Reviewerly/exHarmony huggingface dataset:
RETRACTED ARTICLE: Distribution of earthquake activity in mountain area based on embedded system and physical fitness detection of basketball

With the development of embedded system technology in recent years, it has become increasingly mature. The training model is to train the voice in the embedded system, and match the voice to be tested with the pattern, so as to complete the speech recognition of non-specific people to generate the training model. Therefore, in this paper, the embedded system is used to record the 2018–2020 mountain seismic data of the reservoir at a specific location downstream. For these earthquakes, the displacement, activity, and source parameters are studied, so as to analyze the general characteristics of the distribution of seismic activity in the mountain area, and summarize the double location. At the present stage of Chinese basketball, college basketball is one of the important foundations of its development. Basketball is not only one of the most popular and popular sports in the world, but also one of the most popular sports in China. Since it was introduced into China, it has been highly concerned by the masses and the whole country. Improving the level of college basketball plays an important role in promoting the level of Chinese basketball. Taking a college basketball team as an example, this paper expounds the importance of special basketball training and the role of basketball detection system, and provides effective tips for special physical training. Server data transmission technology and database technology design and implement the team sports physical fitness detection and evaluation system, greatly improve the security of basketball and training. In this paper, the embedded system technology is used to study the distribution of earthquake activity, basketball training, and physical fitness detection. In this paper, the distribution of seismic activity in mountainous areas and basketball physical fitness detection are studied and applied in practice, in order to promote its development and application.
Hierarchical cancer heterogeneity analysis based on histopathological imaging features

Abstract In cancer research, supervised heterogeneity analysis has important implications. Such analysis has been traditionally based on clinical/demographic/molecular variables. Recently, histopathological imaging features, which are generated as a byproduct of biopsy, have been shown as effective for modeling cancer outcomes, and a handful of supervised heterogeneity analysis has been conducted based on such features. There are two types of histopathological imaging features, which are extracted based on specific biological knowledge and using automated imaging processing software, respectively. Using both types of histopathological imaging features, our goal is to conduct the first supervised cancer heterogeneity analysis that satisfies a hierarchical structure . That is, the first type of imaging features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. It has satisfactory statistical and numerical properties. In the analysis of lung adenocarcinoma data, it identifies a heterogeneity structure significantly different from the alternatives and has satisfactory prediction and stability performance.
RETRACTED: Analysis of English Teaching Mode in Big Data Environment Based on Hadoop Cloud Platform

Abstract With the continuous deepening of the application of big data technology, the education field is also paying attention to the impact of big data. “Internet +” education has become a hot topic in the current education field. This article relies on the big data Hadoop cloud platform technology through integration lines Online and offline resources and a variety of teaching techniques and methods are used to analyze the business English teaching model, and use the mixed teaching model to promote the development from extensive to standardization, aiming to enhance the richness, interaction and effectiveness of business English teaching.
RETRACTED: Research on Teaching Ability Evaluation Model of University Teachers Based on Computer Big Data Theory

Abstract As the backbone theory to guarantee the teaching level of universities, teachers are essential resources for the healthy development of universities. However, some universities are still lack of data support in the process of carrying out the evaluation of teachers’ teaching ability, which leads to the evaluation process is not scientific and reasonable, and it is difficult to reflect teachers’ real teaching ability objectively and fairly. Based on this, this paper first analyzes the comprehensive evaluation model of university teachers’ teaching ability under the big data theory, and then studies the application effect of the teaching ability evaluation model based on the big data theory.
Agile Enhancement of Critical PMBoK v6 Processes

This article examines PMBOK version six applicability in agile IT projects. Based on existing scientific research about troubling compatibility of PMBOK processes and agile frameworks, enhancements of five critical project processes are postulated. These process enhancements often focus on time-limited disruptions of agile actions and a separation of macro- and micro-management between agile developments and project management practices. To verify the effectiveness of such enhancement approaches, a quantitative survey explores the applicability and current state of usage. Results show that most proposed solutions are currently not widely used in IT project management although practitioners have evaluated them as useful. However, participants tend to increase the use when understanding more about the solutions. In combination with the discovered positive rating of the effectiveness of enhancement approaches, it can be summarized that the improvement approaches are a well-perceived step towards the enhancement of PMBOK processes in agile developed IT projects.
First Arrival Picking on Microseismic Signals Based on K-Means with a ReliefF Algorithm

The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.
Consideration of the Word’s Neighborhood in GATs for Information Extraction in Semi-structured Documents

Most administrative documents take a semi-structured form (invoices, payslips, etc.). Extracting information from this type of document is still challenging because of the variability of its structure brought about by the change of layout style of the different administrations. In this work, we try to face this type of variation by using a multi-layer Graph Attention Network (GAT). We propose a general structure of a semi-structured document. Based on this latter, we adopt a star sub-graph to exploit the surrounding context of words, allowing neighboring words to help locate the searched words and rank them. The GAT makes it possible to exploit this type of neighborhood and to highlight important neighboring words likely to be better identified. Each graph node contains at the same time textual and visual features. We experiment the multi-layer GAT on three different datasets: invoices and payslips (generated artificially), and receipts (issued from SROIE ICDAR competition). For the later dataset, we get an important F1 score of 0.892.
Adversarial Domain Adaptation for Medieval Instrument Recognition

Image classification models have improved drastically due to neural networks. But as a direct consequence of being trained on a specific dataset, neural networks tend to be biased towards theirs training data and provide worse results on other domains. Hence, a new sub field of Transfer Learning emerged namely Domain Adaptation which uses in the most case a combination of adversarial methods and mathematical heuristics which are applied to the model’s latent space. In this paper we present a new method for Unsupervised Domain Adaptation that is both fast and resilient. Our method starts by applying style transformations to the input images and train a transformation discriminator module to predict these style changes. Whereas the feature extractor part of our model is trained on the adversarial part of that loss allowing to forget and not extract style information which in return improves the accuracy of the classifier. Our second contribution is our new dataset of Musical Instruments Recognition In Medieval Artworks which provides a better benchmark for transfer learning and domain adaptation methods and pushes the research in this area further. We evaluated our method on two main benchmarks namely MNIST-USPS-SVHN and MIMI-Musiconis-Vihuelas benchmark and in all cases our method provides state of the art performances.
A Lightweight Hash Function for Cryptographic and Pseudo-Cryptographic Applications

In this paper, we design a lightweight hash function suitable for fast applications. The proposed hash function is intended to be used as a secondary component in cryptographic and pseudo-cryptographic applications (e.g., pseudorandom number generators, cryptosystems), where the required property is the fastness in addition to an acceptable security level. Based on simple logical operations used in designing primitive functions, and based on a Feistel-like network, the proposed hash function exhibits excellent performance (fastness) and good enough security properties.
CINEMA SOUND: A NEW LOOK AT OLD CONCEPTS

The use of third-octave-band equalisation for 'rom' correction and other compensations in cinema studios and public cinemas is virtually de rigueur. Nevertheless, the understanding of psycho-acoustics and audio signal processing has advanced enormously in the almost 40 years since the current concepts of cinema equalisation were introduced, yet some perhaps out-dated procedures still prevail. To some degree, this has been necessary to maintain the standardisation and continuity in the industry, but it has also limited the ability to make significant advances in soundtrack quality. Given what we now know, along with the imminent introduction of Digital Cinema, an opportunity seems to be presenting itself for taking a new look at the strengths and weaknesses of cinema set-up procedures. The responses of 30 Dolby certified cinema rooms, both mixing studios and public theatres, were measured at the recommended reference distance and close to the central loudspeakers. From comparisons of the near and far responses it has been possible to assess how much the direct sound in typical theatres is being compromised in order to meet the notional standardisation of responses. The ramifications of these compromises are discussed.
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation

Predicting the altered acoustic frames is an effective way of self-supervised learning for speech representation. However, it is challenging to prevent the pretrained model from overfitting. In this paper, we proposed to introduce two dropout regularization methods into the pretraining of transformer encoder: (1) attention dropout, (2) layer dropout. Both of the two dropout methods encourage the model to utilize global speech information, and avoid just copying local spectrum features when reconstructing the masked frames. We evaluated the proposed methods on phoneme classification and speaker recognition tasks. The experiments demonstrate that our dropout approaches achieve competitive results, and improve the performance of classification accuracy on downstream tasks.
Analysis of Human Emotion-Based Data Using MIoT Technique

Emotions have a vital role in everyday life. They play an important role in decision making and impact perception, even though we may initially consider them to be irrelevant. A small change in emotions can bring a huge change in behaviour. Ongoing research explores emotion recognition through physiological signals. Many articles propose methodologies for recognition of human emotional states in automated ways. The primary goal of the work is to give a model for monitoring the condition of the patient and predicting his or her health status. The approach of the work is based on various sensors. These heterogeneous sensors will give the data as a social network of things to form the social Internet of Things.
A Lightweight Pedestrian Detection Engine with Two-Stage Low-Complexity Detection Network and Adaptive Region Focusing Technique

Pedestrian detection has been widely used in applications such as video surveillance and intelligent robots. Recently, deep learning-based pedestrian detection engines have attracted lots of attention. However, the computational complexity of these engines is high, which makes them unsuitable for hardware- and power-constrained mobile applications, such as drones for surveillance. In this paper, we propose a lightweight pedestrian detection engine with a two-stage low-complexity detection network and adaptive region focusing technique, to reduce the computational complexity in pedestrian detection, while maintaining sufficient detection accuracy. The proposed pedestrian detection engine has significantly reduced the number of parameters (0.73 M) and operations (1.04 B), while achieving a comparable precision (85.18%) and miss rate (25.16%) to many existing designs. Moreover, the proposed engine, together with YOLOv3 and YOLOv3-Tiny, has been implemented on a Xilinx FPGA Zynq7020 for comparison. It is able to achieve 16.3 Fps while consuming 0.59 W, which outperforms the results of YOLOv3 (5.3 Fps, 2.43 W) and YOLOv3-Tiny (12.8 Fps, 0.95 W).
Tangible reduction in learning sample complexity with large classical samples and small quantum system

Quantum computation requires large classical datasets to be embedded into quantum states in order to exploit quantum parallelism. However, this embedding requires considerable resources in general. It would therefore be desirable to avoid it, if possible, for noisy intermediate-scale quantum (NISQ) implementation. Accordingly, we consider a classical-quantum hybrid architecture, which allows large classical input data, with a relatively small-scale quantum system. This hybrid architecture is used to implement a sampling oracle. It is shown that in the presence of noise in the hybrid oracle, the effects of internal noise can cancel each other out and thereby improve the query success rate. It is also shown that such an immunity of the hybrid oracle to noise directly and tangibly reduces the sample complexity in the framework of computational learning theory. This NISQ-compatible learning advantage is attributed to the oracle’s ability to handle large input features.
A new hourly dataset for photovoltaic energy production for the continental USA

This new dataset is an ensemble of solar photovoltaic energy production simulations over the continental US. The simulations are carried out in three steps. First, a weather forecast system is used for the predictions of incoming insolation; then, forecast ensembles with 21 members are generated using the Analog Ensemble technique; finally, each ensemble member is used to simulate 13 different solar panels. In total, there are 21×13=273 simulated scenarios. Simulations are carried out for the entire year 2019, with a temporal resolution of one hour, and a spatial resolution of 12 km. The data provide a high spatio-temporal analysis of the power production under different weather and engineering scenarios. The size of the entire dataset is about 1 TB but can be openly accessed by days and scenarios. Details on how to access and use such a dataset are provided in this article.
VAENAR-TTS: Variational Auto-Encoder Based Non-AutoRegressive Text-to-Speech Synthesis

This paper describes a variational auto-encoder based non-autoregressive text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based on the sequence-to-sequence architecture can generate high-quality speech, but their sequential decoding process can be time-consuming. Recently, non-autoregressive TTS (NAR-TTS) models have been shown to be more efficient with the parallel decoding process. However, these NAR-TTS models rely on phoneme-level durations to generate a hard alignment between the text and the spectrogram. Obtaining duration labels, either through forced alignment or knowledge distillation, is cumbersome. Furthermore, hard alignment based on phoneme expansion can degrade the naturalness of the synthesized speech. In contrast, the proposed model of VAENAR-TTS is an end-to-end approach that does not require phoneme-level durations. The VAENAR-TTS model does not contain recurrent structures and is completely non-autoregressive in both the training and inference phases. Based on the VAE architecture, the alignment information is encoded in the latent variable, and attention-based soft alignment between the text and the latent variable is used in the decoder to reconstruct the spectrogram. Experiments show that VAENAR-TTS achieves state-of-the-art synthesis quality, while the synthesis speed is comparable with other NAR-TTS models.
Effect of Training Data Selection for Speech Recognition of Emotional Speech

In this paper, we describe the speech recognition from emotional speech.The task treated in this paper is not an emotion recognition from speech but a speech recognition (speech to text) from a speech that contains distinct emotion.First, we compare two acoustic models trained from neutral speech and emotional speech.We expected that the acoustic model trained from emotional speech improves the recognition performance of emotional speech, but the result showed that a larger amount of neutral speech was more effective than a small amount of emotional speech.Next, we applied the data selection method to enhance the phonetic balance of the training data.As a result, the entropy-based selection from the training data enhanced the recognition performance when there is some domain mismatch between the training data and the evaluation data.
PROPOSED E-LEARNING FRAMEWORK FOR SPECIAL NEED STUDENTS IN HIGHER EDUCATION INSTITUTIONS

Education is a weapon to improve one's life. It helps to develop one's character and attitudes. The education of children is very important. It gives them a platform to develop their mind and body in terms of their abilities. Due to the impact of education on a child's development, it is very important that these children are given equal opportunity to succeed in life. Special needs children deserve equal opportunity to get an education. This study aims to introduce an online framework for specially abled children during the outbreak of COVID-19. The research will come up with a well-defined framework in the field of education for specially abled children. Where the learning will be interactive. There have been various experiments in interactive learning for special needs children such as tap to talk, touch to speak. The research will give a framework which is specially designed, and it is interactive for special needs children. The research will formulate a well-defined framework for special needs children's education. It will be an interactive learning environment. The framework should also be designed in such a way that it fits to the needs of the faculty members and the students.
E-Learning as a basis for the development of media competences in students

The study describes the structure of media literacy and its key aspects: digital literacy, content interpretation, content generation and digital awareness. The results of the survey at the end of the training course confirmed the effectiveness of educational practices on digital media platforms in developing media competencies. According to self-assessment results, learning activities have improved digital skills. The study made it possible to determine the key success factors for the development and consolidation of modern media competencies in the context of online learning. The involvement of each student in the generation of educational content has formed the teaching tactics for the stable development of target media competencies and skills. Group interactions of students associated with the course met student demand for socialisation and contributed to educational progress. It was concluded that media literacy is the result of well-planned and well-organised practical activities of students in the digital media space.
Efektivitas Penerapan Model Pembelajaran Think Talk Write (TTW) Secara Online Terhadap Literasi Informasi Siswa SMA

This study aims to analyze the effectiveness of the Think Talk Write (TTW) learning model which is carried out online on the information literacy of high school students. This study used a quasi-experimental method with a non-equivalent control group pretest-posttest design. The study was conducted on students of class XI IPA SMA Negeri 1 Labuapi consisting of 36 people. Research data were in the form of information literacy scores obtained from the application of pretest and posttest. The data analysis technique used in this study was covariance analysis (ancova). The results showed that the class that applied TTW learning had an average information literacy posttest score of 3.26 which was higher than the conventional class with an average gain of 2.97. The conclusion is that the application of the TTW learning model is more effective than the conventional learning model in terms of achieving student information literacy.
A Design of Extreme Learning Machine Based Receiver for 2×2 MIMO-OFDM System

Machine learning has raised the research interest for future communication network. The implementation of machine learning technologies such as Deep Neural Network (DNN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) have enabled the derivation of channel estimation techniques which provide more accurate estimation in comparison with accuracy provided by other well-known estimation techniques. ELM has faster learning ability compared to that of the other aforementioned technologies. This paper focuses on the ELM based receiver design for 2×2 MIMO OFDM system. The determination of the output layer neuron, a component in ELM technique, on the basis of symbol type (number of bits per symbol) has been proposed. The performance of the proposed receiver is evaluated in terms of Bit Error Rate (BER) and compared with that of the Minimum Mean Square Error (MMSE) based receiver. Improved performance has been achieved by means of the proposed scheme.