Reviewerly/exHarmony huggingface dataset:
An End-to-End Joint Learning Scheme of Image Compression and Quality  Enhancement with Improved Entropy Minimization

Recently, learned image compression methods have been actively studied. Among them, entropy-minimization based approaches have achieved superior results compared to conventional image codecs such as BPG and JPEG2000. However, the quality enhancement and rate-minimization are conflictively coupled in the process of image compression. That is, maintaining high image quality entails less compression and vice versa. However, by jointly training separate quality enhancement in conjunction with image compression, the coding efficiency can be improved. In this paper, we propose a novel joint learning scheme of image compression and quality enhancement, called JointIQ-Net, as well as entropy model improvement, thus achieving significantly improved coding efficiency against the previous methods. Our proposed JointIQ-Net combines an image compression sub-network and a quality enhancement sub-network in a cascade, both of which are end-to-end trained in a combined manner within the JointIQ-Net. Also the JointIQ-Net benefits from improved entropy-minimization that newly adopts a Gussian Mixture Model (GMM) and further exploits global context to estimate the probabilities of latent representations. In order to show the effectiveness of our proposed JointIQ-Net, extensive experiments have been performed, and showed that the JointIQ-Net achieves a remarkable performance improvement in coding efficiency in terms of both PSNR and MS-SSIM, compared to the previous learned image compression methods and the conventional codecs such as VVC Intra (VTM 7.1), BPG, and JPEG2000. To the best of our knowledge, this is the first end-to-end optimized image compression method that outperforms VTM 7.1 (Intra), the latest reference software of the VVC standard, in terms of the PSNR and MS-SSIM.
Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures

Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models require the different neural components to be trained separately instead of end-to-end. While a simple genetic algorithm recently showed end-to-end training is possible, it failed to solve a more complex 3D task. This paper presents a method called Deep Innovation Protection (DIP) that addresses the credit assignment problem in training complex heterogenous neural network models end-to-end for such environments. The main idea behind the approach is to employ multiobjective optimization to temporally reduce the selection pressure on specific components in multi-component network, allowing other components to adapt. We investigate the emergent representations of these evolved networks, which learn to predict properties important for the survival of the agent, without the need for a specific forward-prediction loss.
Minimal Codes From Characteristic Functions Not Satisfying The Ashikhmin-Barg Condition

A minimal code is a linear code where the only instance that a codeword has its support contained in the support of another codeword is when the codewords are scalar multiples of each other. Ashikhmin and Barg gave a sufficient condition for a code to be minimal, which led to much interest in constructing minimal codes that do not satisfy their condition. We consider a particular family of codes $\mathcal C_f$ when $f$ is the indicator function of a set of points, and prove a sufficient condition for $\mathcal C_f$ to be minimal and not satisfy Ashikhmin and Barg's condition based on certain geometric properties of the support of $f$. We give a lower bound on the size of a set of points satisfying these geometric properties and show that the bound is tight.
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context. Given the availability of many such datasets, comprehensive and reliable evaluation is tedious and time-consuming for researchers working on this problem. We present an evaluation server, ORB, that reports performance on seven diverse reading comprehension datasets, encouraging and facilitating testing a single model's capability in understanding a wide variety of reading phenomena. The evaluation server places no restrictions on how models are trained, so it is a suitable test bed for exploring training paradigms and representation learning for general reading facility. As more suitable datasets are released, they will be added to the evaluation server. We also collect and include synthetic augmentations for these datasets, testing how well models can handle out-of-domain questions.
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation

Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label. In commonly used pipelines, segmentation and label assignment are solved separately since joint optimization is computationally expensive. We propose a greedy algorithm for joint graph partitioning and labeling derived from the efficient Mutex Watershed partitioning algorithm. It optimizes an objective function closely related to the Symmetric Multiway Cut objective and empirically shows efficient scaling behavior. Due to the algorithm's efficiency it can operate directly on pixels without prior over-segmentation of the image into superpixels. We evaluate the performance on the Cityscapes dataset (2D urban scenes) and on a 3D microscopy volume. In urban scenes, the proposed algorithm combined with current deep neural networks outperforms the strong baseline of `Panoptic Feature Pyramid Networks' by Kirillov et al. (2019). In the 3D electron microscopy images, we show explicitly that our joint formulation outperforms a separate optimization of the partitioning and labeling problems.
Solving $X^{q+1}+X+a=0$ over Finite Fields

Solving the equation $P_a(X):=X^{q+1}+X+a=0$ over finite field $\GF{Q}$, where $Q=p^n, q=p^k$ and $p$ is a prime, arises in many different contexts including finite geometry, the inverse Galois problem \cite{ACZ2000}, the construction of difference sets with Singer parameters \cite{DD2004}, determining cross-correlation between $m$-sequences \cite{DOBBERTIN2006,HELLESETH2008} and to construct error-correcting codes \cite{Bracken2009}, as well as to speed up the index calculus method for computing discrete logarithms on finite fields \cite{GGGZ2013,GGGZ2013+} and on algebraic curves \cite{M2014}. Subsequently, in \cite{Bluher2004,HK2008,HK2010,BTT2014,Bluher2016,KM2019,CMPZ2019,MS2019}, the $\GF{Q}$-zeros of $P_a(X)$ have been studied: in \cite{Bluher2004} it was shown that the possible values of the number of the zeros that $P_a(X)$ has in $\GF{Q}$ is $0$, $1$, $2$ or $p^{\gcd(n, k)}+1$. Some criteria for the number of the $\GF{Q}$-zeros of $P_a(x)$ were found in \cite{HK2008,HK2010,BTT2014,KM2019,MS2019}. However, while the ultimate goal is to identify all the $\GF{Q}$-zeros, even in the case $p=2$, it was solved only under the condition $\gcd(n, k)=1$ \cite{KM2019}. We discuss this equation without any restriction on $p$ and $\gcd(n,k)$. New criteria for the number of the $\GF{Q}$-zeros of $P_a(x)$ are proved. For the cases of one or two $\GF{Q}$-zeros, we provide explicit expressions for these rational zeros in terms of $a$. For the case of $p^{\gcd(n, k)}+1$ rational zeros, we provide a parametrization of such $a$'s and express the $p^{\gcd(n, k)}+1$ rational zeros by using that parametrization.
Glottal Closure and Opening Instant Detection from Speech Signals

This paper proposes a new procedure to detect Glottal Closure and Opening Instants (GCIs and GOIs) directly from speech waveforms. The procedure is divided into two successive steps. First a mean-based signal is computed, and intervals where speech events are expected to occur are extracted from it. Secondly, at each interval a precise position of the speech event is assigned by locating a discontinuity in the Linear Prediction residual. The proposed method is compared to the DYPSA algorithm on the CMU ARCTIC database. A significant improvement as well as a better noise robustness are reported. Besides, results of GOI identification accuracy are promising for the glottal source characterization.
Efficient Data Analytics on Augmented Similarity Triplets

Data analysis require a pairwise proximity measure over objects. Recent work has extended this to situations where the distance information between objects is given as comparison results of distances between three objects (triplets). Humans find the comparison tasks much easier than the exact distance computation and such data can be easily obtained in big quantity via crowd-sourcing. In this work, we propose triplets augmentation, an efficient method to extend the triplets data by inferring the hidden implicit information form the existing data. Triplets augmentation improves the quality of kernel-based and kernel-free data analytics. We also propose a novel set of algorithms for common data analysis tasks based on triplets. These methods work directly with triplets and avoid kernel evaluations, thus are scalable to big data. We demonstrate that our methods outperform the current best-known techniques and are robust to noisy data.
Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis

In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e.g., blood test, urine test, followed by invasive tests. As tests are often costly, one would like to obtain only those features (tests) that can establish the presence or absence of the state conclusively. Another aspect of medical diagnosis is that we are often faced with unsupervised prediction tasks as the true state of the patients may not be known. Motivated by such medical diagnosis problems, we consider a {\it Cost-Sensitive Medical Diagnosis} (CSMD) problem, where the true state of patients is unknown. We formulate the CSMD problem as a feature selection problem where each test gives a feature that can be used in a prediction model. Our objective is to learn strategies for selecting the features that give the best trade-off between accuracy and costs. We exploit the `Weak Dominance' property of problem to develop online algorithms that identify a set of features which provides an `optimal' trade-off between cost and accuracy of prediction without requiring to know the true state of the medical condition. Our empirical results validate the performance of our algorithms on problem instances generated from real-world datasets.
Statement Emotion Decision Model for Human-Friendly Chatbot

A chatbot is a software that gives proper answers and information for users, and provides useful information to users. Users basically express their feelings to others, and seek to exchange their feelings. However, the chatbot does not respond to the user’s emotional state, but focuses on providing information. Since the chatbot understands the intention of the question without considering the user’s emotional state, the accuracy of intention analysis is poor, and cannot provide accurate answers. In this paper, we develop a module that can know the user’s mood, including newly coined terms and emojis, and apply it to the chatbot. The chatbot consists of an intent decision module that analyzes the user’s intention, a mood decision module that infers the user’s internal mood, and an answer decision module that generates answers to the user’s sentences. The mood decision module consists of an emotion classification model that classifies the user’s emotions through the sentences of users, and a mood determination model that infers the user’s mood through the classified emotions. It infers the user’s current mood state in real-time using the emotion classification model and the mood determination model. The chatbot analyzes morphemes and syntax for user sentences. By building a sentiment dictionary based on the Korean dictionary, the emotions of morpheme-analyzed words are classified. To classify the emotions of new words and emojis, the newly coined terms and emojis emotion dictionary is used to classify the emotions of newly coined terms and emojis. Emotions are classified using naive Bayes classification, and for words that are not classified by naive Bayes classification, an artificial neural network model is used to increase the accuracy of emotion classification. The chatbot infers the mood state using the mood determination model. The mood state consists of the emotional state representing the eight emotions to be classified and each emotional value, and infers the user’s mood with the mood state extracted through the mood determination process.
A Novel Software Architecture to Calculate Effort Estimation for Industrial Big Data

Software development effort estimation is one of the main sub-disciplines of software cost estimation, which comes under software project management. To estimate effort accurately, we noted different estimation models. With the combination of expert judgment, data mining, and machine learning, the motive of this study is to propose a new software architecture for effort estimation. The proposed architecture uses techniques such as expert judgment along with K-means clustering and machine learning techniques such as ANN, SVR, LR, RF, and KNN. At last, we used RMSE, MAE, MMRE, and Pred (.25). After the experimentation, we noted the increase in estimation accuracy was seen with the use of the proposed estimation model. Moreover, support vector regression outperformed all other algorithms with K = 3 and 5 and expert input. Therefore, we concluded the effort estimation of industrial big data is an important step and needs to be given attention in software organizations.
Mathematics Education and Performance, through the Prism of PISA, in Greece and Portugal

Greece and Portugal are two Southern European countries, with nearly the same population as well as a centralized educational system, which were both deeply affected by the economic crisis during the last decade.Despite being severely hit by the economic crisis, Portugal has advanced to the OECD average level in its students' mathematical performance in the Programme for International Student Assessment (PISA 2018), while Greece has performed below the OECD average.PISA, as one of the most influential international educational surveys, aims to evaluate educational systems and provides a valuable platform for comparisons.In the first PISA 2000, Portuguese students outperformed their Greek counterparts by only 7 points and went on to widen the difference by 41 points in PISA 2018.What national strategies have been set up and implemented in Portugal so as to foster student's mathematical literacy competencies?The main aim of this study is, through a recording of the Greek and Portuguese students' mathematics achievements in PISA and at the same time of the mathematics education in both countries, through available policy documents and research reports, to comment on the current outcomes of the two educational systems and their students' performance in Mathematics.
The Interplay Amongst Internal Auditors’ Psychological Capital, Creative Performance and Internal Control Efficiency

This study examined internal auditors' psychological capital relationship with their creative performance.It also investigated the total effect of such a relationship on internal control efficiency related to targets' achievement.The partial least squares structural equation modelling was employed to validate and test the study model, based on data collected from 118 internal auditors of companies listed on the Amman Stock Exchange.The main results indicate that internal auditors' psychological capital positively influences their creative performance, indirectly enhancing the degree of internal control efficiency in achieving targets related to financial reporting, operations, and compliance.This study offers empirical evidence on the psychological capital value as an enabler to engage in creative performance and support the perpetual endeavours of those charged with governance to boost internal control efficiency.Also, the findings highlight the importance of providing internal auditors with the required resources and facilities to enhance their psychological capital and performance.
Job Satisfaction of Public and Private Healthcare Centers in Rangpur City Corporation Area: A Comparative Study

This study compares public and private hospital employees' job satisfaction. The study's objective is a comparative analysis of employee job satisfaction in public and private healthcare centers concerning employee management systems and motivational factors. Respondents have been selected from the public and private healthcare centers of Rangpur City Corporation. The study observed that the views of public and private healthcare employees are volatile in many ways. It's also observed that private hospitals follow systematically and scientifically EMM practices, creating high employee satisfaction levels. In addition, public healthcare workers are more satisfied with the terms and conditions of their workplace than private health workers.
Teacher's personality image-design as an element of pedagogical design

In the conditions of modernization of education, the search for modern resources is intensified. It will contribute to the arrangement of an effective educational process and a comfortable educational environment. Such resources include a variety of tools, among which pedagogical design occupies an important place. In research, pedagogical design (instructional design) is interpreted as a systematic approach to the construction of the educational process, taking into account the theoretical principles of psychology, pedagogy, ergonomics, cognitive science and other sciences that study the peculiarities of human perception and cognition. As a holistic process, pedagogical design involves the analysis of educational needs, development of learning environment and teaching aids that are pedagogical efficiency, methodological adequacy and feasibility. An important component of pedagogical design is the image design of the teacher's personality. Its content is most fully revealed through the personal and professional image of the teacher, and its external manifestations are the appearance of a person and his visual image. With the help of visual image, a person demonstrates to the environment his physical and social condition, shows attitude to his own body and health, emphasizes individual style and aesthetic taste in clothing and more. The visual image of the teacher is characterized by the following features: visual attractiveness; communicative attractiveness; aesthetics and ethics of behavior. In our understanding, the image design of the teacher's personality is a process and result of designing and improving his professional appearance, which is a consequence of modern fashion, individual preferences and financial capabilities. It is implemented through the generally accepted requirements for the professional appearance of teachers. The image design of the teacher’s personality is constantly changing under the influence of fashion trends, the practical manifestation of which are the means of external expression (clothing, shoes, hair, hats, accessories, etc.). After all, the spectacular appearance of a teacher directly depends on the design of his clothes, it has a positive effect on students, and the teacher himself acts as a standard of attitude to himself, to students, to professional activities. Therefore, the image design of the teacher's personality is an effective tool of self-presentation and an effective means of his professional activity. Key words: teacher’s personality, pedagogical design, personality image-design, visual image, appearance, clothing design
Modification of RC4 algorithm utilizing the two-state table and initial state factorial

The issue of security and privacy has gained interest in the area of data and information. The inevitable occurrence of threats, both inside and outside, hovers the security and confidence of the stored data. Therefore, it is necessary to secure data by using a certain method in data encryption thereby maintaining and saving the stored data. One of the encrypting methods is conducted through the RC4 Algorithm, a fast encryption method, which is due to its popularity, the RC4 Algorithm becomes a vulnerable object for the attack. Such data vulnerability is generated from the permutation of the initial state, presenting similarities between the stream and secret key. This study is hence expected to improve the security of the RC4 Algorithm by modifying the Two States Table and the Initial State Factorial. This modification thus could improve the randomness of the Key Scheduling Algorithm (KSA), enabling the slower performance than the C4 Algorithm, but on the aspect of security, the modified RC4 Algorithm is considered safer and more resistant to repeal the Brute Force and Bit Flipping attacks than that in the RC4 Algorithm. The testing is conducted by attacking the data using the Brute Force and the Flipping, proving that the modification of the RC4 Algorithm has longer durability than the RC4 Algorithm.
Formation of technological culture in future teachers in technological education in higher education institutions

The article is devoted to the problem of formation of technological culture of future teachers of technological education in the environment of higher education institution. The relevance and necessity of developing technological culture during the professional training of future specialists in the field of technological education are substantiated. A model of formation of technological culture of future teachers of technological education in higher education institutions has been developed. The connection of the university environment with the pedagogical process with the development of information and communication technologies in the post-industrial and post-pandemic society of Ukraine and other countries is illustrated. The most influential approaches to the formation of technological culture are identified: competence, culturological, synergetic, systemic, anthropological, personal-activity, genetic, informational, contextual. Among the principles - developmental and educational training, positive motivation and a favorable emotional climate; scientific; interdisciplinarity; integrity, national orientation of education; openness and awareness; hazard prevention, creation of the environment, etc. Emphasis is placed on pedagogical conditions and qualities that promote the development of technological culture in future teachers of higher education, encourage them to more effective pedagogical work. Among these conditions are defined: purposeful motivation of teachers; updating the content of professional training of future teachers (theory and practice); application of pedagogical technologies; creation of an innovative cultural and educational environment in higher education institutions; application of individual and differentiated approaches; development of pedagogical methods; organization of diagnostics and appropriate adjustment of the level of technological formation; promotion of values. The result of this model helps to trace and establish modern criteria of technological culture, to make a forecast for its formation in future teachers of technological education under these circumstances in the future. The dependences between the pedagogical conditions of the formation of technological culture in the form of a graph are derived. Key words: technological culture, teacher, higher school, formation, graph