Reviewerly/exHarmony huggingface dataset:
Thesaurus-Based Methods for Assessment of Text Complexity in Russian

The study explores the problem of assessing complexity of Russian educational texts. In this paper, we focus on measuring conceptual complexity which is rarely selected as a research question and propose to use a thesaurus (or a linguistic ontology) to this end. We also compiled an original corpus of school textbooks on Social Studies, History used in high school, and textbooks for elementary school specifically for this set of text complexity experiments. On the first stage of the research, RuThes-Lite thesaurus, a linguistic knowledge base with the total size of 100,000 concepts, was used to elicit concepts in the texts of schoolbooks and represent them as graphs. To the best of our knowledge, we a new method for text complexity assessment using RuThes-Lite graphs and identify graphs-based semantic characteristics of texts that impact complexity. The most significant findings of the research include identification of statistically significant correlations of the selected features, such as node degree, with complexity of educational texts.
Transformed Network Based on Task-Driven

In view of the fact that the current networks mostly improve the network performance and robustness by proposing multiple strategies to update the network parameters, this paper puts forward a novel task-driven transformed network idea. With optimizing network parameters, the network structure can be optimized by vertical update, horizontal update and cross update based on the flat network only containing the input layer and the output layer. A multi-branch error back-propagation mechanism is proposed to match the unfixed network on purpose, which is beneficial to explore the function of each hidden layer and flexible to train the network, ensuring optimal network structure for each update. Objective contribution evaluation indicators based on the tasks are also established to analyze the contribution of each strategy to the network performance and guide the subsequent network structure update. Extensive simulation results show that the method proposed is feasible, specially, the recognition accuracy of testing obtains fine results and the network training of speed is real-time on one-dimension and two-dimension image datasets.
The Model for Continuous IT Solution Engineering for Supporting Legal Entity Analysis

Legal entity analysis nowadays is an important issue for enterprises, when they select their providers, clients, or other cooperation partners. Considering regular amendments to normative documents, changes in the performance of legal entities, and continuously developing new schemes for law violations, the paper suggests application of continuous engineering approach in development of IT solutions for legal entity analysis. Continuous engineering helps to ensure that the developed solutions can align with the continuous changes in regulatory requirements, legal entity performance, and available data sources used for analysis. The paper contributes the model for continuous IT solution engineering for legal entity analysis that supports various analysis factors. The model is theoretically evaluated to assess its ability to meet the challenges of legal entity analysis. Ability to implement the model is tested with the help of an IT solution prototype.
Implementing the IT-Transfer2020 Curriculum Guidance for Two-Year IT Transfer Programs

Baccalaureate IT programs are revisiting their curriculum in response to the ACM/IEEE IT curricular recommendations in the IT2017 report. Consequently, two-year colleges will likely need to update their lower division IT transfer programs as well. IT-Transfer2020 uses the same methodology of competencies as IT2017 and IT2yr2014, and it organizes its 27 essential and 29 supplemental competencies within the ten essential and nine supplemental IT competency domains defined in IT2017. IT-Transfer2020 includes an analysis of congruence between its competencies and the IT2yr2014 learning outcomes to assist institutions looking to offer both transfer and applied two-year IT programs with structuring their course offerings. Two-year programs which follow either IT-Transfer2020 or IT2yr2014 are substantially congruent and would likely only need to differ by one or two courses to prepare students for entry-level positions after two years of study or analyst-level positions upon earning a bachelor's degree. IT-Transfer2020 echoes the IT2017 recommendations regarding mathematics and science content and contains an IT Transfer Articulation Worksheet to facilitate communication between transfer partner institutions as well as assessment rubrics to help with continuous assessment of an IT transfer program's quality. The ACM Committee for Computing Education in Community Colleges invites institutions to highlight their IT transfer program by submitting a program example at ccecc.acm.org/correlations.
Incorporating Knowledge and Content Information to Boost News Recommendation

News recommendation, which aims to help users find the news they are interested in, is essential for online news platforms to alleviate the information overload problem. News is full of textual information with some knowledge entities, so recent studies try to leverage knowledge graphs (KGs) as side information to better model user preferences over news. However, most knowledge-enhanced methods assume that users are interested in the knowledge entities that occurred in the news. In real scenarios, users may like the news because of the news content rather than the knowledge entities. To take both knowledge and content factors into consideration, we propose a news recommendation method, namely knowledge and content aware network for news recommendation (KCNR). KCNR represents user and news in terms of knowledge and content, then it predicts the weight of user preferences on knowledge and content via a user preferences prediction mechanism. Besides, based on the weight of user preferences on knowledge, it extends user preferences along with entities in knowledge graphs. Experiments on two real-world datasets show that our approach achieves significant improvements over several state-of-the-art baselines in news recommendation.
What motor vehicles and translation machines have in common - a first step towards a translation automation taxonomy

In professional translation and in society in general, translation is increasingly automated. However, in Translation Studies, we lack an updated taxonomy of translation automation (TA) to understand the evolving and dynamic relationship between humans and digital technologies. Therefore, taking a first step towards a new taxonomy of TA, this paper adapts the Society of Automotive Engineers' taxonomy of six levels of driving automation, ranging from no automation to fully automated driverless cars, to the field of translation. The six levels of the TA taxonomy basically describe whether the translator and/or the system translates by means of source-text analysis and target-text production and checks for and corrects errors and inadequacies; whether the translator or the system responds to system failures; and whether the performance of the system is limited to a certain domain. Adapting the taxonomy was a complex endeavor, and constructive criticism from stakeholders interested in TA would be welcome.
Probabilistic Class-Specific Discriminant Analysis

In this paper we formulate a probabilistic model for class-specific discriminant subspace learning. The proposed model can naturally incorporate the multi-modal structure of the negative class, which is neglected by existing class-specific methods. Moreover, it can be directly used to define a class-specific probabilistic classification rule in the discriminant subspace. We show that existing class-specific discriminant analysis methods are special cases of the proposed probabilistic model and, by casting them as probabilistic models, they can be extended to class-specific classifiers. We illustrate the performance of the proposed model in both verification and classification problems.
Context Modeling with Evidence Filter for Multiple Choice Question Answering

Multiple-Choice Question Answering (MCQA) is one of the challenging tasks in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In OpenbookQA dataset [1], the requirement of extracting "evidence" is particularly important due to the mutual independence of sentences in the context. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. To address the challenge, we propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts with respect to different options collectively, and to potentially highlight the evidence sentences and filter out unrelated sentences. In addition to the effective reduction of human efforts of our approach compared, through extensive experiments on OpenbookQA, we show that the proposed approach outperforms the models that use the same backbone and more training data; and our parameter analysis also demonstrates the interpretability of our approach.
A Hybrid Sentiment Analysis Method

Sentiment analysis has attracted a wide range of attentions in the last few years. Supervised-based and lexicon-based methods are two mainly sentiment analysis categories. Supervised-based approaches could get excellent performance with sufficient tagged samples, while the acquisition of sufficient tagged samples is difficult to implement in some cases. Lexicon-based method can be easily applied to variety domains but excellent quality lexicon is needed, otherwise it will get unsatisfactory performance. In this paper, a hybrid supervised review sentiment analysis method which takes advantage of both of the two categories methods is proposed. In training phrase, lexicon-based method is used to learn confidence parameters which used to determine classifier selection from a small-scale labeled dataset. Then training set which is used to train a Naive Bayes sentiment classifier. Finally, a sentiment analysis framework consist of the lexicon-based sentiment polarity classifier and the learned Naive Bayes classifier is constructed. The optimal hybrid classifier is obtained by obtaining the optimal threshold value. Experiments are conducted on four review datasets.
Development of Informatization Teaching Ability of College English Teachers under the Network Environment

Development of informatization teaching ability of the college English teachers under the network environment is studied in this paper. The technique novelties are focused on two aspects. First, a design-based method is to directly estimate the sample data that well falls into the domain by sampling survey with weighted adjustment to achieve the excellent characteristics of the estimator, this model is optimized by the kernel method. Second, a reliable optimization model is needed to ensure the rationality of data transmission and reception and the quality of data transmission in the sensor network, hence use the kernel model to then represent the efficient framework. The proposed models are applied to the informatization teaching ability enhancement. The experiment results have given the conclusion that the proposed models can improve the overall performance.
Fine grained access control using H-KCABE in cloud storage

Cloud computing is used to store data in cloud storage through remotely. Cloud services can reach from small scale to large scale applications in low cost budget. Through these services users are able to access and retrieve data very constrainedly in low cost budget. It is used for producing data cloud service provider. It’s taking care of data to keep confidentially from entrusted access. It reduces most of the cloud security problems. This is achieved by some firewalls, cryptographic algorithms, antivirus software. The above techniques could not product data effectively which is shared by enterprise users who confidentially maintain the data during transactions among worldwide. Through simple encryption algorithm we cannot provide fine grained access control to the data in cloud storage. But its different from perfomance, scalability, reliability and other security parameters of cloud computing. In this paper we propose H-KCABE encryption algorithm in HABE model with some small modification and make better performance by re-encryption process. Through this we can easily achieve fine grained access control of cloud data than existing technique.
Notes on LCD Codes Over Frobenius Rings

Let R be a commutative Frobenius local ring. A result that the injective hull of an LCD code C over R. is free of dimension 1(C), where 1(C) is the minimum over the cardinalities of the generating sets of C, is proved in this correspondence. Applying this result, a concise proof for the main result in a recent paper by Sanjit Bhowmick et al. is derived. Furthermore, the LCD λ-constacyclic codes with λ being a unit, π(λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) = 1 and λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≠ 1, where π is the natural projection of R. to its residue field, are characterized, as another application of our result.
Integrating Location-Routing with Task and Motion Planning

This paper presents an approach that combines both location-routing and motion planning. The proposed architecture consists of three phases, namely sampling-based motion planning, facility location and vehicle routing. The sampling-based motion planner uses a probabilistic roadmap to generate a distance matrix. The facility location phase consists of a two-stage clustering algorithm, where the first stage cluster the goals with respect to the vehicles' maximum capacity and the second stage decides the facility location. Finally, in the vehicle routing phase, we use an off-the-shelf PDDL 2.1 solver for the routing problem. Experiments using a second-order vehicle model in obstacle-rich environments showcase the efficiency of our approach.
Signature Datatypes for Type Correct Collective Operations, Revisited

In order to provide for type correct implementations of applications in MPI that use derived datatypes to describe complex and possibly heterogeneous data layouts, signature datatypes describing the sequence of basic datatypes comprising the complex data layout in a compact manner have often been proposed and used to communicate and store such data in a type correct way. Signature datatypes are particularly useful in implementations of algorithms for collective communication employing pipelining and/or message-combining.
An evaluation of the library connectivity project through the lens of the digital inclusion model

Purpose It has been a decade, as the collaboration between the Ghana Investment Fund for Electronic Communications (GIFEC) and the Ghana Library Authority (GhLA) to extend information and communication technology (ICT) and library services to rural deprived, unserved and underserved communities in Ghana dubbed the library connectivity project. This paper aims to evaluate this initiative from the perspective of relevant key stakeholders and through the lens of the digital inclusion model. Design/methodology/approach The study adopted a qualitative approach to evaluate the library connectivity project offered to deprived communities in the Upper East Region of Ghana. Interviews were conducted with the head librarians, ICT teachers and school librarians. Focus group discussions were held with pupils from four beneficiary schools of the project. The researchers also witnessed four of the outreach programmes and training sessions and observed the mode of instruction. Findings The study brought to fore the enormous benefits of the library connectivity project as it aided school pupils to acquire practical ICT skills, which were found to be useful towards their final exam. Despite the enormous benefit of the project, it was bereft with a lot of challenges such as inadequate logistics and personnel thereby restricting the project to very few schools. Low staff motivation and unmotorable roads were also found to be a challenge, which could all be as a result of lack of funds. Research limitations/implications The paper underscores the importance of computer and information literacy and reveals how the GhLA is using innovative mobile library services to bridge the digital divide through the library connectivity project. Originality/value This paper makes a further contribution to the paucity of literature on the role of mobile libraries in the promotion of computer and information literacy.
A robust waste detection method based on cascade adversarial spatial dropout detection network

In cities, a large amount of municipal solid waste has impacted on the ecological environment significantly. Automatic and robust waste detection and classification is a promising and challenging problem in urban solid waste disposal. The performance of the classical detection and classification method is degraded by some factors, such as various occlusion and scale differences. To enhance the detection model robustness to occlusion and small items, we proposed a robust waste detection method based on a cascade adversarial spatial dropout detection network(Cascade ASDDN). The hard examples with occlusion in pyramid feature space are generated and used to adversarial training a detection network. Hard samples are generated by the spatial dropout module with Gradient-weighted Class Activation Mapping. The experiment verifies the effectiveness of our method on the 2020 Haihua AI challenge waste classification.
Study of Evolution By Automating Hardy Weinberg Equilibrium With Machine Learning Techniques In TensorFlow And Keras

Evolution transpires in organism populations and includes population diversity, heredity and varying survival. One way to study evolution is to study how the frequency of a population's allele varies from generation to generation. An allele is a type of gene variant. In other words, rather than looking at two parental species, evolution should be investigated by looking at the population as a whole. The only way to do this is therefore to investigate how allele frequencies shift in populations and how these shifts will predict what will happen to a population in the future. Throughout biology, there are basic equations which are used to model this theory basically termed as Hardy-Weinberg principle. This paper describes a new method that integrates Ecology and Engineering by automating the Hardy-Weinberg model using ADAM Optimization technique. The neural network trained based on Keras and Tensorflow has been deployed using python for the study of evolution. The developed model is tested with standard allele frequency database “ALFRED” to prove the efficiency.
Male Students Selecting Biology as the Entrance Examination in Juntendo University Achieve Higher Scores in Computer-based Testing than Those Selecting Physics

Objective: To identify a high-risk group with poor computer-based testing (CBT) results, we hypothesized that CBT scores are higher in students who selected biology than in those who selected physics as a science subject for the university entrance examination.
Logic in High Definition—Trends in Logical Semantics

This introductory chapter has two main aims: first, we provide a characterization of the basic notions of logical semantics; second, we give a short description of the remaining chapters of this book.
Visual symbols as a tool of transport logistics in university environment

Abstract The development trends of the educational space in the direction of the increasing introduction of intellectual technologies actualize the study of visual symbols being used as transport logistics tools in management of educational trajectories of students. Visual symbols serve as markers indicating space and help students’ navigation by forming their identity, including professional one. The article analyzes the data of a sociological study of the visual elements of the educational environment of Peter the Great St. Petersburg Polytechnic University, conducted among students. The conclusion is made about the importance for students of the prestige of the university, traditional symbols, virtual attributes, as well as elements of everyday learning life as important components of the educational environment, which allows us to raise the question of the possibility of using these tools to improve their logistics on their way to developing professional competencies.
Research on Personalized Recommendation Based on Big Data Technology

Aiming at the sparsity problem existing in the traditional collaborative filtering algorithm, this paper proposed an improved similarity computing method that integrated user rating behavior and item attributes. The sparse matrix is evaluated and predicted by the similarity calculation method, then the prediction rating was filled in the sparse matrix. At the same time, in the context of big data, the data scale was too large to affect the execution efficiency of the recommendation system. Hadoop platform was adopted to implement collaborative filtering recommendation algorithm based on the improved similarity model. Based on large-scale data segmentation, the distributed parallel processing was carried out. The proposed improved algorithm is verified by Movielens which was an internationally standard data set. The verification results show that the personalized recommendation system based on Hadoop platform and improved recommendation algorithm has better recommendation performance.
Establishing a Tungsten Deposit Group and a Pattern Grid Exploration in the Nui Phao Area, Northeastern Vietnam

Nui Phao area (northeastern Vietnam) has a high potential for tungsten resources. The tungsten ore bodies mainly appear in lens-shaped within granitic rocks of the Da Lien complex in the studied area. Minerals accompanying tungsten deposits are composed of fluorite, native gold, native bismuth, chalcopyrite, a lesser amount of allanite, cassiterite, and rare molybdenite. Based on collecting, synthesizing, geological processing data, and the mathematical method, studied objects of the exploration process are quantitatively described. Tungsten oxide (WO3) contents of the major ore body vary from 0.20 to 1.11% with a coefficient of variation (Vc) of 91.2% (unevenly). Generally, the tungsten oxide contents compliance with rules of the standard lognormal distribution. Major tungsten orebody average 56.7 m in thickness and its coefficient of variation (Vm) of 61.2% (unstable). Quantitative calculations reveal the Nui Phao tungsten deposit belongs to the III-type of mining exploration group. To explore this type of minerals, a linear grid pattern should be applied. Results show that the appropriate pattern grid exploration for reserve level 122 is 50 ÷ 60 × 30 ÷ 35 m; these values can be applied to other deposits occurring in similar geological settings.