Reviewerly/exHarmony huggingface dataset:
An O(N) Time Algorithm for Finding Hamilton Cycles with High Probability.

We design a randomized algorithm that finds a Hamilton cycle in $\mathcal{O}(n)$ time with high probability in a random graph $G_{n,p}$ with edge probability $p\ge C \log n / n$. This closes a gap left open in a seminal paper by Angluin and Valiant from 1979.
Feature Space Singularity for Out-of-Distribution Detection.

Out-of-Distribution (OoD) detection is important for building safe artificial intelligence systems. However, current OoD detection methods still cannot meet the performance requirements for practical deployment. In this paper, we propose a simple yet effective algorithm based on a novel observation: in a trained neural network, OoD samples with bounded norms well concentrate in the feature space. We call the center of OoD features the Feature Space Singularity (FSS), and denote the distance of a sample feature to FSS as FSSD. Then, OoD samples can be identified by taking a threshold on the FSSD. Our analysis of the phenomenon reveals why our algorithm works. We demonstrate that our algorithm achieves state-of-the-art performance on various OoD detection benchmarks. Besides, FSSD also enjoys robustness to slight corruption in test data and can be further enhanced by ensembling. These make FSSD a promising algorithm to be employed in real world. We release our code at \url{this https URL}.
The Algebraic Creativity in The Neutrosophic Square Matrices

The objective of this paper is to study algebraic properties of neutrosophic matrices, wherea necessary and sufficient condition for the invertibility of a square neutrosophic matrix is presented by defining the neutrosophic determinant. On the other hand, this work introduces the concept of neutrosophic Eigenvalues and vectors with an easy algorithm to compute them. Also, this article finds a necessary and sufficient condition for the diagonalization of a neutrosophic matrix
Implementasi Metode GOST(Government Standard) dan LSB-1(Least Significant Bit) Untuk Mengamankan Teks

Securing text data is one of the activities carried out so that the text of an information is kept confidential so that it cannot be known by others except those who are entitled to it. One technique that can be done to optimize the security of text data is to combine cryptographic techniques with the steganography technique. Cryptographic techniques function to encode text data that is kept secret through the encryption process. While the steganography technique serves to hide (embedding) encrypted text data into a media such as image, video, audio. This study describes the process of combining cryptographic techniques and steganography techniques in securing sent text. Cryptographic techniques are used to encode secret messages based on the GOST (Government Standard) method and the steganography technique is used to hide encrypted text into a digital image based on the LSB-1 (Least significant bit-1) algorithm. In order to make the process easier, a secret text security application was built using Visual Basic 2008 programming language
#andràtuttobene: Images, Texts, Emojis and Geodata in a Sentiment Analysis Pipeline.

This research investigates Instagram users’ sentiment narrated during the lockdown period in Italy, caused by the COVID-19 pandemic The study is based on the analysis of all the posts published on Instagram under the hashtag #andratuttobene on May 4, May 18 and June 3, 2020 Our research carried out a view on a national, regional and provincial scale We analyzed all the different languages and forms (i e captions, hashtags, emojis and images) that constitute the posts The aim of this research is to provide a set of procedures revealing the different polarity trends for each kind of expression and to propose a single comprehensive measure Copyright © 2020 for this paper by its authors
Challenge Closed-Book Science Exam: A Meta-Learning Based Question Answering System

Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus from Wikipedia or SimpleWikipedia. However, retrieving knowledge from the large corpus is time-consuming and questions embedded in complex semantic representation may interfere with retrieval. Inspired by the dual process theory in cognitive science, we propose a MetaQA framework, where system 1 is an intuitive meta-classifier and system 2 is a reasoning module. Specifically, our method based on meta-learning method and large language model BERT, which can efficiently solve science problems by learning from related example questions without relying on external knowledge bases. We evaluate our method on AI2 Reasoning Challenge (ARC), and the experimental results show that meta-classifier yields considerable classification performance on emerging question types. The information provided by meta-classifier significantly improves the accuracy of reasoning module from \(46.6\%\) to \(64.2\%\), which has a competitive advantage over retrieval-based QA methods.
Natural Language Semantic Representation Method Based on the Scene Framework

This paper constructs a standard semantic reference system with a large number of scene semantic representations, then interprets and understands the reference system with low-level semantics, that is, commonsense, and compares the semantic representations of the standard reference system with the input text, forming the premise and foundation of machine analogy thinking. This paper discusses the method of using the scene framework to construct standard semantic reference system, describes the dynamic process of semantic recognition by analogy with standard reference system, describes the application examples of machine thinking oriented semantic representation, finally, summarizes the paper and explains the application prospect of natural language semantic representation method based on the scene framework.
LibGuides: SUMMER 2021 Hybrid Access to Library Services & Resources: Additional Local Services

Whether you're here on campus or studying remotely, you still have access to library resources and services!
Формування злочинної поведінки через вплив публічної інформації

Theses are devoted to a comprehensive analysis of the negative impact of information, including public information, on human consciousness.
Ολοκληρωμένο πλαίσιο αξιολόγησης πλατφορμών διαχείρισης υπηρεσιών βιβλιοθήκης (Library Service Platforms - LSP)

Contemporary library automation systems, known as Library Service Platforms (LSP) or 3rd generation library systems, have rapidly started to replace the previous generations' systems used globally so far, known as Library Management Systems (LMS). As the LSP take advantage of the newest technologies, their features and functions can effectively serve the modern libraries' needs. However, the new features, modules and characteristics that differentiate the LSP from older generation systems are scattered throughout the relevant literature and there are discrepancies in the terminology used by different researchers or practitioners. Furthermore, there has been no attempt so far by the Greek Librarianship community to gather and clarify their role, to group and translate them. The purpose of this research therefore is to identify and record the innovative features of the library automation systems that define an LSP and differentiate it from an LMS, as well as to evaluate their importance and relevance, based on the opinions provided by the Greek information scientist.In total, 23 features and characteristics have been identified and they have been grouped into 3 categories: New features, Architectural - Technological dimension and Social dimension. Also, the researcher has attempted to provide their Greek translation whenever it was necessary. The method used in order to conduct the research is the Delphi method, which is widely used worldwide in topics that require experts opinion polling and consensus. Two rounds of questionnaires were sent out to the experts: during the first round, each of the experts individually ranked the LSP features and characteristics according to their importance, while in the second round they were able to alter their rankings as they could compare the order they had set in the 1st round to the team's average scores. This resulted in two classifications (a preliminary and a final one) of the features, while both classifications' results converged, achieving Delphi's goal of reaching a consensus. After analyzing the findings, the Greek information scientists' preferences over their requirements for an LSP were clear. Indicatively, the experts hold the management of new metadata standards in high regard, while they want a system covering their current needs with the prospect of incorporating new schemes (linked data etc.) when they become widely available. On the other hand, the use of mobile applications has been placed last in 2 sections and with a significant distance from the previous features. Distinct and specific trends have also been discovered, providing predictions about the future of these systems, such as the high ranking of the collaboration between software providers and libraries for exchanging data, feedback etc., an example of how LSPs can be collaboratively designed in the near future, by receiving feedback from the libraries.
Salient Object Detection Using Spatially Weighted Multiple Contrast Cues

Detecting objects that capture visual attention has played a key role in computer vision. In this paper, we present a model that incorporates multiple bottom-up cues depending on the following concepts: connectivity to the outstanding parts where each superpixel will take a saliency score based on its connectivity strength to the enclosed interest regions, global contrast by measuring how every superpixel differs from all superpixels in the image and utilizing regional frequency tuning and center-bias. The final saliency map is produced by integrating the resulted foreground maps of each cue and refining the result with the optimization framework. An extensive experimental evaluation is done on three challenging datasets to evaluate the proposed model using common classification criteria. Our model has the superiority in performance over the other models qualitatively and quantitatively.
BOINC: A CONTRIBUIÇÃO DA COMPUTAÇÃO DISTRIBUÍDA NOS AVANÇOS CIENTÍFICOS / BOINC: THE CONTRIBUTION OF COMPUTING DISTRIBUTED IN SCIENTIFIC ADVANCES

This paper presents a research on scientific advances made possible through the BOINC platform in the area of distributed computing and its ease in participating in one or more projects involved.The overall objective of the study is to describe BOINC, its objectives, contributions, characteristics and the diversity of projects available.Specific objectives were defined: Search on how the platform came about and why it was created; Know more about the precursor project; Learn more about the best known projects; Run the platform and gain practical knowledge of the projects; Analyze the results generated from the execution.The methodology of this study involves a bibliographical research, starting from distinct branches of science, such as astronomy, physics and biology.The study tool revolved around the use of the platform in a personal computer to validate the proposal that distributed computing is, in fact, efficient in finding new scientific discoveries using only idle processing, thus eliminating the need for supercomputers in such discoveries.BOINC is a software for distributed computing that allows communication between the operating system and the applications running on it.Through this research, it was possible to know and understand distributed computing, as BOINC is important in the advancement and scientific discoveries, the ease of becoming voluntary providing only idle processing of a personal machine, as this processing contributes to great research without the need for investment in supercomputers by institutions, making projects, previously impossible, feasible and promising.
Preventing misuse of discount promotions in e-commerce websites: An application of rule-based systems

E-commerce websites continue to get affected by fraudulent online activities in spite of the substantial efforts made by different stakeholders such as card issuers, banking intermediaries, merchants, and law enforcement agencies. First-time promotional discounts are offered by e-commerce websites for gaining and retaining new and existing customers. However, in several instances, such discounts are abused by fraudsters. Surprisingly, little attempt has been put in the past to detect such abuses. This study is the first attempt in this direction and uses transaction data of an e-commerce company to develop a rule-based detection system. The rules-based system is developed in two-stage processes, generation of facts and rules, respectively; and it is further validated by experts. An architecture of a rule-based fraud detection system is also proposed. Using rule-based detection system, the company can flag-off the probable abusers, and can subsequently monitor their behaviour and take decisive actions.
LibGuides: IN151 - CWRR II - Gilpin: Online Periodicals

Research resources and techniques for Professor Gilpin's IN151 class Tips for distinguishing types of periodicals online
Desain ME Rumah Sakit PKU Muhammadiyah Karangayar

Mechanical electrical installation and plumbing system in a building it is a completeness of the building facilities used to promoted the achieving of elements of comforts, health, safety, communication and mobility in a building. PKU Muhammadiyah Karanganyar Hospital located in tasikmadu district. The Karanganyar PKU hospital owns 1675.21 m 3 building area 11.5 meters and consist of 3 floors. This design dictates the number of points of light in each room, the air conditioning and air capacity used in each room, and the number of other electrical outlets designed, this design also designed the mechanical part like plumbing. the methods used to detemine the number of point lights, determine capacity. air conditioning, and the plumbing system use available equations by determining the living factors, the rooms and buildings as a counting point, the software used to define light points, capacity air conditioning, and plumbing USES Ms. Word while for a singgle line design picture and a diagram Lights on AutoCad 2010. The result of the planning calculations is 370 a large shipping container using NYY 2 (4 x 150 mm2 ). Then for fresh water demand calculation obtained by 21.189 m3 and demand damkar 510.9 m3 so the capacity of ground tank 609 m3 with dimension 17 meter x 9 meter x 4 meter
LibGuides: Copyright : Please Give Us Your Feedback!

This guide will give you basic information on copyright law as it relates to educational use of sources.
Statistical considerations in spectral histopathology and performance evaluation

Spectroscopic imaging offers a potential path to all-digital molecular pathology by relating the spectral data to histopathological details of tissue. In the different statistical approaches applied to spectral data, there are gaps in the appropriate sample size estimation for a significant statistical power and accurate model assessment especially for multiclass problems. Underestimation of the sample size can lead to statistically insignificant diagnostic tests while an overestimation can greatly increase experimental costs and time frames.Since the receiver operating characteristic (ROC) curve is designed primarily for a binary test, there are no straightforward approaches to use it for multiple classes in a typical pathology image. In this study, we have described sample size estimation (power analysis) and multiclass diagnostic ROC curve generation for hyperspectral datasets.
THE EFFECTIVENESS OF MENTORING AND MONITORING CONSUMPTION OF FE TABLETS AND THEIR EFFECTS ON INCREASING HB LEVELS

80, 9% of young women who received Fe Tablets were 98.6% consuming < 52 Fe Tables. The factors are most related to the low consumption of iron tablets in students concerning digestive disorders, the influence of others, student aversion, and environmental situations. This study aims to determine the effectiveness of mentoring and monitoring in the administration of Fe Tablets and its effect on increasing Hb levels in adolescent girls. The design of this study used quasi-experiment, with the population in the study were young women whose samples were those who were anemic and had experienced a failure in Fe Tablets consumption. The sample is divided into two groups, namely 1 group (experimental) mentoring (guidance and direction individually) and monitoring (monitoring) of consumption of Fe Tablets and the second group (control) and monitoring without mentoring. Before and after the intervention, hemoglobin concentration was examined. The results of the study showed that mentoring and monitoring of additional blood supplementation (Fe Tablets) were effective in the behavior of consuming Fe Tablets in young women with an Exp (β) (OR) value of 5.36 and p = 0.008. Consumption rates of Fe tables were more elevated in Hb levels compared to those with less Fe Tablets consumption levels. However, there was no relationship between Fe Tablets consumption and increased Hb levels.
Competence of teachers in the process of digitalization in higher education

The big changes that are occurring today in the field of digital technologies and digitalization affectall spheres of life, including higher education. People's knowledge and competencies are built froman early age, and university professors enrich and reinforce this. Changes affecting every aspect oflife, including those related to Covid 19 lead to the acceleration of the digitalization process ineducation. This in turn creates the need to improve existing skills, knowledge and competences. Thechanges in the technologies and economic situation of Bulgaria place new requirements in terms ofthe skills and individual competencies of teachers in higher education. The main objective of thisdevelopment is aimed at identifying the specificities of the digitalization process in higher educationand the necessary competences of teachers. The main research methods used in the developmentare contingent analysis, method of comparison, method of analysis and synthesis, intuitive andsystematic approach.
Towards Visual Generalization with Graph Deep Learning

While the world of computer vision is constantly facing new challenges and moves towards more complex learning architectures, new research directions are investigating how to extend existing deep learning methods to new types of data. Geometric Deep Learning tackles the challenge to extend the most successful image-based deep learning methods, such as convolutional neural networks, to 3D shapes and graphs. Starting from the great potential of graph convolutional neural networks, the core idea of this thesis project is to tackle image-based tasks via graph-based deep learning methods. It requires to find a bridge that re-combines back the two worlds of images and graphs mapping the former into the latter. Our intuition is that, by exploiting graph-based models, it would be possible to maintain the semantic information within a picture while loosing all the specific idiosyncrasies which limit visual generalization in traditional image-based models. In the first part we concentrated on image segmentation, comparing different algorithms while maintaining a fixed basic Graph Convolutional Neural Network, known as ECC Network. As a second step we moved our attention to the graph network, modifying its structure in terms of depth, type of layers and learning procedure. With this analysis we were able to get state-of-the-art digit classification for each dataset with a significant reduction (10×) in the original number of pixels. Then, we shifted our attention on more challenging cases where training and test data belong to two different distributions. We leverage on recent domain generalization methods that have shown how combining supervised and self-supervised knowledge can improve generalization.
Markets for Quantum Enabled Devices

Matt Langione, a Principal at the Boston Consulting Group, authored one of the most frequently cited publications on the topic of quantum computing’s value to business and society, “Where Will Quantum Computers Create Value –and When?”, which forms the basis for a TED talk that will be released in 2021. As part of BCG’s “deep tech” initiative, he advises Fortune 500 companies on building quantum computing into their digital transformation roadmaps and is writing a series of articles on the topic by industry (“Will Quantum Computing Transform BioPharma RD?” and “It’s Time for Financial Institutions to Place Their Quantum Bets” are the first installments). He has addressed audiences at all the major conferences in the field (Q2B, IBM Q Summit, AI World, IEEE). He is a member of the U.S. Government’s Quantum Economic Development Consortium (QED-C). Beyond quantum computing, Matt publishes regularly on AI, startups and other topics at the nexus of science, business and policy.
LibGuides: So you want to know more about Serial Killers?: References

Are you interested in true crime, but don't know where to start? Are you a serial killer aficionado interested in what your library has to offer? You've come to the right place!
WATER INTAKE APPLET BASED ON HUMAN EXCREMENT

In order to function properly the human body requires adequate hydration due to 70% of the human body being built up by water with lots of chemical reactions involved in order to work optimally throughout the day. This paper presents an idea to make a water intake app based on human excrement surveillance. It might be a solution to intensify people's ability to become self-conscious when drinking less water by surveying the excreted substance. Currently, available software that measures one indicator which is between urine and feces detector on how much water should the user drink. But actually, both excrement indicators are needed to detect users' drinking amount. If one of these indicators shows a bad result, it could lead to water intoxication or hydration. The application was created using Python to give feedback regarding the user's water intake based on the condition of their excreted substance.
The Secured Client-Side Encrypted Data with Public Auditing in Cloud Storage

Cloud computing is rising worldview, empowering clients to remotely store their information in a server and give benefits on-request. In cloud computing cloud clients and cloud specialist organizations are practically sure to be from various put stock in areas. Information security and protection are the basic issues for remote information storage. A protected client authorized information get to control instrument must be given before cloud clients have the freedom to outsource touchy information to the cloud for capacity. Quality based encryption is an open key based encryption that empowers get to control over scrambled information utilizing access strategies and credited properties. In this paper, we are going to investigation different plans for encryption and conceivable answers for their constraints that comprise of Attribute based encryption (ABE), KP-ABE, CP-ABE, and Attribute-based Encryption Scheme with Non-Monotonic Access Structures. HABE.To secure outsourced information in cloud storage against debasements, adding adaptation to non-critical failure to cloud storage together with information trustworthiness checking and disappointment reparation winds up plainly basic. As of late, recovering codes have picked up prevalence because of their lower repair data transmission while giving adaptation to non-critical failure. Broad security investigation demonstrates that our plan is provable secure under arbitrary prophet show and test assessment shows that our plan is very proficient and can be practically coordinated into the recovering code-based cloud storage.