Reviewerly/exHarmony huggingface dataset:
Comparison of Knowledge-Based Reasoning Methods to Measure the Effectiveness of Diagnostic Results

Abstract This study describes a comparative analysis of methods in knowledge-based reasoning which aims to determine the best and optimal method in producing a diagnosis of a disease. The methods to be compared are Bayes’ Theorem, Certainty Factor, and Euclidean Probability. The three methods were chosen because they can generate hypotheses from several existing possibilities, this can be seen from the many uses of these methods in previous studies. With this research, it can be used as material for consideration or support in producing a diagnosis conclusion. The process of testing the comparison of these methods is carried out by selecting the highest diagnosis result value and performing a comparative analysis using exponential techniques. The results of this test indicate that the Euclidean Probability produces an accuracy value of 75%, then the Bayes Theorem obtains an accuracy value of 62% and the Certainty Factor obtains an accuracy value of 87%. In addition, the results of the comparison of methods using exponential techniques show that Euclidean Probability gets 84%, then Bayes’ Theorem gets 78% and the Certainty Factor gets 87%. With these results, it can be concluded that the Certainty Factor is better than Euclidean Probability and Bayes’ Theorem in diagnosing disease.
Teaching Skill and Innovation of Application of Computer in Material Science

As the course of Application of Computer in Material Science is high theoretical and operational, it’s difficult to learn for both teachers and students. In this paper, we introduce some teaching skills such as how to present a good introduction lesson, the teaching of theoretical knowledge and software, homework and final exam. It’s expected to inspire the learning interest of students and improve the teaching effect.
The development of the Poggendorff illusion in typically developing children

We examined how the strength of the Poggendorff illusion changes with age in typically developing children. To this end, we recruited children aged 6 to 14 years and quantified the degree to which they experienced the illusion. The illusion was strongest in the youngest children and decreased with age logarithmically-reaching adult levels (as established by an earlier study) by 21.6 years, as determined by nonlinear interpolation. We also measured the ability to align two lines together in a nonillusory condition, receptive language, and abstract reasoning to determine whether changes in illusion strength were also associated with these factors. Alignment-matching abilities, receptive language, and abstract reasoning increased with age. However, only receptive language and abstract reasoning were correlated with illusion strength. Abilities in alignment matching were not related to illusion strength and reached adult levels (as established by a previous study) earlier at 14.7 years, as determined by nonlinear interpolation. A multiple regression analysis further revealed that receptive language and abstract reasoning did not contribute beyond their shared variance with age. Based on these findings, we suggest that the illusion is exaggerated in early development and attenuates as low-level and high-level processes mature. The theoretical implications of these findings are discussed.
Attention Backpropagation

In recent years, deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks. However, they are often perceived as "black box" methods for the lack of interpretability. In this work, we propose an approach to alleviate the opaqueness of deep learning models by providing visual explanations to the predictions of the network. Our approach entitled Attention Backpropagation will backpropagate the network's attention to produce an attention map highlighting the important regions in the image for predicting. This is a generalized method that can be used for all the networks without modification of the network architecture or retraining. We apply the proposed approach to provide visual explanations for the whole inference process of VGG-16 model, and analyze the cause for the erroneous predictions. Furthermore, numerical results suggest that our approach has better ability to localize the discriminative image regions, and outperforms previous methods in weakly-supervised localization task and the pointing game test on ILSVRC-15 dataset.
Using an Ensemble Learning Approach on Traditional Machine Learning Methods to Solve a Multi-Label Classification Problem

This paper explores an interesting way to solve a multi-label classification problem using an ensemble learning with natural language processing approach. The multi-label classification problem selected is that of movie genre prediction where each movie can possibly belong to multiple genres which essentially differentiates it from a much more classical multi-class classification problem. In the current machine learning environment, there are a host of different classifiers, some which are more suited to handle more than binary classes at once such as random forests and decision trees while some which are modelled around solving an inherently binary classification problem. As we are using textual data in the form of synopsis of various movies, we use Word2Vec to vectorize the textual data in order to fit the classifiers. In this project, we use classifiers belonging to both these types, namely Naive Bayes, random forest classifier and XGBoost classifier to individually predict the multiple labels for each movie, and then we construct an ensemble model using a voting classier in the purview of ensemble learning by adjusting the weights of the combining classifiers and show that the ensemble model performs much better than each of the individual classifiers. The final micro-f-score achieved is 0.6738 which is commendable with using non-neural traditional machine learning techniques and is an improvement from the referred work for this paper.
Correlation of “virtual identity” problems and issues of preserving traditional values in modern society

The emergence of social networks has joined people from different parts of the world which has brought unconditional benefit to humanity. At the same time, the possibility of communication between citizens of different countries using the same platforms, foreign hosting companies and providers has created a new form of human identity – virtual identity. In turn, this has contributed to a new phenomenon, such as digital socialization of the individual. This has created comfort and additional opportunities for integration into society, but at the same time, this form has led to the blurring of traditional values, national cultures and the fragmentation of political consciousness.
Cyber Risk Management and Accounting Profession

In todayâ€™s world of information technologies (IT) and digital connectivity cyber risk is considered inevitable for all organizations. Hence, understanding cyber risk and managing it effectively is crucial for all. This paper includes a literature review with the aim to suggest a model for cyber risk management as well as to justify the role of accountants in this model. This review leads to the conclusion that cyber risk is a unity of three elements (threat, IT vulnerability and negative impact) and suggests the following six integrated stages of its management: (1) identification, prioritization, and assessment; (2) control system design; (3) monitoring; (4) incident management; (5) reporting and assurance; (6) informal management. The incremental contribution of the proposed model with respect to the existing frameworks is in the following two differences: first, it is more integrative than the alternatives, and second, it quantifies cyber risk more relevantly and reliably than the alternatives. To apply the suggested model cybersecurity professionals should have some technical knowledge, but the core attributes relate to their personal capabilities. Due to their specific expertise and competencies, accountants can have a key role in risk management. To benefit cybersecurity risk management, however, accounting needs to reform its higher education model.
Quo Vadis, University?

When the new coronavirus induced Covid-19 pandemic spread across the globe in the early spring of 2020, universities around the world closed down and moved quickly to adapt to the ‘new reality’ by relying on modern technology and moving their curricula online. However, these extraordinary circumstances were not taken as an opportunity to reflect on and reform the many ills of the modern university, but rather aimed to secure the status quo and expected the students to accept the new reality of reduced online curricula. At the same time this has, in the author’s opinion, revealed the structural problems of rigid curricula and the lack of much needed flexibility in order to move beyond reproducing instrumentalised knowledge and to reopen the university and its humanities programmes as venues of ‘science in the making’.
Accessible Driver Announcement in Public Transportation: A Solution based on Speech-to-Text and Display

Today, the information in public transport, especially when an unforeseen event such as signal failure, platform change, etc. occurs, is largely provided by making an audio announcement. This, in many situations, is hardly understandable due to various factors, and it rarely helps people with hearing impairment. This work attempts towards an accessible driver announcement in public transportation based on a speech-to-text conversion and on-screen displays. Incorrect speech-to-text conversions are checked and corrected using a GloVe (Global Vector) based algorithm so that passengers get correct information. User testing of the system with different people including hard of hearing shows the effectiveness of such a solution. This work has been done, by taking the current system in public transportation in Oslo, Norway as a reference. However, the proposed solution and workflow can be applied to any mode of public transportation and anywhere in the world.
Evolutionary algorithm for automated formation of decision-making models for predicting the safety of opioid therapy

Abstract In this paper, an evolutionary algorithm for solving the problem of predicting the safety of opioid therapy for patients with pancreatic cancer is proposed. Opioid analgesics such as fentanyl and morphine are used as a therapy for pain syndromes. Using the patient database, based on the results of the therapy applied to them, it is determined whether there is a correlation between the outcome and the combination of input data taken into account. To find a set of informative features, it is proposed to use the genetic algorithm for multi-criterion optimization, in which two criteria are reduced to one generalized criterion using the method of “additive convolution”. The formed combination of the selected input features, which affects the outcome, is used to build a decision support model and to evaluate it afterwards.
A Method to Determine the Rate Random Walk Coefficient of Fiber Optic Gyroscope

The mechanism of FOG’s (fiber optic gyroscope) RRW (rate random walk) is not clear; there are few researches on the estimation of FOG’s RRW coefficient based on orbit data. A data processing method based on the combination of on-board and on-ground processing is proposed; the integral angle of long period can be obtained by on-board processing; the RRW coefficient is estimated by Allan variance method by on-ground processing; the problem of large estimation error caused by long RRW correlation time is solved. The identification of RRW correlation time is given based on Allan variance curve; the calculation of the standard deviation of integral angle error is given; correctness of the coefficient estimation is proved by comparative analysis.
Generalization of a Class of APN Binomials to Gold-Like Functions

In 2008 Budaghyan, Carlet and Leander generalized a known instance of an APN function over the finite field \(\mathbb {F}_{2^{12}}\) and constructed two new infinite families of APN binomials over the finite field \(\mathbb {F}_{2^n}\), one for n divisible by 3, and one for n divisible by 4. By relaxing conditions, the family of APN binomials for n divisible by 3 was generalized to a family of differentially \(2^t\)-uniform functions in 2012 by Bracken, Tan and Tan; in this sense, the binomials behave in the same way as the Gold functions. In this paper, we show that when relaxing conditions on the APN binomials for n divisible by 4, they also behave in the same way as the Gold function \(x^{2^s+1}\) (with s and n not necessarily coprime). As a counterexample, we also show that a family of APN quadrinomials obtained as a generalization of a known APN instance over \(\mathbb {F}_{2^{10}}\) cannot be generalized to functions with \(2^t\)-to-1 derivatives by relaxing conditions in a similar way.
RETRACTED: Research on Countermeasures for the Successful Transition From Egp Teachers to Esp Teachers in Colleges Based on Computer Information Technology

Abstract The special-purpose English course should combine the specific subject content with the language teaching objective, and the teaching activities should focus on solving the language problems encountered by students, and cultivate the English ability related to the major as the teaching focus. This shows that “a post-college English teaching era emphasizing individuality and diversity has arrived, and the only way for college English teaching to meet international standards is to gradually shift from basic English to ESP teaching”. It can be seen that ESP teaching has become a trend in colleges and universities. How to transform EGP teachers into ESP teachers and other problems remain to be solved in the reform of College English teaching in China.
Handwritten Numbers Recognition, Detection and Generation Based Convolutional Neural Network

Convolutional neural network has been widely used in the field of image recognition. It has achieved significantly results in image classification and object detection. Based on those results, the paper discussed the ability of different network structures to recognize handwritten numbers, the effects of fine tuning on the model, and the ability to generate and detect numbers by computer. This paper discussed the structure of CNN and LSTM and compared them in recognition results. We also use the parameters in classifying MNIST to initialize our recognition task. This paper used Faster R-CNN to detect the position of numbers. At last, we used WGAN to discuss the ability that computers generate handwritten numbers.
Learning Vision-Based Physics Intuition Models for Non-Disruptive Object Extraction

Robots operating in human environments must be careful, when executing their manipulation skills, not to disturb nearby objects. This requires robots to reason about the effect of their manipulation choices by accounting for the support relationships among objects in the scene. Humans do this in part by visually assessing their surroundings and using physics intuition for how likely it is that a particular object can be safely manipulated (i.e., cause no disruption in the rest of the scene). Existing work has shown that deep convolutional neural networks can learn intuitive physics over images generated in simulation and determine the stability of a scene in the real world. In this paper, we extend these physics intuition models to the task of assessing safe object extraction by conditioning the visual images on specific objects in the scene. Our results, in both simulation and real-world settings, show that with our proposed method, physics intuition models can be used to inform a robot of which objects can be safely extracted and from which direction to extract them.
Pengembangan Media Game Edukasi berbasis Macromedia flash Mata Pelajaran Matematika Materi Pecahan Di Kelas IV SD NU Sleman

This study aims to develop a macromedia flash-based Educational Game Media suitable for use in fraction mathematics lessons. The research method used is a type of Research and Development (R &amp; D). The model used is a modification of the model from Borg and Gall with the following stages: (1) research and information gathering, (2) planning, (3) initial product development, (4) limited trials, (5) initial product revision, (6) Main field trials, (7) operational product revisions, (8) operational field trials, (9) final product revisions, (10) Dissemination and implementation. The results showed that the educational Game Media based on macromedia flash was valid, so it was suitable for use in the mathematics learning process. Educational Game Media based on macromedia flash is also considered effective for improving learning outcomes based on the T Independent Sig test results. (p) 0.00 with conditions P &lt;0.05 so that H0 is rejected. Paired T test results Sig. (p) 0.000 with conditions P &lt;0.05 so that H0 is rejected also shows that the macromedia flash-based Educational Game Media has a significant impact on improving learning outcomes.
Open Questions for Next Generation Chatbots

Over the last few years, there has been a growing interest in developing chatbots that can converse intelligently with humans. For example, consider Microsoft's Xiaoice. It is a highly intelligent dialogue system that serves as both a social companion and a virtual assistant. Targeted towards Chinese users, Xiaoice is connected to 660 million online users and 450 million IoT devices. Because of the deep learning revolution, the field is moving quickly, so this survey aims to introduce newcomers to the most fundamental research questions for next generation neural dialogue systems. In particular, our analysis of the state of the art reveals the following 4 key research challenges: 1) knowledge grounding, 2) persona consistency, 3) emotional intelligence, and 4) evaluation. Knowledge grounding endows the chatbot with external knowledge to generate more informative replies. Persona consistency grants dialogue systems consistent personalities. We divide each fundamental research challenge into several smaller and more concrete research questions. For each fine grained research challenge, we examine state of the art approaches and propose future research directions.
Surface Defect Detection Method of High Speed Railway Bridge Cables based on Optical Fiber Sensing Technology

Due to environmental loads, fatigue effects, corrosion and material aging and other factors, the surface of high-speed railway bridge cables will inevitably be damaged, which affects the safety of the bridge. Therefore, a method for detecting surface defects of high-speed railway bridge cables based on optical fiber sensing technology is proposed. Secondly, based on the theory of high-speed fiber-optic sensor, the defect of the bridge is detected and detected by the method of neural network. The results show that: under the application of the proposed method, the defect judgment is accurate, and the type identification is also accurate, which provides a reference for bridge safety and health monitoring.
Proximal Deterministic Policy Gradient

This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms. First, we formulate off-policy RL as a stochastic proximal point iteration. The target network plays the role of the variable of optimization and the value network computes the proximal operator. Second, we exploits the two value functions commonly employed in state-of-the-art off-policy algorithms to provide an improved action value estimate through bootstrapping with limited increase of computational resources. Further, we demonstrate significant performance improvement over state-of-the-art algorithms on standard continuous-control RL benchmarks.
Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles

We investigate the autonomous navigation of a mobile robot in the presence of other moving vehicles under time-varying uncertain environmental disturbances. We first predict the future state distributions of other vehicles to account for their uncertain behaviors affected by the time-varying disturbances. We then construct a dynamic-obstacle-aware reachable space that contains states with high probabilities to be reached by the robot, within which the optimal policy is searched. Since, in general, the dynamics of both the vehicle and the environmental disturbances are nonlinear, we utilize a nonlinear Gaussian filter – the unscented transform – to approximate the future state distributions. Finally, the forward reachable space computation and backward policy search are iterated until convergence. Extensive simulation evaluations have revealed significant advantages of this proposed method in terms of computation time, decision accuracy, and planning reliability.
An element-wise generalized singular value decomposition (GSVD) based image encryption algorithm

Abstract The image encryption algorithm aids a secure data transmission. In our proposed method, we use the generalized singular value decomposition (GSVD) as the encryption tool. Here, the secret image will be decomposed with respect to the key and generates the ciphered text. Further, to improve its security, the Arnold transforms and the XOR operation is used respectively. The region-wise applied GSVD reduce the time consumption of the conventional image encryption methods. Here, the GSVD decomposition is element-wise, so, the algorithm is more robust. The generated cipher text is transmitted and the key is generated with the combination of the key used to decompose the secret image pixel in GSVD module, the Arnold transform key and the XOR operator key. In receiver end, the ciphered image is synthesized with the key received in a separate channel. This proposed model is lossless encryption method.
MarioDAgger: A Time and Space Efficient Autonomous Driver

Imitation learning is a promising approach for training autonomous vehicles, where a set of state-action pairs from human demonstrated driving is used as training data in a supervised learning manner. Dataset Aggregation (DAgger) is a common imitation learning algorithm, in which models are trained by iteratively collecting new data, aggregating it with old data, and retraining the model on the entire collected dataset. Data aggregation and retraining, however, lead to two main problems: (i) large memory consumption, and (ii) long training time. In this work, we present a fast and memory-efficient algorithm, called MarioDAgger, that improves DAgger by resolving the aforementioned problems. Unlike DAgger that requires a collection of old and new data to train the models, MarioDAgger uses only the new data and a few samples from the old data stored in a rehearsal buffer, which is updated iteratively using reservoir sampling. To prevent forgetting old knowledge, MarioDAgger uses a recent regularization tech-nique Elastic Weight Consolidation. We evaluate and compare MarioDAgger with SafeDAgger, a recent variant of DAgger that MarioDAgger builds upon, in the context of autonomous vehicles, and show that MarioDAgger achieves the same performance as SafeDAgger in half as many iterations, using significantly less memory space.