Reviewerly/exHarmony huggingface dataset:
QA4PRF: A Question Answering Based Framework for Pseudo Relevance Feedback

Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user’s information need so as to improve the search results. Previous PRF methods mainly select expansion terms with high occurrence frequency in top-retrieved documents or with high semantic similarity with the original query. However, existing PRF methods hardly try to understand the content of documents, which is very important in performing effective query expansion to reveal the user’s information need. In this paper, we propose a QA-based framework for PRF called QA4PRF to utilize contextual information in documents. In such a framework, we formulate PRF as a QA task, where the query and each top-retrieved document play the roles of question and context in the corresponding QA system, while the objective is to find some proper terms to expand the original query by utilizing contextual information, which is similar answers in QA task. Besides, an attention-based pointer network is built to understand the content of top-retrieved documents and select the terms to better represent the original query. We also show that incorporating the traditional supervised learning methods, such as LambdaRank, to integrate PRF information will further improve the performance of QA4PRF. Extensive experiments on three real-world datasets demonstrate that QA4PRF significantly outperforms the state-of-the-art methods.
Understanding the Features of Internet of Things (IoT) and Big Data Analysis

In the information era, enormous amounts of data have become available on hand to decision makers.Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. The Internet of Things, or "IoT" for short, is about extending the power of the internet beyond computers and smartphones to a whole range of other things, processes and environments. IoT is at the epicentre of the Digital Transformation Revolution that is changing the shape of business, enterprise and people’s lives. This transformation influences everything from how we manage and operate our homes to automating processes across nearly all industries. This paper aims to analyse the relationships of AI, big data and IoT, as well as the opportunities provided by the applications in various operational domains.
Geochemical exploration for Li in regional scale utilizing Staged Factor Analysis (SFA) and Spectrum-Area (S-A) fractal model in north central Iran

The main aim of this study was to outline the lithium anomalies by a regional exploration, at an area of 7800 km2, in Semnan province (north central Iran) using the Staged Factor Analysis (SFA) and Spectrum-Area (S-A) fractal model based on stream sediments and rock samples. Results derived via the SFA denote that Li was located in a factor as F2-4 with B, Cs, U and Rb which was utilized for calculation of the threshold values by the S-A method. The F2-4 data were classified by the fractal model for determination of the Li anomalies. Main anomaly for F2-4≥ 1.5 was situated in the SW and northern parts of this region. Furthermore, Li high grades of rock samples were correlated with main F2-4 anomalies. The main anomalies were correlated with geological particulars of Li mineralization types which represent that the main F2-4 anomalies associate with volcanic and tuff units in the SW part, and overlapped with clay minerals in the northern sector of this region. On the other hand, there are proper potential for Li mineralization which is demonstrated by this method.
High speed and reconfigurable optronic neural network with digital nonlinear activation

With its unique parallel processing capability, optical neural network has shown low-power consumption in image recognition and speech processing. At present, the manufacturing technology of programmable photonic chip is not mature, and the realization of optical neural network in free-space is still a hot spot of intelligent optical computing. In this article, based on MNIST datasets and 4f system, three-layer optical neural networks are constructed, whose recognition accuracy can reach 93.66%. Our network is programmable, high speed, reconfigurable and is better than the existing free-space optical neural network in terms of spatial complexity.
Deep Neural Network Guided by Attention Mechanism for Segmentation of Liver Pathology Image

Tumor region segmentation in liver pathological images has become a significant tool of routine diagnosis and it also plays an important role in the liver cancer surgical plannings. Liver lesions are usually manually marked by clinicians on ultra-high resolution pathological images, which is awfully time-consuming and laborious. Aiming at this problem, we propose a new deep neural network for automatically segment liver cancer pathological images in this paper to assist doctors in completing the diagnosis efficiently. The existing U-Net framework was mainly used in previous researches, but not all features extracted by the encoder module are suitable for pathological image segmentation. In order to solve this problem, the network in this paper designs an attention-guided connection module to obtain accurate segmentation of liver cancer, so that the useful context features can be selected adaptively from the low-level features which are guided by the high-level features. The pyramid sampling operation is applied to the global attention module to fuse different levels of features. The results of experiments show that the method has some certain advantages over the previous methods in liver cancer pathological image segmentation datasets, and its IOU is increase by 5.4% and 2.2% compared with U-Net and PSPNet networks. Simultaneously, in order to testify the basic concept preponderance, some related studies about the ablation are carried out.
A Bibliometric Analysis on Performance Appraisal

One of the most significant techniques for evaluating employee performance is performance appraisal, which should be aligned with the organization's goals.This topic is vital in providing knowledge on the evolution of performance appraisal research articles.This study aims to assess the publications of research articles on performance appraisal over the past 20 years (2001 to 2020).Hence, the bibliometric analysis method was performed on articles retrieved from the Scopus database and a total of 1976 articles were analysed.Information obtained such as keywords, source type, number of citations, frequently used publication language, famous author and highest published articles.The annual trend indicated that the frequency of performance appraisal studies was the highest in the years 2008 and 2010.Furthermore, the nursing profession and the medical line were the most studied fields concerning performance appraisal compared to the other disciplines.Based on the keyword analysis, the field of study on performance appraisal needs to be further expanded to training, organizational commitment, fairness, general practice, turnover intentions, and COVID-19 pandemic impact performance appraisal.This study also contributed to the scholarly knowledge in performance appraisal and has identified research gaps to be addressed in the future.
Building a library brand: five strategies for solo marketers

In academic libraries, the marketing and communications resource often can be a single individual, with marketing responsibilities billed as “other duties as assigned.” In addition, this individual may or may not have any previous marketing experience. No matter who is responsible for marketing at your library, there are some achievable strategies for maximizing your marketing effectiveness by becoming more proactive versus reactive, accelerating brand awareness, making data-driven decisions, and leveraging cross-functional networking.
How Fast Is Sign Language? A Reevaluation of the Kinematic Bandwidth Using Motion Capture

Human motion lies within a range of low frequencies. Filtered and down-sampled motion capture (mocap) data can thus provide meaningful representations for computational models. However, little is known about the kinematic bandwidth of Sign Language (SL), apart from isolated signs. Studies examining isolated signs suggested that SL could be limited to relatively low frequencies. This is unlikely to be appropriate for real-life conditions where signs are produced faster and are combined with several other rapid motion features. The present study investigated the spectral content of a multi-signer mocap dataset of continuous signing in French Sign Language. Across six different signers, Power Spectral Density estimation and residual analysis of the mocap data revealed that SL motion can be limited to a 0-12-Hz bandwidth, which is substantially wider than state-of-the-art estimates on isolated signs. More specifically, filtering the movements below 6 Hz caused distortion of the rapid motion, which suggests that SL motion involves higher frequencies in real-life conditions. The importance of kinematic bandwidth estimation is further addressed with a machine learning model trained to identify the six signers of the dataset. The performance of the model significantly decreased when using inappropriate bandwidths.
Improvement of Active Textbook System (A-txt) on Unity Version and Examination of Usage in Consideration of Copyright

This paper is described on the improvement of the Active Textbook System (A-txt) and the consideration for Copyright Law when the A-txt is used. The A-txt is a next-generation electronic educational material system proposed by the authors that can digitally expand the general paper books with general-purpose terminals. This system is the original system that allows you to easily attach additional digital content to any location by using figures and sentences on paper as augmented reality (AR) markers. Initially, A-txt, which was developed for each type of mobile OS, is provided on a unified platform by using Unity, and it has become possible to concentrate budgets and human resources. This time, we have added new functions to make usability better and improved the screen display by modifying the source code. In addition, as a measure to use figures in books as AR markers without infringing copyright, we experimentally have found the minimum necessary range in which A-txt can be used without inconvenience. In the process, we also have found a method to increase the recognition rate of markers.
MULTI-FACTOR STATUS PREDICTION BY 4D FRACTAL CNN BASED ON REMOTE SENSING IMAGES

With the acceleration of industrialization and urbanization, most lakes and reservoirs have been in eutrophication state. Eutrophication of water body will produce a series of environmental problems, among which cyanobacteria bloom is one of the most studied and seriously polluted problems. It is of great significance to effectively control the occurrence of cyanobacteria blooms by predicting and simulating the outbreak process of cyanobacteria blooms and accurately forecasting the relevant governance departments. However, there are two problems in the existing analysis of algal blooms: on the one hand, it is difficult to consider the impact of other factors on cyanobacteria blooms by taking chlorophyll concentration as the main influencing factor, and it is also unable to determine the relationship between various factors. On the other hand, only based on the field monitoring data research, lack of comprehensive analysis of the whole water area. The remote sensing image can reflect the change of the whole water area, but the traditional analysis method is difficult to deal with the massive remote sensing data effectively. In this study, eutrophication level was used as characterization index of cyanobacteria bloom, and the remote sensing image and its inversion map were taken as the main research data, and a new method of cyanobacteria bloom prediction based on four-dimensional (4D) fractal CNN was proposed. The prediction model uses 4D fractal CNN to extract the features of multi factor remote sensing images, capture the temporal and spatial characteristics and the interaction between multiple factors, and predict the eutrophication level of water body. In this study, a total of 216 remote sensing images of Taihu Lake Basin were selected from 29 groups with fine weather from 2009 to 2010 obtained by MODIS satellite. The simulation results show that the method proposed in this paper has excellent prediction performance, and the accuracy rate of 85.71% is better than that of common 3D CNN and 4D CNN models.
Transmission of SARS-CoV-2 by Children and Young People in Households and Schools: A Meta-Analysis of Population-Based and Contact-Tracing Studies

Background: The role of children and young people (CYP) in transmission of SARS-CoV-2 in household and educational settings remains unclear. We undertook a systematic review and meta-analysis of contact-tracing and population-based studies at low risk of bias. Methods: We searched 4 electronic databases on 5 April for contact-tracing studies and population-based studies informative about transmission of SARS-CoV-2 from 0-19 year olds in household or educational settings. We excluded studies at high risk of bias, including from under-ascertainment of asymptomatic infections. We undertook multilevel random effects meta-analyses of secondary attack rates (SAR: contact-tracing studies) and school infection prevalence, and used meta-regression to examine the impact of community SARS-CoV-2 incidence, mitigations and attendance on school infection prevalence. Findings: 4179 abstracts were reviewed, resulting in 37 included studies (17 contact-tracing; 18 population studies; 2 mixed studies). The pooled relative transmissibility of CYP compared with adults was 1.00 (0.73, 1.37) in adjusted household studies. The pooled SAR from 0CYP was significantly lower in school studies (0.5% (0.1, 1.6) than household studies (7.3% (2.5, 21.8) – p<0.0001. There was no difference in SAR from CYP to child or adult contacts. School population studies showed little evidence of clustering in classes within schools. School infection prevalence was associated with contemporary community 14-day incidence (OR 1.003 (1.001, 1.004), p<0.001) but not with school attendance rate. Interpretation: We found no difference in transmission of SARS-CoV-2 from CYP compared with adults within household settings. SAR were markedly lower in school compared with household settings, suggesting that household transmission is more important than school transmission in this pandemic. School infection prevalence was associated with community infection incidence, supporting hypotheses that school infections broadly reflect community infections. These findings are important for guiding policy decisions on shielding, vaccination school operations during the pandemic. Funding Information: No funding obtained.Declaration of Interests: All authors declare no competing interests.
A geometrical description of the universal $$1 \rightarrow 2$$ asymmetric quantum cloning region

We consider the problem of determining the achievable region of parameters for universal $1 \to 2$ asymmetric quantum cloning. Measuring the cloning performance with the figure of merit of singlet fraction, we show that the physical region is a union of ellipses in the plane. Equivalently, we characterize the parameter region of quantum state compatibility of two possibly different isotropic states, considering, for the first time, negative singlet fractions.
Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion

Articulatory features are proved to be efficient in the area of speech recognition and speech synthesis. However, acquiring articulatory features has always been a difficult research hotspot. A lightweight and accurate articulatory model is of significant meaning. In this study, we propose a novel temporal convolution network-based acoustic-to-articulatory inversion system. The acoustic feature is converted into a high-dimensional hidden space feature map through temporal convolution with frame-level feature correlations taken into account. Meanwhile, we construct a two-part target function combining prediction’s Root Mean Square Error (RMSE) and the sequences’ Pearson Correlation Coefficient (PCC) to jointly optimize the performance of the specific inversion model from both aspects. We also further conducted an analysis on the impact of the weight between the two parts on the final performance of the inversion model. Extensive experiments have shown that our, temporal convolution networks (TCN) model outperformed the Bi-derectional Long Short Term Memory model by 1.18 mm in RMSE and 0.845 in PCC with 14 model parameters when optimizing evenly with RMSE and PCC aspects.
Teacher Professional Development: Mobile and Limited Technology-enhanced Pedagogy

This qualitative case study seeks to document and understand the influence of technology integrated/supported professional development (PD) on preK-12 grade teachers in Nepal. The study focuses on the value of collaborative, university-school relationships to foster buy-in among teachers to integrate technology for increased student learning. We used the TPACK framework and sociocultural theory of learning to understand how and why technology integration at a rural school in a poor country would support more engaged pedagogy for learning. The PDs were co-designed by the researchers with teachers’ input, and further supported in its implementation when teachers tried out the new strategies in their classrooms. Data was collected utilizing methods of observations, self-reflective fieldnotes, informal interviews, and artifacts. Data analysis indicated that effective and locally suitable technology integration relies on building relationships with the teachers and school community; technology integration needs to be focused on the local needs of students and teachers based on their resources and curricular demands; and while limited (mobile) technology integration can support better learning, student-centered inquiry-based pedagogy needs to guide technology use in class. Furthermore, the study argues that recognizing and valuing local cultural and school contexts are central to successful PD in technology integration.
HMM-Based Face Recognition Using SVD and Half of the Face Image

Speeding up the system is one of the basic challenges in the real-world applications of Face Recognition (FR), whereas reducing the computational complexity can significantly increase the speed of the system. In recent years, many face recognition methods have been proposed but few of them give attention to this issue. Accordingly, in this article, we take the axis-symmetrical property of faces as a novel idea to speed up the face recognition algorithm as well as to reduce the computational complexity. Taking the axis-symmetrical property of faces leads us to use half of the face image. Proposing a face recognition system using Hidden Markov Model (HMM) as a classifier, we use the Singular Value Decomposition (SVD) to build the observation vectors. Evaluated results of the proposed system on Yale and Faces94 datasets show that the proposed system can achieve a satisfactory recognition rate with a higher speed.
On the regularization effect of stochastic gradient descent applied to least-squares

We study the behavior of the stochastic gradient descent method applied to Ax -b 2 2 → min for invertible matrices A ∈ R n×n .We show that there is an explicit constant c A depending (mildly) on A such that E Ax k+1 -b 2 2 ≤ 1 + c
An LMS-Based Grammar Self-index with Local Consistency Properties

A grammar self-index of a text T (Claude et al. 2012) consists of a grammar \({\mathcal {G}}\) that only produces T and a geometric data structure that indexes the string cuts of the right-hand sides of \({\mathcal {G}}\)’s rules. This representation uses space proportional to G, the size of the grammar, which is small when the text is repetitive. However, the index is slow for matching long patterns; it finds the occ occurrences of a pattern P[1..m] in \(O((m^{2}+occ)\log G)\) time. The most expensive part is a set of binary searches for the different cuts \(P[1..j]P[j+1..m]\) in the geometric data structure. Christiansen et al. 2010 solved this problem by building a locally consistent grammar that only searches for \(O(\log m)\) cuts of P. Their representation, however, requires significant extra space (tough still in O(G)) to store a set of permutations for the nonterminal symbols. In this work, we propose another locally consistent grammar that builds on the idea of LMS substrings (Nong et al. 2009). Our grammar also requires to try \(O(\log m)\) cuts when searching for P, but it does not need to store permutations. As a result, we obtain a self-index that searches in time \(O((m\log m+occ) \log G)\) and is of practical size. Our experiments showed that our index is faster than previous grammar-based indexes at the price of increasing the space by a 1.8x factor on average. Other experimental results showed that our data structure becomes convenient when the patterns to search for are long.
Contemporary realities of communication ethics in the educational space

The article discusses the current ideas of the foreign scientific school about the specificity of the ethical filling of the educational space in conditions of multiple uncertainty. The destructive factors of modern times, including the pandemic, have created a living environment of increased moral and ethical complexity and new challenges to the education system. Ethical changes in the educational space, both traditionally present and new, lead to a decrease in the quality of education, dehumanizing and dissocializing the student who finds himself in impelled isolation. The working hypothesis of the review is that the spatial separation of participants in the educational process is especially dangerous for the ethical integrity of the educational environment. The problems of ethical deficits of the teacher and the student are deduced: bullying, destructive leadership, academic dishonesty (cheating). The article regards several conditions for creating an ethically normalized learning process that might appear helpful in distance learning of students and regular utilization of social networks.
WATER OVERFLOW OF THE RIVER ANALYSIS BASED ON THE COMPUTER PROGRAM SIMULATIONS (CPS)

The purpose of this study is to the analysis of water overflow during wet seasons in Indonesia especially in Palembang district which frequently involved in annual monsoon floods and the question of whether the wells can be used during floods as well as an alternative source when there are no floods.Rainfall intensity is calculated by the first time to calculating the time of concentration and the calculation of the time of concentration using the Kirpich formula.Rainfall intensity has a relationship between the length of rain that lasts and the frequency of rain which is usually called the IDF (Intensity Duration Frequency) curve.From this curve, it can be seen the amount of rainfall intensity with a return period of 2, 5, and 10 years.The result of the research design discharges used for each return period is Q2 of 58.176 m 3 /sec, Q5 of 68.990 m 3 /sec, and Q10 of 76.150 m 3 /sec.Based on the simulation results using the HEC-RAS 4.1.0program there was overflowing of the river at stations 800, 900, 1000, 1100, 1200, 1300 and 1450 with a return period of 2 years and the station of 100, 200, 300, 400, 500, 600 and 700 there was no overflow or still safe.In the return period of 5 and 10 years there was overflow at stations 100, 400, 500, 600, 700, 800,