Reviewerly/exHarmony huggingface dataset:
Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures.

Neural networks have achieved success in a wide array of perceptual tasks, but it is often stated that they are incapable of solving tasks that require higher-level reasoning. Two new task domains, CLEVRER and CATER, have recently been developed to focus on reasoning, as opposed to perception, in the context of spatio-temporal interactions between objects. Initial experiments on these domains found that neuro-symbolic approaches, which couple a logic engine and language parser with a neural perceptual front-end, substantially outperform fully-learned distributed networks, a finding that was taken to support the above thesis. Here, we show on the contrary that a fully-learned neural network with the right inductive biases can perform substantially better than all previous neural-symbolic models on both of these tasks, particularly on questions that most emphasize reasoning over perception. Our model makes critical use of both self-attention and learned soft object-centric representations, as well as BERT-style semi-supervised predictive losses. These flexible biases allow our model to surpass the previous neuro-symbolic state-of-the-art using less than 60% of available labelled data. Together, these results refute the neuro-symbolic thesis laid out by previous work involving these datasets, and they provide evidence that neural networks can indeed learn to reason effectively about the causal, dynamic structure of physical events.
An Augmented Reality-Based Mobile Application Facilitates the Learning about the Spinal Cord

Health education is one of the knowledge areas in which augmented reality (AR) technology is widespread, and it has been considered as a facilitator of the learning process. In literature, there are still few studies detailing the role of mobile AR in neuroanatomy. Specifically, for the spinal cord, the teaching–learning process may be hindered due to its abstract nature and the absence of three-dimensional models. In this sense, we implemented a mobile application with AR technology named NitLabEduca for studying the spinal cord with an interactive exploration of 3D rotating models in the macroscopic scale, theoretical content of its specificities, animations, and simulations regarding its physiology. To investigate NitLabEduca’s effects, eighty individuals with and without previous neuroanatomy knowledge were selected and grouped into control and experimental groups. Divided, they performed learning tasks through a questionnaire. We used the System Usability Scale (SUS) to evaluate the usability level of the mobile application and a complimentary survey to verify the adherence level to the use of mobile applications in higher education. As a result, we observed that participants of both groups who started the task with the application and finished with text had more correct results in the test (p &lt; 0.001). SUS results were promising in terms of usability and learning factor. We concluded that studying the spinal cord through NitLabEduca seems to favor learning when used as a complement to the printed material.
Online teaching quality evaluation based on emotion recognition and improved AprioriTid algorithm

The association rule algorithm in data mining is used to study the factors that may affect students’ performance, to make suggestions for teaching work, and to provide decision-making basis for teachers and teaching administrators, which has practical significance. There are many potential applications for facial expression recognition technology. For example, in the teaching process, facial expression recognition technology helps teachers understand students and judge students’ reactions to certain things. Based on the current research status of emotion recognition and data mining algorithms, this paper improves the AprioriTid algorithm and constructs an online teaching quality evaluation model based on teaching needs. In addition, this article applies the model constructed in this article to the evaluation of English online teaching quality and evaluates teaching quality through data mining. The experimental research shows that the model constructed in this paper has good performance.
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of symbolic regression. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a black-box performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimizes for best-case performance instead of expected performance.
Blockchain-Based Reputation Systems: Implementation Challenges and Mitigation

Reputation expresses the beliefs or opinions about someone or something that are held by an individual or by a community. Reputation Management Systems (RMSs) handle representation, computation, and storage of reputation in some quantitative form, suitable for grounding trust relations among parties. Quantifying reputation is important in situations, like online service provision, which involve interaction between parties who do not know (and potentially distrust) each other. The basic idea is to let parties rate each other. When a party is considered for interaction, its ratings can be aggregated in order to derive a score for deciding whether to trust it or not. While much valuable research work has been done on reputation-based trust schemes, the problem of establishing collective trust in the reputation management system itself has never been fully solved. Recently, several researchers have put forward the idea of using Distributed Ledger Technology (DLT) as the foundation for implementing trustworthy RMSs. The purpose of this paper is to identify some critical problems that arise when DLTs are used in order to manage evidence about previous interaction and compute reputations. The paper proposes some practical solutions and describes methods to deploy them on top of standard DLT of the Ethereum family.
Encryption System for Hiding Information Based on Internet of Things

&lt;p class="0abstract"&gt;One of the unexpected intelligence tactics known in World War II was to conceal the data in images that were reduced to the size of a point that was used in every text and transported in front of the enemy's eyes. In the new age, and after the expansion of Internet science and the use of the Internet worldwide, we will establish a security feature of the IOT service that will work more reliably and more effectively to deal with the Internet of Things and ensure the work of the services that the customer interacts with. A secret-key stenographic scheme that embeds four gray-scale secret size (128*128) pixel images into a size (512*512) pixel cover image in this work. Wavelet transform is the method used in this project to analyze the cover into its frequency components. In this work, combinations of steganography and cryptography were made to increase the level of safety and make the device more difficult for attackers to beat. The resulting stego-image that will be transmitted did not raise any suspicion by both objective and subjective evaluation, so the primary objective of Steganography is achieved. The proposed system was designed by using (MATLAB R2018b) and running on a Pentium-4 computer. The Internet of Things works with the encryption system for data in a synchronized manner with the technological development, and in order to maintain the stability of any Internet of things service, whether it is information signal services, visual or audio data, a remote control system, or data storage in the Internet cloud, we must focus on data preservation from internet pirates and internet system hackers. The picture Figure&lt;strong&gt; &lt;/strong&gt;4 below shows the method of encryption and dealing with the Internet of things system..&lt;/p&gt;
MEC-assisted flexible transcoding strategy for adaptive bitrate video streaming in small cell networks

Adaptive bitrate video streaming (ABR) has become a critical technique for mobile video streaming to cope with time-varying network conditions and different user preferences. However, there are still many problems in achieving high-quality ABR video streaming over cellular networks. Mobile Edge Computing (MEC) is a promising paradigm to overcome the above problems by providing video transcoding capability and caching the ABR video streaming within the radio access network (RAN). In this paper, we propose a flexible transcoding strategy to provide viewers with low-latency video streaming services in the MEC networks under the limited storage, computing, and spectrum resources. According to the information collected from users, the MEC server acts as a controlling component to adjust the transcoding strategy flexibly based on optimizing the video caching placement strategy. Specifically, we cache the proper bitrate version of the video segments at the edge servers and select the appropriate bitrate version of the video segments to perform transcoding under jointly considering access control, resource allocation, and user preferences. We formulate this problem as a nonconvex optimization and mixed combinatorial problem. Moreover, the simulation results indicate that our proposed algorithm can ensure a low-latency viewing experience for users.
Engineering Task-based Augmented Reality Guidance: Application to the Training of Aircraft Flight Procedures

Abstract Training operators to efficiently operate critical systems is a cumbersome and costly activity. A training program aims at modifying operators’ knowledge and skills about the system they will operate. The design, implementation and evaluation of a ‘good’ training program is a complex activity that requires involving multi-disciplinary work from multiple stakeholders. This paper proposes the combined use of task descriptions and augmented reality (AR) technologies to support training activities both for trainees and instructors. AR interactions offer the unique benefit of bringing together the cyber and the physical aspects of an aircraft cockpit, thus providing support to training in this context that cannot be achieved by software tutoring systems. On the instructor side, the LeaFT-MixeR system supports the systematic coverage of planed tasks as well as the constant monitoring of trainee performance. On the trainee side, LeaFT-MixeR provides real-time AR information supporting the identification of objects with which to interact, in order to perform the planned task. The paper presents the engineering principles and their implementation to bring together AR technologies and tool-supported task models. We show how these principles are embedded in LeaFT-MixeR system as well as its application to the training of flight procedures in aircraft cockpits.
RETRACTED: Design of College English Teaching Information Platform Based on Artificial Intelligence Technology

Abstract With the development of Artificial Intelligence(AI) technology, especially in the construction of College English teaching(ET) IP(IP), more and more applications have promoted the current college ET reform and development. We first analyzes the connotation of AI technology and the related technical problems that need to be solved in the design of ET IP, then analyzes its technology in the development of English learning through the discussion of AI technology, and finally analyzes the application of AI technology from many different aspects. Listening, writing, translation and oral ET.Secondly, under the guidance of modern education theory and learning theory, combined with the information environment, combined with teaching practice, using AI technology and information technology to design college students’ English IP. Through the theoretical construction of the traditional ET model, through interviews and questionnaire surveys, some problems that exist in the learning of the English IP are found. AI technology and new media in information technology are applied to the actual teaching practice of college English, and the whole teaching practice process is tracked and analyzed. In the process of tracking and analysis, the teaching model is continuously improved. The analysis of the teaching practice results shows that “under the mode of College English education informatization, the construction of College English based on AI technology” improves students’ learning ability and greatly improves their English learning achievements. The experimental results show that in the current network connected information society, the construction of College English education IP should abide by the traditional education concept, completely innovate the education mode, and provide the greatest advantages of AI technology and information technology.
Knowledge management systems usage: application of diffusion of innovation theory

Purpose The purpose of this study is to investigate the influence of technological antecedents on the usage of decision makers for the implemented knowledge management system (KMS) amongst Jordanian banks. This study extends the investigation by assessing the influence of knowledge or information quality on KMS usage. This study aims to assess whether knowledge or information quality is significantly correlated to system compatibility, relative advantage and complexity (technological antecedents). Design/methodology/approach The study model was developed by using Rogers’ diffusion of innovation (DOI) theory, on which seven hypotheses were developed. To examine these research hypotheses, a self-administered questionnaire was carried out with 341 decision makers who are using the KMS to perform their job-related activities. Structural equation modelling analysis of moment structures software was used for data analysis. Findings The findings revealed that decision makers usage of the implemented KMS’s is affected significantly by relative advantages, system complexity and knowledge quality, but not system compatibility. Moreover, the findings showed that knowledge quality is significantly correlated with DOI technological antecedents. Practical implications Bank managements are now in a better position to understand what kind of resources and supports are needed to achieve the maximum pay-off from KMS usage within their banks. This study has proved that it is not sufficient for Jordanian banks to focus solely on the system quality; they must also take the quality of knowledge or information (system output) as a critical factor that can affect their investments in KMS’s. Originality/value This study is one of the limited conducted studies to investigate the importance of KMS usage and related antecedents in the Arab world; particularly, in the context of the Jordanian banking sector. The findings of this study have contributed to the Jordanian financial sector for its vital evaluation of the KMS actual usage behaviour. Findings can be used by the Jordanian ministry of finance to improve the understanding of the factors influencing KMS usage in the financial sector. This study has contributed to reducing the gap of DOI literature amongst developed and developing countries, particularly in the Jordanian context.
Enhancing Vocational Training in the Post-COVID Era through Mobile Mixed Reality

COVID-19 and the resulting restrictions have had a massive impact on engineering education, particularly vocational and practical aspects of training. In this study, we present a novel mixed reality (MR) tool to simulate and guide learners through a simple fault diagnosis task of a three-phase power supply. The tool was created as a web-based application that could be accessed from budget smartphones in order to cover the majority of users. Comparisons were made between novices using MR guidance and those with more experience in the task who did not have additional guidance, finding that the novices outperformed the experts across all metrics measured. This indicates that MR could be a valuable tool to supplement traditional vocational learning methods, particularly at a time when physical access to equipment and facilities is scarce. MR has applications across the engineering industry, but the target task of a three-phase power supply was chosen as it has particular relevance to the offshore wind industry, which faces a shortage of skilled engineers and technicians in the coming years.
Optimal provable robustness of quantum classification via quantum hypothesis testing

Abstract Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems. These can arise either from noisy implementations or, as a worst-case type of noise, adversarial attacks. In order to develop defense mechanisms and to better understand the reliability of these algorithms, it is crucial to understand their robustness properties in the presence of natural noise sources or adversarial manipulation. From the observation that measurements involved in quantum classification algorithms are naturally probabilistic, we uncover and formalize a fundamental link between binary quantum hypothesis testing and provably robust quantum classification. This link leads to a tight robustness condition that puts constraints on the amount of noise a classifier can tolerate, independent of whether the noise source is natural or adversarial. Based on this result, we develop practical protocols to optimally certify robustness. Finally, since this is a robustness condition against worst-case types of noise, our result naturally extends to scenarios where the noise source is known. Thus, we also provide a framework to study the reliability of quantum classification protocols beyond the adversarial, worst-case noise scenarios.
Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions

In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said.In education, teachers' uptake of student contributions has been linked to higher student achievement.Yet measuring and improving teachers' uptake at scale is challenging, as existing methods require expensive annotation by experts.We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (PJSD), estimated via next utterance classification; (3) conducting a linguisticallymotivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes.We find that although repetition captures a significant part of uptake, PJSD outperforms repetitionbased baselines, as it is capable of identifying a wider range of uptake phenomena like question answering and reformulation.We apply our uptake measure to three different educational datasets with outcome indicators.Unlike baseline measures, PJSD correlates significantly with instruction quality in all three, providing evidence for its generalizability and for its potential to serve as an automated professional development tool for teachers.1
Constructing QUD Trees

We discuss and combine representation formats for discourse structure, in particular ‘d-trees’ from QUD theory and SDRT graphs. QUD trees are derived from SDRT graphs, while changes must apply to QUD theory in order to allow for representations of naturalistic data. We discuss whether QUD s can replace discourse relations. We apply a new method for the identification of implicit Questions under Discussion (QUD s) to examples from an interview, and we address the status of non-at-issue content within our framework.
Accurate and efficient vehicle detection framework based on SSD algorithm

Vehicle detection plays an important role in intelligent transportation systems and security. Using the original Single Shot MultiBox Detector (SSD) directly for vehicle detection, lacks accuracy and stability. Moreover, most of the state-of-the-art methods need cost a lot of time to inference. Vehicle detection is often used in complex traffic environments. Therefore, faster detection speed and higher detection accuracy are required. This study is aimed at developing a trade-off between accuracy and speed vehicle detection framework based on the SSD algorithm. To improve the multi-scale detection performance of SSD, semantic information, detailed features and receptive fields are combined to propose the feature pyramid enhancement strategy (FPES). On the other hand, the cascade detection mechanism is proposed to strengthen the positioning capability of SSD and an adaptive threshold acquisition method for object detection module (ODM) stage to improve model accuracy. Finally, a more efficient convolutional network is deployed through network slimming. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on UA-DETRAC and Udacity benchmarks. Interestingly, the inference time is the lowest for the proposed method than the state-of-the-art methods, promising its application for fast and effective vehicle detection.
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower-resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.