Reviewerly/exHarmony huggingface dataset:
Fuzzy-C means segmentation of lymphocytes for the identification of the differential counting of WBC

In the domain of histology, discovering the population of white blood cells (WBC) in blood smears helps to recognise destructive diseases. Standard tests performed in hematopathological laboratories by human experts on the blood samples of precarious cases such as leukaemia are time-consuming processes, less accurate and totally depending upon the expertise of the technicians. In order to get the advantage of faster analysis time and perfect partitioning at clumps, an algorithm is proposed in this paper that automatically identifies the counting of lymphocytes present in peripheral blood smear images containing acute lymphoblastic leukaemia (ALL) that performs lymphocytes segmentation by fuzzy C-means (FCM) clustering. Afterward, neighbouring and touching cells in cell clumps are individuated by the watershed transform (WT), and then morphological operators are applied to bring out the cells into an appropriate format in accordance with feature extraction. The extracted features are thresholded to eliminate the regions other than lymphocytes. The algorithm ensures 98.52% of accuracy in counting lymphocytes by examining 80 blood-smear image samples of the ALL-IDB1 dataset. The research works in showing this kind of improved accuracy facilitates in identifying leukaemia on starting stages for uncomplicated healing.
Quantum Deep Learning

In the past decade, deep learning has had a profound impact on machine learning and artificial intelligence in general. Around the same time, quantum algorithms have proven to be effective in solving some of the intractable problems on classical computers. Quantum computing can provide a much more efficient framework for deep learning than the existing classical regime by providing better optimization of the underlying objective function. The field of quantum deep learning attempts to build neural networks that can benefit from the quantum information flow through the network. In summary, quantum deep learning networks are accompanied by quantum layers consisting of quantum gates.
Information Diffusion Enhanced by Multi-Task Peer Prediction

Our study aims to strengthen truthfulness of the two-path mechanism: an information diffusion algorithm to find an influential node in non-cooperative directed acyclic graphs (DAGs). This subject is important because the two-path mechanism ensures only weak truthfulness (i.e., nodes are indifferent between reporting true or false out-edges), which restricts node selection accuracy. To enhance the mechanism, we employed an additional reward layer based on a multi-task peer prediction, where an informative equilibrium provides strictly higher rewards than any other equilibrium in virtually all cases (strong truthfulness). Rewards, which are derived from a comparison of each report, encourage a node to report true out-edges without affecting its own probability of being selected by the original two-path mechanism. We have also experimentally confirmed that our proposed {\em strongly truthful two-path mechanism} can sufficiently elicit true out-edges from each node.
Implementation of Personalized Recommendation Algorithm based on Mahout

Abstract In the Information Age, the success of companies and individuals increasingly depends on how quickly and efficiently they can turn massive amounts of data into actionable information. This paper first introduces the definition of recommendation algorithm and the process of collaborative filtering recommendation algorithm, then introduces the classification of algorithms in the Apache Mahout Framework, finally, the principles of user-based personalized recommendation algorithm and project-based personalized recommendation algorithm in Mahout framework are described and implemented respectively.
Classical reduction of gap SVP to LWE: A concrete security analysis

Regev (2005) introduced the learning with errors (LWE) problem and showed a quantum reduction from a worst case lattice problem to LWE. Building on the work of Peikert (2009), a classical reduction from the gap shortest vector problem to LWE was obtained by Brakerski et al. (2013). A concrete security analysis of Regev's reduction by Chatterjee et al. (2016) identified a huge tightness gap. The present work performs a concrete analysis of the tightness gap in the classical reduction of Brakerski et al. It turns out that the tightness gap in the Brakerski et al. classical reduction is even larger than the tightness gap in the quantum reduction of Regev. This casts doubts on the implication of the reduction to security assurance of practical cryptosystems.
Implementing immersive virtual reality in higher education : a qualitative study of instructor attitudes and perspectives

The current study aimed to understand the attitudes and perceptions of higher education (HE) instructors who have previously used immersive virtual reality (I-VR) in teaching. This study employed a qualitative design by conducting semi-structured interviews with HE instructors from several disciplines and institutions. Using thematic analysis, five major themes were formulated. These included: (a) applications and benefits; (b) curriculum integration; (c) classroom logistics; (d) barriers to application; and (e) evaluation. Instructors were generally positive about using I-VR as a pedagogical tool, proposing a range of novel applications and uses. However, logistical and technical problems were prominent which made implementation and widescale adoption challenging. The implications of these prominent attitudes are discussed, alongside a range of practical recommendations for applied future practice.
Modeling User Search Tasks with a Language-Agnostic Unsupervised Approach

Conversational information seeking is a major emerging research area because of the increasing popularity of conversational AI systems users utilize to perform their search tasks. Search systems and multiple other user supporting applications benefit from modeling the search tasks users carry out to satisfy their information needs. Most existing search task modeling methods are monolingual, and few methods leverage user clicks even though clicked URLs are crucial for modeling user intent. We propose a language-agnostic, user intent aware approach to model search tasks from user interactions with search systems. The proposed approach leverages user intent modeling from clicked query-document pairs, latent representations of queries in a language-agnostic space, and graph-based clusteringto model search tasks in an unsupervised approach. Experimental results demonstrate the proposed approach outperforms recent work in search task modeling, supporting user queries in multiple languages. It can also produce search task modeling results in the order of milliseconds, an essential aspect for conversational systems and user support applications requiring realtime results.
Bias in knowledge graph embeddings

In this paper, we study bias in knowledge graph embeddings. We focus on gender bias in occupations, but our approach is applicable to other types of bias. We start by proposing measures for identifying bias in the dataset (i.e., in the KG) and then present two methods for testing whether any bias in the dataset is amplified by the embeddings. First, we look for gender-specific occupation analogies in the embeddings. Second, we test whether link prediction (i.e., occupation prediction in our case) aggregates gender bias by proposing gender-dominated occupations to people of the corresponding gender more often than expected. Then, we use a debiasing approach based on projections on the gender subspace. We present experimental results using the Wikidata dataset and pretrained TransE embeddings. Our results show that there exists gender bias in the dataset and that such bias is amplified by the embeddings. Our debiasing approach removes bias with a small penalty on accuracy.
Optimization and Performance Measurement Model for Massive Data Streams

Massive Data Stream (MDS) is one research section of Big Data. The data streams which have large velocity are also known as Elephant Flows. The challenges of Velocity, as per Gartner’s definition, are addressed. This paper proposes an algorithm that handles and processes Massive Data Streams. The Service Oriented Architecture (SOA) is used to capture and process massive data at higher velocity. Three types of parallel task running strategies are explored for the use in SOA. The error is adjustable according to available memory for getting more accurate results. The results show that 99% confidence is achieved at the cost of hyper-logarithmic space in memory.
Covariance Matrix Adaptation Evolutionary Algorithm for Multi-task Optimization

Multifactorial evolution is a relatively new evolutionary multitasking paradigm which aims to solve multiple optimization problems simultaneously. In order to make a further contribution to multifactorial optimization, this paper introduced a covariance matrix adaptation evolutionary strategy to explore the generality of the multitasking paradigm. A novel encoding and decoding method is employed so that each component problem is solved by a unique population. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm has a superior performance in comparison with other state-of-the-art multifactorial optimization algorithms.
THE DICTIONARY «CORPORATE NAMES» EDITING WITHIN THE ELECTRONIC CATALOGUE OF THE YAKUB KOLAS CENTRAL SCIENCE LIBRARY OF THE NATIONAL ACADEMY OF SCIENCES OF BELARUS

The present report describes the dictionary «Corporate Names» used in the electronic catalogue of the Yakub Kolas Central Science Library of the National Academy of Sciences of Belarus. The publication reflects the current and planned editing of such kind of a dictionary including creation and modification of authority records of corporate names in the context of continuous interaction in the cooperative cataloguing system.
Multi-label Classification of Long Text Based on Key-Sentences Extraction

Most existing works on multi-label classification of long text task will perform text truncation preprocessing, which leads to the loss of label-related global feature information. Some approaches that split an entire text into multiple segments for feature extracting, which generates noise features of irrelevant segments. To address these issues, we introduce key-sentences extraction task with semi-supervised learning to quickly distinguish relevant segments, which added to multi-label classification task to form a multi-task learning framework. The key-sentences extraction task can capture global information and filter irrelevant information to improve multi-label prediction. In addition, we apply sentence distribution and multi-label attention mechanism to improve the efficiency of our model. Experimental results on real-world datasets demonstrate that our proposed model achieves significant and consistent improvements compared with other state-of-the-art baselines.
Automated Context-Aware Phrase Mining from Text Corpora

Phrase mining aims to automatically extract high-quality phrases from a given corpus, which serves as the essential step in transforming unstructured text into structured information. Existing statistic-based methods have achieved the state-of-the-art performance of this task. However, such methods often heavily rely on statistical signals to extract quality phrases, ignoring the effect of contextual information. In this paper, we propose a novel context-aware method for automated phrase mining, ConPhrase, which formulates phrase mining as a sequence labeling problem with consideration of contextual information. Meanwhile, to tackle the global information scarcity issue and the noisy data filtration issue, our ConPhrase method designs two modules, respectively: 1) a topic-aware phrase recognition network that incorporates domain-related topic information into word representation learning for identifying quality phrases effectively. 2) an instance selection network that focuses on choosing correct sentences with reinforcement learning for further improving the prediction performance of phrase recognition network. Experimental results demonstrate that our ConPhrase outperforms the state-of-the-art approach.
Introduction to Machine Learning

This chapter aims to make an introduction to the field of machine learning and to clarify the scope of similar domains, particularly deep learning. It also aims to compare different machine learning approaches, introduce some of the popular machine learning models, mention significant machine learning concepts, and walk you through the steps of machine learning. This chapter is a very significant one since deep learning is a subsection of machine learning, and therefore, most explanations are also valid for deep learning.
WITHDRAWN: Integration patterns of MongoDB GridFS for advanced data science and big data processing

The volume of unstructured data is increasing rapidly with velocity and variety generating enormous large scale datasets which are very difficult in terms of analysis and further extraction of knowledge. Such issues related to the storage, processing and knowledge discovery from huge amount of unstructured data is treated under big data analytics. There are various applications and cases where the volume, velocity and variety of data are increasing very frequently and a significant research work is going on to cope up with the aspects of Big Data processing. Big data is still data, but is of immense scale. Big Data is a concept used to describe an incredibly large volume set of data that is exponentially increasing over time. In short, these files are so massive and complex that no standard data processing systems can store or handle them effectively. In current scenarios, there is need to handle big data with advanced data science approaches and algorithms. In this manuscript, the integration patterns and implementation scenarios of MongoDB GridFS is presented for the advanced applications of data science with big data processing.
A Novel Focused Crawler with Anti-spamming Approach &amp; Fast Query Retrieval

The Web pages are growing in a design of terabytes or even petabytes day by day. In the case of the small Web, it is an easy task to answer a query, whereas robust modus operandi for storage, searching, or spamming is needed in case of large volumes of data. This study gives a novel approach for the detection of malicious URLs and fast query retrieval. The proposed focused crawler checks URL before entering in the search engine database. It discards malicious URLs but allows benign URLs to enter in search engine database. The detection of malicious URLs is done via the proposed feature set of URL which is created by selecting those attributes of URL features which are susceptible to spammers. Thus, a non-malicious database is created. The searching process performed through this search engine database by triggering a query. Search time taken by the proposed crawler is less as compared to the base crawler. The reason behind it is that the proposed focused crawler uses a trie data structure for storing fetched results in Web repository instead of HashSet data structure as used by the base crawler. On behalf of computed average search time (for ten queries), it is observed that proposed focused crawler is 12% faster than base crawler. To check the performance of proposed focused crawler, quality parameters, i.e., precision and recall, are computed which are found to be 92.3% and 94.73%. Detection accuracy is found to be 90% with an error rate of 10%.
How to Staff Software Engineering Team Roles Using the Concept of Personality? – An Exploratory Study

Nowadays, software is developed in teams. But how should software development teams be staffed in order to build a high performing team? This study investigates how the different roles in a software development team derived from the software lifecycle – project leader, requirements engineer, architect/designer, and developer/tester/maintainer – should be staffed with respect to HEXACO personality traits in order to form a high performing team. We conduct a qualitative analysis by leading 12 semistructured interviews with experts from the software engineering sector and with working experience of at least 2 years. We follow a Grounded Theory approach to derive personality traits and link them to software engineering roles. Our study shows that different personality profiles are beneficial for different roles and that corresponding staffing decisions can be beneficial for team performance. Our results support the composition of software engineering teams with the aim to successfully build high performing teams. Further studies can build on our findings, e.g. by measuring certain software quality aspects and connecting them to the personality profile of the developing team.
Radar Emitter Identification Based on Stacked Long and Short Term Memory

With the increasing complexity of electromagnetic environment and the rising of operating patterns of new radars, emitter identification is becoming more and more difficult. This paper presents a radar emitter identification method based on stacked long and short term memory (SLSTM). Radar pulse train can be directly used as input without extracting other features, which greatly simplifies the data preprocessing and realizes the "end-to-end" recognition of radar emitter signal. The timing characteristics of the pulses are automatically extracted by SLSTM, and the optimal network parameters are trained to complete radar signal identification. Compared experiments with conventional methods are conducted, and the results show that the proposed model outperforms other existing techniques. Moreover, simulation experiments in different noise and loss pulse environment show that the method is effective and robust in solving problems of radar emitter recognition.
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification

Previous studies on Data Augmentation (DA) mostly use a fine-tuned Language Model (LM) to strengthen the constraints but ignore the fact that the potential of diversity could improve the effectiveness of generated data. To address this dilemma, we propose a Diversity-Enhanced and Constraints-Relaxed Augmentation (DECRA) that has two essential components on top of a transformer-based backbone model, including a $$\mathbf {k}$$ - $$\varvec{\beta }$$ augmentation and a masked language model loss. Extensive experiments demonstrate that our DECRA outperforms state-of-the-art approaches by 3.8% in the overall score.
The Construction of “Dual-qualified” Teachers in Applied Colleges Based on Big Data

Big data mainly refers to large-scale or ultra-large-scale data sets, which are called “massive data” or “massive data”. Nowadays, with the development of information technology, such as mobile Internet, cloud computing, Internet of Things, and data mining, a variety of information is gradually being widely used in various industries. Moreover, they can provide important forces to promote national innovation, scientific development, economic take-off, and educational reform. The characteristics of big data are mainly manifested as massive, fast, diversified and high value. Based on the quality requirements of “double-qualified” teachers in applied colleges, schools need to strengthen the construction of the teaching team and create good conditions to actively cultivate high-quality talents.
MAKING LEARNING MEDIA FOR GREENBOX EFFECT SIMULATOR TO IMPROVE UNDERSTANDING OF THE CONCEPT OF THE GREENHOUSE EFFECT

The earth is experiencing global warming due to an increase in air temperature (greenhouse effect). This is due to the large number of greenhouse gases produced by human activities. In addition, it is also due to the reduced number of plants that absorb greenhouse gases, especially carbon dioxide. This condition causes the study of the greenhouse effect to become an object studied by students at school. Understanding the greenhouse effect is somewhat difficult if only understood in theory. Increasing understanding can be done by making practicum learning media. This study aims to create learning media for the Greenbox Effect Simulator to help understand the concept of the greenhouse effect. The research was conducted using control variables and independent variables (use of plants and without plants). The plants used are Caisim, Sri Gading and Anggrek. The presence of carbon dioxide (CO2) greenhouse gases can be detected by three things, namely changes in the color of the CO2 indicator, changes in temperature, and visibility of the box. The color of the CO2 indicator shows green and green yellow for Box B (with plants) which means the concentration of CO2 in normal conditions. Whereas Box C (without plants) gives a yellow color, which means that the concentration of CO2 is at high conditions. The presence of carbon dioxide gas from combustion will increase the temperature by 1.4 - 1.9 oC in Box C (without plants) and 0.7 - 1.5 oC in Box B (use of plants). The visibility of Box B shows a higher brightness level than Box C. The best plants that can absorb CO2 concentrations are orchids. The ability of orchids to absorb CO2 is assisted by their roots which also function to carry out photosynthesis. The existence of plants functions to absorb CO2 quite well when viewed from changes in temperature, color indicators and visibility.