Reviewerly/exHarmony huggingface dataset:
Estimation of High School Entrance Examination Success Rates Using Machine Learning and Beta Regression Models

Education is the foundation of economic, social, and cultural development for every individual and society as a whole.Students are accepted to secondary education institutions with the high school entrance examination made by the Ministry of National Education in Turkey.In this study, the success rates of the students who took the high school entrance examination in Turkey's 81 provinces in 2019 were handled with the machine learning regression and beta regression model.The present paper aimed to model, predict, and explain students' success rates using variables such as divorce rate, gross domestic product, illiteracy, and higher education populations.Support vector regression, random forest, decision tree, and beta regression model were applied to estimate success rates.Two models with the highest R 2 value were found to be beta regression and random forest models.When the prediction errors of beta regression and random forest model were examined, it seemed to be that the random forest model is relatively superior to the beta regression model in predicting the success rates.While the beta regression model was the best predictor of the success rates of Çanakkale province, the random forest model predicted the success rates of Ankara well.Also, it was seen that the variables found to be significant in the beta regression model for success rates were also crucial in the random forest model.It is recommended to use both the beta and random forest models to estimate the students' success rates.
DEEP-LEARNED CROSS-DOMAIN SENTIMENT CLASSIFICATION USING INTEGRATED POLARITY SCORE PATTERN EMBEDDING ON TRI MODEL ATTENTION NETWORK

Classification of sentiments is a challenging task when it is done across multiple domains.The dissimilarity between the domains, reflected in their sentiments makes the classification tougher.This work proposes a deep learned novel architecture named Integrated Polarity Score based Pattern Embedding on Tri Model Attention (IPSPE_TMA) Network which is a model based on Bidirectional Long-short Term Memory (Bi-LSTM), Bi-Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) with three stage novel embedding architecture and attention network.The information is extracted by incorporating attention in word and sentence level.In order to generate robust vectors, a novel polarity score based embedding is proposed and used along with Glove and Fast Text in this paper.Integration of three models with three kinds of embeddings and attention mechanism strengthens this model.This network implementation for sentiment analysis on cross domain dataset gives better performance than many of the previous works.
APPLICATION OF THE METAHEURISTIC TUNICATE SWARM ALGORITHM IN SOLVING APPLIED MECHANICS PROBLEMS

In this paper, the principles of a metaheuristic algorithm based on tunicate swarm behavior are shown. The Tunicate Swarm Algorithm (TSA for short) was used for solving problems in applied mechanics (speed reducer, cantilever beam and three-dimensional beam optimization). In the end, a comparison of results obtained by TSA and results obtained by other methods is given.
Automated Theorem Proving in the Classroom

We report on several scenarios of using automated theorem proving software in university education.In particular, we focus on using the Theorema system in a software-enhanced logic-course for students in computer science or artificial intelligence.The purpose of using logic-software in our teaching is not to teach students the proper use of a particular piece of software.In contrast, we try to employ certain software in order to spark students' motivation and to support their understanding of logic principles they are supposed to understand after having passed the course.In a sense, we try to let the software act as a logic-tutor, the software is not an additional subject we teach.
Research on method of constant false alarm rate of entangled state quantum detection system

Entangled state quantum detection is a new technology that combines quantum mechanics with information science, and is used in the field of target detection. It has the potential to break through traditional detection technologies in terms of sensitivity and anti-interference ability. In the field of radar detection, constant false alarm rate is a technology with important significance and application value. However, there is no research on the method of the constant false alarm rate in the entangled state quantum detection system. Aiming at this problem, in this paper a method of constant false alarm rate for the entangled state quantum detection system is proposed. In the proposed method the system's real-time estimation of noise is adopted, and the detection threshold is adjusted adaptively, so that the entangled state quantum detection system always maintains a constant false alarm rate. The simulation results show that the proposed method of constant false alarm rate is correct and effective, and can realize the function of the constant false alarm rate of the entangled state quantum detection system. The proposed method effectively improves the flexibility and adaptability of the quantum detection system, and provides a solid theoretical foundation for the practical application of entangled state quantum detection technology.
Chest CT Image Super Resolution using Deep Learning Network Models

Image Super Resolution is a process of converting a Low Resolution image into High Resolution image with better visual quality and improved details. It is one of the most popular technique in image processing and computer vision. Super Resolution is applied in wide range of real world applications like medical imaging, surveillance applications and reconstruction of high quality remote sensing images. In the recent years, Super Resolution using deep learning techniques significantly improved accuracy. In this paper, Implemented four deep learning network models to generate High Resolution images of chest CT scans and evaluated the quantitative measure Peak Signal-to-Noise Ratio to compare accuracy of each CNN based deep leaning network models such as Super-Resolution Convolutional Neural Network (SRCNN), Enhanced Deep Super-Resolution (EDSR), Very Deep Super-Resolution (VDSR) and Deep Recursive Convolutional Network (DRCN).
Man In The Space Of Digital Culture

This paper presents a study of the key social, cultural and media area for the 21st century -the Internet.The focused environment and the emerging cultural form in it -digital culture -convey their values, ethical and aesthetic attitudes to the user, and satisfy many human needs.In this regard, it is appropriate to talk about the mass educational significance of digital culture in relation to entire generations.Based on the strong emphasis on a commercial motive (advertising, monetization, etc.) and the lack of ethical and aesthetic control over the content promoted on the Internet, it is concluded that the priority in the value system of the digital culture of commercial interest is made against the background of spiritual and social aspects.The topic of "Internet memes" is touched upon as elements participating in the formation of clip and template thinking of the audience.Using the example of video bloggers as producers and distributors of a semantic product on the Internet, the educational mechanism of the mass consumer of different age levels is described.In this network activity, the promotion of conflict thinking, and behavior is noted, and the negative aspects of the cultural impact of the network environment indicate trade in meanings (with an indifferent attitude of the subjects of activity to the ideological content), Internet dependence of the audience and the degradation of its cognitive, volitional and moral qualities.
XAISec - Explainable AI Security: An Early Discussion Paper on New Multidisciplinary Subfield In Pursuit of Building Trust in Security of AI systems

Today's Artificial Intelligence systems are at the epicenter of security threats across industries. Attackers are trying to turn AI systems against the organization and society, intending to cause harm at various levels. Security, along with the explainability of AI, is a cornerstone for Digital Trust and Trustworthy AI. AI-based systems provide new attack surfaces, and adversaries can utilize attack surfaces to construct attacks to exploit vulnerabilities. Mission-critical systems using AI need to address the crucial problem of AI Security(AISec) and Explainable AI (XAI). In the early version of the discussion paper, we propose the new sub-field of Explainable AI Security (XAISec) at the intersection of AISec, XAI, and Explainable Security (XSec) for Mission-critical systems. We propose that XAISec should aim to explain AI Security's workings (justification of attack and transparency about defense) at an appropriate level considering multiple aspects. XAISec is a niche multidisciplinary greenfield with an ascertained need and validated using informal interview settings. We invite constructive criticism, collaboration, and contribution to jump-start the sub-field. We believe that with XAISec as an integral part of AI, AI can impact millions of lives across the globe, enabling smarter, sustainable, and evolutionary transformations.
How to Empower Machine-Translation-to-Web Pre-Editing From the Perspective of Grice’s Cooperative Maxims

This article takes pre-editing to a new level, shifting its purpose from improving the linguistic accuracy of the machine-produced translation (MT) to boosting the communication effect of MT. Adopting the method of MT textual analysis, this research asked ten university students to read and assess two sets of MT versions generated from pre-edited and non-pre-edited texts retrieved from the web pages of five companies in Taiwan. Students were interviewed to express their preferences and make comments. The findings showed that 90% (9/10) of student respondents perceived the MT texts, which were generated from pre-edited text with a shorter length, as more communicative than the MTs produced from non-pre-edited texts. Additionally, 100% (10/10) of student respondents agreed that the MTs, which were generated from pre-edited texts with concise textual contents and thematic relevance to subheadings, were more communicative than the MTs produced from non-pre-edited texts. The respondents’ feedbacks are compatible with Grice’s maxims of quantity, quality, and relation. This article concludes by highlighting the new direction of revising the information amount, content, and thematic relevance of a web text, and supports extra-linguistic pre-editing from the perspective of Grice’s cooperative maxims.
Solving integer programming problems by using population-based beetle antennae search algorithm

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems. This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.
Configuration Space Exploration for Digital Printing Systems

Abstract Within the printing industry, much of the variety in printed applications comes from the variety in finishing. Finishing comprises the processing of sheets of paper after being printed, e.g. to form books. The configuration space of finishers, i.e. all possible configurations given the available features and hardware capabilities, are large. Current control software minimally assists operators in finding useful configurations. Using a classical modelling and integration approach to support a variety of configuration spaces is suboptimal with respect to operatability, development time, and maintenance burden. In this paper, we explore the use of a modeling language for finishers to realize optimizing decision making over configuration parameters in a systematic way and to reduce development time by generating control software from models. We present CSX, a domain-specific language for high-level declarative specification of finishers that supports specification of the configuration parameters and the automated exploration of the configuration space of finishers. The language serves as an interface to constraint solving, i.e., we use low-level SMT constraint solving to find configurations for high-level specifications. We present a denotational semantics that expresses a translation of CSX specifications to SMT constraints. We describe the implementation of the CSX compiler and the CSX programming environment (IDE), which supports well-formedness checking, inhabitance checking, and interactive configuration space exploration. We evaluate CSX by modelling two realistic finishers. Benchmarks show that CSX has practical performance (&lt;1s) for several scenarios of configuration space exploration.
Introduction to Logical Entropy and its Relationship to Shannon Entropy

We live in the information age. Claude Shannon, as the father of the information age, gave us a theory of communications that quantified an "amount of information," but, as he pointed out, "no concept of information itself was defined." Logical entropy provides that definition. Logical entropy is the natural measure of the notion of information based on distinctions, differences, distinguishability, and diversity. It is the (normalized) quantitative measure of the distinctions of a partition on a set--just as the Boole-Laplace logical probability is the normalized quantitative measure of the elements of a subset of a set. And partitions and subsets are mathematically dual concepts--so the logic of partitions is dual in that sense to the usual Boolean logic of subsets, and hence the name "logical entropy." The logical entropy of a partition has a simple interpretation as the probability that a distinction or dit (elements in different blocks) is obtained in two independent draws from the underlying set. The Shannon entropy is shown to also be based on this notion of information-as-distinctions; it is the average minimum number of binary partitions (bits) that need to be joined to make all the same distinctions of the given partition. Hence all the concepts of simple, joint, conditional, and mutual logical entropy can be transformed into the corresponding concepts of Shannon entropy by a uniform non-linear dit-bit transform. And finally logical entropy linearizes naturally to the corresponding quantum concept. The quantum logical entropy of an observable applied to a state is the probability that two different eigenvalues are obtained in two independent projective measurements of that observable on that state. Keywords: logical entropy, Shannon entropy, partitions, MaxEntropy, quantum logical entropy, von Neumann entropy
Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data

Background: The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media. Method: We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. We applied the pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn text embedding from the Reddit messages, and leveraged the relational information among posted messages to train a graph attention network (GAT) for sentiment classification. Finally, we applied model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment. Results: The results suggest that the odds of negative sentiments in 2020 (pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a $p$-value $<0.001$; and the odds of negative sentiments associated in-person learning were 48.3% higher than with remote learning in 2020 with a $p$-value of 0.029. Conclusions: Our study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states. Our study contributes to the growing real-world evidence on the various negative impacts of the pandemic on our society; it also provides a good example of using both ML techniques and statistical modeling and inference to make better use of real-world data.
Neural Network Quantization for Efficient Inference: A Survey

As neural networks have become more powerful, there has been a rising desire to deploy them in the real world; however, the power and accuracy of neural networks is largely due to their depth and complexity, making them difficult to deploy, especially in resource-constrained devices. Neural network quantization has recently arisen to meet this demand of reducing the size and complexity of neural networks by reducing the precision of a network. With smaller and simpler networks, it becomes possible to run neural networks within the constraints of their target hardware. This paper surveys the many neural network quantization techniques that have been developed in the last decade. Based on this survey and comparison of neural network quantization techniques, we propose future directions of research in the area.
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates

To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a given data point where the model is uncertain. We broaden the exploration to examine δ-CLUE, the set of potential CLUEs within a δ ball of the original input in latent space. We study the diversity of such sets and find that many CLUEs are redundant; as such, we propose DIVerse CLUE (∇-CLUE), a set of CLUEs which each propose a distinct explanation as to how one can decrease the uncertainty associated with an input. We then further propose GLobal AMortised CLUE (GLAM-CLUE), a distinct, novel method which learns amortised mappings that apply to specific groups of uncertain inputs, taking them and efficiently transforming them in a single function call into inputs for which a model will be certain. Our experiments show that δ-CLUE, ∇-CLUE, and GLAM-CLUE all address shortcomings of CLUE and provide beneficial explanations of uncertainty estimates to practitioners.
"Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic

Content warning: This work displays examples of explicit and/or strongly offensive language. Fueled by a surge of anti-Asian xenophobia and prejudice during the COVID-19 pandemic, many have taken to social media to express these negative sentiments. Identifying these posts is crucial for moderation and understanding the nature of hate in online spaces. In this paper, we create and annotate a corpus of tweets to explore anti-Asian hate speech with a finer level of granularity. Our analysis reveals that this emergent form of hate speech often eludes established approaches. To address this challenge, we develop a model and an accompanied efficient training regimen that incorporates agreement between annotators. Our approach produces up to 8.8% improvement in macro F1 scores over a strong established baseline, indicating its effectiveness even in settings where consensus among annotators is low. We demonstrate that we are able to identify hate speech that is systematically missed by established hate speech detectors.
Efficient differentiable quadratic programming layers: an ADMM approach

Recent advances in neural-network architecture allow for seamless integration of convex optimization problems as differentiable layers in an end-to-end trainable neural network. Integrating medium and large scale quadratic programs into a deep neural network architecture, however, is challenging as solving quadratic programs exactly by interior-point methods has worst-case cubic complexity in the number of variables. In this paper, we present an alternative network layer architecture based on the alternating direction method of multipliers (ADMM) that is capable of scaling to problems with a moderately large number of variables. Backward differentiation is performed by implicit differentiation of the residual map of a modified fixed-point iteration. Simulated results demonstrate the computational advantage of the ADMM layer, which for medium scaled problems is approximately an order of magnitude faster than the OptNet quadratic programming layer. Furthermore, our novel backward-pass routine is efficient, from both a memory and computation standpoint, in comparison to the standard approach based on unrolled differentiation or implicit differentiation of the KKT optimality conditions. We conclude with examples from portfolio optimization in the integrated prediction and optimization paradigm.
Parameter identifiability of a deep feedforward ReLU neural network

The possibility for one to recover the parameters—weights and biases—of a neural network thanks to the knowledge of its function on a subset of the input space can be, depending on the situation, a curse or a blessing. On one hand, recovering the parameters allows for better adversarial attacks and could also disclose sensitive information from the dataset used to construct the network. On the other hand, if the parameters of a network can be recovered, it guarantees the user that the features in the latent spaces can be interpreted. It also provides foundations to obtain formal guarantees on the performances of the network. It is therefore important to characterize the networks whose parameters can be identified and those whose parameters cannot. In this article, we provide a set of conditions on a deep fully-connected feedforward ReLU neural network under which the parameters of the network are uniquely identified—modulo permutation and positive rescaling—from the function it implements on a subset of the input space.
Investigation of Quantum Support Vector Machine for Classification in NISQ era

Quantum machine learning is at the crossroads of two of the most exciting current areas of research; quantum computing and classical machine learning. It explores the interaction between quantum computing and machine learning, investigating how results and techniques from one field can be used to solve the problems of the other. Here, we investigate quantum support vector machine (QSVM) algorithm and its circuit version on present quantum computers. We propose a general encoding procedure extending QSVM algorithm, which would allow one to feed vectors with higher dimension in the training-data oracle of QSVM. We compute the efficiency of the QSVM circuit implementation method by encoding training and testing data sample in quantum circuits and running them on quantum simulator and real chip for two datasets; 6/9 and banknote. We highlight the technical difficulties one would face while applying the QSVM algorithm on current NISQ era devices. Then we propose a new method to classify these datasets with enhanced efficiencies for the above datasets both on simulator and real chips.