Reviewerly/exHarmony huggingface dataset:
A Psychological Theory of Explainability

The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI generated explanations. The lack of theory means that validation of XAI must be done empirically, on a case-by-case basis, which prevents systematic theory-building in XAI. We propose a psychological theory of how humans draw conclusions from saliency maps, the most common form of XAI explanation, which for the first time allows for precise prediction of explainee inference conditioned on explanation. Our theory posits that absent explanation humans expect the AI to make similar decisions to themselves, and that they interpret an explanation by comparison to the explanations they themselves would give. Comparison is formalized via Shepard's universal law of generalization in a similarity space, a classic theory from cognitive science. A pre-registered user study on AI image classifications with saliency map explanations demonstrate that our theory quantitatively matches participants' predictions of the AI.
A game comonadic account of Courcelle and Feferman-Vaught-Mostowski theorems

Game comonads, introduced by Abramsky, Dawar and Wang, and developed by Abramsky and Shah, give a categorical semantics for model comparison games. We present an axiomatic account of Feferman-Vaught-Mostowski (FVM) composition theorems within the game comonad framework, parameterized by the model comparison game. In a uniform way, we produce compositionality results for the logic in question, and its positive existential and counting quantifier variants. Secondly, we extend game comonads to the second order setting, specifically in the case of Monadic Second Order (MSO) logic. We then generalize our FVM theorems to the second order case. We conclude with an abstract formulation of Courcelle's algorithmic meta-theorem, exploiting our earlier developments. This is instantiated to recover well-known bounded tree-width and bounded clique-width Courcelle theorems for MSO on graphs.
Market failures and costs of intermediation cause obstacles to the delivery of transactions

To understand economic growth, this paper looks at transaction costs and how they affect how much money people make. It shows transaction costs, market failure, and economic underdevelopment. Many people think that the problems with development are because markets can't do their job of allocating resources. Some people don't believe that the market doesn't work and blame the government instead. If you look at the people who say that market failure is the root of economic backwardness, very few, if any, look at transaction costs and how they are linked to market failure. People who read this paper want to connect development economics with transaction cost theory and new institutional analysis.
Analysis of the scientific productions about companies, accounting information and sustainability in the pandemic context (Covid-19)

The pandemic (Covid-19) decreed by the World Health Organization on March 11, 2020, originating in China in 2019, has generated drastic changes for the world society, and so far is impacting negatively, and changing the quality of life of the entire population. Humanity has been facing an invisible disease and man needs to rethink consumption patterns, as well as the negative environmental impact generated by these patterns. To this end, we aimed to analyze the scientific productions on companies, accounting information and sustainability in the context of the pandemic (Covid-19). This study is classified as a descriptive, bibliographic and qualitative research, as it sought to identify information to highlight the socioeconomic and environmental impacts generated by the pandemic (Covid-19) in organizations. In this sense, the quality of information about these impacts are indispensable issues for analyzing the sustainable development of corporate business. Other impacts were identified in relation to environmental quality such as the companies' accounting technical standards, and theoretical reflections on integrated reporting studies.
Ensemble K-Nearest Neighbors Method to Predict Composite Stock Price Index (CSPI) in Indonesia

The Composite Stock Price Index (CSPI) is a guide for investors to see the movement of stock prices as a whole from time to time. These movements always change from time to time, so it is necessary to use analytical methods to make predictions. The method that can be used to examine this is the K-Nearest Neighbor method. The combination of the results of several K-NN predictions is an effective way to get one final prediction result, namely the method ensemble K-NN. The response variable used in this study is the Composite Stock Price Index (CSPI), while the predictor variables are the gold price, the rupiah exchange rate against the dollar, and the Dow Jones Industrial Average (DJIA) index. The data used are 52 periods. The data used for training are 39 periods and the data used for testing is 13 periods. The prediction results from the ensemble have better results than the K-NN. The prediction results from the ensemble have better results than the single K-NN. The prediction results from the method are ensemble K-NN average of 6078, 634 with a MAPE value of 7,16% including high accuracy
novel approach for file encryption

A novel approach to file Encryption is concerned with protecting files being shared among users. The idea revolves around storing files securely so that If someday database is hacked, then data doesn’t leaked. To ensure files are protected they are split in 3:1 ration and encrypted using various algorithms like Advance Encryption System (AES) and Data Encryption Standard (DES). Audio steganography method has also been used to improve protection standard.
Detection Masking for Improved OCR on Noisy Documents

Optical Character Recognition (OCR), the task of extracting textual information from scanned documents is a vital and broadly used technology for digitizing and indexing physical documents. Existing technologies perform well for clean documents, but when the document is visually degraded, or when there are non-textual elements, OCR quality can be greatly impacted, specifically due to erroneous detections. In this paper we present an improved detection network with a masking system to improve the quality of OCR performed on documents. By filtering non-textual elements from the image we can utilize document-level OCR to incorporate contextual information to improve OCR results. We perform a unified evaluation on a publicly available dataset demonstrating the usefulness and broad applicability of our method. Additionally, we present and make publicly available our synthetic dataset with a unique hard-negative component specifically tuned to improve detection results, and evaluate the benefits that can be gained from its usage
A Personalized and Scalable Machine Learning-Based File Management System

In this work, we present a hybrid image and document filing system that we have built. When a user wants to store a file in the system, it is processed to generate tags using an appropriate open-source machine learning system. Presently, we use OpenCV and Tesseract OCR for tagging files. OpenCV recognizes objects in the images and TesserAct recognizes text in the image. An image file is processed for object recognition using OpenCV as well for text/captions process using TesserAct, which are used for tagging the file. All other files are processed using Tesseract only for generating tags. The user can also enter their own tags. A database system has been built that stores tags and the image path. Every file is stored with its owner identification and it is time-stamped. The system has a client-server architecture and can be used for storing and retrieving a large number of files. This is a highly scalable system.
The Development of PowerPoint-Based Video Intructional Media for Islamic Education Teachers in MTs Qudsiyyah Kudus

&lt;em&gt;This research aims to develop a PowerPoint-based video instructional media. PowerPoint is an office application that teachers can easily operate. There are three steps in the development process of this research, namely, testing the prototype of the material on individual students with three students. It was followed by trials in small classes and large class trials (field tryouts). This small class trial involved eight students, while the large class involved 20 students by taking the students in the class. The first evaluation results of this learning media product were 89.9, followed by the second and third tests, which resulted in the same score of 89.3. It means that the design development of this media product is feasible for the teaching and learning process and can be developed according to the learning needs of the madrasah. The evaluation and product improvement process needs to be carried out to produce good learning media products. This PowerPoint-based video learning media is expected to provide new learning experiences for students because learning is more interesting.&lt;/em&gt;
An Algorithm of Deformation Image Correction Based on Spatial Mapping

Abstract The original image has geometric deformation in geometric position, shape, size and orientation due to the shooting angle or capturing in the process of image shooting. It brings a lot of inconveniences and great challenges in image processing field such as image fusion, de-noising, recognition and segmentation. In order to improve the processing ability and recognition accuracy of deformation images, an adaptive image deformity correction algorithm for the quadrilateral and the triangular is proposed. Firstly, the deformation image is pre-processed and the contour of image edge is extracted. And then, discrete points of image edge are found to locate image edge accurately. Finally, the deformation of the quadrilateral or the triangle is processed into standard rectangle or equilateral triangle image by using the proposed algorithm of three-dimensional homography transformation, so as to complete the transformation from irregular image to regular image adaptively. A lot of experiments show that the proposed algorithm is superior to the traditional methods such as Hough transform and Radon transform, which improves the effect of deformation images correction, solves the problem of geometric deformation images well, and provides a new technical method for image processing of deformation images.
Learning and Exploiting Progress States in Greedy Best-First Search

Previous work introduced the concept of progress states. After expanding a progress state, a greedy best-first search (GBFS) will only expand states with lower heuristic values. Current methods can identify progress states only for a single task and only after a solution for the task has been found. We introduce a novel approach that learns a description logic formula characterizing all progress states in a classical planning domain. Using the learned formulas in a GBFS to break ties in favor of progress states often significantly reduces the search effort.
PECULIARITIES OF MOTIVATION OF EDUCATIONAL AND PROFESSIONAL ACTIVITY OF EDUCATORS WITH DIFFERENT STYLES OF COGNITIVE ACTIVITY

The article presents an analysis of the current state of the problem of the formation of cognitive activity of applicants for higher education, the insufficiency of its development in the theory and practice of vocational education. The development of cognitive activity contributes to the formation of a positive attitude toward theoretical knowledge, educational and professional activities, the acquisition of practical skills, the formation of professional and personal qualities of applicants, their motivational, cognitive and organizational readiness for professional self-education, creating the basis for the development of an individual style of professional activity, ensuring professional mobility of the future specialist.An empirical study of the characteristics of educational and professional motivation of applicants for higher education with different styles of cognitive activity allows us to state that the subjects have divergent, accommodative and assimilative styles of cognitive activity, among which divergent and accommodative ones predominate. The leading motives for the educational and professional activity of applicants with a divergent style of cognitive activity are the acquisition of knowledge, obtaining a diploma, the desire to become a highly qualified specialist, and achieving the approval of parents and others. For applicants with an accommodation style – the acquisition of knowledge, obtaining a diploma, the desire to become a highly qualified specialist, ensuring the success of future professional activities, acquiring deep and strong knowledge. Applicants with an assimilating style of cognitive activity have mastered a profession, received a diploma, successfully continued their studies in subsequent courses, and successfully passed exams. Thus, it has been proven that a different style of cognitive activity has a significant impact on educational and professional motivation. When organizing the cognitive activity of applicants for higher education in order to activate it, it is necessary to take into account the following individual psychological characteristics of a person: temperament, which manifests itself primarily in the style of activity; degree of education – the level of development of intellectual abilities, one’s own experience in the knowledge of the world around; cognitive styles, intellectual lability; the dominant type of perception of information: visual (visual), auditory (auditory), kinesthetic; motivation of cognitive activity; manifestation of will; relationships with teachers, fellow students; emotional state, ability to self-organize.&#x0D; Keywords: motivation of educational and professional activit; applicant of higher education; cognitive activity; style of cognitive activity; activity style; cognitive style; education; higher education.
Fast high-fidelity composite gates in superconducting qubits: Beating the Fourier leakage limit

We present a method for quantum control in superconducting qubits, which overcomes the Fourier limit for the gate duration imposed by leakage to upper states. The method uses composite pulses, which allow for the correction of various types of errors, which naturally arise in the system, by destructive interference of these errors. We use our approach to produce complete and partial population transfer between the qubit states, as well as the three basic single-qubit quantum gates. Our simulations show a substantial reduction of the typical errors and a gate speed-up by an order of magnitude.
Aplikasi Pendaftaran Pelatihan di Dinas Perindustrian Kota Medan Berbasis Android

This study aims to optimize training registration in the Medan City Industry Service through an android-based application. The benefits of this research are to save people's time and energy and reduce the workload of the Medan City Industry Office staff, because registration is done online through RED APPEL. The application is designed using Java programming on Android Studio, MySQL system management database on XAMPP software. The results of the research are training registration, taking training materials through APPEL MERAH (Medan Berkah Training Application) without having to come to the Medan City Industry Office. This application is very important among people who want to take part in training at the Medan City Industry Office because it can be accessed via their respective Smartphones.
Approaches to the Formation of Systemic Ecological Thinking among Undergraduate Students

Today, the formation of an ecological or environmental component in the worldview of a specialist in any field of activity is becoming one of the necessary conditions for the sustainable development of society as a whole. The article is devoted to highlighting the approach to the formation of universal competence in the field of ecological worldview among undergraduate students of SPbPU on the example of the direction &amp;quot;Innovation&amp;quot;.
Object Detection using Open CV and Deep Learning

Object Detection using SSD (Single Shot Detector) and MobileNets is efficient because this technique detects objects quickly with fewer resources without sacrificing performance. In this, every class of item for which the classification algorithm has been trained generates a bounding box and an annotation describing that class of object. This provides the foundation for creating several types of analytical features such as the volume of traffic in a certain area over time or the entire population in an area is real-time detection and categorization of objects from video data. Keywords: Object Detection, OpenCV, SSD.
Bivariate Negative Binomial Regression for Modelling Covid-19 Confirmed Cases and Death Cases in Kalimantan

Coronavirus disease (Covid-19) caused a pandemic severely affecting various sectors and paralyzed health services in Indonesia. As of June 2020, the percentage of Covid-19 confirmed cases in Kalimantan, the second largest island in Indonesia, contributed about 7% of the total national cases. In the same period, the percentage of Covid-19 deaths reached 12% of the national figure. This study used regression models to respond to bi-response count data consisting of Covid-19 confirmed cases and Covid-19 deaths in regencies/cities in Central Kalimantan and South Kalimantan provinces. This study compared the results of bivariate Poisson regression and bivariate negative binomial regression. There were thirteen predictors representing the determinants of health, social, economic, and demography indicators. The results showed that the prevalence of pneumonia had positive effect on Covid-19 confirmed cases and Covid-19 deaths. The percentage of elderly had negative effect on confirmed cases, while it had no significant effect on Covid-19 deaths. Bivariate negative binomial regression showed more satisfying performance on modeling Covid-19 cases and Covid-19 deaths jointly because it produced lower AIC and deviance than that of Poisson one. The negative bivariate model was also better than the Poisson one because it was able to overcome over-dispersion.
Soap bubbles on the surface of a liquid: a playful universe in 3+1 dimensions, a real universe in 4+1 dimensions

Very simple experiments based on an analogy between two theories (gravitation and capillarity) allowed us to construct a two dimension space toy-universe composed of the surface of a three-dimensional water tank with floating soap bubbles and bubble clusters.This very simple approach leads to a surprising description of our universe with, in particular, the existence of a fourth dimension inaccessible to us. Such a 4th macroscopic dimension suggests the existence of a huge 4 D tank of heavy fluid (analogous to our 3 D water tank) sitting below our three dimensional universe (which is the surface of the main 4 dimensions universe). Among other consequences:an original interpretation of the event horizon of black holes or a simple answer to the problem of missing dark matter.
GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data

Electronic health records (EHRs) are usually highly dimensional, heterogeneous, and multimodal. Besides, the random recording of clinical variables results in high missing rates and uneven time intervals between adjacent records in the multivariate clinical time-series data extracted from EHRs. Current works using clinical time-series data for patient representation regard the patients' physiological status as a discrete process described by sporadically collected records. However, changes in the patient's physiological condition are continuous and dynamic processes. The perception of time and velocity of change is crucial for patient representation learning. In this study, we propose a time- and velocity-aware gated recurrent unit model (GRU-TV) for patient representation learning of clinical multivariate time-series data in a time-continuous manner. The neural ordinary differential equations (ODEs) and velocity perception mechanism are applied to perceive the time interval between adjacent records and changing rate of the patient's physiological status, respectively. Our experiments on two real clinical EHR datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on computer-aided diagnosis (CAD) tasks, especially on sequences with high-variance time intervals.
Quantifying Non-Markovianity in Open Quantum Dynamics

Characterization of non-Markovian open quantum dynamics is both of theoretical and practical relevance. In a seminal work [Phys. Rev. Lett. 120, 040405 (2018)], a necessary and sufficient quantum Markov condition is proposed, with a clear operational interpretation and correspondence with the classical limit. Here we propose two non-Markovianity measures for general open quantum dynamics, which are fully reconciled with the Markovian limit and can be efficiently calculated based on the multi-time quantum measurements of the system. A heuristic algorithm for reconstructing the underlying open quantum dynamics is proposed, whose complexity is directly related to the proposed non-Markovianity measures. The non-Markovianity measures and the reconstruction algorithm are demonstrated with numerical examples, together with a careful reexamination of the non-Markovianity in quantum dephasing dynamics.
A Framework of “Quantitative ⨂ Fixed Image ⇒ Qualitative” Induced by Contradiction Generation and Meta Synthetic Wisdom Engineering

Due to the past tP and the future tF being divided into a pair of opposing times by the now tN, the generation mechanism of the contradiction is attributed in this paper as the process in which the time increment &Delta;t and &Delta;t&rsquo; are transmitted from the past tP and the future tF to the present moment tN, respectively, and then reverse each other. The category and topos of time contradictorily constructed by the mechanism is discussed. It is shown that not only can the laws of the &ldquo;Unification of Opposites&rdquo;, &ldquo;Mutual change of Quality and Quantity&rdquo; and &ldquo;Negation of negation&rdquo; of the contradiction be represented in this form of category, but some of classic constructions appearing in the fields of mathematics, physics, logic, life, nerves, thinking, and intelligence can also be considered as morphosm- or pattern-induced and emerge via this mechanism as well. On the other hands, a series of concepts, models, and algorithms for noetic science, such as the attribute conjunctive monoid category (ACMC), attribute reasoning lattice category (ARLC), attribute coordinate system (ACS), attribute coordinate analysis method based on the learning of ACS for perception, cognition, and decision-making (ACAM), qualitative (conversion degree) mapping from quantity to quality (QM), attribute grid computer based on qualitative mapping, qualitative criteria transformation (AGC), etc., which have been verified through corresponding experiments, have been proposed, so that not only a set of attribute theory methods from perception to cognition and thinking have been constructed, but the synthetized framework of &ldquo;Quantitative &#10754; Fixed Image &rArr; Qualitative&rdquo;, called &ldquo;Framework of Syntenic Three Approaches&rdquo; (FSTA) can also be induced. It is possible to provide an alternative reference path and technical solution for noetic science and open complex giant systems because FSTA is consistent with the framework of &ldquo;Quantitative Intelligence &#10754; Fixed Image Intelligence &rArr; Qualitative Intelligence (Meta Synthetic Wisdom)&rdquo;, as proposed by Hsue-shen Tsien.