Reviewerly/exHarmony huggingface dataset:
Human-like Explanation for Text Classification With Limited Attention Supervision

Human-like explanation for text classification is essential for high-impact settings such as healthcare where human rationales are required to support specialists’ decisions. Conventional approaches learn explanations using attention mechanisms to assign heavy weights to words that have a high impact on a model’s prediction. However, such heavily-weighted words often do not reflect human intuition. To advance human rationale, recent studies propose to supervise attention mechanisms assuming access to a huge set of attention labels collected from humans, called human attention maps (HAMs). Unfortunately, acquiring such HAMs for a huge dataset is very tedious, error-prone, and expensive in practice. Thus, we propose the novel problem of text classification with limited human attention supervision. Specifically, we study the learning of human-like attention weights from a dataset in which all documents contain classification labels but only a few documents provide HAMs. To this end, we design a deep learning architecture, HELAS: Human-like Explanation with Limited Attention Supervision to adaptively learn attention weights that focus on words analogous to a human with very limited attention supervision. HELAS effectively unifies joint learning improving both tasks of text classification and humanlike explanation even with only insufficient supervision labels for the latter task. Our experiments show that HELAS generates attention maps similar to real human annotations raising similarity scores up to 22% over state-of-the-art alternatives, even with as little as 2% of the documents having HAMs. It concurrently improves text classification by driving accuracy up to 19% over four state-of-the-art methods.
Motion Path Planning of Sorting Robot Based on Extended RRT-Connect Algorithm

Aiming at the problems of slow planning velocity, low efficiency and high path cost in the path planning of sorting robot by manual teaching or off-line planning, an improved rapid-exploration random tree algorithm is proposed to realize the autonomous obstacle avoidance movement of robot in the process of grasping in dynamic and unstructured environment. Based on the neighborhood radius of Voronoi visible area, the algorithm effectively controls the blind sampling near obstacles, improves the efficiency of effective sampling nodes. After introducing the idea of target gravity, the guidance of path generation and the operation efficiency of the algorithm are improved. For the motion path obtained by the planning algorithm, the Shortcut algorithm and cubic B-spline are used for optimization and smoothing, and finally the smooth motion path planning is realized. Through MATLAB two-dimensional static simulation and ROS (robot operating system) dynamic experiments, the results show that compared with the RRT algorithm and RRT-connect algorithm, the extended RRT-connect algorithm has been greatly optimized in path cost, search time and the number of sampling nodes, which proves the correctness, effectiveness and practicability of the proposed algorithm.
Privacy-Preserving Support Vector Machines with Flexible Deployment and Error Correction

Support vector machines (SVMs) are one of the most commonly used models for classification problems in machine learning. Nowadays there is an important scenario that many different parties jointly perform SVM training by integrating their individual data, while at the same time it is required that privacy of data can be preserved. At present there are three main routes to achieving privacy-preserving SVM. First, all parties jointly generate kernel matrices privately and then use them for remaining training (e.g. Yu et al. 2006). Second, based on the first route, an additional randomization is adopted to randomize kernel matrices in order to (heuristically) hide information exposed by kernel matrices (e.g. Mangasarian et al. 2008). Third, also the securest one, all parties run MPC protocols for computing whole optimization algorithms privately (not merely the generation of kernel matrices as the first two routes do) (e.g. Liu et al. 2018 and Wang et al. 2020).In this paper we propose a new efficient privacy-preserving SVM protocol in the third route that privately realizes the gradient descent method to optimize SVM and its security is proven in the semi-honest model. Our protocol admits the following advantages. The protocol is of flexible deployment. It supports the deployment of arbitrarily multiple servers and multiple clients. The protocol can tolerate dropping-out of some servers. The protocol admits the ability of malicious-error-message correction (which is actually beyond the semi-honest security). If a small number of messages are corrupted, it can still recover correct messages as desired. We remark that none of the above advantages can be obtained by some known work. Moreover, when compared to the privacy-preserving SVM by Liu et al. 2018 and Wang et al. 2020, our protocol achieves higher efficiency. We implement our protocol in Python and the experiments verify its efficiency.
The Internet of Value in Consumer Markets

This chapter focuses on how the Internet of Value can address e-commerce business models in the areas of loyalty, direct to consumer, servitisation, sustainability, data and self-sovereign identity. All these areas have a direct impact on the evolving role of e-commerce business models and how these will affect consumer markets.
Quantum Multiple Hypothesis Testing Based on a Sequential Discarding Scheme

We consider the quantum multiple hypothesis testing problem, focusing on the case of hypothesis represented by pure states. A sequential adaptive algorithm is derived and analyzed first. This strategy exhibits a decay rate in the error probability with respect to the expected value of measurements greater than the optimal decay rate of the fixed-length methods. A more elaborated scheme is developed next, by serially concatenating multiple implementations of the first scheme. In this case each stage considers as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> hypothesis probability the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> probability of the previous stage. We show that, by means of a fixed number of concatenations, the expected value of measurements to be performed decreases considerably. We also analyze one strategy based on an asymptotically large concatenation of the initial scheme, demonstrating that the expected number of measurements in this case is upper bounded by a constant, even in the case of zero average error probability. A lower bound for the expected number of measurements in the zero error probability setting is also derived.
AI Future: From Internal Vectors to Simple Objects States Subspaces Maps

The gradient descent ideas in the backpropagation error (BPE) form with some additional algorithms development allowed to realize deep learning of neural networks and led to a revolution in machine learning. A huge learning parameters number made it possible to replace discrete concepts with a vector description. There are some other ideas that are no less profound than gradient descent. Such as memory localization, decomposition of complex objects, and transformations linearization. The transition from internal vectors to maps of simple objects is considered as the promising direction for neural network algorithms development that can implement these ideas. It seems possible with states subspaces maps to overcome some of the problems that deep learning cannot cope without new ideas. A general plan for constructing an interacting maps hierarchical structure is considered, which can work in the Systems 1 and 2 modes according to Kahneman. Mathematical ideas for the linear approximation implementation using neural network maps are given. Other aspects of the general plan also have been preliminary worked out, but a significant amount of work remains to be done to improve the algorithms, ensure the stability of their convergence and confirm their performance by modeling. It will take time and funding. Nevertheless, the success in each of other ideas implementation: localization, decomposition, and linearization can lead to progress in the development of AI, comparable to the introduction of the gradient descent idea into the deep neural networks training, which caused a revolution in machine learning.
Certain Investigation On Cause Analysis Of Accuracy Metrics In Sentimental Analysis On News Articles

In the modern world, news is reaching the people through online mode. News readers are inclined to news based on interactivity and immediacy. The current technology facilitates all users a direct news on any event in real world. The sentimental analysis can be used to determine an emotional rating of text data sources from news articles such as Times of India, India Today etc., TFIDF methodology of word vectorization and Word Embedding of word vectorization is used to analyze the effectiveness in accuracy of models. The experiments have been performed on datasets using linear SVM and Gaussian Naive Bayes classifiers, in which consideration in decision making is based maximum scores acquired at certain class is ranked to be an important news to public. The accuracy for unigram by TFIDF vectorization is 62%, which is lesser than the accuracy of bigram by TFIDF vectorization which is 65% using SVM classifier. For Gaussian Naive Bayes classifier, the accuracy of unigram model is 61 %. Even though the accuracy of Gaussian Naive Byes classifier is less, independent feature considering nature of this classifier makes this model more effective in predicting the socially important news than other classifiers and also common occurrence of token of word between each class will be cause of affect the F1 score of models.
Meticulous Use of Turkish Informatics Terms to Deter Defective Turkish

Our working group on the meticulous use of Turkish Informatics terms is a working group of the Informatics Association of Turkey. The aims of our working group are clarified as: To clean the Turkish informatics terms from defective terms; to develop a dictionary to offer proper Turkish equivalents to especially contemporary informatics terms; to be a role model to other domains in the cleaning of Turkish from defective use; and to promote awareness of the value of meticulous Turkish in lieu of defective Turkish. After a brief review of the goals of language planning, our rationale and activities are presented and our future activities are outlined.
Research on the Influence of Calibration Image on Reprojection Error

This paper aims to capture calibration pictures by adopting three shooting methods at arbitrary angles, micro angles, and fixed angles, conducting camera calibration tests on data from multiple sets of pictures, and determining the average reprojection error of every dataset. Therefore, it is possible to evaluate the effects of calibration boards, number of calibrated pictures, and shooting methods of the calibrated pictures on calibration results. Based on the change curve of reprojection errors, it can be concluded that calibration effects of the circular array calibration board are more stable than those of the checkerboard; however, the impact is not significant. If there are a few calibration pictures, camera parameters will evidently be prone to a certain error rate. Moreover, too many calibration pictures will not only escalate the computation load but also result in a certain accumulation of errors and multiply the probability of error. The shooting method of a calibration image can greatly affect the calibration outcome. In other words, the standardization of a shooting image plays a central role in reducing reprojection errors and improving the calibration accuracy.
APPLICATION OF THE PROJECT METHOD IN REMOTE BIOLOGY LESSONS

The project approach was applied in remote biology lessons in the seventh grades. Each student chose the topic of their science work, set a goal, formulated a hypothesis, and decided on the ways to do the work. Students were required to submit intermediate results and a description of the entire work within the agreed time. Students received two points. One was for writing assignments and the other was for classroom presentation. In summary, we can confirm that the project approach in science education can take into account the different learning abilities and interests of students, as each student can individually choose the topic of the work and the way of doing it. So teaching is replaced by learning. In this method, the theory is applied in practice and vice versa - theoretical generalizations are made from practical results. All the listed arguments increase students' motivation to learn and develop students' creativity. By working on the project method and doing the work that suits him or her best, the student can reveal himself or herself in the best way. This choice increases the intrinsic motivation of the learner and fosters personal responsibility. The project simulates real life in the school and the student learns to achieve the set goal within a strict time frame. Thus, it can be stated that according to the achieved results, the project method corresponds to the challenges of modern school for science education, as students are taught to think critically, their creativity is developed and various problem-solving methods are applied. Keywords: project method, remote lessons, science work
Multi-task Perceptual Occlusion Face Detection with Semantic Attention Network

Face detection has been well studied for many years and one of the remaining challenges is to detect complex occluded faces in a real-world environment. Hence, this paper introduces a Multi-task Perceptual Occlusion Face Detection framework with a semantic attention network (MTOFD), which can detect face under complex occlusion conditions, especially, it takes the discrimination of occlusion type as a learning task, and use occlusion semantic information to improve face detection. In addition, an adaptive semantic attention network is employed to solve the conflict problem caused by multi-task in feature fusion, in which the potential semantic information of the occlusion task is learned adaptively, and the most important semantic information is selected and aggregated automatically to the task of occlusion face detection. Finally, MTOFD is tested and compared with some typical algorithms, such as FAN and AOFD, and it is found that our algorithm achieves state-of-the-art performance on dataset MAFA.
Machine Learning based Book reading level Prediction in Bangladesh

Bangladesh is called the most densely populated country. The most potent weapon for changing a person and the planet is a book. The majority of Bangladeshis read a variety of literature. The habit of reading has an impact on one's development as well as social progress in general. Now a days in Bangladesh, the rising of book reading among students is become growing. The paper's key is to identify the reading level of students that started during the Covid-19 pandemic period. This study looked at the reading levels of Bangladeshi students using machine learning techniques. The Xgb boost algorithm's closeness was determined to play the most important function in predicting reading level detection in this paper.
Gaussian Process Regression for Forecasting of Boston Housing Prices

Regression for forecasting of Boston housing prices is a classical problem in the field of machine learning. However, it is difficult to obtain satisfactory results using traditional statistical regression methods or machine learning algorithms. Part of the reason is that the interpretability of the parametric regression models is relatively limited. This article gradually introduces ordinary least square regression, ridge regression and Gaussian process regression to make forecasts about Boston housing prices and derives the progressive relationship among the three methods. This article focuses on the Gaussian process regression and optimizes the regression model by selecting appropriate kernel function and adjusting hyperparameters. By comparing the sum of residuals of regression models, it is concluded that owing to the characteristics of small sample size and excess explanatory variables, as a Bayesian non-parametric method, Gaussian process regression can more effectively forecast Boston housing prices.
Design of Vector Quantization Code Book Based on Clone Selection Algorithm and its Application

In order to improve the drawback that the LBG algorithm is more sensitive to the initial code book, the similarity measure of Euclidean distance only reflects the local consistency characteristics of the clustering results, but not the global consistency of the clusters. The clone selection algorithm is introduced to generate the initial code book using the splitting method. Further, it is proposed to generate the final code book using the flow distance based clone selection clustering method for optimization. The experimental results show that MDCSA achieves relatively high PSNR values for different code book sizes. The design method of clone selection based on manifold distance has better performance and effectiveness.
Cover Systems for the Modalities of Linear Logic

Ono’s modal FL-algebras are models of an extension of Full Lambek logic that has the modalities ! and ? of linear logic. Here we define a notion of modal FL-cover system that combines aspects of Beth-Kripke-Joyal semantics with Girard’s interpretation of the ! modality, and has structured subsets that interpret propositions. We show that any modal FL-algebra can be represented as an algebra of propositions of some modal FL-cover system.
RDPlot – An Evaluation Tool for Video Coding Simulations

RDPlot is an open source GUI application for plotting Rate-Distortion (RD)-curves and calculating Bjøntegaard Delta (BD) statistics [1]. It supports parsing the output of commonly used reference software packages, parsing *.csv-formatted files, and *.xml-formatted files. Once parsed, RDPlot offers the ability to evaluate video coding results interactively. Conceptually, several measures can be plotted over the bitrate and BD measurements can be conducted accordingly. Moreover, plots and corresponding BD statistics can be exported, and directly integrated into LaTeX documents.
Balanced News Neural Network for a News Recommender System

News recommender systems face unique challenges due to the rapidly changing readers&#x2019; interests over time. Some of the reader&#x2019;s interests are long-term, and some are short-term that need to be addressed in a news recommender system. Diversification is also required in a news recommender system to keep readers engaged in the reading process and expose them to various viewpoints. We propose a deep neural network for the news recommendation problem that learns multi-faceted news representations from the news content. The proposed model also learns the reader&#x2019;s long-term interests from the whole click history and the short-term ones from the click history using LSTMs. The attention mechanism is used to learn a reader&#x2019;s diversified interests. We give different levels of attention to the news and reader components. Experiments on two news datasets have shown the superiority of our proposed method compared to state-of-the-art methods.
Interactive Design Of a Museum Website

This engineering design process is creating a strategy to develop a museum website with desired performance targets. This process consists of several phases, and some of them need to be performed several times before ultimate product development can begin. The formulation of objectives and criteria, synthesis, analysis, building, testing, and assessment are essential aspects of the web design process. This engineering design process can be summarized, including research, conceptualization, feasibility evaluation, design requirements, feasibility study, system architecture, scheduling and tool design, and, eventually, actual coding.
Research on the evaluation method of ship damage-control ability based on rough set and cloud gravity center theory

An new evaluation method of ship damage-control ability based on rough set and cloud gravity center theory is given in this paper. The reduction of redundant information in standardized decision matrix is given by rough set theory, and the combined weight of evaluation index system of ship damage -control ability is obtained by rough set theory. Then the cloud theory is applied to model the expert evaluation language value, and the weighted deviation between evaluation value and ideal state value is calculated by cloud gravity center method. On the basis of establishing the evaluation index system of ship damage-control ability, an example is given to verify the effectiveness of the evaluation method.
A Real-Time Approach of Fall Detection and Rehabilitation in Elders Using Kinect Xbox 360 and Supervised Machine Learning Algorithm

Nowadays, fall in elders is a major issue almost in all the countries. Sometimes, heavy fall in elders cause serious injuries which leads to major medical care. Fall may lead to disability and also cause mortality to the elderly people. Due to the development of science and technology, the life of the fallen elders is rescued, and the injuries are healed. The newly developed technologies bring happiness and makes the elders life comfortable. At present, fall detection and prevention draws the attention of researchers throughout the world. New technology like Kinect Xbox 360 brings a new way to develop new intelligent system, which could be used to monitor the elderly people in their daily activities. Kinect Xbox 360 is a low-cost device. It tracks the body movements. It is used by the elders doing rehabilitation exercises in the homely environment. Elders who are living alone face the risk of fall. Activity recognition system is a very important technology for elderly people to do their daily activities in their life. Physiotherapy is one of the branches of rehabilitation science which brings differences in the ability and makes the individual to lead a healthy life. In this paper, we are going to analyze various methods of human fall detection and techniques by noticing the daily activities of the elders. We are also going to see different types of machine learning algorithm used for fall detection.