Reviewerly/exHarmony huggingface dataset:
Advance Self Driving Car Using Machine Learning

The Autonomous car is about to enter the mass-market. The question is not about when it will happen but in which conditions, under which form or who will be the first car manufacturer to release an efficient and reliable final product. Entirely unexpected ways to deal with building up the AI frameworks for self-driving vehicles exist and most of them are horribly best in class and with extremely high equipment needs. The appropriate response presented during this paper proposes the AI fundamentally based framework to be as simple as conceivable with low equipment needs. A straight forward three layers profound, totally associated neural system was prepared to outline pictures from a forward looking QVGA camera to directing orders. Upheld an information picture the neural system should settle on one among the four offered orders (Forward, Left, Right or Stop). With least of the instructing information (250 pictures) the framework figured out how to follow the street ahead and keep in its path.The framework precisely learns essential street alternatives with exclusively the directing point in light of the fact that the contribution from the human driver. it had been near explicitly prepared to watch lines out and about. Contrasted with rather progressively confounded arrangements like express decay of the issue, similar to path identification and the board and convolutional neural systems simply like the conclusion to complete the process of learning arranged by the N-Vidia this technique demonstrated to be amazingly solid and affordable. we will in general attempt to demonstrate that this methodology would bring about better and lower equipment necessities so making the occasion of oneself driving vehicles simpler and more financially savvy. Simple counterfeit neural system, much the same as the one gave during this paper, is sufficient for relatively muddled technique like path keeping.
Asymmetric Attributional Word Similarity Measures to Detect the Relations of Textual Generality

In this work, we present a new unsupervised and language-independent methodology to detect the relations of textual generality. For this, we introduce a particular case of Textual Entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE Recognition (RTE) task, systems have been asked to automatically judge whether the meaning of a portion of the text, the Text (T), entails the meaning of another text, the Hypothesis (H). Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signaling renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect the relations of textual generality. In text, there are different kinds of entailments, yielded from different types of implicative reasoning (lexical, syntactical, common sense based), but here, we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more general one. Therefore, we have T→GH whenever the premise T entails the hypothesis H, this also being more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair ⟨T,H⟩ having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize about the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is highlighting the importance of this inference mechanism. Consequently, the new annotation data seem to be a valuable resource for the community.
Classifiers Ensemble of HMM and d-Vectors in Biometric Speaker Verification

This paper presents a novel approach on text-dependent biometric speaker verification (SV) based on the ensemble of two feature extraction and classification processes using Hidden Markov Models in a Universal Background Model framework (HMM-UBM) and d-Vectors derived from a Deep Learning Network (DNN) structure. Once the individual SV systems are trained, a third classifier is trained/tuned over individual test scores in the same dataset using three different approaches for comparison purposes: Multilayer Perceptron (MLP), Support Vector Machine (SVM) with three different kernels, and a Fuzzy Inference System (FIS). Obtained results over a proprietary speech database in Spanish, indicate an improved performance, providing an Equal Error Rate (EER) within the range of 0.7%–2.54% when classifier ensembles are used, versus an EER of 3.6% and above obtained in average with individual classifiers. Results in detail corresponding to comparison of the several approaches used in this experimental work are further described.
Barriers to Social Services Fuel Racial Inequality

With a few changes to existing systems, human-centered technology can help counter the racial bias embedded in the social safety net by creating more opportunity for people of color to access servi...
Machine proof of Equivalence between Axiom of Choice and Product Theorem of Standard Family Sets

Based on the computer proof assistant Coq, the establishment of a formal system of naive set theory is realized, referring to Morse-Kelley set theory. On this basis, the formal descriptions of the axiom of choice and the product theorem of standard family sets are given, and the proof code for the equivalence between axiom of choice and the product theorem of the standard set family is completed and verified in Coq. All process fully embodies that machine proof of mathematical theorem based on Coq has the characteristics of readability and interactivity.
Criminalizzare l’hate speech per scongiurare la collective violence? Ipotesi di lavoro intorno al reato di “propaganda razzista”

The essay provides an unusual approach about criminalization of racist propaganda (and also about the interpretation of Art 604-bis of Italian Penal Code), in order to focus the trend of racism toward institutionalization due to political “hate speech” strategies. Therefore, only propaganda which can actually be converted into discriminatory public actions, or even into contextual elements of international crimes, should be qualified as a crime: an outcome constitutionally untenable in itself, regardless of ability to urge “hate crimes”. The risk of restricting individual freedom of speech seems to be reduced, since individuals, quite to the contrary, are going, in this way, to be protected from abuses of public authorities. In addition, a definition of racism is adopted, which necessarily implies false assertions, that don’t deserve a strong constitutional protection. Besides, such assertions are perhaps better exposed to the dynamics of the “free market of ideas” in a criminal trial, than in a public debate widely biased by new media.
Fuzzy Transfer Learning in Heterogeneous Space Using Takagi-Sugeno Fuzzy Models.

Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge (a source domain with a large amount of labeled data) to assist in completing a prediction task in a related domain (a target domain with little labeled data). Many transfer learning methods have been proposed, and especially the fuzzy transfer learning method, which is based on fuzzy systems, has been developed because of its capability to deal with the uncertainty. However, there is one issue with fuzzy transfer learning that has not yet resolved: the domain adaptation methods for regression tasks in heterogeneous space are still scarce, and the relation of features in two domains have not been explored to assist the construction of target model. In this work, we proposed a new fuzzy transfer learning method, which constructs the transformed mappings for the domain-independent and domain-dependent features, separately. The existing fuzzy rules of the source domain are transferred to the target domain through modifying the input space using the mappings, and the parameters of the mappings are optimized by the few labeled target data. The experiments on real-world datasets validate the effectiveness of the proposed method and discuss the impact of some important parameters to the performance of the constructed target model.
Graph Problems: NP-Hard

A cynical view of graph algorithms is that “everything we want to do is hard.” Indeed, all problems in this section are provably NP-complete with the exception of graph isomorphism—whose complexity status remains an open question. The theory of NP-completeness demonstrates that either all NP-complete problems have polynomial-time algorithms, or none of them do. The former prospect is sufficiently unlikely that NP-completeness suffices to say that no efficient algorithm exists to solve the given problem.
Conceptual Optimization of a Generalized Net Model of a Queuing System

The problem of conceptual optimization of Generalized Nets (GNs) models is discussed.An overview of some operators for complexity of GNs and relations with respect to them is presented.Some new operators and relations are defined.A GN model of a queuing system with finite capacity of the buffer and server, and FIFO discipline of service of the requests, is optimized with respect to some of the operators for complexity.
An Automated Method with Feature Pyramid Encoder and Dual-Path Decoder for Nuclei Segmentation

Nuclei instance segmentation is a critical part of digital pathology analysis for cancer diagnosis and treatments. Deep learning-based methods gradually replace threshold-based ones. However, automated techniques are still challenged by the morphological diversity of nuclei among organs. Meanwhile, the clustered state of nuclei affects the accuracy of instance segmentation in the form of over-segmentation or under-segmentation. To address these issues, we propose a novel network consists of a multi-scale encoder and a dual-path decoder. Features with different dimensions generated from the encoder are transferred to the decoder through skip connections. The decoder is separated into two subtasks to introduce boundary information. While an aggregation module of contour and nuclei is attached in each decoder for encouraging the model to learn the relationship between them. Furthermore, this avoids the splitting effect of independent training. Experiments on the 2018 MICCAI challenge of Multi-Organ Nuclei Segmentation dataset demonstrate that our proposed method achieves state-of-the-art performance.
Building Functional Encryption: In Search of a Seed, II

In this section, we continue introducing candidate realizations of bootstrappable primitives, now focusing on those ones that yield full-fledged iO through constructing an FE scheme first. The main benefit of this approach is that—up to now—it has led to a better characterization of the problem of building iO.
한국어 교육에서 시각텍스트 사용의 변천 과정 : 최근 30년 대학기관 초급 교재를 중심으로

This study analyzed Korean language textbooks focusing on how and for what purpose visual texts have been used over the last 30 years. The analysis centered on types of visual texts, changes in language usage, and methods of composing textbook units. The result of the analysis shows that there were changes in the use of images one of the types of visual texts at the unit introductory section. And visual texts were used only partially in the vocabulary section, but recently they are used also in teaching and learning grammar. Furthermore, the study also found that visual texts were used only in part of the textbook unit composition, but recently they are used in all parts of it.The Korean language education is generally divided into several areas such as vocabulary, grammar, listening, speaking, reading, and writing. This study is meaningful because, in each of these areas, it analyzed how visual texts have been used for teaching and learning.
How To realize student-centered teaching in Cloud Class

The student-centered teaching innovation has certain universality, from the teaching method angle it aims at the student's study and the growth demand, uses the information method to create the innovation teaching method.The innovation practice centering on the development of students can improve the students' learning experience and learning achievement.We Use the three-dimensional network problem-oriented map to stimulate students' learning motivation, and use questionnaire survey, celebrity interviews, real-life happiness model analysis to achieve the student-centered teaching practice of mixed cloud class, improve the well-being of students' life value, and create a happy teaching model.
Normative aspects of instructional planning activities of mathematics teacher candidates

It is quite difficult to adequately describe and evaluate the developmental processes of teachers without knowing and analyzing the pedagogical communities they participate in.Teachers acquire their own professional knowledge by carrying their experiences to the educational environment.Therefore, classroom microcultures experienced by teachers and teacher candidates are important for the formation and improvement of their professional knowledge.In this context, the quality of the classroom microcultures constituted by the teacher training programs come to the forefront.Within this context, in the present study, classroom microculture belonging to mathematics education course (Methods of Teaching Mathematics-II (MTM)) which has an important place in the teacher education was examined within the framework of the classroom norms.In other words, the classroom norms regulating the instructional planning activities performed by teacher candidates were investigated and the qualities of these norms were discussed.The data of the study were obtained through observing the learning environments of the MTM course in a mathematics teacher education program at a state university in Turkey.The data of the study, which is a case study, were analyzed through constant comparative analysis method.According to the findings of the study, it can be said that teacher candidates had difficulty in sustaining and participating the norms which are related to assessment-evaluation, teaching knowledge, specifying objectives and social language used.
VGG based Convolutional Neural Network for Disguised Face Recognition

Nowadays, with the increase in the number of data, including image, signal and audio data, the need for computer systems and automated algorithms seems more than ever. The field that automates this operation is called machine learning, which is a subset of artificial intelligence. In recent years, new methods of deep learning have been proposed that are based on neural networks and have a large number of layers. Disguised face processing is one of the subsets of image processing that has many applications such as identifying criminals. Convolutional neural networks are among the deep learning structures that have been very successful in image processing. In this article we used Disguised Faces in the Wild (DFW) which contains approximately 11000 images from 1000 subjects. These images include normal faces and also face with different lightning, pose, background and different make up. We used batch normalization and dropout to overcome overfat. We used activation function RELU that leads negative numbers to 0 and its function is: max (0, x ) which means that it would be never saturated in positive region. In addition, ADAM (adaptive momentum estimation) was used as optimization algorithm. Our proposed architecture has 16 layers including 14 convolution layers and two fully connected layers. Finally, the proposed method reached 84 percent accuracy.
Research Guides: Resnet (MyResnet) - How to Set Up Devices and Access: MyResnet

Instructions to help students set up access to MyResnet for wifi access in resdience halls.
PERENCANAAN ARSITEKTUR SISTEM INFORMASI BERBASIS CLOUD COMPUTING DENGAN MENGGUNAKAN METODE EAP (ENTERPRISE ARCHITECTURE PLANNING) (Studi Kasus: PT. Ma’soem)

PT. Ma'soem is one of Pertamina fuel distributor in West Java, which has 33 retail outlets. The operations are run Pertamina fuel is sold directly to customers and supplying the fuel directly from the manufacturer Pertamina. PT. Ma'soem so far in implementing the business processes have not been using an information system that requires the Enterprise Architecture Planning for PT. Ma'soem. Enterprise architecture models used in this research is by using a model of Enterprise Architecture Planning (EAP). EAP is a process of defining the enterprise architecture, which focuses on data architecture, application and technology in support of business and plans to implement the architecture, where the EAP method, there are several stages ranging assessment of the Initialization of planning (Planning Initiation), Modeling Business (Business Modeling) Systems and Technology today (current Systems & Technology), Architecture Data (Data Architecture), Application Architecture (Applications Architecture), Architectural Technology (Technology Architecture), Plan of Implementation (Implementation Plans).The results of this analysis, information systems on PT. Ma'soem form a blueprint for enterprise architecture planning planing the cloud-based managed defines four core business processes, the data architecture resulted in 17 entities, 6 as well as the application for the technology architecture technology architecture resulted in the proposal.
Psychological research of the formation of professional identity in psychology students

In article are considered questions of formation of psychologists in the conditions of educational space of the University, where one of the main criteria of professional competence of future psychologists is the level of development of professional self­consciousness and identity. A multidimensional approach to the analysis of professional identity is one of the possible directions of the formation and correction of the professional identity of the future specialists. A person becomes “completely human” when he realizes his identity. Identity is the mental component of self­awareness, which is formed and exists in the human world. We know who we are, we are aware of our identity in the world of people, professions, nations, etc. In this regard, the experience of identity and in the professional sphere of human life is of particular importance. In the theory and practice of professional development, the need for a new understanding of the nature and meaning of work of specific professionals, the specifics of professional self­determination is increasingly felt, and the problems associated with the development of professional identity are coming to the fore. Key words: identity, self­consciousness, psychologists, institution of higher education, formation, training.
Training Effectiveness Evaluation System of Migrant Workers along Yangtze River Valley

Mo, J.; Mao, C.M., and Zuo, Z.Y., 2020. Training effectiveness evaluation system of migrant workers in coastal cities. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 984–987. Coconut Creek (Florida), ISSN 0749-0208.Based on Kirkpatrick's four-level model ,combined with the principle of index selection and the characteristics of migrant workers coastal cities, the paper initially establishes the evaluation index of training effect on the four levels of reaction layer, learning layer, behavior layer and result layer. Using the method of AHP and fuzzy comprehensive to evaluate the training effect of migrant workers along Yangtze River valley. Finally, the paper put forward the way to improve the training effectiveness of migrant workers along Yangtze River valley.
Non-Asymptotic Analysis of Monte Carlo Tree Search

In this work, we consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of infinite-horizon discounted co...
A Robust Correlation Filtering Tracker with Resampling-Detection and Adaptive Fusion Multi-features

Recently, correlation filter is widely used in visual tracking for its robust and accuracy. However, it is still a challenge in tracking with complex situations such as target blurring, occlusion, and scale variation. In this paper, a correlation filter-based tracker with resampling-detection and scale estimation is proposed. We use multiple features with adaptive fusion to describe the target appearance, and resampling-detection module will be performed on the frame which tracking confidence determined by PSR is lower than a threshold. Besides, scale pyramid is introduced to estimate the scale. The extensive experimental evaluates on the OTB benchmark and results show that our approach outperforms the baseline trackers and has excellent performance in accuracy and robust, especially on the challenge of fast motion and motion blur. Additionally, our approach is computationally efficient and suitable for real-time applications.
Visualization of Solution of Probability Tasks Using Schemes and Tables in Training

Researchers are considering the issues of teaching method of mathematics, in particular probability theory and statistics. Combinatorial, probabilistic, and statistical objectives have a number of features that must be taken into account in methodological planning. These tasks are distinguished from the usual school course by increased abstractness, multivariance. Increasing the level of mastering probabilistic tasks is considered using schemes and tables. A nonstandard transition from a verbal to a mathematical form of data presentation is illustrated, an attempt is made to systematize the allocation and the relationship between the original set and a number of properties of the compiled sets. The advantage of this technique is visualization, reliance on the didactic principle of visualization, which facilitates the perception of knowledge by students of the digital generation, especially in the age of visual culture. The school program in mathematics is a symbiosis of the academic and practical component of training. Today it is difficult to imagine a student’s functional literacy without the ability to perceive and analyze different forms of information, to realize the probabilistic basis of the most important patterns, and the ability to perform the necessary probabilistic analysis. A technique for presenting tasks through a visual code is proposed. Key words: visualization, «probability tree» probabilistic schemes, «classics» tables, event probability, random event.&nbsp; &nbsp
Demodulación de franjas de interferencia usando diferentes configuraciones

Non-contact techniques for the analysis of deformations on surfaces has had great boom and acceptance in various sectors of science. When a beam of light with characteristics of temporal and spatial coherence radiates about optically rough surface, it presents a random distribution of intensity, giving rise to the phenomenon called speckle; said phenomenon is a representation of bright and dark dots formed by the random distribution of said coherent light, this distribution of overlapping speckle patterns generate interference fringes, its fringes are presented when in second pattern fringes has phase difference between them, which, give information about the state of deformation suffered by the material that is analyzed. This work reports the procedure that is followed for the analysis of deformations in materials using interferometric techniques. Different configurations of interference fringes are presented, and the procedure for their demodulation is described.