Reviewerly/exHarmony huggingface dataset:
With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms

Sentence Compression is the task of generating a shorter, yet grammatical version of a given sentence, preserving the essence of the original sentence.This paper proposes a Black-Box Optimizer for Compression (B-BOC): given a black-box compression algorithm and assuming not all sentences need be compressed -find the best candidates for compression in order to maximize both compression rate and quality.Given a required compression ratio, we consider two scenarios: (i) single-sentence compression, and (ii) sentences-sequence compression.In the first scenario, our optimizer is trained to predict how well each sentence could be compressed while meeting the specified ratio requirement.In the latter, the desired compression ratio is applied to a sequence of sentences (e.g., a paragraph) as a whole, rather than on each individual sentence.To achieve that, we use B-BOC to assign an optimal compression ratio to each sentence, then cast it as a Knapsack problem, which we solve using bounded dynamic programming.We evaluate B-BOC on both scenarios on three datasets, demonstrating that our optimizer improves both accuracy and Rouge-F1-score compared to direct application of other compression algorithms.
Incremental Beam Manipulation for Natural Language Generation

The performance of natural language generation systems has improved substantially with modern neural networks.At test time they typically employ beam search to avoid locally optimal but globally suboptimal predictions.However, due to model errors, a larger beam size can lead to deteriorating performance according to the evaluation metric.For this reason, it is common to rerank the output of beam search, but this relies on beam search to produce a good set of hypotheses, which limits the potential gains.Other alternatives to beam search require changes to the training of the model, which restricts their applicability compared to beam search.This paper proposes incremental beam manipulation, i.e. reranking the hypotheses in the beam during decoding instead of only at the end.This way, hypotheses that are unlikely to lead to a good final output are discarded, and in their place hypotheses that would have been ignored will be considered instead.Applying incremental beam manipulation leads to an improvement of 1.93 and 5.82 BLEU points over vanilla beam search for the test sets of the E2E and WebNLG challenges respectively.The proposed method also outperformed a strong reranker by 1.04 BLEU points on the E2E challenge, while being on par with it on the WebNLG dataset.
Bangla Missing Data Imputation using HexaGAN Framework

Missing values within data affect the performance of machine learning models. HexaGAN is a framework that addresses the problem of missing data along with the issues of class imbalance and missing labels via a technique called imputation. Frequently, real-world datasets encounter the aforementioned problem. In our work, we attend the missing data problem by introducing missing data into MNIST handwritten-numerical dataset and Bangla handwritten-numerical dataset named NumtaDB. Root mean square error (rmse) and F1 score are computed to evaluate the performance of missing data imputation and digit-recognition classification of imputed images. K-Nearest Neighbor is used for digit-recognition. Results indicated lower rmse and F1 scores which implies that the imputation performed by HexaGAN is reliable.
Digital Turn: Global Trends and Local Specifics

The article reviews emerging research programs and analyzes new research strategies, including media epistemological and media archeological approaches, interface semiotics, economic theory of communication between market entities, etc. The main questions of the article: how is a digital turn in culture possible, what are its preconditions and consequences that radically transform the context of life? The development of information technologies has led to the emergence of a number of research areas such as chaos theory, transformational linguistics, bioinformatics, etc. Applied theories today can already change not only basic epistemological, but also ontological concepts. Thus, the spheres of everyday existence, habitually labeled as marginal, become decisive for global political, economic and socio-cultural subjects. So, the message on social networks affects the stock markets, the behavior of computer players – on the ways of interacting with digital gadgets, and the instrumental and communication skills of our time, the network content of private video bloggers can sometimes determine the main agenda in the media. Software applications of the digital environment developed in the IT industry determine the set of tools available to the user, and with them, the ways of expressing and perceiving thoughts. The authors share the conclusion: whatever we work on, we work together with the digital mind, which in the form of Big Data, Big Five, Cyber DNA and other technologies determines our actions. The essence of the digital turn is not taken by the arsenal of pre-digital methodological attitudes that require a return to the original unity of logic, epistemology, ethics and aesthetics in an era of epistemological uncertainty. The situation becomes more complicated in connection with the declared counter-tendencies of globalization – glocalization.
Stylometry of Short Stories through Voyant Corpus Summary Tool: A Text Mining Study

Text mining tools quantify big data precisely to express stylistic features of any text within a few seconds. This study aims to extract quantified stylistic qualities computationally in five American short stories (1. Button, Button, 2. Clearing in the Sky, 3. Dark They Were and Golden Eyed, 4. Thank You M?am, 5. The Piece of String). Students of literature encounter complications in grasping precise stylistic qualities of a writer in a short span of time or even before reading the text since exact comprehension of stylistic qualities facilitates in understanding the target text, its form, literary function and readers? critiquing ability. Current study mines the selected five short stories to derive certain stylistic characteristics quantitatively and qualitatively. Summary panel, a component of Voyant text mining tools, is applied to unveil quantified and qualitative stylistic features of literary texts. One advantage of implementation of Voyant Summary tool for data generation is that its results are statistical, qualitative, accurate and efficient. Mixed method is applied to produce authentic and valid results of this research. On philosophical grounds, Knowledge Discovery Theory by Rakesh Aggrawal is applied to extract potentially worthwhile new knowledge patterns. Major findings of this study are that all short stories employ dialogic technique and the common theme “said” is found in all short stories. Three short stories among five demonstrate their vocabulary density from 0.307 to 0.362 which exhibit ratio of unique words and total words in a short story. Average length of sentences ranges from 9.9 to 12.9 words which express small and simple construction of sentences by the writer.
Latent Gaussian process with composite likelihoods and numerical quadrature

Clinical patient records are an example of high-dimensional data that is typically collected from disparate sources and comprises of multiple likelihoods with noisy as well as missing values. In this work, we propose an unsupervised generative model that can learn a low-dimensional representation among the observations in a latent space, while making use of all available data in a heterogeneous data setting with missing values. We improve upon the existing Gaussian process latent variable model (GPLVM) by incorporating multiple likelihoods and deep neural network parameterised back-constraints to create a non-linear dimensionality reduction technique for heterogeneous data. In addition, we develop a variational inference method for our model that uses numerical quadrature. We establish the effectiveness of our model and compare against existing GPLVM methods on a standard benchmark dataset as well as on clinical data of Parkinson's disease patients treated at the HUS Helsinki University Hospital.
Traffic Light Control System Using Image Processing

In present times traffic plays a huge role in people’s busy lifestyle, this requires a more effective and efficient method to deal with today’s consistent growth of traffic at big intersections. The paper proposes using Image Recognition to incorporate a smart traffic controller. This system detects the vehicle on the lane and based on detection it estimates the vehicle count. The vehicle count tells the traffic condition, is it high, low or normal on a lane.[3] If it is normal then basic traffic management will work or if it is low or high, then only on its basis certain type of traffic management will work. Not now, but in future the system will be going to work on some other functionalities like emergency vehicle detection to deliver the fast emergency to the one who need and the system will also estimate the traffic and its condition on the lane using database and predictive analytics to predict the traffic situation on the lanes that not only help system for its proper functionality but also help people to decide their movement. Based on the number of vehicles in either direction, it was customised to be used in the future to monitor the traffic light sign by allowing each sign ample time.[1]
Completing the Picture: Complexity of Graded Modal Logics with Converse

Abstract A complete classification of the complexity of the local and global satisfiability problems for graded modal language over traditional classes of frames has already been established. By “traditional” classes of frames, we mean those characterized by any positive combination of reflexivity, seriality, symmetry, transitivity, and the Euclidean property. In this paper, we fill the gaps remaining in an analogous classification of the graded modal language with graded converse modalities. In particular, we show its NE xp T ime -completeness over the class of Euclidean frames, demonstrating this way that over this class the considered language is harder than the language without graded modalities or without converse modalities. We also consider its variation disallowing graded converse modalities, but still admitting basic converse modalities. Our most important result for this variation is confirming an earlier conjecture that it is decidable over transitive frames. This contrasts with the undecidability of the language with graded converse modalities.
It Should Be Remembered Before Peter Kapitsa Our Laboratory Was the First and sole in the USSR

"If I were offered to declare the names of only two physicists who were most famous in Ukrainian science, I would call the theorist L.D. Landau and experimenter L.V. Shubnikov" Academician of NAS of Ukraine OI Akhiezer
A Study on the Application of Output-Oriented Approach in College English Writing in Private Colleges

Based on the theory of output-oriented approach, this paper designs the teaching process from the following three aspects: motivating, enabling and assessing, so as to explore the effect of output-oriented method in college English writing teaching in private colleges.
Defect detection in code characters with complex backgrounds based on BBE

<abstract> Computer vision technologies have been widely implemented in the defect detection. However, most of the existing detection methods generally require images with high quality, and they can only process code characters on simple backgrounds with high contrast. In this paper, a defect detection approach based on deep learning has been proposed to efficiently perform defect detection of code characters on complex backgrounds with a high accuracy. Specifically, image processing algorithms and data enhancement techniques were utilized to generate a large number of defect samples to construct a large data set featuring a balanced positive and negative sample ratio. The object detection network called BBE was build based on the core module of EfficientNet. Experimental results show that the mAP of the model and the accuracy reach 0.9961 and 0.9985, respectively. Individual character detection results were screened by setting relevant quality inspection standards to evaluate the overall quality of the code characters, the results of which have verified the effectiveness of the proposed method for industrial production. Its accuracy and speed are high with high robustness and transferability to other similar defect detection tasks. To the best of our knowledge, this report describes the first time that the BBE has been applied to defect inspections for real plastic container industry. </abstract>
An Assessment of Flood Shocking Tears Down: A Case Study in Bogura District

This study was conducted on two chars of Bogura district using primary data and aims at understanding of how flood affects the rural poor living in the char lands. The study revealed that floods have long-term negative implications on socio-economic status. According to survey the most affected sector was agriculture (54.03%), followed by property (28.66%) and health (19.71%), diseases as Diarrhea occurred at alarming levels (76.07%) in the study areas. In the study year food availability and roads communications were also highly affected by flood. Floods make people vulnerable; as they take away their livelihoods at the first instance and leave them with little resources to overcome from the situation. Because of floods, rural poor communities face job loss, and two-thirds of their income is reduced, which limits their capabilities of preparedness, response, and recovery to subsequent floods. People cope with the situation by bearing substantial debts and a loss of productive assets. It also examines the impacts of flood on the livelihood of the rural poor and explores their coping strategies and also observers it as a positive ingredient for fertility and soil improvement. However the present study captures both positive and negative.&#x0D; Res. Agric., Livest. Fish.8(1): 65-73, April 2021
Prototyping of creation, implementation and visualization of correlation rules and microDocs

Semantic Correlation Rules (SCR) and microDocs are new concepts in the field of content delivery. SCR allow to define relationships between information units based on their metadata and, therefore, allow for the dynamic aggregation of microDocs. The creation of SCR heavily relies on the capabilities of modern content management systems (CMS) or ontology editors. The evaluation and visualization of the emerging microDocs, on the other hand, relies on the capabilities of content delivery portals (CDP). At this time, the support of both concepts in most software solutions currently being used, is only partly existent. This paper aims to demonstrate, how these currently existing limitations can be overcome, to reveal important factors to be considered and to showcase future possibilities of the aforementioned concepts. For this purpose, we developed a series of prototypes and conceptional visuals regarding creation of SCR, aggregation of microDocs and their visual appearance taking human perception into account.
Integration Model between Heterogeneous Data Services in a Cloud

With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.
Online Customer Reviews on Restaurant Using Blockchain

Modern day businesses are largely dependent on digital technologies. People prefer viewing the reviews before making any decisions. It applies to all consumables like buying Electronic items, Clothing, Travel, Guest-House, Restaurant, Rental, Housing, Automobile, Cosmetics, Jewellery, Movies, etc. Online services like Mantra, Yelp, Amazon, Facebook, Google My Business, Trip Advisor offer great services to the customer. However, drawbacks of these systems are fake reviews, negative reviews and sometimes even tampering of the reviews given by the customers, which has a huge impact on the business leading to huge financial losses. Sometimes a competitor in the business might also influence the ratings being provided. The centralized storage of these reviews also leads to problems like tampering or manipulation of the data being stored. In this paper we propose an application in the restaurant industry that solves all these drawbacks by making use of the Ethereum blockchain. The food reviews given by the customers are stored as smart contracts in the blockchain, which can't be altered, thus guaranteeing the authenticity of the reviews. Validity of the reviews is ensured because it is difficult for the restaurants to delete or create new accounts to wipe away the bad reviews given. Blockchain is immutable so we ensure that the reviews are genuine and the system is trustable.
AORM: Fast Incremental Arbitrary-Order Reachability Matrix Computation for Massive Graphs

Processing a reachability query in large-scale networks using existing methods remains one of the most challenging problems in graph mining. In this paper, we propose a novel incremental algorithmic framework for arbitrary-order reachability computation in massive graphs. The proposed method is intuitive and significantly outperforms the currently known methods in terms of computation time. We focus on the arbitrary-order reachability matrix framework called AORM, which can handle directed and disconnected networks such as citation networks. The AORM can handle diverse types of real-world datasets. We conduct extensive experimental studies with twenty synthetic networks generated from five random graph generation models and twenty massive real-world networks. The experimental results show the advantages of the method in terms of both computational efficiency and approximation controllability. In particular, the proposed method outperforms up to 10 times compared to NetworkX for incremental all-pairs shortest paths computation. Moreover, the computational results of the method rapidly converge to the ground truths. Thus, we can get the correct solution in the early stage of the incremental approximation. We can employ the method as a versatile feature extraction framework for network embedding. Overall, the experimental results present a remarkable improvement in speed-up for reachability computation.
[Characteristics of extreme precipitation in Ansai District of Yan'an City, China.]

The extreme precipitation characteristics of Ansai District in 40 years was analyzed from the perspective of precipitation intensity and precipitation frequency with 11 extreme precipitation indices, based on daily precipitation data of Ansai Meteorological Station from 1980 to 2019. The trend of extreme precipitation in the future was predicted. The results showed that the extreme precipitation indicators generally showed a downward trend during 1980-2019. The downtrend of heavy precipitation days and simple daily precipitation intensity reached significant level, with their climate tendency slopes being -0.65 d·(10 a)-1, -0.32 mm·d-1·(10 a)-1, respectively. Except for consecutive wet days and extremely wet day precipitation, the other extreme precipitation indices had mutation points. After the mutation, most of them had a downward trend, with significant decreases of annual precipitation, moderate precipitation days, heavy precipitation days, very heavy precipitation days, very wet day precipitation, simple daily precipitation intensity. The correlation between the consecutive dry days and other indicators was low and negatively correlated with some indicators, while the consecutive wet days were only correlated with a few indicators. In addition, other extreme precipitation indicators were significantly correlated. Results of the Hurst index analysis showed that the trend of extreme precipitation in Ansai District was sustainable.
Evidence-Based Statistical Evaluation of Japanese L2-Learners’ Proficiency using Principal Component Analysis

This paper aims at an automatic evaluation of second language (L2) learners’ proficiencies and tries to analyze English conversation data having 94 statistics and Global Scale scores of the Common European Framework of Reference (CEFR) given to each participant. The CEFR defines Range, Accuracy, Fluency, Interaction and Coherence as 5 subcategories, which constitute the CEFR Global Scale score. The statistics were classified into the CEFR’s 5 subcategories. We used the Principal Component Analysis (PCA), an unsupervised machine learning method, on each subcategory and obtained the participants’ principal component scores (PC scores) of the 5 subcategories for estimation parameters. We predicted the participants’ CEFR Global scores using the Multiple Regression Analysis (MRA). The proposed prediction method using the PC scores was compared with conventional methods with the 94 statistics. Based on the coeﬃcients of determination (R 2 ), the value of the proposed method (0.82) was nearly equivalent to one of values obtained by the conventional methods. Meanwhile, as for standard deviation, the proposed method showed the smallest value in the comparison. The results indicated usability of the PCA and PC scores calculated from the CEFR subcategory data for objective evaluation of L2 learners’ English proficiencies.
Research on quantitative evaluation method of teachers based on multiple linear regression

According to the nature of multiple linear regression evaluation method, the quantitative evaluation method of teachers is divided into quantitative evaluation method and qualitative evaluation method. On the basis of meta linear regression, the quantitative evaluation method of teachers' teaching is standardized, and the quantitative evaluation standard of teachers is designed. A spiral dynamic evaluation process of "data collection evaluation feedback" is formed by collecting and combining the evaluation information method with the expectation evaluation and grade evaluation standard. Combined with the multiple linear regression algorithm, the quantitative evaluation criteria and evaluation index of teachers are calculated To identify, identify and differentiate the main functions of quantitative evaluation of teachers, teaching effect of high teachers, targeted evaluation and improvement of teaching methods and models.