Reviewerly/exHarmony huggingface dataset:
Enhancing Student’s Writing Ability in Paragraph Writing Through an Outline Strategy at Primary Level

Among language skills, writing is considered the most challenging and complicated skill compared to other skills. The main objective of this research is to analyze the improvement of students' writing skills in writing paragraphs through the outline strategy. The participants in this study were 15 fifth grade students. The participants were selected through random sampling technique. The data obtained were analyzed in percentage. Pre-Test and Post-Test were used as instruments in this study. In the Pre-Test and Post-Test the required items such as writing paragraph titles, grammar, spelling and total sentences (length) of paragraphs are the same. According to the findings, in the Pre-Test the students writing paragraphs were 20.00%, 26.66%, 20.00%, 26.66% (Good), 46.66%, 20.00%, 33.33%, 33 ,33% (Average), 33.33%, 53.33%, 46.66% , 40.00% (Weak) in paragraph headings, use of proper grammar, correct spelling and number of sentences (length ) paragraphs, respectively. After the Pre-Test, the instructor taught the participants about writing paragraphs through an outline strategy for 4 weeks and then conducted a Post-Test. In this test, participants in writing paragraphs through the outline strategy were 46.66%, 53.33%, 60.00%, 53.33% (Good), 40.00%, 26.66%, 33.33%, 40 0.00% (Average), 13.33%, 20.00%, 6.66% , 6.66% (Weak) in paragraph headings, use of proper grammar, correct spelling and number of sentences (length ) paragraphs, respectively. This action-based research shows that the outline strategy can improve students' writing skills.
Improving BERT With Self-Supervised Attention

One of the most popular paradigms of applying large pre-trained NLP models such as BERT is to fine-tune it on a smaller dataset. However, one challenge remains as the fine-tuned model often overfits on smaller datasets. This problem manifests as irrelevant or misleading words in the sentences, which are obvious to humans, but can substantially degrade the performance of the fine-tuned BERT models. In this paper, we propose a novel technique, called Self-Supervised Attention (SSA) to help facilitate this generalization challenge. Specifically, SSA automatically generates weak, token-level attention labels iteratively by probing the fine-tuned model from the previous iteration.We investigate two different ways of integrating SSA into BERT and propose a hybrid approach to combine their benefits. Empirically, through a variety of public datasets, we illustrate significant performance improvement using our SSA-enhanced BERT model.
Symplectic decomposition from submatrix determinants

An important theorem in Gaussian quantum information tells us that we can diagonalize the covariance matrix of any Gaussian state via a symplectic transformation. While the diagonal form is easy to find, the process for finding the diagonalizing symplectic can be more difficult, and a common, existing method requires taking matrix powers, which can be demanding analytically. Inspired by a recently presented technique for finding the eigenvectors of a Hermitian matrix from certain submatrix eigenvalues, we derive a similar method for finding the diagonalizing symplectic from certain submatrix determinants, which could prove useful in Gaussian quantum information.
Managing Dependencies in Large-Scale Agile

Abstract Delivering results iteratively and frequently in large-scale agile requires efficient management of dependencies. We conducted semi-structured interviews and virtual observations in a large-scale project during the Covid-19 pandemic to better understand large-scale dependency management. All employees in the case were working from home. During our data collection and analysis, we identified 22 coordination mechanisms. These mechanisms could be categorized as synchronization activities, boundary-spanning activities and artifacts, and coordinator roles. By using a dependency taxonomy, we analyzed how the mechanisms managed five different types of dependencies. We discuss three essential mechanisms for coordination in our case. First, setting Objectives and Key Results (OKRs) in regular workshops increased transparency and predictability across teams. Second, ad-hoc communication, mainly happening on Slack because of the distributed setting, was essential in managing dependencies. Third, the Product Owner was a coordinator role that managed both inter-team and intra-team dependencies.
An effective library website: a data-driven approach to its management using analytical tools

This article is devoted to topical issues of increasing the efficiency of a library website and the introduction of a data-driven approach. A data driven approach is considered as a management strategy for the development of a site in a web environment, based on data from web analytical tools as the basis for decision-making. The author has developed a website management model that includes the sequence of actions: goal setting, performance monitoring, data analysis, planning, decision-making, implementation of changes, control. The proposed management model allows one the application of a unified approach and management tools to a library website, depending on the goals set, identify current problems and, on the basis of objective decisions, develop its further development strategy in the information environment. The article presents the results of approbation of the model on the example of website management of the State Public Scientific Technological of the Siberian Brach of the Russian Academy of Sciences and notes the advantages of managing a library website based on a data-driven approach.
Spectral compression and entanglement reduction in the cascaded biphoton state with cavities

The cascaded biphoton state generated from a cold atomic ensemble presents one of the strongly correlated resources that can preserve and relay quantum information. Under the four-wave mixing condition, the emitted signal and idler photons from the upper and lower excited states become highly correlated in their traveling directions and entangled in continuous frequency spaces. In this system, we theoretically study the spectral compression of the biphoton source using an external cavity and show the reduction in its frequency entanglement entropy. This indicates, respectively, an improved light absorption efficiency for the idler photon as well as an almost pure biphoton source which is useful in optical quantum networks. We further investigate the limit of the spectral compression that can be achieved by using multiple cavities. Our results show the capability and potential of the biphoton source with external cavities, where the performance of atom-based quantum memory can be enhanced and the entanglement property can be manipulated by tailoring the spectral compression.
Necessary conditions on effective quantum entanglement catalysts

Quantum catalytic transformations play important roles in the transformation of quantum entangled states under local operations and classical communications (LOCC). The key problems in catalytic transformations are the existence and the bounds on the catalytic states. We present the necessary conditions of catalytic states based on a set of points given by the Schmidt coefficients of the entangled source and target states. The lower bounds on the dimensions of the catalytic states are also investigated. Moreover, we give a detailed protocol of quantum mixed state transformation under entanglement-assisted LOCC.
Software energy consumption prediction using software code metrics

In recent years, a significant amount of energy consumption of ICT products has resulted in environmental concerns. Growing demand for mobile devices, personal computers, and the widespread adaptation of cloud computing and data centers are the main drivers for the energy consumption of the ICT systems. Finding solutions for improving the energy efficiency of the systems has become an important objective for both industry and academia. In order to address the increase in ICT energy consumption, hardware technology, such as production of energy efficient processors, has been substantially improved. However, demand for energy is growing faster than improvements are being made on these energy-aware technologies. Therefore, in addition to hardware, software technologies must also be a focus of research attention. Although software does not consume energy by itself, its characteristics determine which hardware resources are made available and how much electrical energy is used. Current literature on the energy efficiency of software, highlights, in particular, a lack of measurements and models. In this dissertation, first, the relationship between software code properties and energy consumption is explored. Second, using static code metrics regression based energy consumption prediction models are investigated. Finally, the models performance are assessed using within product and cross-product energy consumption prediction approaches. For this purpose, a quantitative based retrospective cohort study was employed. As research methods, observational data collection, mining software repositories, and regression analysis were utilized. This research results show inconsistent relationships between energy consumption and code size and complexity attributes considering different types of software products. Such results provide a foundation of knowledge that static code attributes may give some insights but would not be the sole predictors of energy consumption of software products.
Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602.
Estimating 3D Motion and Forces of Human–Object Interactions from Internet Videos

In this paper, we introduce a method to automatically reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person together with the object pose, the contact positions and the contact forces exerted on the human body. The main contributions of this work are three-fold. First, we introduce an approach to jointly estimate the motion and the actuation forces of the person on the manipulated object by modeling contacts and the dynamics of the interactions. This is cast as a large-scale trajectory optimization problem. Second, we develop a method to automatically recognize from the input video the 2D position and timing of contacts between the person and the object or the ground, thereby significantly simplifying the complexity of the optimization. Third, we validate our approach on a recent video + MoCap dataset capturing typical parkour actions, and demonstrate its performance on a new dataset of Internet videos showing people manipulating a variety of tools in unconstrained environments.
Comprehensive Evaluation Method of High-Rise Building Green Construction Based on Cloud Model

With resource conservation, environmental protection and people's health being valued by the whole society, high-rise building green construction has become an irresistible trend.For accurate evaluation of high-rise building green construction assessment level, considering the fuzziness and randomness, green construction assessment based on the perspective of construction general contracting unit, through literature research and the influence factors of in-situ investigation to determine the influence of green construction, using the PSR theory constructing evaluation index system of green construction, adopt AHP, entropy weight method to calculate the combination weight of index system.Finally, the transformation between qualitative and quantitative was realized by using cloud model, and the comprehensive evaluation level was established by drawing comprehensive cloud map and calculating membership degree.Combined with a municipal representative project, the green construction evaluation simulation calculation, the calculation results show that the project green construction evaluation grade is good, feedback of the results to the general contractor has been generally recognized.
Combating Ransomware in Internet of Things: A Games-in-Games Approach for Cross-Layer Cyber Defense and Security Investment

The recent surge in ransomware attacks has threatened many critical infrastructures such as oil pipeline systems, hospitals, and industrial Internet of Things (IoT). Ransomware is a cryptoviral extortion attack that involves two phases: the cyber infection of the malware and the financial transaction of the ransom payment. As the ransomware attackers are financially motivated, the protection of the infrastructure networked systems requires a cross-layer risk analysis that not only examines the vulnerability of the cyber system but also consolidates the economics of ransom payment. To this end, this paper establishes a two-player multi-phase and multi-stage game framework to model cyber and economic phases of a ransomware attack. We use a zero-sum Markov game to capture the multi-stage penetration of ransomware in the lateral movement. A sequential-move game is proposed to model the ransom payment interactions at the second phase. Two games are composed to form a multi-phase and multi-stage game-in-games (MPMS-GiG) that enables a holistic risk assessment of ransomware in networks and a cross-layer design of cyber defense and investment strategies to mitigate the attack. We provide a complete equilibrium characterization of ransomware game and design interdependent optimal strategies for cyber protection and ransom payment. We use prospect theory to analyze the impact of human factors on equilibrium strategies. Finally, we use a prototypical industrial IoT network as a case study to corroborate the results.
Photonic convolutional accelerator and neural network in the Tera-OPs regime based on soliton crystal Kerr microcombs

We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds. We demonstrate a single neuron perceptron at 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We then demonstrate a convolutional accelerator operating beyond 11 TeraOPs/s. We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively.
Discriminative Metric Learning for Partial Label Learning

One simple strategy to deal with ambiguity in partial label learning (PLL) is to regard all candidate labels equally as the ground-truth label, and then solve the PLL problem using existing multiclass classification algorithms. However, due to the noisy false-positive labels in the candidate set, these approaches are readily mislead and do not generalize well in testing. Consequently, the method of identifying the ground-truth label straight from the candidate label set has grown popular and effective. When the labeling information in PLL is ambiguous, we ought to take advantage of the data's underlying structure, such as label and feature interdependencies, to conduct disambiguation. Furthermore, while metric learning is an excellent method for supervised learning classification that takes feature and label interdependencies into account, it cannot be used to solve the weekly supervised learning PLL problem directly due to the ambiguity of labeling information in the candidate label set. In this article, we propose an effective PLL paradigm called discriminative metric learning for partial label learning (DML-PLL), which aims to learn a Mahanalobis distance metric discriminatively while identifying the ground-truth label iteratively for PLL. We also design an efficient algorithm to alternatively optimize the metric parameter and the latent ground-truth label in an iterative way. Besides, we prove the convergence of the designed algorithms by two proposed lemmas. We additionally study the computational complexity of the proposed DML-PLL in terms of training and testing time for each iteration. Extensive experiments on both controlled UCI datasets and real-world PLL datasets from diverse domains demonstrate that the proposed DML-PLL regularly outperforms the compared approaches in terms of prediction accuracy.
A Survey on Computer Vision Architectures for Large Scale Image Classification using Deep Learning

The advancement in deep learning is increasing day-by-day from image classification to language understanding tasks.In particular, the convolution neural networks are revived and shown their performance in multiple fields such as natural language understanding, signal processing, and computer vision.The property of translational invariance for convolutions has made a huge advantage in the field of computer vision to extract feature invariances appropriately.When these convolutions trained using back-propagation tend to prove their results ability to outperform existing machine vision techniques by overcoming the various hand-engineered machine vision models.Hence, a clear understanding of current deep learning methods is crucial.These convolution neural networks have proven to show their performance by attaining state-of-the-art performance in computer vision over years when applied on humongous data.Hence in this survey, we detail a set of state-of-the-art models in image classification evolved from the birth of convolutions to present ongoing research.Each state-of-the-art model evolved in the successive year is illustrated with architecture schema, implementation details, parametric tuning and their performance.It is observed that the neural architecture construction i.e. a supervised approach for an image classification problem is evolved as data construction with cautious augmentations i.e., a self-supervised approach.A detailed evolution from neural architecture construction to augmentation construction is illustrated by provided appropriate suggestions to improve the performance.Additionally, the implementation details and the appropriate source for the execution and reproducibility of results are tabulated.
Development of Mobile Facilities of Neuro-like Cryptographic Encryption and Decryption of Data in Real Time

The requirements are formed, the method is chosen and the main stages of development of mobile means of neuro-like cryptographic encryption and real-time data decryption are considered. It is shown that the development of mobile means of neuro-like cryptographic encryption and decryption of real-time data with high efficiency of equipment is reduced to minimize hardware costs while providing a variety of requirements, characteristics and limitations. The tabular-algorithmic method of calculating the scalar product has been improved. Namely, the ability to work with floating-point operands has been added and it is focused on hardware and software implementation. Developed on the basis of a universal processor core, supplemented by specialized modules, mobile means of neuro-like cryptographic encryption and data decryption. Which due to the combination of universal and specialized approaches, software and hardware provides effective implementation of algorithms for cryptographic encryption and decryption of data in real time. It is proposed to use a multioperand approach, tables of macroparticle products and bases of elementary arithmetic operations to achieve high technical and economic indicators in the implementation of specialized modules of neuro-like cryptographic encryption and real-time data decryption. Specialized modules of neuro-like cryptographic encryption and data decryption have been implemented using the VHDL hardware programming language and the Quartus II development environment (version 13.1) on the FPGA. The evaluation of hardware and time parameters of the developed specialized module of neurosimilar cryptographic data decryption is carried out.
An Empirical Study of Corpus Application in the Teaching of English Writing

The cultivation of writing ability has always been one of the teaching goals of English majors.It is listed as the five skills of English learning along with listening, speaking, reading and translation.Nowadays, people gradually realize that most Chinese college students' English writing ability is not very satisfactory.One of the problems is that most students are affected by their mother tongue.The Chinese way of thinking hinders the development of their language skills, thus restricting further language learning.English corpus is of great help in terms of vocabulary, grammar usage, monitoring and verification of the correctness of language output.In this paper, the author combines the teaching practice of English writing with the application of English corpus, and conducts research on its effectiveness.Results show that students' language errors in compositions are reduced, their vocabulary becomes richer and more diversified, and students' feedback is very good.Besides, this method can also encourage students to learn independently to improve their writing skills.