Reviewerly/exHarmony huggingface dataset:
Perceiving Multiple Representations for scene text image super-resolution guided by text recognizer

Single image super-resolution (SISR) aims to recover clear high-resolution images from low-resolution images, which has made great progress with the development of deep learning these years. Scene text image super-resolution (STISR) is a subfield of SISR with the goal of increasing the resolution of a low-resolution text image and enhancing the readability of characters in the image. Despite significant improvements in recent approaches, STISR remains a challenging task due to the diversity of background, text appearances and layouts, etc. This paper presents a Perceiving Multiple Representations (PerMR) method for better super-resolution performances in scene text images. PerMR is a unified network that combines super-resolution with text recognition and exploits the recognizer’s feedback to facilitate super-resolution. Specifically, contextual information from the text decoder is extracted to provide sequence-specific guidance and enable the super-resolution model to pay more attention to the text region. Meanwhile, low-level and high-level visual features from the vision backbone of the recognition network are integrated to further improve visual quality. Additionally, we incorporate a frequency branch into the vanilla convolution unit, which efficiently enhances global and local feature representations. Experiments on the STISR benchmark dataset TextZoom validate that PerMR can not only generate more distinguishable images, but also outperforms the current state-of-the-art methods. PerMR boosts the average recognition accuracy by 5.9% using ASTER, 5.8% using MORAN and 10.6% using CRNN compared to the baseline model TSRN. PerMR outperforms the advanced method TPGSR-3 by 1.4% on ASTER, 0.1% on MORAN, 0.2% on CRNN and boosts TATT by 0.6% on ASTER and 1.1% on MORAN respectively. Furthermore, PerMR demonstrates good robustness and generalization when tackling low-quality text images in multiple scene text recognition datasets. The experiment results verify the capabilities of PerMR to boost text recognition performance.
Large‐scale characterization of Java streams

Abstract Java streams are receiving the attention of developers targeting the Java virtual machine (JVM) as they ease the development of data‐processing logic, while also favoring code extensibility and maintainability through a concise and declarative style based on functional programming. Recent studies aim to shedding light on how Java developers use streams. However, they consider only small sets of applications and mainly apply manual code inspection and static analysis techniques. As a result, the large‐scale dynamic analysis of stream processing remains an open research question. In this article, we present the first large‐scale empirical study on the use of streams in Java code exercised via unit tests. We present stream‐analyzer, a novel dynamic program analysis (DPA) that collects runtime information and key metrics, which enable a fine‐grained characterization of sequential and parallel stream processing. We use a fully automatic approach to massively apply our DPA for the analysis of open‐source software projects hosted on GitHub. Our findings advance the understanding of the use of Java streams. Both the scale of our analysis and the profiling of dynamic information enable us to confirm with more confidence the outcome highlighted at a smaller scale by related work. Moreover, our study reports the popularity of many features of the Stream API and highlights multiple findings about runtime characteristics unique to streams, while also revealing inefficient stream processing and stream misuses. Finally, we present implications of our findings for developers of the Stream API, tool builders and researchers, and educators.
Non-separability classification of degrees of freedom of light assisted by machine learning

In this work, we present a study about the non-separability of degrees of freedom (DoF) of light in mixed modes, which emulates entangled mixed states. We explore bipartite spin–orbit modes and, by adding path DoF to spin–orbit modes, we propose an optical circuit to prepare tripartite non-separable mixed modes that extend the scenario of the classical-quantum analogy of non-separable modes. By using Machine Learning it was possible to show that we can characterize non-separability unambiguously with partial tomography measurements only on Sz basis, which is a remarkable advantage for higher dimension modes avoiding many tomographic measurements.
Legacy Statistical Disclosure Limitation Techniques for Protecting 2020 Decennial US Census: Still a Viable Option

In our article “Database Reconstruction Is Not So Easy and Is Different from Reidentification”, we show that reconstruction can be averted by properly using traditional statistical disclosure control (SDC) techniques, also sometimes called legacy statistical disclosure limitation (SDL) techniques. Furthermore, we also point out that, even if reconstruction can be performed, it does not imply reidentification. Hence, the risk of reconstruction does not seem to warrant replacing traditional SDC techniques with differential privacy (DP) based protection. In “Legal Statistical Disclosure Limitation Techniques Were Not an Option for the 2020 US Census of Population and Housing”, by Simson Garfinkel, the author insists that the 2020 Census move to DP was justified. In our view, this latter paper contains some misconceptions that we identify and discuss in some detail below. Consequently, we stand by the arguments given in “Database Reconstruction Is Not So Easy…”.
Lung Cancer Malignancy detection Using Voting Ensemble Classifier

Lung Cancer is a deadly disease caused by the abnormal and uncontrollable procreation of cells. Cancer can invade other body parts in case of late diagnosis. In this paper, we designed a computer-assisted lung cancer malignancy detection system using the Voting Ensemble classifier. The ensemble was designed by combining Decision Tree, SVM, and KNN as base learners. Each of the base learners was defined five times with different parameters. A total of fifteen weak learners were incorporated while defining the ensemble. The ensemble model attained an accuracy of 97.72%. The Sensitivity, F1-Score, and Precision were obtained as 94.33%, 96.33%, and 98.00% respectively. Our suggested model scored better than other current state-of-the-art approaches.
Cooperative dual medical ontology representation learning for clinical assisted decision-making

Predicting clinical events and providing assisted decision-making using Electronic Health Records (EHRs) play a central role in personalized healthcare. Despite the promising performance achieved for diagnosis and procedure predictions, most of the existing predictive models regard different medical codes as the same type and generally ignore the dependence between diagnoses and procedures in patients' admission history. To address these issues, we propose an end-to-end cooperative dual medical ontology representation learning framework for clinical assisted decision-making.The framework consists of two primary modules: (1) dual medical ontology representation learning to facilitate the learning of medical concepts and (2) task dependent multi-task prediction to capture the correlation between diagnoses and procedures in patients' admission history. We evaluate our method with EHRs from the MIMIC-III Clinical Database, covering 6321 patients and 16335 visits.Experiments conducted on the MIMIC-III dataset show that the proposed model achieves the best performance, with a top-20 accuracy of 58.20% for diagnosis prediction and a top-20 accuracy of 75.85% for procedure prediction. In addition, a series of experimental analyses and case studies further illustrate the excellent performance of our model.We propose an end-to-end cooperative dual medical ontology representation learning framework, which achieves superior performance on multi-task diagnosis and procedure predictions. The source code is available at https://github.com/mhxu1998/CoDMO.
Synonym Insensitive Searching: A Novel Synonym Weighted-Vector Space Model for Document Retrieval

Document retrieval will become challenging when it deals with the unique capability of natural languages to present content in different forms using synonyms, usages, and their complex combinations. Most of the existing information retrieval systems are struggling to retrieve documents with a similar meaning, and they are helpful only to get documents based on matching keywords. The query expansion is a logically simple and straightforward technique to improve the effectiveness of information retrieval in this background. The existing statistical approach depends mainly on the term frequency to generate candidate documents for the expanded or normal query. Most of the existing works do not consider the ways in which the content in a particular document can be represented differently by keeping the same context. This paper proposes a novel Synonym Weighted - Vector Space Model and query expansion technique for an effective synonym-incorporated method for document retrieval. The combination of modified Term Frequency - Inverse Document Frequency(TF-IDF) and synonym extended VSM has given a promising outcome for the experiments throughout the study. The proposed method is validated with two English-written publicly available datasets - CACM and CISI. The quantitative measures, like mean average precision, precision, recall, and F-measure obtained in the experiments are found to be better for the proposed method compared with the classical VSM and other baseline methods in the problem domain. We could obtain the highest precision of 0.83 and 0.65 for the CACM and CISI datasets respectively.
A Multi-Source Information Attention Network for Short Sequential Location Prediction

Sequential location prediction has been studied extensively for various applications, including mobility prediction, route planning, and location-based advertising. The existing models generally work on sequences containing hundreds or even hundreds of check-in records but do not perform well on short sequences with only a dozen check-in records. To solve this problem, we design a Multi-Source Information Attention Network (MSIAN). In this network, we consider information from various sources, including global spatio-temporal information and local personalized information, while using a time indicator with personal preference - stay-time. Specifically, we construct a global graph to use spatio-temporal information from the global. The location and visit time are then used in conjunction to extract the personalization information when modeling the check-in records. Finally, utilize stay-time with personalized preferences when performing the attention operation to improve the prediction effect. The experimental results prove that it makes sense to consider multi-source information and shows that our model significantly outperforms the baselines.
Continuous Knowledge Graph Refinement With Confidence Propagation

Although Knowledge Graphs (KGs) are widely used, they suffer from hosting false information. In the literature, many studies have been carried out to eliminate this deficiency. These studies correct triples, relations, relation types, and literal values or enrich the KG by generating new triples and relations. The proposed methods can be grouped as closed-world approaches that take into account the KG itself or open-world approaches using external resources. The recent studies also considered the confidence of triples in the refinement process. The confidence values calculated in these studies affect either the triple itself or the ground rule for rule-based models. In this study, a propagation approach based on the confidence of triples has been proposed for the refinement process. This method ensures that the effect of confidence spreads over the KG without being limited to a single triple. This makes the KG continuously more stable by strengthening strong relationships and eliminating weak ones. Another limitation of the existing studies is that they handle refinement as a one-time operation and do not give due importance to process performance. However, real-world KGs are live, dynamic, and constantly evolving systems. Therefore, the proposed approach should support continuous refinement. To measure this, experiments were carried out with varying data sizes and rates of false triples. The experiments have been performed using the FB15K, NELL, WN18, and YAGO3-10 datasets, which are commonly used in refinement studies. Despite the increase in data size and false information rate, an average accuracy of 90% and an average precision of 98% have been achieved across all datasets.
Analysis of awareness, satisfaction and support needs for intellectual property education (IPA) for teachers in charge of IPA

Objectives The purpose of this study is to analyze the perception, satisfaction, and support demand of intellectual property education teachers in charge of intellectual property education, and to provide implications for the future intellectual property education teacher education. Methods In Korea, 203 (92.3%) copies were extracted through an online survey targeting teachers in charge of intellectual property education at invention education centers designated by the Korean Intellectual Property Office, specialized high schools, and education offices, etc. The trend was identified. Through group comparison, differences in response results according to teaching career, training participation, class experience, etc. were statistically verified using the t-test and one-way analysis of variance (ANOVA). Results Result As a result of analyzing the understanding of intellectual property education, about 18.37% of the teachers responded that they did not know well, and there was a significant difference in understanding according to teaching experience, training, and class experience. Awareness was high. Regarding the content to be included in intellectual property education, teachers at all school levels recognized that intellectual property protection should be dealt with most importantly, and there was a difference in perception of the part to be treated as important by school level. Teachers' satisfaction with intellectual property education was low at 2.85 points, and as a result of an analysis of current state awareness on school or education office support, 47.41% of respondents recognized that it was not sufficient. Conclusions It is necessary to organize the scope and contents of intellectual property education according to the school level, and it is necessary to establish support measures in terms of supporting learning and experience programs, utilizing assistant instructors and external human resources, and developing and distributing textbooks and tutorials.
Personalized page-transition recommendation model based on contents and students' logs in digital textbooks

The e-book systems have been widely used in educational activities, such as college studies. At the same time, a huge amount of data regarding the learning history are collected by using the E-book system. Data mining and machine learning method have been used for analyzing these educational data in order to study the learning behaviors of students. Most rereading behaviors to study relevant contents on other pages again are inefficient due to that students are difficult to accurately navigate to the desired page with just a one-time page transition on the e-book system. In this study, we develop a hybrid recommendation model, which can recommend students to find the desired pages when rereading. The developed model is combining a content-based method (TF-IDF model) and a data-driven method (Page jumping model) linearly. By optimizing its parameters with the collected learning data, the developed model can generate ranking lists of relevant pages for students. Finally, the performance of the developed model is evaluated by two metrics: precision and recall.
Progressive Practices for Achieving Inclusiveness and Openness for People with Disabilities by Top Universities in Less Developed Countries

Purpose: The purpose of the chapter is to study progressive practices in ensuring inclusivity and openness for people with disabilities in top universities in developing countries. Design/methodology/approach: To achieve the indicated research purpose, the authors evaluated the contribution of distance learning to inclusiveness and openness for people with disabilities at top universities in developing countries using the Difference in Differences (DiD) method. Progressive practices of achieving inclusivity and openness for people with disabilities by top universities from developing countries are investigated by the case study method. Findings: As a result of the conducted research, it was revealed that the inclusiveness of leading universities in developing countries is achieved through progressive practices of achieving openness for people with disabilities: distance learning, inclusive social environment, as well as inclusive higher education systems. The experience of top universities in developing countries is successful, but it is fundamentally different and, therefore, not comparable with the experience of universities from developed countries. Originality/value: The theoretical significance of the results obtained in this chapter lies in the fact that they revealed the features of the institutions of the “knowledge economy,” thanks to which the inclusiveness of leading universities in developing countries is achieved. The practical significance of the findings is that they revealed the prospects for increasing inclusiveness and openness for people with disabilities in developing countries.
Identification of Privacy Challenges and Social Discrimination on Internet of Things

The Internet of Things (IoT) notion anticipates the ubiquitous connectivity and collaboration of intelligent objects across the existing and forthcoming Internet connectivity. IoT is essentially the expansion of the internet into physical sphere, allowing numerous innovative applications that will enhance people’s daily lives, generate start-ups, and make structures, societies, and mobility increasingly intelligence. However, these valuable capabilities are also instances of confidentiality risk and digital unequal treatment and discrimination that are already restricting the Internet of Things aim’s viability if they are not implemented properly. These risks include IoT-based personal invasion and other types of discrimination as well as new problems. This paper will study outline of the IoT and application into the world. Then the study will next analyse numerous regulations strengthen Privacy and Anti-discrimination laws suggested by the United Nations and examples of countries information technology provisions that provide effective confidential safeguards and discrimination regulation of user authentication for the IoT. The conclusion of the article is that, in order to minimize the privacy impact and social discrimination problems resulting from the abuse of IoT, we must: ensure the better legal provisions with self-consciousness among users which are needed, as well as advance technology and software application needed to curb the challenges.
Efficient Deep Learning Algorithm for Egyptian Sign Language Recognition

Although most people can communicate effectively through speech, some have difficulties doing so due to physical or mental impairments. Communication is a significant obstacle for individuals with these disabilities. Methods of deep learning can aid in the elimination of communication barriers. This article proposes a model based on deep learning for detecting and recognizing words from gestures. Deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used to recognize signs from Egyptian Sign Language (ESL) video frames. There are many activation functions, and every type has advantages and disadvantages. All these activation functions were applied to our dataset for ESL. To overcome the main disadvantage of the Relu activation function, we proposed a gesture recognition method for ESL using Mediapipe and modified GRU with a new activation function (Talu). The proposed model achieves approximately 94.95% accuracy across ten different signs. This method may assist people unfamiliar with Egyptian sign language in communicating with people with speech or hearing impairments.
Pengaruh Self Leadership Terhadap Performa Mahasiswa Universitas Riau dengan Gamiification Sebagai Variabel Pemoderasi

Performance or performance is a concept and theory which by a number of academics has become a topic that is continuously studied with the aim of being able to find the most appropriate alternative to the obstacles that arise. Based on the results of the initial search, it was found that the leadership factor is a variable that is often used as a cause of performance improvement, while the self-leadership variable is a factor that will be investigated in 2021. The researcher also found that regulatory factors and rewards for achievements are similar to the concept of a known game. in Gamification theory is a factor that strengthens (and is translated as a moderating factor) the influence between self-leadership and student performance. This research is a quantitative study using the SEM-PLS analysis technique on primary data in the form of a questionnaire instrument which was distributed to 97 samples representing the student population of the University of Riau. The results showed that the variable value increase in the self-leadership variable had a significant impact on increasing student performance scores, and this was positively moderated (reinforced) by the increasing level of gamification carried out by the university environment.
Design of Efficient Point-Mass Filter for Linear and Nonlinear Dynamic Models

This letter deals with the state estimation of nonlinear stochastic dynamic systems in the Bayesian framework. The emphasis is laid on the numerical solution to the Chapman-Kolmogorov equation by the widely-used point-mass method. It is shown, that the standard prediction step of the point-mass filter can be decomposed into two parts; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">advection</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">diffusion</i> solution. This decomposition allows application of the fast Fourier transform, which <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speeds up</i> the prediction step by several orders of magnitude making the point-mass filter attractive even for higher dimensional models. The proposed efficient point-mass filter is illustrated in a numerical simulation with available source codes and is compared with the particle filter.
Adaptive Task-Oriented Chatbots Using Feature-Based Knowledge Bases

Task-oriented chatbots relying on a knowledge base for domain-specific content exploitation have been largely addressed in research and industry applications. Despite this, multiple challenges remain to be fully conquered, including adaptive knowledge mechanisms, personalization for user-specific demands, and composite intent resolution. To address these challenges, in this paper, we present a work-in-progress summary of a task-oriented, knowledge-based chatbot in the field of mobile software ecosystems. The chatbot is designed to assist users in the combined use of multiple features from different applications. The proposed knowledge base and the machine learning pipeline supporting the chatbot technical core are designed to: (i) effectively use user context, (ii) process runtime feedback, (iii) use user historical data, and (iv) automatically infer slot values and dependent actions. With this report, we expect to lay the groundwork for future development stages and user validation studies.