Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis through Word Vectors: A Study on Movie Reviews from IMDb

Roy, Abhijit Roy, ParthajitThe present study emphasizes the challenges of labeling the correct semantic orientation of movie review texts due to the high degree of ambiguity and varied narratives of the reviewers. The study uses a vector-based model of sentiment analysis on IMDb movie review data set. The model computes the attributes of text reviews based on singular words and the longest common subsequence of words. We present a comparative assessment of unsupervised vector-based model (K-means clustering) and supervised models (support vector machine with linear kernel and naïve Bayesian classifier) for sentiment-based classification of movie review data set. The study reports that supervised models are outperforming unsupervised model in terms of accuracy. Despite the high degree of ambiguity of movie reviews, the results are encouraging in the context of natural language processing.
Self-Adaptive Word Segmentation Model in Military Domain Based on Conditional Random Field

To reinforce the adaptability of word segmentation model and make up for the inaccessibility of large-scale annotated corpus in military domain, we propose a self-adaptive word segmentation model in military domain based on conditional random field. The model adequately extracts the features of character, position and part-of-speech from texts to increase the accuracy of the general word segmentation model. Meanwhile, it introduces the boundary entropy and dictionary features, and realizes the self-adaptability of the word segmentation model in military domain, based on reverse maximum matching of word segmentation results using the military dictionary. The experiment shows when the training corpus is general and the testing corpus is military, the precision, recall and F1-measure value increased by 9.45%, 9.97% and 9.72%, respectively, compared with the CRF model in general domain, which effectively improves the self-adaptability of segmentation method in military domain.
CPRIC: Collaborative Parallelism for Randomized Incremental Constructions

Randomized algorithms often outperform their deterministic counterparts in terms of simplicity and efficiency. In this paper, we consider Randomized Incremental Constructions (RICs) that are very popular, in particular in combinatorial optimization and computational geometry. Our contribution is Collaborative Parallel RIC (CPRIC) -a novel approach to parallelizing RIC for modern parallel architectures like vector processors and GPUs. We show that our approach based on a work-stealing mechanism avoids the control-flow divergence of parallel threads, thus improving the performance of parallel implementation. Our extensive experiments on CPU and GPU demonstrate the advantages of our CPRIC approach that achieves an average speedup between 4× and 5× compared to the naively parallelized RIC.
Data Analysis of Online and Offline Guiding Evaluation Mode of Ideological Courses based on Oracle

In the course of education reform, colleges and universities need to transform and upgrade the existing management mode, so as to meet the needs of the ever-expanding scale of students in colleges and universities. At the same time, it is also necessary to upgrade and transform the network teaching in colleges and universities. The education quality assessment of colleges and universities is very important for the management of colleges and universities. It can help the leaders of colleges and universities comprehensively and accurately grasp the teaching status quo, and improve the education quality of the whole college. The teaching quality directly reflects the teaching level of the whole university, so the evaluation of the whole university is very important. Teaching quality assessment is the overall assessment of teachers' teaching activities according to certain rules, including information statistics, students' learning conditions and students' feedback in class. Oracle databases are versatile and difficult to maintain and optimize. In the case of Oracle databases, the use of indexes, parallel control, SGA and PGA management, and the query speed of the database all interfere with the overall performance of the database.
Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.
Governance and net-import dependency on food and agricultural products in Sub-Saharan Africa: does any causality exist?

Though most countries in Sub-Saharan Africa (SSA) are agricultural-based, the region is a net importer of food and agricultural products and experiences the highest level of food insecurity globally. The government have a joint goal of achieving a favourable balance of trade and food security; hence this study examines the causal relationship between quality of governance and net-import dependency on food and agricultural products for 25 SSA countries during the period 1995-2015. Principal component analysis is employed to develop a governance index based on the six worldwide governance indicators and a multivariate panel vector error correction framework applied to infer causality in the short and long run. The results reveal that a higher governance index is correlated with a lower net-import dependency ratio and the relationship is statistically significant. Evidence of unidirectional causality running from governance to net-import dependency is reported in 14 SSA countries, mainly in the long run. In conclusion, improving governance quality could support reduced food and agricultural net-import dependency through promoting agriculture production, exports and consequently reduced trade deficits in the long run. Hence, governance reforms in the region should be placed at the heart of the agricultural development agenda.
Retaliation in Bitcoin Networks

Due to counterparty risks, some Bitcoin trading platforms allow users to rate the level of trust they have in others. We examine users' feedback behaviour on two Bitcoin trading platforms and provide statistically strong evidence that the feedback behaviour of Bitcoin users is dependent on how they are rated themselves, that is, they retaliate. In addition, user's reputation is strongly and positively associated with the scores they deliver, and there is a certain persistence in the scores a user gives to others. We find that peers deliver negative feedback relatively quickly to users with bad reputation. Moreover, well-reputed users withhold negative feedback longer and give positive feedback faster than users with bad reputation.
Convergent series of integers with missing digits

A classical theorem of Kempner states that the sum of the reciprocals of positive integers with missing decimal digits converges. This result is extended to much larger families of "missing digits" sets of positive integers with convergent harmonic series.
Reception and criticism of I. Kant’s theory of space and time in the philosophy of V. D. Kudryavtsev-Platonov

The subject of this research is the reception of I. Kant's theory of space and time in the theory of cognition of V. D. Kudryavtsev-Platonov (hereinafter Kudryavtsev). In his theory of cognition, Kudryavtsev not only engages into an indirect dispute with the German philosopher and criticized his point of views, but also used a number of his ideas, namely from the theory of space and time. The relevance of this article is substantiated by the fact that the problem of reception of Kantian ideas in Kudryavtsev's philosophy has not been fully developed. The goal of this research is to determine the specificity of perception of Kantian theory of space and time in the analogous theory of Kudryavtsev. For achieving this goal, the author employed conceptual-analytical, system-structural and comparative methods, as well as the method of historical reconstruction. The scientific novelty lies in elucidation of the nature of reception of gnoseological ideas of I. Kant in the philosophy of V. D. Kudryavtsev. The author outlines the positions of Kantian theory of space and time that were borrowed by Kudryavtsev: 1) space and time have an a priori nature, i.e. they are universal and necessary non-empirical forms of sensory cognition; 2) at the same time, they are conditions of sensory experience. Kudryavtsev also follows Kant in division of reality into phenomena and things-in-themselves, but does not agree with uncognizability of the things-in-themselves. Such divergence is associated with the objective meaning of space and time, which Kudryavtsev insists on, while Kant underlines their subjective meaning alone. In conclusion, it is emphasized that Kudryavtsev formulates his theory of space and time leaning on Kantian apriorism. Kudryavtsev simultaneously agrees with Kant's definitions of space and time and denies Kant's conclusions on their subjective nature.
CASR: A Network for Single-Image Super-Resolution with Cascading and Residual connections

CNNs facilitate the significant process of single-image super-resolution (SISR). However, most of the existing CNN-based models suffer from numerous parameters and exceeding deeper structures. Moreover, these models relying on deep features commonly ignore the hints of low-level features, resulting in poor performance. In this paper, we demonstrate an effective network named CASR, which addresses these problems by extracting features in Head Module via the strategies based on the depth-wise separable convolution and includes a cascading residual Block (CAS-Block) for the upsampling process, which benefits the gradient propagation and feature learning while eases the model training. Extensive experiments conducted on four benchmark datasets demonstrate the proposed method outperforms the state-of-the-art methods for SISR.
Context-based understanding of food-related queries using a culinary knowledge model

Dietary practices are governed by a mix of ethnographic aspects, such as social, cultural and environmental factors. These aspects need to be taken into consideration during an analysis of food-related queries. Queries are usually ambiguous. It is essential to understand, analyse and refine the queries for better search and retrieval. The work is focused on identifying the explicit, implicit and hidden facets of a query, taking into consideration the context – culinary domain. This article proposes a technique for query understanding, analysis and refinement based on a domain specific knowledge model. Queries are conceptualised by mapping the query term to concepts defined in the model. This allows an understanding of the semantic point of view of a query and an ability to determine the meaning of its terms and their interrelatedness. The knowledge model acts as a backbone providing the context for query understanding, analysis and refinement and outperforms other models, such as Schema.org , BBC Food Ontology and Recipe Ontology.
Real-Time Deep Learning Face Mask Detection Model During COVID-19

COVID-19 pandemic has affected the whole world not only physically but also financially. Thousands of people have died due to this COVID-19 (Coronavirus) epidemic, and still the whole world is struggling to prevent this epidemic. Initially, the government had only an option to stop this epidemic and that was to declare complete lockdown. But during this time phase of lockdown, many people became unemployed. To stop this scenario and for the sake of employment and other financial economy, our government has decided to unlock various sectors. This unlock phase has increased the risk of outbreak of COVID-19 so to avoid risk and to stop spreading of this pandemic disease, the government has issued some important guidelines to be followed mandatorily by each individual during this unlock process. The face mask is one of the compulsory guidelines issued by the government. Wearing a face mask may reduce the chances of spreading corona virus. Many people roaming in public places without wearing a face mask. To monitor such kind of people at a time is not easy manually. So in this paper, we are proposing deep learning-based model that can automatically detect person wearing a mask or not. In this method, a model convolutional neural network (CNN) was trained over thousands of images to detect whether a person wearing a mask or not. Proposed model achieved 93.36 validation accuracy and 98.71 training accuracy. This model can be helpful to stop spreading of coronavirus in such organizations where human interaction is necessary for smooth functioning like hospitals, colleges, gyms, supermarkets.
A Literature Review of Data Mining Techniques Used in Collaborative Filtering Recommender Systems

As a result of the ever-increasing number of available items in e-services, users are often overwhelmed. Therefore, it is essential to develop and apply algorithms to address the challenge of selection overload. Collaborative Filtering (CF) systems have been developed to help users to find what they might be interested in among a range of available selections. Moreover, CF systems have been widely discussed as an efficient approach to cope with the selection overload issue. This paper presents a literature review of the common CF techniques and data mining techniques used for CF.
Badge: Towards a Domain Ontology for SPI Resources

Software Process Improvement (SPI) consists of a set of changes in software development companies, which introduces new and improved methods, techniques, and tools. SPI projects generally are performed based on a reference model, such as CMMI, ISO 9001, ISO 15504, among others. In most cases, there is a need to use more than one reference model or add aspects of other models. Based on this, it is necessary to harmonize the chosen models to reach the expected goals, but it is not a simple task. The goal of this paper is to introduce a domain ontology called Base of Knowledge about Software Engineering Practices (Badge). To evaluate our proposal we performed a focus group session with practitioners and academic subjects. The focus group outcome is that Badge is able to generalize the models that say “what should be done”. We also advocate that using a generalization structure enables the possibility to automate some tasks in SPI initiatives.
Imbalanced-type Incomplete Data Fuzzy Modeling and Missing Value Imputations

Missing values are a common phenomenon in real-world datasets, which caused by many factors such as errors in data acquisition or storage, equipment failure, or human fault in storage. Incomplete data modeling and missing values imputation have become an increasingly important task. Since the regression relationship between attributes is usually different in different clusters, this paper proposes a method called DS-TS-ALI model to model incomplete data that rely on clusters. The precise regression model between attributes is established for incomplete data in the framework of Takagi-Sugeno (TS) fuzzy model. In the premise parameter identification part, a distance density (DS) algorithm based on a partial distance strategy is proposed given the distribution of data categories is imbalanced. Moreover, a membership reconstruction strategy is proposed on this basis. In the consequence parameter identification part, we propose an alternating iterative (ALI) scheme which treats missing values as variables to identify the parameters of the attribute regression model. The imputation will be completed at the end of the modeling process. Experiments on several datasets are conducted to demonstrate the effectiveness of the proposed method.
Justice-Centered Design Engagements with Children and Teens:

We perpetuate, reinforce, and have the opportunity to disrupt systemic injustices through the actions we take as individuals. As an IDC community, we have a responsibility to reflect upon our behaviors and the commitments we make to ensure our engagements with youth, especially those that identify as Black or brown, are justice-centered. In this workshop, we invite researchers and practitioners to reflect on what is at stake if the IDC community does not intentionally take a justice-centered approach throughout our methods, our research questions, and our everyday interactions with youth. The goals of our workshop are two-fold: (1) To share stories about the actions we take as researchers with power to influence change in our interactions with youth, and (2) To establish the commitments the IDC community can make for more equitable design and research engagements with youth.
Towards Evaluating Students’ Digital Capabilities: An Analysis of UK Further Education Student Surveys

The ability of students to access learning via technology is a key factor in sustainable development goals. During the COVID-19 emergency most students’ educational experience moved from face-to-face physical classroom to web-based environments which exposed disparities in students’ digital resources and competence and placed greater attention on the need to address these inequalities. Digital competence is typically measured in terms of an individual's ability to use digital technology to achieve their work, study or personal objectives. Being digitally competent is significant with regard to an individual being able to achieve things that they value, but is only part of an overall evaluation of their digital capability. This paper argues that in addition to competence, assessment of digital capability should include an evaluation of a person's access to technology as well as their attitudes towards its value in achieving their goals.
Projection Region Detection Model Based on BASNet

As smart devices are used to assist in understanding presentation slides, such as machine translation and augmented reality, in seminars and conferences, separating the projection region from the foreground and background will improve the accuracy of recognition of slides. To take on this challenge, this paper proposes a novel detection model, BASNet with channel and space attention (CS-BASNet), to accurately detect projection area. Firstly, channel module and space attention module are added to the backbone network of ResNet18 to improve the detection evaluation metrics. Secondly, to reduce the influence of color on detection, we used gray images as inputs to train the model. We also propose a projection region dataset which sets projection region as foreground. The test results on this dataset show that the evaluation metrics of CS-BASNet is better than that of BASNet, and it showed that the evaluation metrics of CS-BASNet in detecting projection region has outperformed the latest five salient object detection models.
Complexity and Algorithms for MUL-Tree Pruning

A multiply-labeled tree (or MUL-tree) is a rooted tree in which every leaf is labeled by an element from some set $${\mathcal X}$$ , but in which more than one leaf may be labeled by the same element of $${\mathcal X}$$ . MUL-trees have applications in many fields. In phylogenetics, they can represent the evolution of gene families, where genes are represented by the species they belong to, the non-uniqueness of leaf-labels coming from the fact that a given genome may contain many paralogous genes. In this paper, we consider two problems related to the leaf-pruning (leaf removal) of MUL-trees leading to single-labeled trees. First, given a set of MUL-trees, the MUL-tree Set Pruning for Consistency (MULSETPC) Problem asks for a pruning of each tree leading to a set of consistent trees, i.e. a collection of label-isomorphic single-labeled trees. Second, processing each gene tree at a time, the MUL-tree Pruning for Reconciliation (MULPR) Problem asks for a pruning minimizing a reconciliation cost with a given species tree. We show that MULTSETPC is NP-hard and that MULPR is W[2]-hard when parameterized by the duplication cost. We then develop a polynomial-time heuristic for MULPR and show its accuracy by comparing it to a brute-force exact method on a set of gene trees from the Ensembl Genome Browser.
Dog Breed Classification Using Convolutional Neural Network

Dogs are one of the most common domestic animals. Due to a large number of dogs, there are several issues such as population control, decreased outbreak such as Rabies, vaccination control, and legal ownership. At present, there are over 180 dog breeds. Each dog breed has specific characteristics and health conditions. In order to provide appropriate treatments and training, it is essential to identify individuals and their breeds. Machine learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks ( CNNs ) gives single often used methods for image classification and detection. In this exertion , we define a CNN based approach for spotting dogs in perchance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm ( CNN ) gives good analysis accuracy for all the tested datasets.
Cluster analysis of mixed data based on Feature Space Instance Cluster Closeness Metric

Clustering Algorithms have recently attracted considerable attention in machine learning applications due to their high efficiency. However, the existing clustering methods still have some issues that need to be further solved. For example, most existing methods convert one type of feature into another type, which ignores the specific properties of data. In addition, most of them consider entire features, which may lead to complexity in computation and result in sub-optimal performance. To address the above problems, this paper proposes a novel method for clustering categorical and numerical features based on Feature Space Instance Cluster Closeness Metric (FSICCM). In the first stage, given training data, FSICCM exploits the similarity metric for numerical features. In the second stage, we design a novel metric for categorical features. Meanwhile, we design a new learning algorithm to cluster mixed datasets. Extensive experimental results on benchmark datasets show that the proposed FSICCM outperforms several state-of-art clustering methods in terms of accuracy and efficiency.