Reviewerly/exHarmony huggingface dataset:
Belief Change without Compactness

&#x0D; &#x0D; &#x0D; Dealing with dynamics is a vital problem in Artificial Intelligence (AI). An intelligent system should be able to perceive and interact with its environment to perform its tasks satisfactorily. To do so, it must sense external actions that might interfere with its tasks, demanding the agent to self-adapt to the environment dynamics. In AI, the field that studies how a rational agent should change its knowledge in order to respond to a new piece of information is known as Belief Change. It assumes that an agent’s knowledge is specified in an underlying logic that satisfies some properties including compactness: if an information is entailed by a set X of formulae, then this information should also be entailed by a finite subset of X. Several logics with applications in AI, however, do not respect this property. This is the case of many temporal logics such as LTL and CTL. Extending Belief Change to these logics would provide ways to devise self-adaptive intelligent systems that could respond to change in real time. This is a big challenge in AI areas such as planning, and reasoning with sensing actions. Extending belief change beyond the classical spectrum has been shown to be a tough challenge, and existing approaches usually put some constraints upon the system, which are either too restrictive or dispense some of the so desired rational behaviour an intelligent system should present. This is a summary of the thesis “Belief Change without Compactness” by Jandson S Ribeiro. The thesis extends Belief Change to accommodate non-compact logics, keeping the rationality criteria and without imposing extra constraints. We provide complete new semantic perspectives for Belief Change by extending to non-compact logics its three main pillars: the AGM paradigm, the KM paradigm and Non-monotonic Reasoning.&#x0D; &#x0D; &#x0D;
Real Time Face Detection using Improvised Machine Learning Algorithm

Face recognition has become an important field in computer-based application development in the last few years. This is because of the wide range of areas in which it is used. In addition, because of the wide variations of faces, face recognition from database images to real-time images/capture images are a challenging problem in front of the face recognition system. Image processing, pattern recognition, and computer vision are relevant technologies which are used in the face recognition field. The innovation of new approaches for the face recognition technology is a continuous subject for building much stronger face recognition algorithms. In this work, to identify a face, we are using Machine learning algorithms and python as the programming language. We are using Open-CV, the most popular library for computer vision, provides bindings for Python.Open-CV uses machine learning algorithms to search for faces within a picture.In this system we already store some images of the person in our database along with his details.By using this system, the surveillance camera system present at some public Space which automatically matches the input faces with that persons database and gives Notification if the results are matched.
Improved Topic Modeling with Parallel-Supervised LDA

In the modern era of digitalization, our day-to-day life is entirely dependent on digital platform-from raising our voice in social media to online shopping. Our collective knowledge is continued to be accumulated in the form of electronic texts, blogs, news, images, audios, videos and in many more ways and on account of this there is a greater need of analyzing these huge contents to get rid of the difficulties in searching the object, we aim for. Topic modelling is an efficient machine learning techniques for discovering the hidden semantic structure of contents. “Latent Dirichlet Allocation” (LDA) is a generative probabilistic topic modelling, which is the basis of other generative topic modelling techniques. New models are coming up with advanced algorithms in order to improve the topic modelling. Existing models have their own limitation. In case of obtaining more accuracy, the processing time of topic modelling goes high while in consideration of achieving more speed, accuracy gets low. Most of the algorithms implemented earlier cannot perform well in above-mentioned area. In this paper, we would like to introduce parallel-supervised LDA model where supervised Latent Dirichlet Algorithm (sLDA) and parallel Latent Dirichlet Algorithm (pLDA) are applied together to obtain high accurate results with quicker response time.
Virtuoso View of Semantic Web

Semantic web is an extension of the World Wide Web (WWW) which makes the machine to understand the content of the web. The main idea behind the semantic web is to share the interlinked documents over Internet in a meaningful way. Web contents are created and designed to be read by humans that may not be understood by computer programs. Thus, they fail to communicate with each other. Semantic web brings the web structure to meaningful form of information. This article describes building blocks, several tools and techniques to implement a killer application on the semantic web.
True Sensor Captured Radiance and Surface Reflectance from Landsat-8 Multispectral Image sets

Proper validation of multispectral image sets that are produced by Satellite Sensors, in this era of big-data computing, is an important aspect in signal processing of natural semantic scenes. In this research article we explore fundamental issues that are cropped up in radiance correction and surface reflectance measurements. We calibrate the raw Landsat-8 imagery digital numbers by using standard functional oriented modular programs in R software, for true sensor radiance and reflectance values. This important preprocessing step is essential for activities that use multispectral images for natural resources monitoring and change-detection studies. It is noted that the procedures for preprocessing can be applied in machine learning algorithms for scientific studies.
A Framework Design for Collaborative GIS Applications: Based on Hybrid Architecture

Geographical information systems (GIS) software tools that support synchronous collaboration efforts among distributed decision-making participants can be very useful in many application areas, such as urban planning, engineering design, disaster and emergency response, and distant learning. However, most existing GIS tools do not provide adequate support for group interaction on decision-making and design scenarios. Early efforts on developing collaborative GIS tools have focused on collaborative geospatial information sharing and presentation in a group environment, mostly adapted to centralized client-server architecture for specific applications. This thesis presents the results of a research project, aiming at providing such GIS software tools over the Internet. Based on the analysis of two mainstream architectures used in collaborative applications: centralized architecture and replicated architecture, a hybrid architecture is selected to develop a collaborative GIS framework as the platform for prototyping the aforementioned GIS tools. The discussion focuses on synchronous collaboration where people interact with each other using the system at the same time from different places. The prototype system, called GeoLink, addresses some important design and development issues such as session management and floor control through a message sending approach.
Image Tampering Detection Based on Combined Feature Extraction Methods

Image manipulated identification is varied from longstanding old semantic location since it demonstrates more arrangement to intruding relics than to picture material, which recommend that favored highlights should be illuminated.We propose a cross breed highlight model like convolutional neural Network System (CNN), histogram of situated slope (HOG), scale-invariant component change (sift) highlights to identify the altered areas given a controlled picture.Initially, we propose the SIFT based alter area location.At that point the framework contains the information picture is portioned into covering squares, whereupon a HOG-CNN highlight is applied to each square.HOG -CNN highlights are removed and decreased to encourage the estimation of closeness.At long last, highlight vectors are lexicographically arranged, and altered picture alter district squares are distinguished by K-Nearest Neighbor.Finally, joining of SIFT based alter area location result and HOG-CNN based alter locale recognition result.Investigation yields show that the proposed way can distinguish different instances of numerous kinds of duplicate and move , grafting imitation, expulsion fraud and precisely find the twinned replicated areas, in any event, when taking care with pictures distorted by interpretation including any little changes like revolutions, obscuring, modifying the brilliance, and shading changes.
Synthesizing Multicast Solutions using Heterogeneous Information

The producer-consumer problem and online algorithms, while natural in theory, have not until recently been considered extensive. In this position paper, we confirm the development of evolutionary programming, which embodies the practical principles of operating systems. In our research we propose new secure theory (CHIT), proving that semaphores can be made “smart”, stochastic, and classical.
The Impact of Scenario-based Settings on Cognitive Reading-to-write Processes

In Scenario-based assessment (SBA), a real-life scenario is created for students to do reading and writing tasks in a computer-mediated environment.It has been recognized that task authenticity will help strengthen assessment validity and task settings and characteristics will influence cognitive efforts.This study attempts to explore the underlying reading-to-write processes in an SBA setting by contrast with those in a non-SBA setting, and thereby investigate the impact of task settings on cognitive processes.Four participants were invited to complete three forms of integrated reading-writing tasks in different settings and then recall their thinking processes stimulated by screen capture recordings and their writing outcomes.The data were analyzed through parsing, encoding, and classifying.The results showed that there were differences between the processes elicited from the SBA setting and the non-SBA setting in the saliency and frequencies of some process categories, and in the ways the source texts were used.Comparatively, the processes summarized in the SBA setting were more congruent with those summarized in theoretical frameworks of integrated reading-writing processes and findings from previous studies in other contexts.The SBA setting not only encouraged a more comprehensive understanding of the multiple texts provided but also found the participants more active in making connections between new and prior ideas rather than scanning for supporting piecemeal information and patch-making.The findings affirmed the effectiveness of the authentic scenario created by computer technology, which provides insights into the constructs and validity of the assessment.
The Impact of Feedback Distribution and Performance-Contingent Bonuses on Learning and Performance.

This study investigates how the distribution of cognitive feedback interacts with bonus incentives in an environment with incomplete information.We experimentally examine how identical feedback influences performance when it is either provided evenly distributed throughout a task, or distributed early at the beginning of the task, and whether the effect is moderated by the presence of a performance-contingent bonus.We predict and find that evenly distributed feedback produces higher performance when there is no bonus.However, when a bonus is offered, identical feedback distributed early is more effective.Additional tests reveal that without a bonus, feedback distributed early leads to cognitive overload, and consequently lowers performance.However, when a bonus is provided, workers take a more active approach to learning and spend more time trying to understand the feedback, which leads to higher performance.This study adds to the growing body of accounting studies on the effects of feedback distribution on performance.Importantly it highlights that performance-contingent incentives can influence the effectiveness of feedback.
Applying Multi Property Tree for Multi Keyword Rank Searching and Dynamic Update in Cloud

Various types of data structures are used for keyword searching like binary tee, KBB tree, inverted tree, inverted index, Multi Property Tree (MPT). These data structures are used for searching keywords in cloud space after getting instructions from data user. On the basis of MPT data structure the authors have introduced a search scheme called MPTsearch algorithm. Experiments show that the proposed scheme performs better than linear search. It also achieves lower time consumption and computing overhead for queries and trapdoor formation. Moreover this scheme not only fulfills the searching part but also plays vital role in dynamic update (insertion and deletion) of data provided by the data owner
An Architecture for Automated Verification of Academic Testimonials in E-Learning

Universities offering e-learning courses often provide their students with a hard copy of the marksheet. When that same student wants to apply for a job through the online application portal of a company, he/she must scan the marksheet and upload the scanned copy. This is a nuisance because there can be many such marksheets and not everyone has access to a scanner at home. The candidate is also required to provide the name of the University which issued the degree as well as the marks obtained, because these information cannot be extracted from the scanned marksheet image using OCR with 100% success rate due to many factors including: varying marksheet formats, presence of background watermarks, differing fonts, loss in quality during scanning, etc. The company must now manually verify each such application by matching the entered marks against the marks printed in the marksheet, which is a tedious process. In this paper, we propose an alternative approach where the data printed on the marksheet is also embedded in a digital copy of the marksheet. This digital copy, in the form of an image, can then be downloaded by the students from the University portal thereby eliminating the need for scanning. Furthermore, when this image is uploaded, the company, i.e. job provider, can easily verify the information by invoking a standard API exposed by the University (or some nodal agency), which will then extract the embedded information. This eliminates the need for any manual verification and the entire process is automated, simple, fast and hassle-free. Security features are also inherent in our approach thereby reducing any chances of fraud.
PEMODELAN KASUS PNEUMONIA PADA BALITA DI PROVINSI BALI MENGGUNAKAN METODE REGRESI NONPARAMETRIK B-SPLINE

Pneumonia is an inflammatory lung disease caused by bacterium Streptococcus pneumonia, Chlamydophila pneumonia bacteria, influenza virus, and fungi. Bali Provincial Health Service data in 2018 shows that one of the diseases that causes many deaths in children under five is pneumonia. This study aims to model the number of pneumonia cases in children under five in Bali Province in 2018 with six research variables, namely percentage of low birth weight, percentage of coverage of infants who receive vitamin A, percentage of infants who receive exclusive breastfeeding, percentage of under-five health services, percentage of villages that implement community-based total sanitation, and percentage of neonatal complications management. In the B-Spline function there are connecting points called knots. The best estimation of the B-Spline regression model is obtained from selecting the optimum knot point with the criteria for the value of generalized cross validation (GCV) and the selected mean square error (MSE) having the minimum value. The B-Spline regression model that is formed is the quadratic B-Spline model (order 3) with five knots resulting in an value of 87.79%.
Implementing No/Low Code Development in a Systems Analysis and Design Course

No/Low Code is the next phase in the code evolution. Enterprise companies build complex solutions using platforms that are primarily no/low code. According to Gartner - “By 2024, low-code application development will be responsible for more than 65% of application development activity.” Thus, adding a no/low code tool at Tuskegee University’s System Analysis &amp; Design class prepares students for the future. During class, students select projects, analyze, design, build, and implement full systems using the latest expertise tools and processes such as the agile The paper highlights why tools such as Mendix are important to incorporate in the classroom.
Applications of Virtual Reality in Mental Health

This objective of this research is to study the application of virtual reality in health care and to especially comprehend its role in mental health care. The methodology employed in qualitative in nature, mainly descriptive that takes in to consideration the databases of various journals and academic websites. The research paper aims at providing a review and analysis on how virtual reality or the artificial environment is used to treat various health related issues, paying specific attention on the mental health and related ailments. Virtual reality has taken a great leap in the past two decades where its various applications are being used to not only treat different medical conditions but also provide training to the medical professionals, thereby reducing the cost of training manifold. The use of artificial environment has improved the quality of health care in the recent years. However, there are still few challenges that the virtual reality world faces and needs to address. The applications of virtual reality need to be communicated widely as to increase the awareness among people and lessen the stigma attached with getting help for mental health issues.
An Analysis of Omission in Students’ English Writings

This paper is part of research with a larger scope covering errors students made related to subject-verb agreement. This part aims to elaborate grammatical problems faced by foreign language learners of English in relation to the omission they made in their writings. A qualitative data collection method was applied in gathering the data. The researchers collected pieces of writing from the students, read them, and analysed the omission errors found in the collected writings. As result, the most common omissions found in the data are related to verb markers. Omission of main verbs and auxiliaries also occur, creating sentences without any verbs or grammatically necessary auxiliaries. Absense of these kinds of function in Indonesian is the main reason for this grammatical problem faced by the learners. In addition, omissios of suffix -ing were also found by the researchers in the data. These findings should be able to contribute to existing works on grammatical errors and increase the success of both teaching and learning of a foreign language like English.Key Words: error, grammar, omission, writing
On the randomness that generates biased samples: The limited randomness approach

We introduce two new algorithms for creating an exponentially biased sample over a possibly infinite data stream. Such an algorithm exists in the literature and uses O(log n) random bits per stream element, where n is the number of elements in the sample. In this paper we present algorithms that use O(1) random bits per stream element. In essence, what we achieve is to be able to choose an element at random, out of n elements, by sparing O(1) random bits. Although in general this is not possible, the exact problem we are studying makes it possible. The needed randomness for this task is provided through a random walk. To prove the correctness of our algorithms we use a model also introduced in this paper, the limited randomness model. It is based on the fact that survival probabilities are assigned to the stream elements before they start to arrive.
Towards Interactive Data Exploration

Enabling interactive visualization over new datasets at “human speed” is key to democratizing data science and maximizing human productivity. In this work, we first argue why existing analytics infrastructures do not support interactive data exploration and outline the challenges and opportunities of building a system specifically designed for interactive data exploration. Furthermore, we present the results of building IDEA, a new type of system for interactive data exploration that is specifically designed to integrate seamlessly with existing data management landscapes and allow users to explore their data instantly without expensive data preparation costs. Finally, we discuss other important considerations for interactive data exploration systems including benchmarking, natural language interfaces, as well as interactive machine learning.
Multiresolution analysis and adaptive estimation on a sphere using stereographic wavelets

We construct an adaptive estimator of a density function on $d$ dimensional unit sphere $S^d$ ($d \geq 2 $), using a new type of spherical frames. The frames, or as we call them, stereografic wavelets are obtained by transforming a wavelet system, namely Daubechies, using some stereographic operators. We prove that our estimator achieves an optimal rate of convergence on some Besov type class of functions by adapting to unknown smoothness. Our new construction of stereografic wavelet system gives us a multiresolution approximation of $L^2(S^d)$ which can be used in many approximation and estimation problems. In this paper we also demonstrate how to implement the density estimator in $S^2$ and we present a finite sample behavior of that estimator in a numerical experiment.
Mining predicate-based entailment rules using deep contextual architecture

Semantic inference plays an essential role in numerous Natural Language Processing (NLP) tasks, such as question answering, machine reading and text summarization. Reasoning in natural language is inseparable from the knowledge about inference, which is often represented in the form of predicate-based entailment rules. Many efforts have been dedicated to extracting entailment rules from text corpora by utilizing statistical methodology including distributional hypothesis and Latent Dirichlet Allocation (LDA). However, these studies could not give equal consideration to both coverage and accuracy of the mined rules, which brings instability to downstream applications. To solve this problem, this paper proposes a novel model named Deep Contextual Architecture (DCA), which is driven by Deep Belief Networks (DBNs), for the task of mining predicate-based inference rules from texts. Besides previously used statistical contextual information, we also involve semantic meanings represented by word embeddings into DBNs to learn topic level representation of predicates. Combining benefits from both kinds of information, the proposed DCA model shows potential for better modeling the context of predicates. Evaluation on public datasets demonstrates that our method outperforms several strong baselines.
Time evolution of multiphoton added-then-subtracted coherent states in thermal channel

In the present paper, the time evolution in thermal channel of the multiphoton added-then-subtracted coherent states are investigated based on the compact expression of normalization factor relating to Laguerre polynomials. We have analytically derived the time evolution of normally ordered density operator, Wigner function, and tomogram function in thermal channel respectively, and analyzed the decoherence effect in detail accordingly. The decoherence process and several novel evolution phenomenons of the resulting states evolving in thermal channel are clearly unveiled numerically.