Reviewerly/exHarmony huggingface dataset:
Analysis of Coherence and Cohesion on Students’ Academic Writing: A Case Study at the 3rd Year students at English Education Program

Purpose - This research aims to analyze the understanding of coherence and cohesion made by the 3rd year students of English education program on their academic writing at Islamic Institute of Bukittinggi.&#x0D; Design/methodology/approach - This was descriptive research which explains the phenomena concerns on the data due to field finding. This research applied for the library based work where the data was taken by using document analysis and interview.&#x0D; Findings - The result of the research showed that the numbers of students were still difficult to construct the idea cohesively and coherently. The result of the students’ understanding on coherence and cohesion showed that there were 59% students who were considered in need to improve and also followed by good work and could be better with 27% and 14% respectively. It can be explained that students were lack of ability to write the idea smoothly and grammatically correct. The interview result showed that there were three main reasons for this. Those are lack of motivation, limited lecture duration and prior English knowledge.&#x0D; Originality/value - coherence and cohesion are parts of academic writing that should be mastered by students in order to create a good thesis. Specifically for the students who will graduate from Higher Education.&#x0D; Paper type – Research paper
Probability of Pulse Overlap as a Quantitative Indicator of Signal Environment Complexity

Introduction. Simultaneous operation of numerous sources of radio emission form complex signal environment. Different devices with the common name “wideband analyzers” (WBA) are widely used to analyze and to control such environment. There is currently a need for developing the quantitative characteristics of a complex signal environment, which will make it possible to predict the stability of the WBA operation. Aim. The development of the indicator of the signal environment complexity, which will make possible the quantitative assessment of such environment. Materials and methods. To provide the desired indicator, simulation and mathematical tools for random events description are used. All calculations are performed using MatLab. Results. The principles of disturbances in the WBA receiver and algorithmic errors in the processing of overlapped signals are described. To quantify the “complexity” of the signal environment it is proposed to use the probability that pulses from several sources overlap in time. This allows one to compare signal environments with each other. The new analytical expression for estimating the pulse overlap probability is proposed. Functions of the pulse overlap probability from the complex signal environment parameters were obtained. Conclusion. According to the comparative analysis of the calculations using proposed analytical expression and simulation, the new expression allows one to achieve the calculation speed up to 6 orders of magnitude higher with an error below 7% compared to the simulation. The high performance of the calculations using the proposed expression allows one to simulate the complex signal environment in dynamics more efficiently.
Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

This paper presents a method, which is developed based on the Discrete Cosine (DC) coefficient and multivariate parametric statistical tests, such as tests for equality of mean vectors and the covariance matrices. Background scenes and forefront objects are separated from the key-frame, and the salient features, such as colour and Gabor texture, are extracted from the background and forefront components. The extracted features are formulated as a feature vector. The feature vector is compared to that of the feature vector database, based on the statistical tests. First, the feature vectors are compared with respect to covariance. If the feature vector of the key-frame and the feature vector of the feature vector database pass the test, then the test for equality of mean vector is performed; otherwise, the testing process is stopped. If the feature vectors pass both tests, then it is inferred that the query key-frame represents the target video in the video database. Otherwise, it is concluded that the query key-frame not representing the video; and the proposed system takes the next feature vector for matching. The proposed method results in an average retrieval rate of 97.232%, 96.540%, and 96.641% for CC_WEB, UCF101, and our newly constructed database, respectively. Further, the mAP scores computed for each video datasets, which resulted in 0.807, 0.812, and 0.814 for CC_WEB, UCF101, and our newly constructed database, respectively. The output results obtained by the proposed method are comparable to the existing methods.
Pedagogical Innovation in the Conditions of Informatization of Humanities Education

Horizon Research Publishing,USA (HRPUB) is a worldwide open access publisher serving the academic research and scientific communities by launching peer-reviewed journals covering a wide range of academic disciplines. As an international academic organization for researchers & scientists, we aim to provide researchers, writers, academic professors and students the most advanced research achievements in a broad range of areas, and to facilitate the academic exchange between them.
Quantum Photonic Simulation of Spin-Magnetic Field Coupling and Atom-Optical Field Interaction

In this work, we present the physical simulation of the dynamical and topological properties of atom-field quantum interacting systems by means of integrated quantum photonic devices. In particular, we simulate mechanical systems used, for example, for quantum processing and requiring a very complex technology such as a spin-1/2 particle interacting with an external classical time-dependent magnetic field and a two-level atom under the action of an external classical time-dependent electric (optical) field (light-matter interaction). The photonic device consists of integrated optical waveguides supporting two collinear or codirectional modes, which are coupled by integrated optical gratings. We show that the single-photon quantum description of the dynamics of this photonic device is a quantum physical simulation of both aforementioned interacting systems. The two-mode photonic device with a single-photon quantum state represents the quantum system, and the optical grating corresponds to an external field. Likewise, we also present the generation of Aharonov–Anandan geometric phases within this photonic device, which also appear in the simulated systems. On the other hand, this photonic simulator can be regarded as a basic brick for constructing more complex photonic simulators. We present a few examples where optical gratings interacting with several collinear and/or codirectional modes are used in order to illustrate the new possibilities for quantum simulation.
Anomaly Detection by STL Decomposition and Extended Isolation Forest on Environmental Univariate Time Series

&amp;lt;p&amp;gt;Atmospheric pollution is one of the primary problems and high concentration levels are critical for human health and environment. This requires to study causes of unusual high concentration levels which do not conform to the expected behavior of the pollutant but it is not always easy to decide which levels are unusual, especially, when data is big and has complex structure. A visual inspection is subjective in most cases and a proper anomaly detection method should be used. Anomaly detection has been widely used in diverse research areas, but most of them have been developed for certain application domains. It also might not be always a good idea to identify anomalies by using data from near measurement sites because of spatio-temporal complexity of the pollutant. That&amp;amp;#8217;s why, it&amp;amp;#8217;s required to use a method which estimates anomalies from univariate time series data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This work suggests a framework based on STL decomposition and extended isolation forest (EIF), which is a machine learning algorithm, to identify anomalies for univariate time series which has trend, multi-seasonality and seasonal variation. Main advantage of EIF method is that it defines anomalies by a score value.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, a multi-seasonal STL decomposition has been applied on a univariate PM10 time series to remove trend and seasonal parts but STL is not resourceful to remove seasonal variation from the data. The remainder part still has 24 hours and yearly variation. To remove the variation, hourly and annual inter-quartile ranges (IQR) are calculated and data is standardized by dividing each value to corresponding IQR value. This process ensures removing seasonality in variation and the resulting data is processed by EIF to decide which values are anomaly by an objective criterion.&amp;lt;/p&amp;gt;
Use of Snapchat to Enhance Primary School English as Second Language Learners in the Writing of Personal Information

Writing is one of the fundamental skills in language production. Writing simple sentences is therefore important, especially for pupils learning English as second language (ESL). Teachers can use multiple ways to teach how to write simple sentences. However, pupils need a platform wherein they can explore and express their ideas and observe their peers’ work to develop their skills. In this Covid-19 pandemic era, online learning plays an essential role in providing a mobile platform wherein pupils can learn without any restrictions. Teachers use social media applications such as WhatsApp, Facebook and Telegram as a bridge to convey the teaching and learning processes and encourage active engagement amongst the pupils. Only a few studies attempt to investigate the use of Snapchat, which is also a social media application that can be used in the teaching and learning processes. Other scholars suggested that Snapchat may have negative effects on the academic development of pupils. This study sought to investigate the implementation of Snapchat to enhance primary school ESL learners’ sentence construction of personal information through pre and post-writing tests. It employs a paired sample t-test to compare the results. The participants comprised 30 Year 2 students from a national type primary school. Findings reveal that most of the pupils improved sufficiently in writing simple sentences of personal information using Snapchat. Some limitations and possible avenues for further research are also discussed.
Banking 4.0: Digital Ecosystems and Super-Apps

Banking 4.0 is characterised by digital innovations, fusing the physical, artificial, and biological worlds, affecting societies, industries, and companies with ever higher speed and intensity. In the process to a new world order with new competitive forces and uncertainty, where goals are continually changing, and resources must be flexibly reorganised, there is no room for traditional ways of strategic thinking. With old management concepts, linear value chains, and rigid and closed organisational structures, established firms will be the losers. This article explains why the banking industry is ripe for disruption. It introduces a conceptual framework based on a case study research of Chinese juggernauts, including value constellations, platform business models and super-apps. Our journey from the industrial economy to the digital era opens up new vistas on how to create and capture value for businesses and clients of the next generation. We describe why modern leaders must embrace change, learn from Asia, and develop strategies through the lens of the ecosystem theory. Digital ecosystems focus on clients and data and consolidate interconnected goods and services. To achieve sustainable financial growth, we suggest an agile management approach that takes the digital transformation as a chance and builds upon partnerships to connect with diverse actors—technologically, socially, and culturally.
A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap

Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F), where F is a random distribution function. Efron’s and Rubin’s bootstrap procedures can be extended, introducing an informative prior through the Proper Bayesian bootstrap. In this paper different bootstrap techniques are used and compared in predictive classification and regression models based on ensemble approaches, i.e., bagging models involving decision trees. Proper Bayesian bootstrap, proposed by Muliere and Secchi, is used to sample the posterior distribution over trees, introducing prior distributions on the covariates and the target variable. The results obtained are compared with respect to other competitive procedures employing different bootstrap techniques. The empirical analysis reports the results obtained on simulated and real data.
SOME ASPECTS OF EVALUATING COGNITIVE COMPLEXITY OF MATHEMATICAL CONCEPTS

The article is devoted to the development of a method for assessing the cognitive complexity of mathematical concepts by summing the complexities of the terms included in their definitions. A measure of the concept complexity is the number of words that need to be pronounced to define a concept using terms included in a fifth-grader’s thesaurus. The amount of information contained in an ordinary word is taken as a conventional unit of information. Difficulty equations were compiled for the evaluated concepts. As a result of the analysis of definitions, the complexity of mathematical concepts was estimated and it was found that it varies from 1–3 (add, multiply) to 100–200 (gradient, divergence, rotor). The resulting comparison base can be used to assess the complexity of other mathematical terms.
Blurred Facial Expression Recognition System by Using Convolution Neural Network

A facial expression is a visual impression of a person's situations, emotions, cognitive activity, personality, intention and psychopathology, it has an active and vital role in the exchange of information and communication between people. In machines and robots which dedicated to communication with humans, the facial expressions recognition play an important and vital role in communication and reading of what is the person implies, especially in the field of health. For that the research in this field leads to development in communication with the robot. This topic has been discussed extensively, and with the progress of deep learning and use Convolution Neural Network CNN in image processing which widely proved efficiency, led to use CNN in the recognition of facial expressions. Automatic system for Facial Expression Recognition FER require to perform detection and location of faces in a cluttered scene, feature extraction, and classification. In this research, the CNN used for perform the process of FER. The target is to label each image of facial into one of the seven facial emotion categories considered in the JAFFE database. JAFFE facial expression database with seven facial expression labels as sad, happy, fear, surprise, anger, disgust, and natural are used in this research. We trained CNN with different depths using gray-scale images from the JAFFE database.The accuracy of proposed system was 100%.
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis

In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest.However, the classification and recognition of memes is much more complicated than that of social text since it involves visual cues and language understanding.To address this issue, this paper proposed a parallel-channel model to process the textual and visual information in memes and then analyze the sentiment polarity of memes.In the shared task of identifying and categorizing memes, we preprocess the dataset according to the language behaviors on social media.Then, we adapt and fine-tune the Bidirectional Encoder Representations from Transformers (BERT), and two types of convolutional neural network models (CNNs) were used to extract the features from the pictures.We applied an ensemble model that combined the BiLSTM, BIGRU, and Attention models to perform cross domain suggestion mining.The officially released results show that our system performs better than the baseline algorithm.Our team won nineteenth place in subtask A (Sentiment Classification).The code of this paper is availabled at : https://github.com/YuanLi95/Semveal2020-Task8-emotion-analysis.
Improving Context Modeling in Neural Topic Segmentation.

Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention. Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets. We also the robustness of our proposed model in domain transfer setting by training a model on a large-scale dataset and testing it on four challenging real-world benchmarks. Furthermore, we apply our proposed strategy to two other languages (German and Chinese), and show its effectiveness in multilingual scenarios.
Making Renewable Energy Certificates Efficient, Trustworthy, and Anonymous

Although renewable energy costs are declining rapidly, producers still rely on additional incentives, such as Renewable Energy Certificates (RECs), when making an investment decision. An REC is a proof that a certain amount of energy was generated from a renewable resource. It can be traded for cash in an REC market. Unfortunately, existing mechanisms to ensure that RECs are trustworthy-not fraudulently generated and from a universally-agreed renewable energy source-require periodic audits of the generation plant, which adds costly administrative overheads and locks out small producers. Although prior work has attempted to address these issues, existing solutions lack privacy and are vulnerable to tampering. In this work, we design, implement, and evaluate a system that is efficient, trustworthy, and anonymous, thus opening the REC market to small-scale energy producers. Our implementation is based on the commercially-available Azure Sphere micro-controller unit and the Algorand public blockchain.
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text Using Feature Engineering

In today's interconnected and multilingual world, code-mixing of languages on social media is a common occurrence.While many Natural Language Processing (NLP) tasks like sentiment analysis are mature and well designed for monolingual text, techniques to apply these tasks to code-mixed text still warrant exploration.This paper describes our feature engineering approach to sentiment analysis in code-mixed social media text for SemEval-2020 Task 9: SentiMix.We tackle this problem by leveraging a set of hand-engineered lexical, sentiment, and metadata features to design a classifier that can disambiguate between "positive", "negative" and "neutral" sentiment.With this model we are able to obtain a weighted F1 score of 0.65 for the "Hinglish" task and 0.63 for the "Spanglish" tasks.
Formalization of the Equivalence among Completeness Theorems of Real Number in Coq

The formalization of mathematics based on theorem prover becomes increasingly important in mathematics and computer science, and, particularly, formalizing fundamental mathematical theories becomes especially essential. In this paper, we describe the formalization in Coq of eight very representative completeness theorems of real numbers. These theorems include the Dedekind fundamental theorem, Supremum theorem, Monotone convergence theorem, Nested interval theorem, Finite cover theorem, Accumulation point theorem, Sequential compactness theorem, and Cauchy completeness theorem. We formalize the real number theory strictly following Landau’s Foundations of Analysis where the Dedekind fundamental theorem can be proved. We extend this system and complete the related notions and properties for finiteness and sequence. We prove these theorems in turn from Dedekind fundamental theorem, and finally prove the Dedekind fundamental theorem by the Cauchy completeness theorem. The full details of formal proof are checked by the proof assistant Coq, which embodies the characteristics of reliability and interactivity. This work can lay the foundation for many applications, especially in calculus and topology.
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect given the cause to quantile regression. Based on this theory, we develop Bivariate Quantile Causal Discovery (bQCD), a new method to distinguish cause from effect assuming no confounding, selection bias or feedback. Because it uses multiple quantile levels instead of the conditional mean only, bQCD is adaptive not only to additive, but also to multiplicative or even location-scale generating mechanisms. To illustrate the effectiveness of our approach, we perform an extensive empirical comparison on both synthetic and real datasets. This study shows that bQCD is robust across different implementations of the method (i.e., the quantile regression), computationally efficient, and compares favorably to state-of-the-art methods.
Communication Emitter Motion Behavior’s Cognition Based on Deep Reinforcement Learning

Considering the successful application of deep reinforcement learning (DRL) on tasks of moving objects, this paper innovatively applies deep deterministic policy gradient algorithm (DDPG) to complete the cognition task on multi-dimension and continuous communication emitter motion behavior.First, we propose a DDPG-based behavior cognition algorithm (DDPG-BC).It chooses direction, velocity, and communication frequency as state space, gains experience from interaction between network and environment and outputs deterministic cognition results.Second, under the condition of sufficient prior information such as geographic information, we further propose a novel algorithm named DDPG-based behavior cognition with Attention algorithm (DDPG+A-BC).It introduces attention mechanism into DDPG-BC which limits exploration scope and the randomness of initial state and improves the exploration efficiency and accuracy.The simulation experiments verify that DDPG-BC and DDPG+A-BC show good cognition ability on two different data set.And the algorithms are all superior to other DRL algorithm and existing cognition method with higher cognition accuracy and less time.In addition, we also discuss the influence of episode, reward function, and added attention mechanism on algorithm performance.
SPX64

General-purpose computing systems employ memory hierarchies to provide the appearance of a single large, fast, coherent memory. In special-purpose CPUs, programmers manually manage distinct, non-coherent scratchpad memories. In this article, we combine these mechanisms by adding a virtually addressed, set-associative scratchpad to a general purpose CPU. Our scratchpad exists alongside a traditional cache and is able to avoid many of the programming challenges associated with traditional scratchpads without sacrificing generality (e.g., virtualization). Furthermore, our design delivers increased security and improves performance, especially for workloads with high locality or that interact with nonvolatile memory.