Reviewerly/exHarmony huggingface dataset:
Continuous Weighted Neural Cognitive Diagnosis Method for Online Education

With the rapid development of online education, extensive data records from online education are accumulated in large quantities, therefore the educational evaluation industry is of great potential. Cognitive diagnosis based on machine learning has drawn considerable attention from both the research community and industry, and a lot of works have been proposed. However, many models ignored the point that different knowledge concepts have different important degrees on each exercise. In this paper, we propose the Continuous Weighted Neural Cognitive Diagnosis (CWNCD) model, which is extended from the Neural Cognitive Diagnosis (NCD) framework, a cognitive diagnosis framework based on neural network, to get a more accurate diagnosis result and ensure its interpretability. Specifically, we added information about the importance degree of different knowledge concepts in each exercise for modeling their interactions, in which case we can more comprehensively model the cognitive level of a student. Extensive experiments conducted on real-world datasets show that the CWNCD model is feasible and obtain excellent performance. Finally, the possible future research directions are discussed.
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text

Procedural text contains rich anaphoric phenomena, yet has not received much attention in NLP.To fill this gap, we investigate the textual properties of two types of procedural text, recipes and chemical patents, and generalize an anaphora annotation framework developed for the chemical domain for modeling anaphoric phenomena in recipes.We apply this framework to annotate the RecipeRef corpus with both bridging and coreference relations.Through comparison to chemical patents, we show the complexity of anaphora resolution in recipes.We demonstrate empirically that transfer learning from the chemical domain improves resolution of anaphora in recipes, suggesting transferability of general procedural knowledge.
Expressing the minimum distance, weight distribution and covering radius of codes by means of the algebraic and numerical normal forms of their indicators

<p style='text-indent:20px;'>We consider the algebraic normal form (ANF) of the indicators (i.e. characteristic functions) of linear binary codes, and characterize the minimum distance of such codes in a very simple way by means of this ANF. We extend this characterization to nonlinear binary codes, via another representation, the numerical normal form (NNF). We further extend these characterizations to linear codes over finite fields and (after introducing a generalization of the NNF to functions from <inline-formula><tex-math id="M1">\begin{document}$ {\Bbb F}_p^n $\end{document}</tex-math></inline-formula> to <inline-formula><tex-math id="M2">\begin{document}$ {\Bbb R} $\end{document}</tex-math></inline-formula>) to unrestricted codes over prime fields. We also study the weight distribution by means of the NNF, and the covering radius of binary codes with the same approach; the latter is more difficult to address, but we obtain some results as well.
Combining Extraction and Generation for Constructing Belief-Consequence Causal Links

In this paper, we introduce and justify a new task-causal link extraction based on beliefsand do a qualitative analysis of the ability of a large language model-InstructGPT-3-to generate implicit consequences of beliefs.With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task.
A Framework for Self-Inspection Buildings Based on Augmented Reality Agents

Emergent technologies are being adopted at all the stages of smart building lifecycles. More specifically, mobile, pervasive, and communication technologies are being deployed to achieve a wide range of functions that improve the building performance (including ventilation, air conditioning, heating, lighting, and security) and reduce their related costs. Augmented Reality (AR) has arisen as a promising tool to achieve these goals. However, in spite of the multiple solutions that have integrated AR within smart buildings, several shortcomings are yet to be solved. In addition to the limited user experience and the lack of AR content, current solutions do not provide effective collaborations between construction stakeholders as well as do not include intelligent mechanisms for the management of inspection activities. In order to address some of the smart building challenges, we are proposing in this paper a new framework for intelligent collaborative self-inspection buildings based on the concept of awareness wheel as well as the multi-agent system paradigm.
Modern Drug Situation as a Criminogenic Factor of Crime Growth

A sociological analysis of the state of the drug threat has been carried out, the main regions for the production of narcotic drugs have been described, and components that characterize the drug situation both at the global and Russian levels have been formulated. The danger of expanding the production and consumption of synthetic drugs as one of the criminogenic factors in the growth of crime is emphasized. As a result, the need to intensify efforts to integrate the world community in countering the illicit trafficking in narcotic drugs and psychotropic substances is substantiated.
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer

Pre-trained language models are still far from human performance in tasks that need understanding of properties (e.g.appearance, measurable quantity) and affordances of everyday objects in the real world since the text lacks such information due to reporting bias.In this work, we study whether integrating visual knowledge into a language model can fill the gap.We investigate two types of knowledge transfer: (1) text knowledge transfer using image captions that may contain enriched visual knowledge and (2) cross-modal knowledge transfer using both images and captions with vision-language training objectives.On 5 downstream tasks that may need visual knowledge to solve the problem, we perform extensive empirical comparisons over the presented objectives.Our experiments show that visual knowledge transfer can improve performance in both low-resource and fully supervised settings.
Improving Specificity in Review Response Generation with Data-Driven Data Filtering

Responding to online customer reviews has become an essential part of successfully managing and growing a business both in e-commerce and the hospitality and tourism sectors.Recently, neural text generation methods intended to assist authors in composing responses have been shown to deliver highly fluent and natural looking texts.However, they also tend to learn a strong, undesirable bias towards generating overly generic, one-size-fits-all outputs to a wide range of inputs.While this often results in 'safe', high-probability responses, there are many practical settings in which greater specificity is preferable.In this work we examine the task of generating more specific responses for online reviews in the hospitality domain by identifying generic responses in the training data, filtering them and fine-tuning the generation model.We experiment with a range of data-driven filtering methods and show through automatic and human evaluation that, despite a 60% reduction in the amount of training data, filtering helps to derive models that are capable of generating more specific, useful responses.
Contextual Sentence Embeddings for Obtaining Food Recipe Versions

Food and culinary activities related to cooking are present in our daily lives. The rise of food-related data has led to the term food computing, which refers to the study and development of computer systems to solve food-related tasks. Despite the large number of food computing systems focused on the collection, recommendation, retrieval, and creation of recipes, very few have used existing recipes to get adapted versions for user requirements. In this work, we have developed a method for adapting recipes that suggests food options for substituting their ingredients based on food relations and text similarity. For this purpose, we employ different deep learning language models based on BERT. These models incorporate attention mechanisms to extract contextual representations of foods using different strategies to build the word embeddings. We use them to conduct a semantic comparison task for detecting similar ingredients between the recipe ingredients and a food dataset. The results show that the method obtains high-quality recipe versions, thanks to context data, attention mechanisms, and the token representation strategy used for the foods.
English Language Learning Using WhatsApp Application in a Classroom

Numerous high schools are now using educational platforms to enhance educational practices and benefit all members of their institutions via online learning. According to the researchers’ early investigation, instructors have not yet completely embraced the WhatsApp application in Education. Thus, the research responds to an effort to ascertain the extent to which the WhatsApp Application is associated with English language acquisition. The study used a non-experimental, descriptive, and correlational approach and included 90 students from a Peruvian high school. A questionnaire and a test were used to gather data, both of which had been verified earlier against the study’s goals. The findings reveal the presence of a significant correlation, with r = 0.016 (p-value < 0.005) as the significance value. WhatsApp can assist English instructors and students in teaching and studying the English language, as well as helping students enhance their English proficiency.
Natural Gaze Informatics: Toward Intelligence Assisted Wheelchair Mobility

Controllers such as the joystick, sip “n” puff, and head mount (gyro control) in the electric wheelchair cannot accustom to people with severe disabilities. Artificial intelligence (AI) mediated mobility could be a suitable solution, but the human gaze is rarely included in the loop. This chapter focuses on natural gaze informatics for intelligence assisted wheelchair mobility. Based on the natural gaze behavior during joystick-controlled wheelchair navigation in different scenarios, we developed two decoders for natural gaze-based powered wheelchair control. “Semantic Empirical Bayesian Decoder” was found to be most intuitive and user-friendly especially, while navigating in more complex environments. Control of backward motion, one of the major drawbacks of our previous “Look Where You Want To Go” interface, was solved using the “Semantic Empirical Bayesian Decoder.” The “Continuous Control Field Decoder” was preferred when the users had to follow a straight trajectory. Furthermore, we have harvested natural gaze informatics to decode user’s driving intention, which in fusion with an autonomous wheelchair platform allows users to accomplish their routing and steering requirement, thus enabling a natural gaze data-driven powered wheelchair. The human-in-the-loop approach combined with AI systems allows subjects to navigate their wheelchair without the need to interact with a controller in dynamic urban environments. Our natural gaze informatics-based AI control modules provide a new approach to enable wheelchair users to navigate indoors, and we aim to provide an independent urban continuum to the severely disabled and the elderly.
APPLICATION OF DIGITAL TECHNOLOGIES DURING REMOTE TRAINING LESSONS

The article is devoted to the discussion of the issues of using information and communication technologies in organizing a remote training process and conducting online training sessions. The authors consider the use of information and communication technologies as one of the effective and affordable means to maintain physical performance and technical and tactical readiness during distance training sessions. The paper identifies the features of online training, their focus, as well as the difficulties of the widespread introduction of this form of training. The authors reviewed various platforms and applications for online training sessions and analyzed their advantages and disadvantages. It is noted that within the framework of the current situation, it is necessary to use the principles and various methods of distance training, which will allow the introduction of unique technologies aimed at developing the educational and training process. The authors concluded that the training process using information and communication technologies is an effective way to train an athlete along with other methods.
Image Steganalysis Based on CNN and Transfer Learning for Small-Sample Condition

Image steganography has drawn severe multimedia security threat because it can transmit secret message. Existing steganalysis methods depend on known stegnography algorithm or existing big stego datasets to detect stego image existence. However, real world stego image sample is hard to collect and their stegnography algorithm are mainly unknown, which becomes two obstracles for image steganalysis. In this paper, we propose a transfer learning based image steganalysis model for small-sample condition. Parameter sharing method is used in our model to achieve transfer learning between different stegnography algorithms. The features are that the same series of steganography algorithms have similar data prior distribution. The similarities among different steganography algorithms is searched to realize transfer learning among different algorithms and optimize detection training. It makes algorithm can train quickly without losing the accuracy in small number of dataset. Comparing the detection accuracy and learning efficiency of the detection algorithm before and after, the performance of our model (on a small training set) is better than small sample training. At the same time, we analyzes the relationship and correlation between steganography algorithms. Our model optimizes the existing research results from a posteriori perspective.
An Investigation of Cryptocurrencies: Behavior Finance Perspective

The cryptocurrency market has ignited heated debates since its large fluctuation during 2013.This project first discusses a brief connection in certain representative cryptocurrency assets, with certain properties raising from the fields of behavior finance.Then I study the link between behaviors in cryptocurrency market with these properties using event study analysis.In order to proxy such behaviors, I extract confidence and uncertainty index which is a proxy of fully rational measurement and any deviations would imply the space of behavior finance theories.I discuss how such measurements could explain various individuals' reactions to cryptocurrencies both quantitatively and qualitatively.
The Effect of Proactive Action, Innovation and Risk Taking on Business Performance

The role of SMEs and its development become increasingly important for the economy.The purpose of this study is to examine whether: 1) Proactive action can affect business performance 2) Innovation can affect business performance 3) Risk taking can affect business performance.The sample used in this study includes 50 respondents who own a food and beverage business in West Jakarta.The results show that there is a significant effect of innovation and risk taking on business performance, but proactivity has no significant effect on business performance.
New Method for 4D Reconstruction of Medical Images

This paper proposes a new optimized method that is fast in rendering for 4D reconstruction from 2D medical images of human anatomy permitting their real-time refined visualization.This method uses the 3D reconstruction algorithm based on contour matching of medical image sequences and on the tessellation of recent GPU.In our framework, the construction of the low-resolution mesh that is based on contour extraction allows to create a 3D mesh without any ambiguity and exactly matches the real shape of the human anatomy.Such preliminary result is of great interest, since it permits to lead to other valuable realizations such as reducing the computation burden of basic meshes and displacement vectors.Moreover, one can achieve a very low storage memory, as well as one can ease the fast real-time 4D visualization with a high desired resolution.Hence, it is then straight forward that this study can contribute to easing the diagnosis and detection in real-time of human organs in motion damage and deterioration.Especially, 4D visualization technology that is still under development is highly important and needed for assessing some dangerously evaluative diseases, as in the case of lung diseases.
The Development and Impact of Central Bank Digital Currency

Digital currency, which is a kind of digital token that exists on a distributed and decentralized ledger, is under the spotlight of the public's discussion.Chinese government desires to promote the establishment of a currency service system that is oriented to the digital economy era, which is beneficial to the public, and universally innovative and open.This essay introduces the reasons that the Chinese government aims to promote a new type of digital RMB, and the disadvantages the digital currency is facing now such as regulatory problems compared with existing third-party payment methods and the impact of the promotion of digital currency on third-party payment.To solve these problems, this study focuses on the integration of third-party payment and digital currency, the traffic of third-party payment and digital currency users, and the security of the blockchains.This study aims to solve the practical problems which the digital currency might face and help to promote the development of digital RMB.
Internet Skills in Using Smartphones for Children with Intellectual Disability at SLB Negeri Wonogiri

The development of ICT makes it easier for people to acquire information, show expression, and fulfill other needs.Although information can be accessed easily by anyone, people need digital skills to master digital technology that develops rapidly.Digital skill is a person's ability to determine information needs from digital technology sources.This study aims to reveal how people with Intellectual Disability (ID) at SLB Negeri Wonogiri access ICT and internet skills.Sampling was done by a purposive sampling technique on four informants who met the sample criteria.Data collection techniques were conducted using in-depth interviews and nonparticipant observations.The results showed that internet skills among students with ID varied depending on several factors, including understanding buttons and operating several applications to support social skills in digital media interactions.The use of digital technology, especially smartphones by informants, can meet communication and information needs.The informants' internet skills could be observed from related medium, such as operational skills and formal skills, while the related content included information skills, communication skills, strategic skills, and content-creation skills.
Problems Existing in Practice Teaching of Financial Management Specialty and Improvement Ideas

In the context of rapid economic development, talent competition in enterprises is very fierce, especially in the financial management professional talent demand is increasing, financial management has become a popular professional courses in colleges and universities. However, most of the teaching activities carried out by the financial management major are teaching the theoretical knowledge of the financial management course, which cannot meet the practical needs of enterprises to carry out financial management, leading to the contradiction between teaching and practice. Based on this, this study analyzes the current teaching situation of financial management specialty, and solves the problems of practical teaching of financial management specialty curriculum to improve the teaching effect, meet the needs of compound talents in enterprises, and achieve long-term development.
Czech sign language single hand alphabet classification with MediaPipe

The paper presents the classification of static images of the single-handed Czech sign language alphabet.It uses the framework MediaPipe for annotation, and the classification is performed by a neural network using the TensorFlow computational library.The flow of the proposed method, data acquisition, preprocessing, and training are described in the paper.Obtained results consist of the classification success rate of the validation dataset for various MediaPipe configurations.The overall success rate was around 94%.
KANT: A Tool for Grounding and Knowledge Management

The roboticist community divides the knowledge representation and decision-making in the symbolic and sub-symbolic fields. Thus, real-world robotics grounded representation requires specific software techniques for landing continuous and discrete state variables in both fields. This research designs and develops an Open Source tool called KANT (Knowledge mAnagemeNT). It will be used to translate sub-symbolic properties and attributes from the robot environment to a symbolic paradigm in the Knowledge Base, in particular to PDDL. Based on Python, KANT includes mechanisms to enhance the process of knowledge manipulation in real-world scenarios through the use of in-memory or non-SQL databases, which would mean a time enhancement of around 30% of total time in middleware such as ROS 2.
Development of Hausa Acoustic Model for Speech Recognition

Acoustic modeling is essential for enhancing the accuracy of voice recognition software.To build an automatic speech system and application for any language, building an acoustic model is essential.In this regard, this research is concerned with the development of the Hausa acoustic model for automatic speech recognition.The goal of this work is to design and develop an acoustic model for the Hausa language.This is done by creating a word-level phonemes dataset from the Hausa speech corpus database.Then implement a deep learning algorithm for acoustic modeling.The model was built using Convolutional Neural Network that achieved 83% accuracy.The developed model can be used as a foundation for the development and testing of the Hausa speech recognition system.