Reviewerly/exHarmony huggingface dataset:
Optimizing Instance Selection Strategies in Interactive Machine Learning: An Application to Fraud Detection

Machine Learning systems are generally thought of as fully automatic. However, in recent years, interactive systems in which Human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time. In this paper we show that the order by which instances are evaluated by the auditors, and their feedback incorporated, influences the evolution of the performance of the system over time. The goal of this paper is to study of different instance selection strategies for Human evaluation and feedback can improve the learning speed. This information can then be used by the system to determine, at each moment, which instances would improve the system the most, so that these can be suggested to the users for validation.
A multi-task approach to argument frame classification at variable granularity levels

Abstract Within the field of argument mining, an important task consists in predicting the frame of an argument, that is, making explicit the aspects of a controversial discussion that the argument emphasizes and which narrative it constructs. Many approaches so far have adopted the framing classification proposed by Boydstun et al. [3], consisting of 15 categories that have been mainly designed to capture frames in media coverage of political articles. In addition to being quite coarse-grained, these categories are limited in terms of their coverage of the breadth of discussion topics that people debate. Other approaches have proposed to rely on issue-specific and subjective (argumentation) frames indicated by users via labels in debating portals. These labels are overly specific and do often not generalize across topics. We present an approach to bridge between coarse-grained and issue-specific inventories for classifying argumentation frames and propose a supervised approach to classifying frames of arguments at a variable level of granularity by clustering issue-specific, user-provided labels into frame clusters and predicting the frame cluster that an argument evokes. We demonstrate how the approach supports the prediction of frames for varying numbers of clusters. We combine the two tasks, frame prediction with respect to media frames categories as well as prediction of clusters of user-provided labels, in a multi-task setting, learning a classifier that performs the two tasks. As main result, we show that this multi-task setting improves the classification on the single tasks, the media frames classification by up to +9.9 % accuracy and the cluster prediction by up to +8 % accuracy.
Explicit Facial Expression Transfer via Fine-Grained Representations

Facial expression transfer between two unpaired images is a challenging problem, as fine-grained expression is typically tangled with other facial attributes. Most existing methods treat expression transfer as an application of expression manipulation, and use predicted global expression, landmarks or action units (AUs) as a guidance. However, the prediction may be inaccurate, which limits the performance of transferring fine-grained expression. Instead of using an intermediate estimated guidance, we propose to explicitly transfer facial expression by directly mapping two unpaired input images to two synthesized images with swapped expressions. Specifically, considering AUs semantically describe fine-grained expression details, we propose a novel multi-class adversarial training method to disentangle input images into two types of fine-grained representations: AU-related feature and AU-free feature. Then, we can synthesize new images with preserved identities and swapped expressions by combining AU-free features with swapped AU-related features. Moreover, to obtain reliable expression transfer results of the unpaired input, we introduce a swap consistency loss to make the synthesized images and self-reconstructed images indistinguishable. Extensive experiments show that our approach outperforms the state-of-the-art expression manipulation methods for transferring fine-grained expressions while preserving other attributes including identity and pose.
On the Bernstein-von Mises theorem for the Dirichlet process

We establish that Laplace transforms of the posterior Dirichlet process converge to those of the limiting Brownian bridge process in a neighbourhood about zero, uniformly over Glivenko-Cantelli function classes. For real-valued random variables and functions of bounded variation, we strengthen this result to hold for all real numbers. This last result is proved via an explicit strong approximation coupling inequality.
Enhancement of Sentiment Analysis Using Clause and Discourse Connectives

The sentiment of a text depends on the clausal structure of the sentence and the connectives’ discourse arguments. In this work, the clause boundary, discourse argument, and syntactic and semantic information of the sentence are used to assign the text’s sentiment. The clause boundaries identify the span of the text, and the discourse connectives identify the arguments. Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence, a deeper-level semantic analysis is required for the correct analysis of sentiments. Hence, in this study, explicit connectives in Malayalam are considered to identify the discourse arguments. A supervised method, conditional random fields, is used to identify the clause boundary and discourse arguments. For the study, 1,000 sentiment sentences from Malayalam documents were analyzed. Experimental results show that the discourse structure integration considerably improves sentiment analysis performance from the baseline system.
Artificial intelligence for pancreatic cancer detection: Recent development and future direction

Artificial intelligence for pancreatic cancer detection: Recent development and future direction
Detecting Acute Lymphoblastic Leukemia Through Microscopic Blood Images Using CNN

Leukemia refers to cancer, takes place when bone marrow generates excess amount of abnormal blood cells. These abnormal blood cells grow rapidly interrupting the functioning of the other normal cells. Hence, body loses its capability to fight with exterior organism like viruses, bacteria, fungi, and so on, hereby, affecting the immunity of the person. Leukemia, if not treated on time can lead to death. This disease affects adults as well as children. Thus, early detection of the disease would help in proper treatment. The manual diagnosis of this disease is quite laborious, tedious, and time-consuming which involves blood test and biopsy. Thus, to overcome these shortcomings, this paper discusses a computer-aided automated diagnosis system for detection of acute lymphoblastic leukemia (ALL) using deep-learning models. A pretrained AlexNet model is deployed for performing this task. Experiment is done using microscopic blood cell images. This overall framework helps in detecting the malignant cells easily. From the experimental results, it is evident that this proposed method achieves an accuracy of 98% without the use of any image segmentation technique or feature extraction technique. Hence, the work reported in the paper would provide a framework to aid pathologist in diagnosing acute lymphoblastic leukemia accurately and quickly.
Sign Language Recognition System

In this paper an attempt has been made to design a sign language recognition system. An intelligent glove has been designed to automate the communication between a deaf-mute with others by converting sign language into speech or understandable language. The sensory gloves provide data of the human hand shape or movement and translate it to text and speech. It comprises hardware and software for translating sensor data. It is wearable devices that can be put on human hands and convert hand's gestures into signs letter by letter and send the data into the firebase for further processing. The glove is equipped with flex sensors and an inertial measurement unit to recognize the movement by monitoring the finger orientation and hand motion in three-dimensional spaces that senses a person's gestures in the form of finger bend and hand fist tilt. The Hall sensor has been used to process and collect data for training and model development. The three different machine learning algorithms, i.e., support vector machine, Naïve Bayes, decision tree, have been used for analysis. It has been observed that the support vector machine has the highest accuracy, i.e., 90%. After Analyzing, the data has been sent to the speech converting function, and then audible results have been produced.
Management and maintenance of research data by researchers in Zimbabwe

Purpose The concept of research data management (RDM) is new in Zimbabwe and other developing countries. Research institutions are developing research data repositories and promoting the archiving of research data. As a way of creating awareness to researchers on RDM, the purpose of this paper is to determine how researchers are managing their research data and whether they are aware of the developments that are taking place in RDM. Design/methodology/approach A survey using a mixed method approach was done and an online questionnaire was administered to 100 researchers in thirty research institutions in Zimbabwe. Purposive sampling was done by choosing participants from the authors of articles published in journals indexed by Google Scholar, Scopus and Web of Science. Interviews were done with five top researchers. The data was analysed using NVIVO. The results were presented thematically. The questionnaire was distributed using the research offices of the selected 30 research institutions. There was a 75% response rate. Findings The findings indicated that all the researchers are aware of the traditional way of managing research data. A total of 70% of the respondents are not aware of the current trends in RDM services, as they are keeping their data on machines and external hard drives, while 97.3% perceive RDM services as useful, as it is now a requirement when applying for research grants. Librarians have a bigger role to play in creating awareness on RDM among researchers and hosting the data repositories for archiving research data. Practical implications Research institutions can invest in research data services and develop data repositories. Librarians will participate in educating researchers to come up with data management plans before they embark on a research project. This study also helps to showcase the strategies that can be used in awareness creation campaigns. The findings can also be used in teaching RDM in library schools and influence public policy both at institutional and national level. Social implications This study will assist in building capacity among stakeholders about RDM. Based on the findings, research institutions should prioritise research data services to develop skills and knowledge among librarians and researchers. Originality/value Few researches on RDM practices in Zimbabwe were done previously. Most of the papers that were published document the perception of librarians towards RDM, but this study focused mainly on researchers’ awareness and perception. The subject is still new and people are beginning to research on it and create awareness amongst the stakeholders in Zimbabwe.
SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

Purpose Topic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These models also exist the readability problem when displaying the discovered topics. The purpose of this paper is to propose a novel model called the Sense Unit based Phrase Topic Model (SenU-PTM) for both the sparsity and readability problems. Design/methodology/approach SenU-PTM is a novel phrase-based short-text topic model under a two-phase framework. The first phase introduces a phrase-generation algorithm by exploiting word embeddings, which aims to generate phrases with the original corpus. The second phase introduces a new concept of sense unit, which consists of a set of semantically similar tokens for modeling topics with token vectors generated in the first phase. Finally, SenU-PTM infers topics based on the above two phases. Findings Experimental results on two real-world and publicly available datasets show the effectiveness of SenU-PTM from the perspectives of topical quality and document characterization. It reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time. Originality/value The originality of SenU-PTM lies in the new procedure of modeling topics on the proposed sense units with word embeddings for short-text topic discovery.
An evaluation of documentation requirements for ISO 9001 compliance in scrum projects

Purpose Quality management standards (e.g. ISO 9001) lead to process conformance in the realization of quality goods and services; however, they can be rather document intensive. This paper investigates documentation practices used for aligning “light-weight” Scrum methods with ISO 9001 in a leading healthcare software firm. Design/methodology/approach The authors investigated how “light-weight” Scrum approaches fit with organizational documentation practices for ISO 9001 compliance in one leading healthcare software development firm. Three investigative rounds were conducted with software professionals having different Scrum roles to understand their challenges in maintaining process documentation with Scrum methods. Findings ISO standards stipulate certain mandatory documentation as evidence that certain pre-defined processes are followed in the build-up of quality goods and services. However, this may result in “heavy-weight” document driven approaches that interfere with “light-weight” Scrum methods. Case study findings reveal tensions faced by software professionals in maintaining the ISO 9001 documentation. That is, while some level of documentation is considered useful, software professionals consider certain other documentation tasks to be excessive and cumbersome. Further, many operational documents were written retrospectively for administrative compliance, leading to reduced, incomplete and ambiguous descriptions. Practical implications The study provides much value for practitioners in adapting their documentation with ongoing operational processes. Further, the critique on current ISO 9001 implementations in Agile environments has implications for future documentation practice. Originality/value The empirically drawn findings showcase some of the challenges in maintaining ISO 9001 documentation within Scrum projects. The study has contributed to both theory and practice in relation to the co-existence of ISO drawn standards with Agile approaches used for software development.
Analysis and Research on Enterprise Resumption of Work and Production Based on K-means Clustering

In response to the impact of the Novel coronavirus infection in 2020, State Grid Corporation has introduced 12 important measures to fully promote enterprises to resume work and production. The evaluation method based on the ratio of user's current daily electricity consumption to the average electricity consumption of December 2019 is straightforward and effective, but difficult to scientifically defining the judgment threshold. In view of the problem of lacking rationality in the evaluation of resumption of work and production, this project deeply analyzes the current enterprise resumption index, then further proposes a K-means clustering analysis method. By extracting power consumption data of enterprises, characteristic indexes reflecting their production shutdown, resumption and peak periods are established. K-means clustering algorithm is applied to cluster characteristic samples, accurately analyzing the resumption situation of enterprises consequently, which helps improve the accuracy of the evaluation on resumption of work and production and achieve significant management, economic and social benefits.
The application of basketball technology multimedia computer-assisted instruction courseware in physical education

Computer-assisted instruction, as a new type of education method, uses modern media to optimize the classroom teaching structure. Multimedia technology integrates graphics, text and sound into one, with a large amount of information, rich content, good human-computer interaction, timely feedback, can effectively stimulate students’ interest, attract students’ attention, and enable students to produce strong Learn the desire to develop students’ active learning ideas. We should adapt to the changes of modern teaching methods as soon as possible, combine traditional physical education teaching methods with multimedia computer networks, and effectively carry out physical education teaching activities. Basketball is one of the majors in college sports competition teaching, and it is also one of the sports that college students like to see. For a long time, the teaching of basketball has been carried out through the teaching and demonstration of teachers, which is a bit boring and weak in the face of the infinite charm of basketball. Nowadays, CAI courseware will inject new vitality and vitality into traditional basketball teaching. In order to combine this method organically with basketball teaching, this research develops a set of CAI courseware based on network basketball tactics based on the characteristics of basketball tactics and the development of computer multimedia courseware. CAI is the abbreviation of computer-assisted instruction. The basketball class of the second-grade students of the general department of the university conducted an eight-week teaching experiment to explore the feasibility of applying CAI courseware in basketball teaching, and provided a feasible reference for the physical education teachers to develop teaching courseware and timely application in basketball teaching.
A Multi-Task Neural Network for Action Recognition with 3D Key-Points

Action recognition and 3D human pose estimation are fundamental problems in computer vision and closely related areas. In this work, we propose a multi-task neural network for action recognition and 3D human pose estimation. Results of previous methods are usually error-prone especially when tested against the images taken in-the-wild, leading error results in action recognition. To solve this problem, we propose a principled approach to generate high quality 3D pose ground truth given any in-the-wild image with a person inside. We achieve this by first devising a novel stereo inspired neural network to directly map any 2D pose to high quality 3D counterpart. Based on the high-quality 3D labels, we carefully design the multi-task framework for action recognition and 3D human pose estimation. The proposed architecture can utilize shallow, deep features of images, and in-the-wild 3D human key-points to guide a more precise result. High quality 3D key-points can fully reflect morphological features of motions, thus boost the performance on action recognition. Experimental results demonstrate that 3D pose estimation leads to significantly higher performance on action recognition than separated learning. We also evaluate the generalization ability of our method both quantitatively and qualitatively. The proposed architecture performs favorably against the baseline 3D pose estimation methods. In addition, the reported results on Penn Action and NTU datasets demonstrate the effectiveness of our method on the action recognition task.
Early and Precision-Oriented Detection of Cervical Cancer

Cancer, a deadly disease with very high mortality, is the abnormal growth of a specific region in the body. The high mortality rate due to cancer is because of delay in its detection as well as misdiagnosis. Early detection of solid tumors such as breast cancer and cervical cancer is critical for treating patients. The inclusion of digital colposcopy in the position of manual diagnosis for the screening of cervical cancer can enhance the precision and can significantly lower the chances of error subsequently. Application of Artificial Intelligence (AI) in medical diagnostics not only has the potential to revolutionize the provisioning of healthcare but can also significantly improve the health of patients afflicted with cancers such as cervical cancer. Artificial Intelligence based Deep Learning models have been extensively used for Image Analytics based detection of anomalous tumors. In this chapter, the use of deep learning based convolutional neural networks (CNN) is examined for the cervical cancer classification.
Enhancing Usability of E-Learning Platform: A Case Study of Khan Academy

Human-Computer Interaction (HCI) is a subject that measures usability and evaluates the interface of a system, software, or product to see its efficiency, effectiveness, and user satisfaction. On the other hand, at the time, the pandemic spread of Coronavirus (Covid-19) having an immense impact on schools, universities, and other educational sectors. In this current unpredictable situation, the importance of online education is visible, therefore, the usability of e-learning platforms truly plays an essential role in our lives. This study is about to evaluate the usability of a very common e-learning portal i.e., Khan Academy by user testing and heuristic evaluation techniques. Effectiveness, efficiency, and satisfaction of Khan Academy were measured quantitatively, while usability issues were identified qualitatively through heuristic evaluation. The study result shows that Khan Academy suffers from certain usability problems which need to be addressed. The study also presents relevant recommendations which are scrutinized and approved by IT specialists and UI/UX designers. The study concluded that suggested amendments are important to consider enhancing the usability of different e-Learning portals generally and Khan Academy particularly.