuning on poetic data.
Can peers help reduce violations of information security policies? The role of peer monitoring

Peers may help others avoid violating organisational information security policies (ISPs). This study explores how peer monitoring reduces employee ISP violation intention. We propose that peer monitoring discourages employees from violating ISP. Moreover, trust plays an important role. Trust not only facilitates peer monitoring, but also moderates the effect of peer monitoring on employee ISP violation intention. In addition, collective responsibility leads to peer monitoring. We test our research model with data from two waves of surveys of 254 employees in the United States conducted two weeks apart. We utilise four scenarios in the second wave of surveys capturing the dependent variable and measure all other constructs in the first wave of surveys. Our results suggest that peer monitoring decreases one’s intention to violate ISPs. Furthermore, both collective responsibility and trust contribute to peer monitoring. Finally, trust amplifies the effect of peer monitoring on employees’ intention to violate ISPs. We discuss the theoretical contributions and practical implications.
Reproducible families of codes and cryptographic applications

Abstract Structured linear block codes such as cyclic, quasi-cyclic and quasi-dyadic codes have gained an increasing role in recent years both in the context of error control and in that of code-based cryptography. Some well known families of structured linear block codes have been separately and intensively studied, without searching for possible bridges between them. In this article, we start from well known examples of this type and generalize them into a wider class of codes that we call ℱ-reproducible codes. Some families of ℱ-reproducible codes have the property that they can be entirely generated from a small number of signature vectors, and consequently admit matrices that can be described in a very compact way. We denote these codes as compactly reproducible codes and show that they encompass known families of compactly describable codes such as quasi-cyclic and quasi-dyadic codes. We then consider some cryptographic applications of codes of this type and show that their use can be advantageous for hindering some current attacks against cryptosystems relying on structured codes. This suggests that the general framework we introduce may enable future developments of code-based cryptography.
Role of Human Body Posture Recognition Method Based on Wireless Network Kinect in Line Dance Aerobics and Gymnastics Training

With the rapid development of the information society, human body gesture recognition has become an important technology for human-computer interaction. This paper combines Kinect’s human bone monitoring technology with auxiliary gymnastics training. The gymnastics and dance training can correct students’ wrong movements in time through feedback and improve the training efficiency, so as to meet the needs of nature and harmony of human-machine interaction. In this paper, based on the wireless network Kinect, the human body posture recognition method and tracking technology are studied, and the joint point angle representation method based on the fixed axis is proposed, and the posture recognition method based on the joint point angle is improved, which can accurately recognize the human body posture. Aiming at the situation that the human joint points are occluded, the human joint point repair algorithm is improved. The algorithm is based on the proportion of human bone nodes and the characteristics of human motion, and based on geometric principles, it repairs the occluded points. The feasibility of the original joint point data, angle feature, and distance feature in expressing human behavior is analyzed through experiments, a standard gymnastics movement database is established, and new gymnastics movements can be entered at any time. A gymnastics auxiliary training system is designed, which can analyze and evaluate the exercises of the trainer from the joint point coordinates and the angle formed by the joints and provide the trainer with intuitive error correction prompts. The human body posture recognition method studied in this paper can accurately give the difference between the trainer’s movement and the standard movement, and the trainer can adjust the movement posture according to the prompts, improve the level of gymnastics, and achieve the purpose of auxiliary training. Experiments show that the algorithm model has an accuracy rate of 95.7% for human body posture recognition, and it plays a huge role in line dance aerobics and gymnastics training.
Internet of Things (IoT) and Sustainability

Internet of Things (IoT) technologies have started to impact society as a whole and have become a key enabler for sustainable development.The purpose of this paper is to examine the connection between IoT and sustainability providing a critical reflection on current literature.The research methodology consists of a literature review in the research field of IoT technologies and their beneficial impact in various sectors, such as sustainable urban development or farming.The aim is to provide an in-depth, detailed analysis on the various ways in which IoT can help with current global environmental issues and achieve sustainable development.The paper highlights the importance of IoT technologies in protecting the environment and emphasizes the need for all stakeholders to adapt such technologies nowadays and in the future.The main value of the current paper is to help enrich the literature on IoT technologies, sustainable development, and environmental protection.The Internet of Things (IoT) is believed to be one of the enabling paradigms of sustainable digital transformation and environmental protection (Salam, 2019).IoT is emerging as a powerful enabler in many application domains, such as water and energy management, environmental monitoring, health, smart cities, smart industry and supply chain management.This paper contributes to academic literature and business by revealing a possible link between sustainability and Internet of Things.This subject sparks interest among both marketers and researchers.
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases

Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence.Recently, much progress toward this goal has been made with automatic knowledge base (KB) construction techniques.However, such techniques focus primarily on the acquisition of positive (true) KB statements, even though negative (false) statements are often also important for discriminative reasoning over commonsense KBs.As a first step toward the latter, this paper proposes NegatER, a framework that ranks potential negatives in commonsense KBs using a contextual language model (LM).Importantly, as most KBs do not contain negatives, NegatER relies only on the positive knowledge in the LM and does not require ground-truth negative examples.Experiments demonstrate that, compared to multiple contrastive data augmentation approaches, NegatER yields negatives that are more grammatical, coherent, and informative-leading to statistically significant accuracy improvements in a challenging KB completion task and confirming that the positive knowledge in LMs can be "repurposed" to generate negative knowledge.
Research on an Obstacle Avoidance Method for UAV

With the continuous development of UAV technology, UAV has been widely used in various industries. In the flight process of UAV, UAV often changes the given path because of obstacles (including static nonliving body and moving living body). According to the properties of obstacles and the characteristics of UAV, standard Kalman filter is used for nonmaneuvering targets, and sigma point Kalman filter is used for maneuvering targets. In the aspect of obstacle avoidance, the minimum search method is used to get the initial population of local programming. Then, the improved genetic algorithm is run. Combined with the predicted obstacle features, the local planning path can be obtained. Finally, the local planning path and global planning path are combined to generate the planning path with new obstacles. At the end of the paper, the obstacle avoidance strategies of static and moving obstacles are simulated. The simulation results show that this method has fast convergence speed and good feasibility and can flexibly deal with the obstacle avoidance and local path planning of various new obstacles.
Numerical reasoning in machine reading comprehension tasks: are we there yet?

Numerical reasoning based machine reading comprehension is a task that involves reading comprehension along with using arithmetic operations such as addition, subtraction, sorting, and counting.The DROP benchmark (Dua et al., 2019) is a recent dataset that has inspired the design of NLP models aimed at solving this task.The current standings of these models in the DROP leaderboard, over standard metrics, suggest that the models have achieved near-human performance.However, does this mean that these models have learned to reason?In this paper, we present a controlled study on some of the top-performing model architectures for the task of numerical reasoning.Our observations suggest that the standard metrics are incapable of measuring progress towards such tasks.
Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings

Simultaneous translation is vastly different from full-sentence translation, in the sense that it starts translation before the source sentence ends, with only a few words delay.However, due to the lack of large-scale, high-quality simultaneous translation datasets, most such systems are still trained on conventional fullsentence bitexts.This is far from ideal for the simultaneous scenario due to the abundance of unnecessary long-distance reorderings in those bitexts.We propose a novel method that rewrites the target side of existing fullsentence corpora into simultaneous-style translation.Experiments on Zh!En and Ja!En simultaneous translation show substantial improvements (up to +2.7 BLEU) with the addition of these generated pseudo-references.
360°-Based Virtual Field Trips to Waterworks in Higher Education

360° models are a form of virtual reality (VR) that allow the viewer to view and explore a photorealistic object from multiple locations within the model. Hence, 360° models are an option to perform virtual field trips (VFT) independent of time and location. Thanks to recent technical progress, 360° models are creatable with little effort. Due to their characteristics of visualization and explorability, 360° models appear as excellent learning tools, especially when additional didactic features, such as annotations, are used. The subject of this explorative field study is a 360° model of a waterworks that has been annotated for learning purposes. Data are collected from a total of 55 learners in four cohorts from study programs in environmental engineering and urban studies using a questionnaire that included standardized measurement instruments on motivation, emotion, and usability. Furthermore, the eight learners of cohort 1 are surveyed using semi-structured interviews on learning, operation and features of the 360° model. Overall, a very positive view on learning suitability of 360° models in VFTs is revealed. In addition, further potential for development of the 360° model could be identified. The results indicate that VTFs based on 360° models might be valuable learning tools, because of their applicability without great effort on the part of either the lecturers or the students. VFTs based on 360° models might serve as a supplement to conventional learning activities or in self-directed learning activities.