Reviewerly/exHarmony huggingface dataset:
Classification of benign and malignant breast masses using entropy from nonlinear ultrasound radiofrequency signal

In this paper the classification of benign and malignant breast masses is investigated by using the entropy of nonlinear ultrasound radio frequency (RF) signal. The parameters (entropy and weighted entropy) derived from the nonlinear ultrasound RF signal and the conventional ultrasound parameters (image grayscale, aspect ratio, irregularity, breast mass size, and depth) are extracted from 306 image samples (158 benign and 148 malignant); t-test and linear-discriminant classifier (LDC) are used to test the distinction between benign and malignant breast masses by each parameter; furthermore the effective parameters are combined to classify benign and malignant breast masses. The results show that except the image grayscale, the other parameters are significantly different between benign and malignant breast masses. Multi-parameter combined with support vector machine (SVM) is used to classify breast masses as benign and malignant. The accuracy is 81.4%, the sensitivity is 78.4%, and the specificity is 84.2%. The present work shows that the combination of the nonlinear entropy of ultrasound RF signal and traditional ultrasound parameters can more effectively characterize the benign and malignant breast masses. The entropy of nonlinear ultrasound RF signal can become a new parameter for characterizing the benign and malignant breast masses.
Tiny Word Embeddings Using Globally Informed Reconstruction

We reduce the model size of pre-trained word embeddings by a factor of 200 while preserving its quality.Previous studies in this direction created a smaller word embedding model by reconstructing pre-trained word representations from those of subwords, which allows to store only a smaller number of subword embeddings in the memory.However, previous studies that train the reconstruction models using only target words cannot reduce the model size extremely while preserving its quality.Inspired by the observation of words with similar meanings having similar embeddings, our reconstruction training learns the global relationships among words, which can be employed in various models for word embedding reconstruction.Experimental results on word similarity benchmarks show that the proposed method improves the performance of the all subword-based reconstruction models.
Perceptions of Students Toward Digital Transformation in University Teaching to Improve Sustainability in Education

The management of a cultural change in higher education is becoming necessary and unavoidable: changes in teaching, changes in research and changes in governance. Digital transformation is an essential part of many countries&amp;rsquo; modernisation agendas. The aim of this non-experimental, descriptive, survey-based study was to explore the perceptions of university students toward the digital transformation that took place in university teaching as a consequence of COVID-19. The specific objectives proposed were to: a) analyse the perceptions of students toward digital transformation in university teaching; b) determine the valuation of students about the digital transformation that occurred in university teaching as a result of COVID-19; and c) explore the resources (hardware-software), professional collaboration, digital pedagogy and student empowerment (motivation) with respect to digital education and the recent changes in university teaching due to the pandemic. The results show that a large number of items are positively correlated. In conclusion, further research should delve into motivation, collaboration, reflective experience sharing, self-learning and initiatives that promote the development of competences in future teachers.
Distributed Ledger Technologies for Inclusive Postal Financial Services: Blockchain and More

Distributed ledger technologies are emerging as important tools that can positively impact almost all major industries and activities in our society. They are increasingly used in various supply (value) chain applications including financial, governance, regulations, health, logistics, and other industrial sectors. In this letter, we are presenting various ongoing work related to different distributed ledger technologies and their applications in postal sector. In particular, we will highlight some of the innovations in the inclusive financial services for the underbanked and unbanked populations using distributed ledger technologies.
INNOVATIONS OF TEACHER TRAINEES PREGRADUAL TRAINING AIMED AT FORMING THEIR DIGITAL COMPETENCES

In the paper there are presented some results of research survey of current state and needs to innovate teacher trainees pregradual training in the field of development their didactic-technological competences.Within the research survey, from the point of view of a research sample of 280 teacher trainees, measure of significance of selected software applications in relation to the development of professional digital literacy of teachers was assessed.Results of statistical analysis of the collected data pointed out some important facts that should be reflected in innovations of the relevant area of curricula of teacher study programs at universities, or also in programs of further education of inservice teachers.As follows from the presented results, into the teacher training study programs, in addition to the traditionally taught use of software applications Microsoft Word, Microsoft Excel and Microsoft PowerPoint, there should be included, or strengthened, also teaching of such software applications as ActivInspire, FreeMind, Flow! Works, Google Docs and, if possible, FreeMind, Mindomo, XMind and Prezi.
Information and Communicative Competence Development in Prospective Teachers

The article discusses role of teaching by taking into account modern features in the teaching profession, in the process of improving the prospective teachers' professional competence, and the learning process organization.Effectiveness of the competence formation in prospective teachers determined by the qualitative criteria, the relevance of which relates to the educational process content and pedagogical conditions.Implementation of pedagogical activities following the principles and competent approaches to person-centered learning, new teaching methods used in pedagogical practice, plans of proposed methods for basic competencies formation in teachers, analysis effectiveness of decision making in certain pedagogical situations were discussed.Social implications in this study are resulted in society development changes, mainly the problem of globalization, the growth of the strategic direction of the market economy, changes in international relations and modern requirements for the teaching profession, ability to organize humanization processes, the ability to strengthen international relations, interpersonal and intercultural ties, etc., in-depth training, taking into account the above-mentioned modern requirements in the formation of the ICT competence in prospective teachers.The practical meanings associated with the learning process organized in a systematic way, where students will be able to create new ideas, further develop mentally and gain professional knowledge through a variety of activities, and gain experience in using the obtained skills in the right situations.
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs

Recurrent neural networks (RNNs) are powerful dynamical models for data with complex temporal structure. However, training RNNs has traditionally proved challenging due to exploding or vanishing of gradients. RNN models such as LSTMs and GRUs (and their variants) significantly mitigate these issues associated with training by introducing various types of gating units into the architecture. While these gates empirically improve performance, how the addition of gates influences the dynamics and trainability of GRUs and LSTMs is not well understood. Here, we take the perspective of studying randomly initialized LSTMs and GRUs as dynamical systems, and ask how the salient dynamical properties are shaped by the gates. We leverage tools from random matrix theory and mean-field theory to study the state-to-state Jacobians of GRUs and LSTMs. We show that the update gate in the GRU and the forget gate in the LSTM can lead to an accumulation of slow modes in the dynamics. Moreover, the GRU update gate can poise the system at a marginally stable point. The reset gate in the GRU and the output and input gates in the LSTM control the spectral radius of the Jacobian, and the GRU reset gate also modulates the complexity of the landscape of fixed-points. Furthermore, for the GRU we obtain a phase diagram describing the statistical properties of fixed-points. We also provide a preliminary comparison of training performance to the various dynamical regimes realized by varying hyperparameters. Looking to the future, we have introduced a powerful set of techniques which can be adapted to a broad class of RNNs, to study the influence of various architectural choices on dynamics, and potentially motivate the principled discovery of novel architectures.
Parallel Key Insulated ID-Based Public Key Cryptographic Primitive with Outsourced Equality Test

Parallel key-insulation allows the use of multiple helper keys to protect private decryption keys during secret decryption key updates.This approach prevents decryption key leakage or exposure in insecure environment.We combined parallel key-insulated encryption (PKIE) with multiple helper keys and identity-based encryption with the equality test (IBE-ET) to obtain parallel key insulated ID-based public key encryption with outsourced equivalent test (PKI-IBPKE-ET).The scheme inherits the advantages of identity-based encryption (IBE), which simplifies certificate management for public key encryption.Furthermore, the parallel key-insulation with multiple helper mechanism was introduced in our scheme, which perfectly reduced the possibility of helper key exposure.Our scheme will enable the protection and periodic update of decryption keys in insecure environment.Our scheme achieves a weak indistinguishable identity chosen ciphertext (W-IND-ID-CCA) security in the random oracle model.Ultimately, it is observed that our scheme is feasible and practical through the experimental simulation and theoretical analysis.
Dynamic scene Image deblurring using modified scale-recurrent network

In recent times, the non-uniform blind deblurring for dynamic scenes is considered as one of the severely ill-posed challenges in computer vision because the blur is not only caused from various different object motions, and also it will come from the shake of the camera and also from scene depth variation. To get rid of the complex motion blurs, the conventional energy optimization technique that depends on some simple assumptions such that the blur kernels are partially uniform and as well as locally linear. Furthermore, in present days the machine learning (ML) construct approach will depend on synthetic blur dataset made down with this kind of assumptions. Hence, this makes conventional deblurring technique to grief-in-order to eliminate the complicated blurs, where the blur kernel itself is hard to parameterized or approximated [ex: different objects motion boundaries], So in the proposed experiment the multi-scale strategy called scale recurrent network (SRN), that restores the sharp images in an end-to-end manner is employed, where the blur is originating from different sources. When it is compared with some recent learning based approaches, the SRN-Deblur Net has the simpler network structures and it has compact number of parameters and simpler to train the network and the proposed approach is analyzed on the large-scale deblurring datasets with the complicated motions and thus the result showed that the use of modified SNR-Deblur Net can produce a better quality of latent sharp images, that remains state-of-the-art in both quantitatively and qualitatively.
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation System Based on Ensemble of Language Model

In this paper, we present language model system submitted to SemEval-2020 Task 4 competition: "Commonsense Validation and Explanation".We participate in two subtasks for subtask A: validation and subtask B: Explanation.We implemented with transfer learning using pretrained language models (BERT, XLNet, RoBERTa, and ALBERT) and fine-tune them on this task.Then we compared their characteristics in this task to help future researchers understand and use these models more properly.The ensembled model better solves this problem, making the model's accuracy reached 95.9% on subtask A, which just worse than human's by only 3% accuracy.
Population Recovery from the Deletion Channel: Nearly Matching Trace Reconstruction Bounds

The population recovery problem asks one to recover an unknown distribution over $n$-bit strings given query access to independent noisy samples of strings drawn from the distribution. Recently, Ban et. al. [BCF+19] studied the problem where the unknown distribution over $n$-bit strings is known to be $\ell$-sparse for some fixed $\ell$, and the noise is induced through the deletion channel. The deletion channel is a noise model where each bit of the string is independently deleted with some fixed probability, and the retained bits are concatenated. We note that if $\ell = 1,$ i.e., we are trying to learn a single string, learning the distribution is equivalent to the famous trace reconstruction problem. The best known algorithms for trace reconstruction require $\exp\left(O(n^{1/3})\right)$ samples. For population recovery under the deletion channel, Ban et. al. provided an algorithm that could learn $\ell$-sparse distributions over strings using $\exp\left(n^{1/2} \cdot (\log n)^{O(\ell)}\right)$ samples. In this work, we provide an algorithm that learns the distribution using only $\exp\big(\tilde{O}(n^{1/3}) \cdot \ell^2\big)$ samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17]. We also give the first algorithm with a runtime subexponential in $n$, which solves population recovery in $\exp\big(\tilde{O}(n^{1/3}) \cdot \ell^3\big)$ samples and time. Notably, our dependence on $n$ nearly matches the known upper bound when $\ell = 1$, and we reduce the dependence on $\ell$ from doubly to nearly singly exponential. Therefore, we are able to learn the mixture even for much larger values of $\ell$. For instance, Ban et. al.'s algorithm can only learn a mixture of $O(\log n/\log \log n)$ strings with a subexponential number of queries, whereas we are able to learn a mixture of up to $n^{o(1)}$ strings in subexponential queries and time.
BERT-based Contextual Semantic analysis for English Preposition Error Correction

Abstract In the writing of English as a Second Language (ESL) learners, preposition related errors account for nearly 30 percent in the all grammatical errors (Bitchener et al. 2005) [1] . Unlike verbs and nouns, isolated preposition does not mean anything, but in different contexts, prepositions usually have different meanings so that ESL students hardly master how to use all prepositions correctly. In this publication, we present a preposition error correction (PEC) model based BERT (Devlin et al., 2018) [2] which contains rich contextual semantic information, fine-tuned BERT models to intensify sentence semantic in output, and calculate the preposition matching scores according to the formula.
DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection

Light field data exhibit favorable characteristics conducive to saliency detection. The success of learning-based light field saliency detection is heavily dependent on how a comprehensive dataset can be constructed for higher generalizability of models, how high dimensional light field data can be effectively exploited, and how a flexible model can be designed to achieve versatility for desktop computers and mobile devices. To answer these questions, first we introduce a large-scale dataset to enable versatile applications for RGB, RGB-D and light field saliency detection, containing 102 classes and 4204 samples. Second, we present an asymmetrical two-stream model consisting of the Focal stream and RGB stream. The Focal stream is designed to achieve higher performance on desktop computers and transfer focusness knowledge to the RGB stream, relying on two tailor-made modules. The RGB stream guarantees the flexibility and memory/computation efficiency on mobile devices through three distillation schemes. Experiments demonstrate that our Focal stream achieves state-of-the-arts performance. The RGB stream achieves Top-2 F-measure on DUTLF-V2, which tremendously minimizes the model size by 83% and boosts FPS by 5 times, compared with the best performing method. Furthermore, our proposed distillation schemes are applicable to RGB saliency models, achieving impressive performance gains while ensuring flexibility.
Advantages and disadvantages of application of the most common information and communication technologies in the educational process of institutions of general secondary education

The purpose of this article is to analyze the current state of application of the most common information and communication technologies in the educational process of institutions of general high school in Ukraine and in the world under the influence of information and the technological revolution to modernize. General scientific methods were used, such as analysis and synthesis, the theory of innovation by I. Schumpeter with additions by B. Twiss and M. Lapin, as well as the theory of social interaction by P. Sorokin. An empirical study was carried out on the advantages and disadvantages of the introduction of information and communication technologies in the educational process, research was carried out with 120 people, including 20 parents, 50 students and 50 teachers. As a result of the study, the basic trends of modern reform of general secondary education under the influence of information and communication technologies were investigated and the main positive and negative aspects of global information and communication trends were analyzed, as well as the consequences of incorporations as well. and delayed urgent reforms.
El aprendizaje del álgebra en Educación Secundaria: las estrategias metacognitivas desde la tecnología digital

The learning situation in Spanish schools changed when the state of alarm was declared in March 2020, closing schools abruptly. This article shows the adaptation to a fully digital learning environment, carried out in a secondary education center in southern Spain. The study took place in a third-grade algebra class. Various materials were used to facilitate autonomous learning and teacher-student communication. The assessment instruments used were two problem-solving templates based on metacognitive strategies. The results show that the students exceeded the assessment criteria for this block of content, and optimal feedback levels were achieved throughout the teaching and learning process.
Incremental Neural Lexical Coherence Modeling

Pretrained language models, neural models pretrained on massive amounts of data, have established the state of the art in a range of NLP tasks.They are based on a modern machine-learning technique, the Transformer which relates all items simultaneously to capture semantic relations in sequences.However, it differs from what humans do.Humans read sentences one-by-one, incrementally.Can neural models benefit by interpreting texts incrementally as humans do?We investigate this question in coherence modeling.We propose a coherence model which interprets sentences incrementally to capture lexical relations between them.We compare the state of the art in each task, simple neural models relying on a pretrained language model, and our model in two downstream tasks.Our findings suggest that interpreting texts incrementally as humans could be useful to design more advanced models.
A Comparative Study between the Use of Role Play and Discussion Method in Teaching Speaking Ability

The study aims to investigate if role play and discussion methods can improve students’ speaking skills significantly. The researcher also attempts to investigate whether role play or discussion method that better enhancing the students’ speaking performance after the teaching-learning process conducted. To achieve these aims, the quasi-experimental research design is employed. The data were gathered from 60 students of twelfth grade in one of the vocational high schools in Pandeglang, Banten. Students from two classes were taken randomly and divided into experimental and control groups. Some instruments like pre-test - post-test were employed to collect the data. At the end of the study, many advantages were found to improve the students’ speaking skill including vocabulary, grammar, and pronunciation aspect. Both groups indicated to experience improvement, but the experimental group showed more advanced enhancement indicated by their achieving to reach the ‘good’ category in speaking. While none of the students from the control group could get that. Conclusively, students taught by the discussion method show more improvement with 19.93 as their mean score. While students by role-play method have 13.73 as their mean score. It is significantly different from both methods indicated by the significance value score of 0.14 which is less than 0.05. Hence, the discussion method is more effective than the role-play method in improving students’ speaking ability. For that reason, it is recommended for teachers to use discussion method to improve students’ speaking comprehension.
A Novel Genetic Grey Wolf optimizer for Global optimization and Feature Selection

In this paper, a new stochastic search strategy inspired by the Grey Wolf optimization theory is proposed for feature subset selection. Grey Wolf optimization algorithm (GWO) is a new metaheuristic optimization technique. Its principle is to reproduce the behavior of grey wolves in nature to hunt in a cooperative way. In this work, we have used the Grey Wolf optimizer and Genetic algorithm to select the most relevant features in a dataset. Then we have proposed a new Genetic Grey Wolf optimization algorithm. In our proposed strategy, feature selection algorithm is formulated as an optimization problem that searches an optimum with less number of features in a feature space and a good accuracy. The goal of our study is to achieve a balance between the classification accuracy and the size of the feature subsets selected. Our proposed approach has been evaluated on 10 standard datasets taken from UCI repository and validated on 02 big datasets used in literature. The experimental results show the superiority of GWO algorithm in classification performance and dimensionality reduction.