Reviewerly/exHarmony huggingface dataset:
Recent Advancements in Self-Supervised Paradigms for Visual Feature Representation.

We witnessed a massive growth in the supervised learning paradigm in the past decade. Supervised learning requires a large amount of labeled data to reach state-of-the-art performance. However, labeling the samples requires a lot of human annotation. To avoid the cost of labeling data, self-supervised methods were proposed to make use of largely available unlabeled data. This study conducts a comprehensive and insightful survey and analysis of recent developments in the self-supervised paradigm for feature representation. In this paper, we investigate the factors affecting the usefulness of self-supervision under different settings. We present some of the key insights concerning two different approaches in self-supervision, generative and contrastive methods. We also investigate the limitations of supervised adversarial training and how self-supervision can help overcome those limitations. We then move on to discuss the limitations and challenges in effectively using self-supervision for visual tasks. Finally, we highlight some open problems and point out future research directions.
Grover-based ashenhurst-curtis decomposition using quantum language quipper

Functional decomposition plays a key role in several areas such as system design, digital circuits, database systems, and Machine Learning. This paper presents a novel quantum computing approach ba...
Arquitetura da informação e o comércio eletrônico à luz das cinco leis de Ranganathan

In general, the aim is to show the relationship between the five laws of Library Science created by Ranganathan and electronic commerce through information architecture. It conceptualizes information architecture and presents the organization, navigation, labeling and search systems proposed by Rosenfeld, Morville and Arango, in addition to providing an overview of the role of the information architect. It characterizes e-commerce and highlights the techniques of Search Engine Optimization - SEO and the reasons why the skills of the librarian match this activity. Briefly historicizes the trajectory of Shiyali Ramamrita Ranganathan as a librarian and emphasises the creation of the five laws of Library Science. It displays adaptations of each of these laws for the digital environment and relates them to the context of e-commerce.Keywords: Information Architecture. E-commerce. Ranganathan. Five Laws of Library Science. Information Organization.
A STUDY ON COMPLEX NEUTROSOPHIC GRAPH

This paper introduces various types of Complex Neutrosophic Graphs(CNG) called complete complex neutrosophic graph, bipartite complex neutrosophic graph, spanning complex neutrosophic graph and path-forest-tree of complex neutrosophic graph. Further, it proposes and identifies new and innovative concepts of dominations in complex neutrosophic graph
Finding Fuzziness in Neural Network Models of Language Processing.

Humans often communicate by using imprecise language, suggesting that fuzzy concepts with unclear boundaries are prevalent in language use. In this paper, we test the extent to which models trained to capture the distributional statistics of language show correspondence to fuzzy-membership patterns. Using the task of natural language inference, we test a recent state of the art model on the classical case of temperature, by examining its mapping of temperature data to fuzzy-perceptions such as cool, hot, etc. We find the model to show patterns that are similar to classical fuzzy-set theoretic formulations of linguistic hedges, albeit with a substantial amount of noise, suggesting that models trained solely on language show promise in encoding fuzziness.
PROFESSIONAL VALUES AND SOCIETAL REQUIREMENTS AS CHALLENGES IN THE FORMATION OF PROSPECTIVE SKILLS AMONG STUDENTS

The accelerated change generates new challenges, entails new requirements for the educational system in general and, in particular, for societys value system. Important in this system is the students personality and values they possess. The hierarchy of the values formed based on the interaction with the social and cultural environment, which they are part of, helps the person make decisions regarding the selection of the profession. However, with the development of technologies and in the context of the gradual disappearance of some professions and the emergence of new ones, it is essential to reform education, to be effective in developing values in the form of prospective skills(anticipation, planning, targeting) for students. In conclusion, the requirements of the society provoke and orient the university educational system towards the prospective formation of the personality. And obtaining valid results in solving educational problems and challenges is strictly necessary to define the type of personality focused on values and emphasis on the formation of prospective skills among students.
Learning generative models for valid knockoffs using novel multivariate-rank based statistics.

We consider the problem of generating valid knockoffs for knockoff filtering which is a statistical method that provides provable false discovery rate guarantees for any model selection procedure. To this end, we are motivated by recent advances in multivariate distribution-free goodness-of-fit tests namely, the rank energy (RE), that is derived using theoretical results characterizing the optimal maps in the Monge's Optimal Transport (OT) problem. However, direct use of use RE for learning generative models is not feasible because of its high computational and sample complexity, saturation under large support discrepancy between distributions, and non-differentiability in generative parameters. To alleviate these, we begin by proposing a variant of the RE, dubbed as soft rank energy (sRE), and its kernel variant called as soft rank maximum mean discrepancy (sRMMD) using entropic regularization of Monge's OT problem. We then use sRMMD to generate deep knockoffs and show via extensive evaluation that it is a novel and effective method to produce valid knockoffs, achieving comparable, or in some cases improved tradeoffs between detection power Vs false discoveries.
Optimized Hybrid Decoupled Visual Servoing simulation and code

The data for the paper Optimized hybrid decoupled visual servoing with supervised learning is presented here.The zip file includes the presented simulation environment in the paper created in ROS/GAZEBO. In addition, the proposed HDVS code is provided in C++.
Миллий таълим-тарбия тарихимизда баркамол шахс тарбиясининг ўзига хос хусусиятлари

In order for a person to reach the level of a person, it is expedient to create opportunities for him to develop mentally, morally, emotionally, physically, to show his creative abilities, in which to compose human qualities. The socio-educational purpose and its essence are reflected in the views of Eastern thinkers. The use of them in the process of pedagogical education in accordance with modern requirements is an urgent task of today.
The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation

Accurate detection and segmentation of marine debris is important for keeping the water bodies clean. This paper presents a novel dataset for marine debris segmentation collected using a Forward Looking Sonar (FLS). The dataset consists of 1868 FLS images captured using ARIS Explorer 3000 sensor. The objects used to produce this dataset contain typical house-hold marine debris and distractor marine objects (tires, hooks, valves,etc), divided in 11 classes plus a background class. Performance of state of the art semantic segmentation architectures with a variety of encoders have been analyzed on this dataset and presented as baseline results. Since the images are grayscale, no pretrained weights have been used. Comparisons are made using Intersection over Union (IoU). The best performing model is Unet with ResNet34 backbone at 0.7481 mIoU. The dataset is available at this https URL
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models.

There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstrate the use of Bayesian Hidden Physics Models to first uncover the physics governing the propagation of acoustic impulses in metallic specimens using data obtained from a pristine sample. We then use the learned physics to characterize the microstructure of a separate specimen with a surface-breaking crack flaw. Remarkably, we find that the physics learned from the first specimen allows us to understand the backscattering observed in the latter sample, a qualitative feature that is wholly absent from the specimen from which the physics were inferred. The backscattering is explained through inhomogeneities of a latent spatial field that can be recognized as the speed of sound in the media.
FIE-GAN: Illumination Enhancement Network for Face Recognition

Low-light face images not only are difficult to be perceived by humans but also cause errors in automatic face recognition systems. Current methods of image illumination enhancement mainly focus on the improvement of the visual perception, but less study their applications in recognition systems. In this paper, we propose a novel generative adversarial network, called FIE-GAN to normalize the lighting of face images while try to retain face identity information during processing. Besides the perceptual loss ensuring the consistency of face identity, we optimize a novel histogram controlling loss to achieve an ideal lighting condition after illumination transformation. Furthermore, we integrate FIE-GAN as data preprocessing in unconstrained face recognition systems. Experiment results on IJB-B and IJB-C databases demonstrate the superiority and effectiveness of our method in enhancing both lighting quality and recognition accuracy.
IMPROVING THE PROFESSIONAL COMPETENCE OF PROFESSIONAL TEACHERS

The article analyzes the motivational, conceptual, technological, reflexive principles of the development of professional competence of teachers of special subjects in higher education institutions. The basis for ensuring the interaction of these principles with the requirements of the activity has been developed. Teachers are given the content and priorities for the development of their professional competence on the basis of instructions, self-assessment, analysis, reflection, professional and social growth, methods of improving professional skills, diagnosis of communicative skills.
Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence

The Holy Grail of a decentralised stablecoin is achieved on rigorous mathematical frameworks, obtaining multiple advantageous proofs: stability, convergence, truthfulness, faithfulness, and malicious-security. These properties could only be attained by the novel and interdisciplinary combination of previously unrelated fields: model predictive control, deep learning, alternating direction method of multipliers (consensus-ADMM), mechanism design, secure multi-party computation, and zero-knowledge proofs. For the first time, this paper proves:- the feasibility of decentralising the central bank while securely preserving its independence in a decentralised computation setting- the benefits for price stability of combining mechanism design, provable security, and control theory, unlike the heuristics of previous stablecoins- the implementation of complex monetary policies on a stablecoin, equivalent to the ones used by central banks and beyond the current fixed rules of cryptocurrencies that hinder their price stability- methods to circumvent the impossibilities of Guaranteed Output Delivery (G.O.D.) and fairness: standing on truthfulness and faithfulness, we reach G.O.D. and fairness under the assumption of rational partiesAs a corollary, a decentralised artificial intelligence is able to conduct the monetary policy of a stablecoin, minimising human intervention.
Persona’s Dilemma between Academics and practitioners - Ideation Guidelines for Co-designing Future Technologies Workshops

The introduction of Industry 4.0 interactive technologies and automated systems in complex organizations have imposed novel challenges and burdens on academics and industrial practitioners for developing systems that work for future workplaces.Developing such systems need sufficient knowledge and understanding of the trends and technological developments and viability from industry and academic experts before introducing them to the general population.Co-designing workshops with employees and users supported by various design tools can provide better ideation for designing future scenarios.We conducted a qualitative study to analyze academics' and industrial practitioners' views on a persona as a design tool during a conference workshop.These participants empirically test the co-creation of personas and find conceptual differences between the groups in their tool use.We propose guidelines on using personas for idea management in the co-design of future scenarios using pre and post-workshop surveys and workshop transcripts to code and clustered our findings.The conclusion is that considering the differences in academics' and industrial practitioners' perspectives, using the right design tools for ideation in a prepared environment for a combined team is substantial and can lead to designing positive experiences in future workplaces.
Multiliteracy education challenges: Will narrative texts used in textbooks open students' minds to critical literacy?

This study investigates critical literacy dimensions implemented in narrative texts and how they are presented. Narrative texts in junior high school and senior high school textbooks were taken as the subject of study since they are compulsory materials taught in the classroom but are regularly considered as a non-powerful material regardless of their multiple insights. A qualitative content analysis was conducted to explore critical literacy dimensions in narrative texts, whereas datasheets consisting of the development of critical literacy dimensions were utilized as the data collection method. The result revealed that the dimensions consisting of disrupting the commonplace, interrogating multiple viewpoints, focusing on sociopolitical issues, taking action, and promoting social justice were covered in narrative texts presented through learning unit and learning activity. Those covered skills indicated that narrative texts assist students to develop their critical literacy skills but teachers’ multimodalities and strategies are still needed to give students guidance.
pix2rule: End-to-end Neuro-symbolic Rule Learning.

Humans have the ability to seamlessly combine low-level visual input with high-level symbolic reasoning often in the form of recognising objects, learning relations between them and applying rules. Neuro-symbolic systems aim to bring a unifying approach to connectionist and logic-based principles for visual processing and abstract reasoning respectively. This paper presents a complete neuro-symbolic method for processing images into objects, learning relations and logical rules in an end-to-end fashion. The main contribution is a differentiable layer in a deep learning architecture from which symbolic relations and rules can be extracted by pruning and thresholding. We evaluate our model using two datasets: subgraph isomorphism task for symbolic rule learning and an image classification domain with compound relations for learning objects, relations and rules. We demonstrate that our model scales beyond state-of-the-art symbolic learners and outperforms deep relational neural network architectures.
Research on the Application of “Full Online” Model in Chorus Teaching under the Background of Epidemic Situation

After novel coronavirus pneumonia outbreak, online teaching has become a trend. Various educational institutions actively promote online teaching mode and build online teaching courses, which has played an important role in promoting the development of teaching informatization, also solved the teaching problems during the epidemic period, and has kept classes suspended. In terms of chorus teaching, this is a collective performance training activity. Under the background of the epidemic situation, this collective singing training cannot be carried out offline. Therefore, the construction of online chorus teaching mode is one of the important directions of chorus teaching in the new era. This paper analyzes the advantages of the “full online” chorus teaching mode under the background of the epidemic situation, analyzes the difficulties faced in promoting the “full online” chorus teaching mode, and analyzes the development countermeasures of the “full online” chorus teaching mode under the epidemic situation.
Gaussian states are enough for universal and powerful quantum reservoir computing

Trabajo presentado en el 2nd International Workshop on Quantum Network Science (NetSci 2020 Satellite Workshop), celebrado el 18 de spetiembre de 2020.
LibGuides: Online Research and Resources: Ebooks Beyond Reed

This guide provides an overview of how to use the Reed College Library website to find electronic sources for your projects and class assignments.
Revisiting the Timpson Induced Earthquake Sequence: A System of Two Parallel Faults

The 17 May 2012 M4.8 Timpson earthquake is the largest known earthquake in eastern Texas. It is suspected to have been induced by wastewater injection from two nearby, high-volume wells. Its cataloged aftershocks form a NW-SE trend, which unlike other induced earthquakes sequences, is unfavorably oriented for failure in the local stress field. To understand this, we enriched the catalog using PhaseNet, a deep-learning-based picker followed by double-difference relocation with cross-correlation-based differential traveltimes. We clustered the aftershocks based on waveform similarity. Most of the seismicity falls into two-clusters, which define a complex fault structure of two parallel subfaults that are more favorably oriented than the overall trend. We inferred from waveform similarity that the sequence initiated on the northern subfault with a M3.9 foreshock and M4.8 mainshock, then extended to the southern subfault with a M4.1 aftershock, and was finally reactivated on the northern subfault with two more M4 events.
Text Mining in Python for Social Scientists

Helge-Johannes Marahrens is a doctoral student in the department of Sociology at Indiana University. He recently earned an MS in Applied Statistics and is currently working toward a PhD in Sociology. His research interests include cultural consumption, stratification, and computational social science with a particular focus on Natural Language Processing (NLP).
Fast robust peg-in-hole insertion with continuous visual servoing.

This paper demonstrates a visual servoing method which is robust towards uncertainties related to system calibration and grasping, while significantly reducing the peg-in-hole time compared to classical methods and recent attempts based on deep learning. The proposed visual servoing method is based on peg and hole point estimates from a deep neural network in a multi-cam setup, where the model is trained on purely synthetic data. Empirical results show that the learnt model generalizes to the real world, allowing for higher success rates and lower cycle times than existing approaches.
CVS Identification through Live streaming using Machine Learning: An Elaborative Framework

Use of technology in the health sector has brought substantial expansion in identification of disease at an early stage. Our current piece is an effort to contribute more in the same direction. In the current piece of work authors are trying to identify the presence of CVS (Computer vision Syndrome) from a live video. Identification is based on few traceable and non-traceable factors that are carefully chosen under the guidance of a medical concern. Artificial Intelligence plays an important role in the detection of eye fatigue through a classification and prediction algorithm. Our current piece of work will be a great help for all those who have to spend multiple hours continuously in front of a screen or device due to Education, Professional commitments, entertainment or any other valid or invalid reason. The prominence of our work is more relevant in the current scenario where most of the jobs are executed through online mode due to the pandemic.
Dynamic Trace Estimation

We study a dynamic version of the implicit trace estimation problem. Given access to an oracle for computing matrix-vector multiplications with a dynamically changing matrix A, our goal is to maintain an accurate approximation to A's trace using as few multiplications as possible. We present a practical algorithm for solving this problem and prove that, in a natural setting, its complexity is quadratically better than the standard solution of repeatedly applying Hutchinson's stochastic trace estimator. We also provide an improved algorithm assuming slightly stronger assumptions on the dynamic matrix A. We support our theory with empirical results, showing significant computational improvements on three applications in machine learning and network science: tracking moments of the Hessian spectral density during neural network optimization, counting triangles, and estimating natural connectivity in a dynamically changing graph.
DESCRIPTIVE ANALISIS BELAJAR DARING DENGAN VIDEO CONFERENCE PADA MAHASISWA BAHASA INGGRIS UNIVERSITAS SISINGAMANGARAJA XII TAPANULI

Teaching and learning process during the Covid-19 pandemic is held online and online. In the learning process using several models, namely video conferencing such as zoom, google meet and messenger room. This online factor makes various assumptions in which learning effectiveness does not feel right on target in lectures, including in the teaching and learning process and also attendance. This situation creates an abandoned learning atmosphere where in the use of various video conferences not all can operate, including lecturers and students. This study aims to 1) find out the description of online learning using Video Conference during the covid-19 pandemic for English language study students at Sisingamangaraja XII Tapanuli University, 2) explain the positive & negative impacts of learning by video conferencing on English language study students due to covid-19 . This research method is a qualitative descriptive that focuses on online learning outcomes with video conferencing. The subjects of this study were students of the English language study program at the University of Sisingamangaraja XII Tapanuli. Instruments of data collection using questionnaires and interviews. This research was carried out in one implementation stage, namely regarding the description of students in learning and interviewing how positive and negative learning was during the Covid-19 Pandemic with various Video Conferences.
Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews

With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user’s experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.