Reviewerly/exHarmony huggingface dataset:
A Probabilistic Approach to Neural Network Pruning

Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network. Many algorithms have been developed for pruning both over-parameterized fully-connected networks (FCNs) and convolutional neural networks (CNNs), but analytical studies of capabilities and compression ratios of such pruned sub-networks are lacking. We theoretically study the performance of two pruning techniques (random and magnitude-based) on FCNs and CNNs. Given a target network {whose weights are independently sampled from appropriate distributions}, we provide a universal approach to bound the gap between a pruned and the target network in a probabilistic sense. The results establish that there exist pruned networks with expressive power within any specified bound from the target network.
Dynamic Time-Division Multiple Access in Noisy Intermediate-Scale Quantum Networks

Quantum networks are networks composed of quantum processors that facilitate the exchange of information in the form of quantum bits, also called qubits. Qubits observe a physical phenomenon known as entanglement that enables the transmission of quantum information over long distances as well as the realization of novel protocols and applications that are impossible in classical networks such as the Internet. Due to the limitations of state of the art Noisy Intermediate-Scale Quantum (NISQ) devices, the establishment of entanglement over multi-hop networks demands strict coordination among the network nodes that connect two hosts. The delivery of entanglement in multi-hop quantum networks is further complicated when the network must support Quality of Service requirements for multiple users at the same time. The main challenges are ensuring that connected quantum processors agree when to establish entanglement with their neighbors and that processors use the correct pieces of entanglement to connect source/destination pairs. In this thesis, we propose a novel dynamic time-division multiple access (TDMA) method for multiplexing network resources used to connect multiple users in quantum networks. We investigate the behavior of scheduling heuristics in constructing the TDMA schedules and the effects of resource allocation on network performance. We additionally propose a novel scheduling problem and heuristic based on limited preemption that improves achievable network throughput in the case that devices may tolerate interruptions during connection of users.
PENGARUH EDUKASI MENGGUNAKAN BUKU INFORMASI KESEHATAN TERHADAP PENGETAHUAN DAN SIKAP REMAJA TENTANG KESEHATAN REPRODUKSI DI SMP N 1 SIBOLGA TAHUN 2020

Teenagers need to have knowledge about reproduction health. Not only take care of the organs’ health and function but the right information can make them avoid doing unexpected cases. The purpose of this research was to analyze the influence of education by using the health information books on the knowledge and attitude of the student in SMPN I Sibolga in 2020. The design of this research was one group pretest and posttest design.It was performed through lecture method and had them read the health information books during 2 days.The sample of this research was third year students with 70 members. They were taken by simple random sampling method.The data were got based their answers on the questionnaire of knowledge and attitude. The implemented assessement was Paired Simple T-Test. The result of this research showed that there was increase in their average score either knowledge and attitude after education. For knowledge weat up from 62,86 to 89,20 and 104,24 to 142,44 for attitude. So we could see they had 26,34 point increase in knowledge and 38,2 in attitude. The conclusion is education by using health information books gave a positive influence on the teenagers’ knowledge and attitude concerning with reproduction health. The p-value was gotten 0,000 < 0,05.Health service activities at schools especially about reproduction health of teenager need to do more and more in Sibolga.
LibGuides Home: Lyman Beecher Brooks Library Virtual Assistance (ENG 102.45A): Help for the Professor

This guide will show you how to search for various library resources to support your assignment..  Click on one of the tabs below to get more information
ИЧКИ ВА ТАШҚИ ТАҲДИДЛАРГА НИСБАТАН МУСТАҲКАМ МАФКУРАВИЙ ИММУНИТЕТ ШАКЛЛАНТИРИШ - МАЪНАВИЙ ХАВФСИЗЛИКНИ ИШОНЧЛИ ВОСИТАСИ СИФАТИДА

The article, along with many positive processes taking place in our social life, explores spiritual threats aimed at capturing the mind and consciousness of the younger generation, and ways to combat them. According to the results of scientific research in many fields, many of our young people suffer from the consequences of internal and external threats caused by their age-related psychological characteristics.Some aspects of the participation of military personnel in the formation of morale and the elimination of emerging threats are also mentioned.
Spin-entangled squeezed state on a Bloch four-hyperboloid

The Bloch hyperboloid H2 underlies the quantum geometry of the original SO(2, 1) squeezed states. In Hasebe (2020 J. Phys. A: Math. Theor. 53 055303), the author utilized a non-compact 2nd Hopf map and a Bloch four-hyperboloid H2,2 to explore an SO(2, 3) extension of the squeezed states. In the present paper, we further pursue the idea to derive an SO(4, 1) version of squeezed vacuum based on the other Bloch four-hyperboloid H4. We show that the obtained SO(4, 1) squeezed vacuum is a particular four-mode squeezed state not quite similar to the previous SO(2, 3) squeezed vacuum. In view of the Schwinger's formulation of angular momentum, the SO(4, 1) squeezed vacuum is interpreted as a superposition of an infinite number of maximally entangled spin-pairs of all integer spins. We clarify basic properties of the SO(4, 1) squeezed vacuum, such as von Neumann entropy of spin entanglement, spin correlations and uncertainty relations with emphasis on their distinctions to the original SO(2, 1) case.
Perception of Referencing and Plagiarism Detection Tools Among Students: A case study of Pondicherry University

The present study tries to assess the perception of referencing and plagiarism detecting tools among the students of Pondicherry University. The main objectives of the study are: to assess the perception of referencing among the students of Pondicherry University and to find out their awareness and use of reference management software and plagiarism detection tools. A questionnaire survey was conducted among the P.G. and Ph.D. students of the Pondicherry University. The results shows that majority of the respondents are aware of the word ‘referencing’ and are aware of various plagiarism detection tools like Turnitin and Urkund. More than half of the respondents are not aware of reference management software and a few of them always use Mendeley. Regarding the use of style manuals, the highest number of the respondents are using APA style manual followed by the MLA style manual.
High Performance SqueezeNext for CIFAR-10

CNNs is the foundation for deep learning and computer vision domain enabling applications such as autonomous driving, face recognition, automatic radiology image reading, etc. But, CNN is a algorithm which is memory and computationally intensive. DSE of neural networks and compression techniques have made convolution neural networks memory and computationally efficient. It improved the CNN architectures and made it more suitable to implement on real-time embedded systems. This paper proposes an efficient and a compact CNN to ameliorate the performance of existing CNN architectures. The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module and to develop a compact architecture with a competitive accuracy. Further, explores the bottleneck module and squeezenext basic block structure. The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture. The proposed architecture is further trained on the CIFAR-10 dataset from scratch. All the training and testing results are visualized with live loss and accuracy graphs. Focus of this paper is to make an adaptable and a flexible model for efficient CNN performance which can perform better with the minimum tradeoff between model accuracy, size, and speed. Finally, the conclusion is made that the performance of CNN can be improved by developing an architecture for a specific dataset. The purpose of this paper is to introduce and propose high performance squeezenext for CIFAR-10.
Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models

Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely used for students learning English as a Second Language (ESL) and building computational approaches to automatically solve such questions is beneficial to language learners. In this work, we propose a neural framework to solve SC questions in English examinations by utilizing pre-trained language models. We conduct extensive experiments on a real-world K-12 ESL SC question dataset and the results demonstrate the superiority of our model in terms of prediction accuracy. Furthermore, we run precision-recall trade-off analysis to discuss the practical issues when deploying it in real-life scenarios. To encourage reproducible results, we make our code publicly available at https://github.com/AIED2021/ESL-SentenceCompletion .
Gendang Pahang augmentation and technological symbiosis / Siti Khodijah Mohd Annuar and Mohd Kamal Sabran

Gendang Pahang is one of the traditional musical instruments that are less popular and known since modern music is more appealing to the generation today. Nowadays, there are many musical instruments that have been augmented to make it more interesting in this modern world; however, most of them are western or contemporary musical instruments while there are only a few studies that focused on augmented traditional musical instruments. This paper describes the development of Robotic Gendang Pahang, a technological innovation of traditional musical instruments using the original instruments. The aim of this paper is to describe the construction of the actuator control system and the mechanism of the beater to automatically hit the drums and gong without interfering with the original features of the instruments. This is to help introduce the traditional musical instruments to the public along with the real sound coming from the instruments itself without using any recording audio and real players. This paper also describes the prototype development using the prototyping method with few steps which are requirement gathering and analysis, preliminary design, building the prototype, initial evaluation, refining prototype and implementation. The results are discussed in terms of the design and performance analysis based on musical instruments’ robotic theory and future development
Improving the Quality of Laminates in Liquid Composite Molding Using Magnetic Compaction: Experiments and Process Model

Despite the extensive use of liquid composite molding (LCM) processes such as wet lay-up vacuum bagging (WLVB) and vacuum assisted resin transfer molding (VARTM) in composite manufacturing, they have two major drawbacks. First, the fiber volume fraction of the composite parts made by LCM is lower than those made under an elevated pressure using either autoclave or hot press, leading to lower mechanical properties. Second, the process induced defects in LCM parts are quite high, which may significantly reduce the mechanical performance and environmental durability of composites. The focus of this dissertation is to tackle the important problems encountered with WLVB and VARTM to improve the quality of molded parts.The first part of this dissertation introduces a novel technique, magnet assisted composite manufacturing (MACM), to improve the quality of WLVB laminates. In this technique, the composite lay-up is sandwiched between a magnetic tool plate and a set of Neodymium-Iron-Boron (NdFeB) permanent magnets during cure. The details and effectiveness of MACM process are investigated by fabricating of E-glass/epoxy composite laminates with and without magnetic pressure and comparing their void content and morphology, fiber volume fraction, and mechanical properties. The results clearly show that the quality of composite laminates is significantly improved in the presence of magnetic consolidation pressure, where the fiber volume fraction increases by more than 50% to almost 30% and process-induced voids decrease to less than 3%. As a result, the flexural strength and modulus of the parts are enhanced by approximately 60% and 50% to ~245 MPa and ~10 GPa, respectively.The second part of the dissertation extends the application of the MACM technique to fabricate high-quality VARTM laminates. In VARTM, unlike the WLVB process, the preform impregnation takes place under vacuum, which results in different mechanisms of void formation and different ranges of fiber content. Thus, enhancing VARTM is quite different than enhancing the WLVB process which provides the motivation to investigate the effectiveness of utilizing MACM in VARTM. In this regard, thin (i.e. 6-ply), as well as moderately thick (i.e. 12- and 18-ply) E-glass/epoxy laminates are fabricated by applying MACM either before or after infusion. The results prove the effectiveness of MACM in fabricating high-quality VARTM laminates where a fiber volume fraction of more than 50% and void content of less than 1% is achieved. In addition, a transient magnetic consolidation model is developed, predicting the final thickness and fiber volume fraction of the VARTM/MACM parts.The third part of this dissertation introduces a novel technique of compacting dry fibrous reinforcement to control the resin flow rate, thus eliminating the void formation in VARTM parts. In this technique, the fibrous preform is compacted by either stationary or moving magnets prior to resin infusion. As a result, the pore size between the fabric layers and permeability are reduced, and the…
Artificial Intelligence based Intelligent Tourism System-Go My Way

At the moment, we find that tourists usually spend more time planning their trip because they need to spend every minute. In this context, this application aims to identify the main computer needs to support the improvement of the tourist promotion point, using the mobile application proposal. Currently, for regular tourists and travelers they spend a lot of time planning and deciding on their trip to achieve maximum satisfaction. In this case, the app aims to identify the main computer needs to support the development of the tourist promotion point. This paper suggests a model for use in an intelligent visitor information system. It uses the concept of knowledge-base. The model will be based on a study of human behavior as a tourism guide. It builds a relationship between an information-based system and a guide, to provide a service to any visitor who meets their needs and the purpose of obtaining location information. There are different modules, different methods of acquisition methods and a shorter way to acquire the ingenuity of the artificial intelligence in this thesis. The proposed system should be designed in such a way that it works on most devices namely palmtop and mobiles. So it can be helpful when visiting new places. This application will find the route using user terms. The short-term method of finding an algorithm should work well and in the right way in most cases. The system must find a method that fulfills the user's terms, indicating the name of the item, images related to a brief description of the location. It should also be able to find the distance, time and travel costs to your destination and over time the user can also make bookings using the app interface only.
Devassas em devassa: uma fonte para a reconstituição da história da cultura escrita no Brasil Colonial e Imperial

The present study aims to address the multiplicity of research fields made possible by the documentary typology devassa from the analysis of the characteristics identified in the manuscripts that are part of the collection of the historical event entitled Conjuration Baiana, an important movement of revolutionary intent that took place in colonial Brazil in the 18th century which motivated vast documentary production currently held, mostly, by the Public Archive of the State of Bahia and by the Manuscripts Division of the National Library. Alluding to the need for a rigorous scientific systematization that takes care of the preparation of this type of material, at the level of paleographic and diplomatic editing, which values the scientific rigor that it demands, the aim is to draw the attention of researchers to the need for editions of historical sources are carried out in order to guarantee the reliability of the original sources, establishing a bridge between the physical and digital document.
Imaginaries of Digital Culture in Publics Schools

In Colombia, technology has gained importance in education, especially in schools that aim to develop innovative knowledge, with teaching models that integrate ICT. This article focuses its objective on the approach on the imaginaries that young students from the department of Magdalena in Colombia have regarding digital culture in their educational contexts. The methodology was based on a qualitative approach, on a hermeneutical epistemological basis and a case study inquiry strategy with an ethnographic perspective. The techniques applied were the documentary review, focus group and survey. The results reflected students from rural and urban institutions relate digital culture to the customs of a society linked to technologies. It is concluded that it is necessary to know the realities of the population when formulating educational policies related to the incorporation of ICT in school environments.
Structured World Belief for Reinforcement Learning in POMDP

Object-centric world models provide structured representation of the scene and can be an important backbone in reinforcement learning and planning. However, existing approaches suffer in partially-observable environments due to the lack of belief states. In this paper, we propose Structured World Belief, a model for learning and inference of object-centric belief states. Inferred by Sequential Monte Carlo (SMC), our belief states provide multiple object-centric scene hypotheses. To synergize the benefits of SMC particles with object representations, we also propose a new object-centric dynamics model that considers the inductive bias of object permanence. This enables tracking of object states even when they are invisible for a long time. To further facilitate object tracking in this regime, we allow our model to attend flexibly to any spatial location in the image which was restricted in previous models. In experiments, we show that object-centric belief provides a more accurate and robust performance for filtering and generation. Furthermore, we show the efficacy of structured world belief in improving the performance of reinforcement learning, planning and supervised reasoning.
Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine & Market Topics & Sentiment

Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts(KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.
Extracting Appointment Spans from Medical Conversations

Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan.In this paper, we introduce a novel task of extracting appointment spans from medical conversations.We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time.However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise.Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models.The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32.
ECC Data Hide – Using Steganoraphy and Deep Neural Network

In the field of computer networks, cryptography and steganography are the well-known features for best security purpose. The main idea is to transmit the data securely. So, providing acceptable level of security is essential for data transmission. Also it should reduce the time complexity of the security algorithm. Here we have employed the “Elliptic Curve Cryptography” scheme to encrypt the data and image. A “Least Significant Bit” steganography algorithm is used to insert the encrypted data to be hidden inside the image in order to send the data securely. The encrypted data from the image is then decrypted by the decryption algorithm. Finally the hidden data is taken from the decrypted data. Then the image is compressed before sending through the internet. MATLAB is utilized to mimic outcomes which show that it has great inserting limit and security.