Reviewerly/exHarmony huggingface dataset:
Detecting and analyzing missing citations to published scientific entities

Proper citation is of great importance in academic writing for it enables knowledge accumulation and maintains academic integrity. However, citing properly is not an easy task. For published scientific entities, the ever-growing academic publications and over-familiarity of terms easily lead to missing citations. To deal with this situation, we design a special method Citation Recommendation for Published Scientific Entity (CRPSE) based on the cooccurrences between published scientific entities and in-text citations in the same sentences from previous researchers. Experimental outcomes show the effectiveness of our method in recommending the source papers for published scientific entities. We further conduct a statistical analysis on missing citations among papers published in prestigious computer science conferences in 2020. In the 12,278 papers collected, 475 published scientific entities of computer science and mathematics are found to have missing citations. Many entities mentioned without citations are found to be well-accepted research results. On a median basis, the papers proposing these published scientific entities with missing citations were published 8 years ago, which can be considered the time frame for a published scientific entity to develop into a well-accepted concept. For published scientific entities, we appeal for accurate and full citation of their source papers as required by academic standards.
Detection and Classification of Leukemia Using Deep Learning

Abstract: The practice of medicine is getting modernized every year and continuously moving towards more automated systems that help and improves the healthcare practice to be more productive with treatments and accurate in their assessments. Leukemia is a form of cancer that can be a fatal disease, and to rehabilitate and treat it requires a correct and early diagnosis. Standard methods have transformed into automated computer tools for analyzing, diagnosing, and predicting symptoms. Advanced methods can be used to help patients detect terminal disorders such as leukemia, which is a fatal disorder and common cancer type amongst children. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Because advanced CNN models, such as ResNet and DenseNet, are deeper and more complex having the ability to learn better. Keywords: Leukemia, Deep Learning, Classification, DenseNet, ResNet, CNN
Evaluation of Apriori, FP growth and Eclat association rule mining algorithms

Association rule mining means to discover the guidelines which empower us to anticipate the event of a particular thing dependent on the events of different things in the exchange. Incessant thing set mining prompts the disclosure of affiliations and connections among things in enormous value-based or social informational collections. With monstrous measures of information constantly being gathered and put away, numerous enterprises are becoming keen on mining such examples from their data sets. The disclosure of intriguing connection connections among immense measures of deal records can help in numerous business dynamic cycles, for example, inventory configuration, cross-advertising, and client shopping conduct examination. In this we assess diverse sort of calculations like Apriori, FP Growth and Eclat calculation for affiliation decide mining that deals with regular thing sets. Affiliation rule mining between various things in enormous scope data set is a significant information mining issue. We assess these calculations by considering different elements like number of exchanges, least help, memory utilization and execution time. Assessment of calculations is created dependent on exploratory information, which give end.
Object Tracking: An Experimental and Comprehensive Study on Vehicle Object in Video

Tracking objects on camera or video is very important for automated surveillance systems.Along with the development of techniques and scientific research in object tracking, automatic surveillance systems have gradually become better.With the input of a frame including the object to be tracked and the location information of the object to be tracked in that video.The output will be the prediction of the position of the object to be tracked on the next frame.This paper presents the comparison and experiment of some traditional object tracking methods and suggestions for improvement between them.Firstly, we examined related studies, traditional object tracking models.Secondly, we examined image and video data sets for verification purposes.Thirdly, experimenting with some related research works in traditional object tracking problems, evaluation of the existing model, what has been achieved and what has not been achieved for the current models.Propose improvements based on the combination of traditional methods.Finally, we aggregate these results to evaluate for each type of object tracking model.The results show that Particles Filter method has the highest CDT with TO score of 0.907971 on VOT dataset and 0.866259 on UAV123 dataset.However, the most stable are the two hybrid methods, the Particle filter base on Mean shift method has a TF score of 31.1 on the VOT dataset and the Kalman Filter base on Mean shift method has a TME score of 28.8233 on the UAV dataset.Because low-level features cannot represent all the information of an object to be tracked during the completion of the experiment, we can conclude that combining deep learning network and using high-level feature into the tracking model can bring better performance in the future.
Understanding How In-Visualization Provenance Can Support Trade-Off Analysis

In domains, such as agronomy or manufacturing, experts need to consider trade-offs when making decisions that involve several, often competing, objectives. Such analysis is complex and may be conducted over long periods of time, making it hard to revisit. In this paper, we consider the use of analytic provenance mechanisms to aid experts recall and keep track of trade-off analysis. We implemented VisProm, a web-based trade-off analysis system, that incorporates in-visualization provenance views, designed to help experts keep track of trade-offs and their objectives. We used VisProm as a technology probe to understand user needs and explore the potential role of provenance in this context. Through observation sessions with three groups of experts analyzing their own data, we make the following contributions. We first, identify eight high-level tasks that experts engaged in during trade-off analysis, such as locating and characterizing interest zones in the trade-off space, and show how these tasks can be supported by provenance visualization. Second, we refine findings from previous work on provenance purposes such as recall and reproduce, by identifying specific objects of these purposes related to trade-off analysis, such as interest zones, and exploration structure (e.g., exploration of alternatives and branches). Third, we discuss insights on how the identified provenance objects and our designs support these trade-off analysis tasks, both when revisiting past analysis and while actively exploring. And finally, we identify new opportunities for provenance-driven trade-off analysis, for example related to monitoring the coverage of the trade-off space, and tracking alternative trade-off scenarios.
Shap-enhanced counterfactual explanations for recommendations

Explanations in recommender systems help users better understand why a recommendation (or a list of recommendations) is generated. Explaining recommendations has become an important requirement for enhancing users' trust and satisfaction. However, explanation methods vary across different recommender models, increasing engineering costs. As recommender systems become ever more inscrutable, directly explaining recommender systems sometimes becomes impossible. Post-hoc explanation methods that do not elucidate internal mechanisms of recommender systems are popular approaches. State-of-art post-hoc explanation methods such as SHAP can generate explanations by building simpler surrogate models to approximate the original models. However, directly applying such methods has several concerns. First of all, post-hoc explanations may not be faithful to the original recommender systems since the internal mechanisms of recommender systems are not elucidated. Another concern is that the outputs returned by methods such as SHAP are not trivial for plain users to understand since background mathematical knowledge is required. In this work, we present an explanation method enhanced by SHAP that can generate easily understandable explanations with high fidelity.
Art of (Data) Storytelling

The extent to which data visualizations are used, and the quality of these visualizations, has consistently been shown to influence human decision-making relative to static (non-visual) presentations of findings or ideas (Boldosova &amp; Luoto, 2019; El-Wakeel et al., 2020; Liem et al., 2020). Why are visualizations so impactful? Likely because most decision-makers do not want to sort through spreadsheets or read a novel-length narrative to understand what is important—they want it straight and quick. They want color, novelty, storytelling, and interactivity (Dykes, 2020; Kostelnick, 2016; Kosara &amp; MacKinley, 2013). This is the purpose of data storytelling: to literally tell a story about the data analyses to, in some way, impart knowledge or affect change among the audience. Data and data analysis are never neutral—they are always political–and storytelling is how the data analyst can attempt to influence how data findings are perceived by the audience. This paper discusses the basis of data storytelling and why it is important for creating a narrative around data visualizations that compels readers and viewers to act upon findings. It then discusses (in the form of a reflective discussion) how the art of data storytelling may be improved and activated to promote social justice themes by reflecting on the effectiveness of storytelling in hip hop music.
A Framework for Short Video Recognition Based on Motion Estimation and Feature Curves on SPD Manifolds

Given the prosperity of video media such as TikTok and YouTube, the requirement of short video recognition is becoming more and more urgent. A significant feature of short video is that there are few switches of scenes in short video, and the target (e.g., the face of the key person in the short video) often runs through the short video. This paper presents a new short video recognition algorithm framework that transforms a short video into a family of feature curves on symmetric positive definite (SPD) manifold as the basis of recognition. Thus far, no similar algorithm has been reported. The results of experiments suggest that our method performs better on three changeling databases than seven other related algorithms published in the top issues.
Evaluation of Tacotron Based Synthesizers for Spanish and Basque

In this paper, we describe the implementation and evaluation of Text to Speech synthesizers based on neural networks for Spanish and Basque. Several voices were built, all of them using a limited number of data. The system applies Tacotron 2 to compute mel-spectrograms from the input sequence, followed by WaveGlow as neural vocoder to obtain the audio signals from the spectrograms. The limited number of data used for training the models leads to synthesis errors in some sentences. To automatically detect those errors, we developed a new method that is able to find the sentences that have lost the alignment during the inference process. To mitigate the problem, we implemented a guided attention providing the system with the explicit duration of the phonemes. The resulting system was evaluated to assess its robustness, quality and naturalness both with objective and subjective measures. The results reveal the capacity of the system to produce good quality and natural audios.
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance

Human-translated text displays distinct features from naturally written text in the same language.This phenomena, known as translationese, has been argued to confound the machine translation (MT) evaluation.Yet, we find that existing work on translationese neglects some important factors and the conclusions are mostly correlational but not causal.In this work, we collect CAUSALMT, a dataset where the MT training data are also labeled with the human translation directions.We inspect two additional critical factors, the traintest direction match (whether the human translation directions in the training and test sets are aligned), and data-model direction match (whether the model learns in the same direction as the human translation direction in the dataset).We show that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on translationese.In light of our findings, we provide a set of suggestions for MT training and evaluation. 1 * Equal contributions. 1 Our code and data are at https://github.com/EdisonNi-hku/CausalMT.
Subverting Fair Image Search with Generative Adversarial Perturbations

In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the ranking model behave unfairly without having access to the model or training data? To investigate this question, we present a case study in which we develop and then attack a state-of-the-art, fairness-aware image search engine using images that have been maliciously modified using a Generative Adversarial Perturbation (GAP) model [75]. These perturbations attempt to cause the fair re-ranking algorithm to unfairly boost the rank of images containing people from an adversary-selected subpopulation.
Using Information Technologies to Train Today Teachers in the Educational Environment

The article studies the characteristics of teacher training for primary school teachers in the information educational environment in higher education institutions (HEIs). It shows that Ukraine’s integration into European and world educational space as a prerequisite for determining effective areas in its development is a strong motive to study the systems of teacher training for primary school teachers, reflect on their positive experience, clarify trends and prospects of their development and discover ways to solve the existing issues in teacher education. The article considers the current state of teacher training for primary school teachers in view of applying information technologies in HEIs. Considering teacher training for primary school teachers as the large-scale nationwide objective, the article analyzes this aspect in those Ukrainian HEIs whose activities are related to this particular area. IT is also essential and necessary to study the experience of individual scholars and practitioners implementing the achievements of informatization in their activities. The detailed study of teacher training for primary school teachers has made it possible to reveal significant shortcomings in this process. The findings prove that HEIs do not create favourable organizational conditions for future primary schools teachers to develop their innovative professional and personal qualities. These include the following: competitiveness; motivation towards lifelong learning and continuing professional development; knowledge about appropriate ways of personal development; creative and IT skills.
A Sparse Non-parametric BRDF Model

This paper presents a novel sparse non-parametric Bidirectional Reflectance Distribution Function (BRDF) model derived using a machine learning approach to represent the space of possible BRDFs using a set of multidimensional sub-spaces, or dictionaries. By training the dictionaries under a sparsity constraint, the model guarantees high-quality representations with minimal storage requirements and an inherent clustering of the BDRF-space. The model can be trained once and then reused to represent a wide variety of measured BRDFs. Moreover, the proposed method is flexible to incorporate new unobserved data sets, parameterizations, and transformations. In addition, we show that any two, or more, BRDFs can be smoothly interpolated in the coefficient space of the model rather than the significantly higher-dimensional BRDF space. The proposed sparse BRDF model is evaluated using the MERL, DTU, and RGL-EPFL BRDF databases. Experimental results show that the proposed approach results in about 9.75dB higher signal-to-noise ratio on average for rendered images as compared to current state-of-the-art models.
RF Micro-Doppler Classification with Multiple Spectrograms from Angular Subspace Projections

Radio Frequency (RF) sensors present distinct ad-vantages over cameras or wearables for hand gesture recognition providing high resolution radial range and velocity measurement, being able to operate in dark and through the objects with high temporal and frequency resolutions. Moreover, the flexibility of the complex formatted data allows users to develop their own algorithms to generate various data representations such as time-frequency (Micro-Doppler - &#x03BC;D) maps, or range-Doppler or - angle as a function of time. However, conventional &#x03BC;-D generation does not regard the angular information of the multiple targets existing in the RF data. Hence, multiple targets with different &#x03BC;-D signatures at various angular positions create a mixed spec-trogram output reducing recognition performance. This paper proposes an angular projection approach on radar data cubes (RDCs) to generate raw radar data for defined angular subspaces. Hence multiple &#x03BC;-D spectrograms for each angular subspace can be constructed from the projected data. The proposed approach has been tested on RF data for gross body movement and American Sign Language (ASL) recognition. It has been showed that the utilization of angular projected spectrograms increases classification accuracy for ASL and achieves recognition accuracy of 92.6&#x0025; for 20 word ASL signs.
Prediction of failures in the project management knowledge areas using a machine learning approach for software companies

Abstract In this paper we propose a novel machine-learning model to predict project management knowledge areas failure for software companies using ten knowledge areas in project management based solely on the criteria of unambiguity, measurability, consistency, and practicability. The majority of software projects fail in software companies due to a lack of software project managers who are unfamiliar with the Project Management Knowledge Areas (PMKAs) that are used without considering the company's conditions or project contexts. By distributing questionnaires, we use an experimental methodology and the snowball sampling method to collect data from software businesses. We employ machine learning techniques including Support Vector Machines (92.13%), Decision Trees (90%), K-Nearest Neighbors (87.64%), Logistic Regression (76.4%), and Naive Bayes (66%) to adapt data from failed software projects. When we look at the results, Support Vector Machine outperforms the other four machine learning methods. High dimensional data is more efficient and contains nonlinear changes since Support Vector Machines deal with categorical data. The study's purpose is to improve project quality and decrease software project failure. Finally, we recommend collecting more failed project datasets from software businesses and comparing them to our findings to predict knowledge domain failure.
Rediscovering Argumentation Principles Utilizing Collective Attacks

Argumentation Frameworks (AFs) are a key formalism in AI research. Their semantics have been investigated in terms of principles, which define characteristic properties in order to deliver guidance for analysing established and developing new semantics. Because of the simple structure of AFs, many desired properties hold almost trivially, at the same time hiding interesting concepts behind syntactic notions. We extend the principle-based approach to Argumentation Frameworks with Collective Attacks (SETAFs) and provide a comprehensive overview of common principles for their semantics. Our analysis shows that investigating principles based on decomposing the given SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in comparison to usual AFs. We introduce the notion of the reduct as well as the modularization principle for SETAFs which will prove beneficial for this kind of investigation. We then demonstrate how our findings can be utilized for incremental computation of extensions and give a novel parameterized tractability result for verifying preferred extensions.
Retos y dificultades de docente de nivel secundaria en tiempos de Covid-19

This article aims to describe some of the main experiences that teachers at the secondary level had, derived from the exercise of the teaching function during the covid-19 pandemic. The results were analyzed by categorizing their responses into five aspects: 1. Difficulties faced 2. Challenges of virtual teaching, 3. Technological tools used, 4. Strengths detected and 5. Achievements. Among the results, it was found that participants faced difficulties associated with student connectivity, communication, the lack of teacher training and among the challenges, teaching practice, the management of different application being that distance education in emergency by Covid-19 mainly involved the use of videoconferencing applications, thus posing new challenges to teachers for their effective management. However, important strengths in teachers´ attitudes and skills are recognized, that favored the achievement of the students´ learning, as well as the improvement of capacities for virtual teaching. It´s concluded the importance of reassessing the personal dimension of teaching, as a relevant element in teaching in emergency situation.
Improving the Application of Blockchain Technology for Financial Security in Supply Chain Integrated Business Intelligence

For major organizations, establishing financial shared service centers to unify the financial activities of entities in multiple countries and regions for bookkeeping and reporting has become the trend for financial and management optimization. Based on this, this paper selects security company A as a case study, introduces the emerging blockchain technology (BCT) as the core and embeds it into the concept of financial shared center, tries to explore a new model of financial shared service model under BCT from the theoretical perspective, and optimizes the original service model by improving its business process, creating visualized, supervisible, and traceable financial data processing capability, and expanding. This paper first examines the financial shared service model of Company A. This paper starts with the actual situation of Company A’s financial shared service model. Complex procedures, highly centralized information, unreasonable division of labor in corporate processes, and a lack of adequate information communication methods are discovered through process analysis. New requirements for accountants have emerged in the digital age, and the security protection of private keys in asymmetric encryption algorithms in organizations, particularly in large and medium-sized enterprises, must be done well.