Reviewerly/exHarmony huggingface dataset:
Learning Evidential Cognitive Diagnosis Networks Robust to Response Bias

As a basic task of the intelligent education system, cognitive diagnosis aims to diagnose the knowledge proficiency of students and capture the complex relationship between students and exercises. Benefiting from big data, deep learning show advantages in cognitive diagnosis tasks. However, the general deep learning-based methods are sensitive to noise, and response bias is an inevitable problem in real-world situations. To address this challenge, we propose the Evidential Cognitive Diagnosis Model (EvidentialCDM), which introduces the evidential deep learning to neural cognitive diagnostic frameworks for estimating the aleatoric and epistemic uncertainties as well as maintaining the predicting performance. In addition, this paper proposes a new dataset, named Uncertainty ASSIST (UncASSIST), in order to better deal with this problem. Experimental results show the effectiveness of our method on both the publicly available ASSIST and our proposed UncASSIST datasets.
Hierarchical Recurrent Contextual Attention Network for Video Question Answering

Video question answering (VideoQA) is a task of answering a natural language question related to the content of a video. Existing methods that utilize the fine-grained object information have achieved significant improvements, however, they rely on costly external object detectors or fail to explore the rich structure of videos. In this work, we propose to understand video from two dimensions: temporal and semantic. In semantic space, videos are organized in a hierarchical structure (pixels, objects, activities, events). In temporal space, video can be viewed as a sequence of events, which contain multiple objects and activities. Based on this insight, we propose a reusable neural unit called recurrent contextual attention (RCA). RCA receives a 2D grid feature and conditional features as input, and computes multiple high-order compositional semantic representations. We then stack these units to build our hierarchy and utilize recurrent attention to generate diverse representations for different views of each subsequence. Without the bells and whistles, our model achieves excellent performance on three VideoQA datasets: TGIF-QA, MSVD-QA, and MSRVTT-QA using only grid features. Visualization results further validate the effectiveness of our method.
Adversarial and Implicit Modality Imputation with Applications to Depression Early Detection

Depression early detection is a significant healthcare task that heavily relies on high-quality multi-modal medical data. In practice, however, learning a robust detection model is challenging because real-world data often suffers from serious modality-level missing issues caused by imperfect data collection and strict data sharing policies. In this study, we propose an Adversarial and Implicit Modality Imputation (AIMI) method to resolve this challenge. In particular, when training multi-modal predictive models, we learn an implicit mechanism to impute the missing modalities of training data at the same time. These two learning objectives are achieved jointly in an adversarial learning framework. Based on the UK Biobank dataset, we demonstrate the effectiveness and superiority of our method on the early detection of depression. Codes are available at https://github.com/rucnyz/AIMI .
Spectral Clustering Based on a Graph Model for Airspace Sectorization

This paper focuses on exploring the usage of clustering techniques to form new sector designs conforming to the constraints imposed by the problem. As an initial step we generate DBSCAN clusters based on the sector’s geographical points for analyzing the structural design of the airspace. As a second step, we form a graph model through the Voronoi Diagram to generate sites for each sector. Next, we transform the diagram into a Delaunay Triangulation. For the weighted graph model, we use two main metrics: the traffic flow and the number of intersections on each link. For the final step, we analyze spectral clustering on sectors to test how suitable the results are via convex bounding.
A Set-Theoretic Representation of Algebraic L-domains

In this paper, we aim to establish a concrete representation, as a family of sets, for every algebraic L-domain. We generalize the notion of a topped algebraic intersection structure to a locally algebraic intersection structure. Just as topped algebraic intersection structures are concrete representations of algebraic lattices, locally algebraic intersection structures are concrete representations of algebraic L-domains. This result extends the classic Stone’s representation theorem for Boolean algebras to the case of algebraic L-domains. In addition, it will be seen that many well-known representations of algebraic L-domains can be analyzed with the framework of locally algebraic intersection structures.
Revisiting Security of Symmetric-Key Encryption with Imperfect Randomness and Correlation Between Source and Keys

In the ideal environment, a secret key for a cryptosystem is generated with perfect randomness and is independent of other keys or sources. It is well-known that such an ideal condition gives the perfect security to many encryption schemes. However, in practice, it is not uncommon that due to limited resources, we can not always guarantee such ideal conditions. Thus, it is very important to know the amount of security we can guarantee under a non-ideal condition in practice. In this paper, we are interested in investigating the security of symmetric encryption schemes under non-ideal conditions. Especially, we focus on a wide category of encryption schemes with the following property: for any plaintext, there is no collision between ciphertexts generated from the encryption of the plaintext by different secret keys. As the main contribution, we derive the sufficient and the necessary condition for guaranteeing the security of any such encryption scheme even in the non-ideal cases where: (1) the source (plaintext) and the secret key used in the encryption process are correlated, and (2) the secret key is not perfectly random.
Route Optimization in Service of a Search and Rescue Artificial Social Intelligence Agent

The success and safety of a Search and Rescue (SAR) team hinge on routing, making it an integral part of any SAR mission. Consequently, an Artificial Social Intelligence (ASI) agent aware of the “good” available routes in a mission is a very desirable asset for a SAR team. Such awareness is contingent on having superior knowledge of the environment and understanding the dynamics of the SAR team. An ASI agent equipped with this capability can utilize it while reasoning about the mission, similar to how a human may use real-time GPS route suggestions from a navigation application. This feature was historically infeasible for real-time ASI agents because the problems were computationally intractable. However, recent advances in Graph Neural Networks, transformers, and attention models make them candidates to be leveraged as neural heuristics in routing problems to quickly generate near-optimal routes. This paper describes a sequential decision framework based on neural heuristics to devise such routes for participants in the DARPA ASIST Minecraft SAR Task and reports our initial findings.
Foundations of Synthetic data

In this chapter we will explore the different types of synthetic data, how to generate fair synthetic data, and the benefits and challenges presented by synthetic data. We will also explore the synthetic-real field gap and how to overcome it with field transfer, field adaptation, and field randomization. We will also discuss how simulation is used to automate data labeling in autonomous vehicle companies and how real-world experience is inevitable. Finally, we will discuss how data can be pre-trained, learned with reinforcement, and self-supervised learning to learn medical images.
Integrating Machine Learning and Cognitive Modeling of Decision Making

Modeling human decision making plays a fundamental role in the design of intelligent systems capable of rich interactions and effective teamwork. In this paper we consider the task of choice prediction in settings with multiple alternatives. Cognitive models of decision making can successfully replicate and explain behavioral effects involving uncertainty and interactions among alternatives but are computationally intensive to train. ML approaches excel in terms of choice prediction accuracy, but fail to provide insights on the underlying preference reasoning. We study different degrees of integration of ML and cognitive models for this task. We show, via testing on behavioral data, that our hybrid approach, based on the integration of a neural network and the Multi-alternative Linear Ballistic Accumulator cognitive model, requires significantly less time to train, and allows to capture important cognitive parameters while maintaining similar accuracy to the pure ML approach.
Greenhouse gas reduction and waste generation prediction from ICT-based smart waste collection system

Due to global warming and climate change, greenhouse gases are considered an important factor to reduce. In this paper, to reduce the amount of greenhouse gas inevitably generated in the process of waste collection and transport, we investigated the optimization of the collection route and predicting the amount of waste generated by implementing an ICT-based smart weight collection system in Jeju city's D area. HES and LSTM models were used as prediction methods. We calculated greenhouse gas emissions assuming the 23 clean houses in the D area were replaced with the smart weight collection system. As a result of the prediction results, both models performed with 85% accuracy. It shows optimizing the collection route can also reduce greenhouse gas emissions by more than 57%. In this study, It is expected that the effect of introducing a smart weight collection system to reduce greenhouse gas emissions will be further increased if long-term data are accumulated in areas with higher populations and waste generation than area D.
Some aspects of the professional track methodology of teaching staff in the continuous training process

In this article, aspects of the methodology of the teacher professionalization path in the continuous training process are revealed: the level of skills for professional development; career development of the teaching staff; the human development path as a work subject; human life cycles; period/stages/stages of professionalization; the postulates of professional career development; the path and goals of the professionalization of the teaching staff; the program, forms and methods of continuous training of teaching staff included in the professionalization route; the estimated time for the continuous training of the teaching staff.
The valences of theatrical game in view of the development of the literary-artistic reception competence of preschool children

The article reflects the importance of theatrical play in the development of preschoolers’ literary-artistic reception skills. The theatrical game facilitates the adequate reception of the literary text and contributes to the development of knowledge of artistic images by the preschooler, on the basis of which the understanding of the literary work takes place. By engaging in theater games, children learn the content of the literary work more easily. Thus occurs: artistic knowledge that includes phenomena of the supersensible; the discovery of the emotional-affective nature of reception, the way of reflecting the external and internal reality in the literary work; understanding the subject-object relationship present in the literary work that contributes to the elaboration of the epistemology of literary-artistic reception; literary communication and identification with the character; the author’s collaboration with the receiver who creates the theatrical play; analysis of the literary work.
Psycho-educational implications of education forms and education models in the development of pre-adolescents’ beliefs for the role of responsible parents

In contemporary society, being trained for life means being prepared to integrate into an expanding technological universe. In the pedagogical literature, this problem is treated very differently, using the following terms: technical education, work education, professional education, polytechnic education. In addition to these types of education, the family, the society in which pre-adolescents live, grow, learn, train, etc. is taken into account. The values they form are shared by society, and they establish the behaviors and attitudes of preadolescents set for the purpose of achieving the desired well-being. Therefore, these values, according to the definitions of today’s sociology, psychology and anthropology, are defined as determining elements of choices in everyday life or are useful tools for pre-adolescents in ordering priorities and their own activities.
Digital technology as a factor in the development of an informatized society: an educational perspective

Scientific exploration aims to analyze the synergistic effects of interaction and the mutual influence of educational and digital potential in the process of society development. The task of the article is to consider the information and digital factors relevant to the current Ukrainian socio-cultural space, affecting the level of education. The methodology used in the study is based on the methods of scientific and pedagogical discourse. So, the digitalization of education is an important element in the development of Ukrainian society, as the skillful use of digital technology expands the worldview and professional skills of the participants in the educational process.
Tool Mediated Operations and the Development of Human Consciousness

Galperin starts this chapter by considering the operations used by the children in his empirical material. He focuses on examining the psychological significance of the types of operations used and difference between tools and means.
Cytological Diagnostic and Prognostic Methods using Immunocytochemistry (Estrogen Receptor) for Surgical Management of Breast Cancer

The purpose of this study was to assess the utility of fine needle aspiration cytology (FNAC), immunocytochemistry(ICC) using estrogen receptor(ER) in diagnosing breast lesions. This was done by comparing it to histopathology with immunohistochemistry(IHC), which serves as the gold standard for diagnosing these lesions. To compare these modalities of investigation,50 samples were collected using FNAC and were compared to the same samples obtained by histopathology. For FNAC the results were as follows, Sensitivity=100%, Specificity=100%,Diagnostic Accuracy=100% Positive Predictive Value(PPV)=100% and Negative Predictive Value(NPV)=100% . For ICC using ER the results were as follows, Sensitivity=100%,Accuracy=100%,Positive Predictive Value=100%,Negative Predictive Value=100%. This indicates that FNAC and ICC using ER can be used as a reliable alternative to gold-standard diagnostic tests when the latter cannot be done due to a lack of resources or in circumstances where there is a need to perform a painless, minimally invasive procedure such as in inoperable breast carcinoma. This study also involved using text data analysis on FNAC reports. On analysis, it was found that the useful words were 11.35% of the data set, implying that the process of normalization, will result in the formation of condensed data, which can then be utilized for assisting clinical chart reviews and clinical decision support systems.
Development of an interactive simulation and app (Pipette Master) to teach undergraduate students how to read, set and use and automatic laboratory pipette

Abstract An automatic pipette is a key piece of laboratory equipment. The ability to accurately and reproducibly deliver any volume is a critical skill to master for all undergraduate students in the laboratory sciences. Over many years we have noted that students struggle in learning several facets of correct pipette usage. These include, choosing the correct pipette for a given volume, correctly setting the pipette and correctly using the plunger to aspirate and dispense the liquid. Students also struggle with unit conversions between ml and μl and vice versa. Lastly, students fail to retain these skills and information in following semesters of teaching. In an attempt to help students with converting between units (μl and ml) as well as choosing the most appropriate pipette for a given volume, I developed an interactive simulation which covered all these major aspects. Using a Gilson pipette as an example, the simulation covered how to set the pipette for a number of defined volumes. An Android/iOS app of the simulation has also been developed which can be used during laboratory classes. In 2017, we polled first year undergraduate students about their level of experience, knowledge of pipettes and level of comfort in converting between units and we then explained the simulation to them. The feedback on their skills and the software will be discussed. We also tested the impact of the simulation using 2nd year students enrolled in genetics. Using a laboratory quiz, we found significant improvements in student performance when students used the simulation compared to those that did not. We suggest this interactive approach is a simple and effective way to enhance student learning of key aspects of laboratory pipettes. The simulation is freely available.
Gesture Replication Robo-Arm

Abstract: In these times robotic arm is used in several areas analogous as military, defense, medical surgeries pick and place function in artificial automation operations. The Gesture Replicating Robotic arm is a servo controlled robotic arm which replicates the mortal arm gestures in a three- dimensional terrain. The main ideal of this design is to control the robotic arm using mortal gestures. This system replicates the conduct of mortal hands, and the robotic arm moves and performs the task. It's coded in such a way that the demanded conduct for the mortal gesture is done. thus, this model we proposed will be truly helpful.
Stock Trend Prediction Based on Social Media Articles

This paper presents a machine-learning approach to predicting stock trends using social media articles. The proposed method consists of three steps: data acquisition, data processing, and sentiment analysis. First, relevant social media articles are collected from different sources. Then, the acquired data is preprocessed by filtering out stop words, punctuation, and non-alphanumeric characters. Finally, sentiment analysis is conducted using the Classification algorithms to determine the sentiment of the article and the corresponding stock trend. The experimental results demonstrate that the proposed method yields a satisfactory prediction accuracy for stock trends.
Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel

In this paper, hybrid texture features are proposed for identification of scripts of bi-lingual camera images for a combination of 10 Indian scripts with Roman scripts. Initially, the input gray-scale picture is changed over into an LBP image, then GLCM and HOG features are extracted from the LBP image named as LBGLCM and LBHOG. These two feature sets are combined to form a potential feature set and are submitted to KNN and SVM classifiers for identification of scripts from the bilingual camera images. In all 77,000-word images from 11 scripts each contributing 7000-word images. The experimental results have shown the identification accuracy as 71.83 and 71.62% for LBGLCM, 79.21 and 91.09% for LBHOG, and 84.48 and 95.59% for combined features called CF, respectively for KNN and SVM.
Data Discovery: A Human-Centered View

This chapter (i) contextualizes the terminology used in the remainder of the manuscript and (ii) situates human-centered data discovery within the context of existing information-seeking research. We end the chapter by providing examples of data seeking in practice, drawn from our research, to further contextualize human-centered data discovery.
FAKTOR-FAKTOR YANG MEMPENGARUHI PELAKSANAAN E-LEARNING DI INSTITUT AGAMA KRISTEN NEGERI KUPANG TAHUN 2021

This study aims to determine the factors that influence the implementation of e-learning in the online learning process at the Kupang State Christian Institute in 2021. The type of research used is a descriptive method using a survey approach. The population in this study were all students. The sampling used in this study used stratified random sampling. The sample in this study was 180 students consisting of 73 semester 1 students, 66 semester 3 students, and 41 semester 5 students. The result showed that there were 104 people who felt that the quality of information during the implementation of e-learning was not good, 62 people felt that the quality of information during the implementation of e-learning was not good. the implementation of e-learning is good, and only 14 people feel that the quality of information during the implementation of e-learning is very good, there are 102 people who feel that the quality of the system during the implementation of e-learning is not good, 61 people feel that the quality of the system during the implementation of e-learning is good , and only 17 people feel that the quality of the system during the implementation of e-learning is very good, and There are 99 people who feel that the network connection during the implementation of e-learning is not good, 53 people feel that the network connection during the implementation of e-learning is good, and only 28 people who feel that the network connection during the implementation of e-learning is very good. Keywords: Electronic learning, distance learning, learning evaluation
A New Computing Techniques on Digital Nano Topology

A definite perspective of this paper is to propose a k-means clustering algorithm with aid of some form of open sets. For this, shape a brand new topology on a non-empty set with widely known closure and interior operators which is called as the digital nano topology. Also define the digital nano topology on a digital plane with khalimsky plane topology, named as digital nano khalimsky plane (In short, Digital $$\mathcal {N}\kappa '$$ ) topology. Using these novel perception, we introduce a k-means clustering algorithm. Further, an illustrative example is given for the proposed algorithm.
Prediction of Software Defects using Ensemble Machine Learning Techniques

During software development and maintenance, predicting software bugs becomes critical. Defect prediction early in the software development life cycle is an important aspect of the quality assurance process that has received a lot of attention in the previous two decades. Early detection of defective modules in software development can support the development team in efficiently and effectively utilizing available resources to provide high-quality software products in a short amount of time. The machine learning approach, which works by detecting hidden patterns among software features, is an excellent way to identify problematic modules. The software flaws in NASA datasets MC1, MW1, KC3, and PC4 are predicted using multiple machine learning classification algorithms in this work. A new model was developed based on altering the parameters of the previous XGBoost model, including N_estimator, learning rate, max depth, and subsample. The results were compared to those obtained by state-of-the-art models, and our model outperformed them across all datasets.