Reviewerly/exHarmony huggingface dataset:
Financing System Using SOA and EA

In other studies that have been conducted, Enterprise Architecture (EA) and Service-Oriented Architecture (SOA) have a relationship. Through this research we try to build a prototype of an SOA-based finance system using the provided EA documents. The research sample is an EA document from a multi finance company in Indonesia. The prototype result can be successfully carried out using BPM and ESB tools.
Evolución de la teoría del aprendizaje significativo y su aplicación en la educación superior

This article reviews the central tenets of David Ausubel's classic perspective on the theory of significant learning. Subsequently, an analysis of its theoretical evolution. Finally, its most important applications, implications and principles of teaching work for higher education were determined. The results obtained show that: previous knowledge should always be considered as the isolated variable that most influences the learning of new contents. The professional exercise implies the resolution of diverse situations or problems according to the knowledge and its degree of connection with reality. If the university student is able to apply the training received in the classroom in an active way and not as a mere memorization of texts, then we will really talk about a successful educational process. The student's predisposition to learn is one of the two key conditions for meaningful learning. The human factor involved in higher education is consolidated in integral formation, avoiding mechanical learning. Concept maps and mental models acquire great importance, because they are built from the knowledge that the individual already has in his cognitive structure, from what he perceives and from the new situation. The mediation that leads to the capture of meanings is no longer only human and semiotic, it also includes technological components. University teachers must promote critical and even self-critical meaningful learning, facilitating the transmission of declarative, procedural and attitudinal curricular content.
Values and aspirations as predicates of professional development of a human being

Professional work in the modern world has become an important value, and for some individuals even the most important. Professional development fits into the personal development of a man and creates an important component of his activity. It is difficult to consider professional development in isolation from personal development. These are processes that, at a certain time in life, run in parallel, permeate and overlap each other. It is therefore important to take into account the values and aspirations of an individual as determinants of the choices made, first educational, then professional. These values serve to formulate aspirations and life goals. Assessment of the associated hierarchy of values is part of the strategy of naming the right life choices, it serves to develop human self-awareness.
Asstech Application Based E-Learning System to Improve The Quality Of 21st Century Inclusive Education

This study aims to describe digital optimization E-learning system based on ASSTECH to improve the quality of 21st century Inclusive education. E-Learning system is an effort digital to facilitate the learning process and strengthening values in each subject to students who have special needs. In the context of inclusive education, E-learning systems based on adjusted to the characteristics and needs of students, such as JAWS functions to change text into sound, i-CHAT to help strengthen hearing, websites and other learning media designed for students with special needs. This study uses a qualitative approach that seeks to obtain and collect and process, analyze and define data qualitative. The qualitative approach in this research various library sources such as prociding papers, journals, newspapers or magazines, reference books and so on which are then constructed as a critical thinking framework so that this approach is expected able to provide accurate about E-Learning Based Learning Technology Asstech in improving the quality of 21st century inclusive education. Keywords : e-learning system ; ASSTECH; inclusi ve education .
BLOCKCHAIN TECHNOLOGIES AND APPLICATIONS

Blockchain is a technology that uses community validation to synchronize the content of ledgers replicated by multiple users. Although Blockchain derives its origins from technologies introduced decades ago, recently it has received an astonishing amount of attention in both academic and industry due to its characteristics of decentralization, point-to-point transmission, transparency, traceability, non-tampering, and data security. Both researchers and practitioners have recognized that Blockchain can be used to solve complex technical or socio-economic problems. In recent times, a number of blockchain-based applications have been released in the literature and Blockchain's approaches are used to other cases. For example, as an emerging distributed architecture and computing paradigm, Blockchain technologies have accelerated the development/application of the Internet of Things, Artificial Intelligence, Cloud/Edge Computing, Social Networking, Finance, Insurance, Healthcare, and Supply Chain Management. The popularity and rapid development of Blockchain bring many technical and regulatory challenges for research communities. There is a pressing need to balance between technical and regulatory requirements.
Efficient Decentralized Random Commitment Key Generation for Mixnet Shuffle Proof

In this paper, we propose a new commitment key generation method for the mixnet shuffle proof developed by Bayer-Groth in Eurocrypt’ 12. The problem of the shuffle proof algorithm is that it gives too much power to a single authority: It has been shown that the authority, who creates commitment keys and generates proofs for verifying electronic voting (e-voting) results, also can produce malicious verification proofs by logging the exponents of commitment keys. We suggest a new way to decentralize the commitment key generation process by allowing multiple parties to jointly participate in the commitment key generation. Therefore, any of the parties, even who operating e-voting system, cannot know the exponents of commitment keys fully. Therefore, our suggestion distributes the power that is concentrated on the single authority and makes the verification process of the proof more sound and prudent.
Braj in the Ergativity Hierarchy

In generative literature, it is often assumed that closely related varieties differ minimally due to local effects of features (Barbiers 2009, Kayne 2000, 2013), with these small-scale differences located on the lower end of the Parameter Hierarchy (Biberauer and Roberts 2012, Roberts 2012). This paper re-visits one such hierarchy on ergativity (Sheehan 2017) by presenting novel dialectal data from Braj (Indic). Specifically, we illustrate that (a) ergative micro-variations in the language ensue not from feature level variation, but from underlying syntactic structures which in turn, have consequences for a v+erg head; these structural changes, however, affect a small domain, and (b) there exist cases of intra-dialectal case alignment variation, unpredicted by the hierarchy, which we relate to a phi-complete T head that is gradually superseding the v+erg head.
Streaming Performance Analysis of Virtual Objects in Augmented Reality Cloud Based on X86 and ARM Processor Types

Augmented Reality is a technology that makes it possible to create 3D objects in the real world with the help of a camera on a gadget. In its development, Augmented Reality technology began to be developed and combined with cloud computing technology. With the existence of Cloud computing technology Augmented reality can work more efficiently because all 3D objects can be stored on the Cloud. With the development of this technology, the author feels the need to conduct an analysis to determine the right processor in making the Augmented Reality Cloud application, because there is no research that is used as a reference in developing hardware and software used for Augmented Reality Cloud. Result of this experiment can be concluded CISC architecture processors have faster time in detecting markers compared to RISC architecture processors, because CISC processor speeds are faster than RISC processors.
Generative Adversarial Models for Unsupervised Domain Adaptation in Semantic Segmentation

In this thesis we implement an unsupervised domain adaptation framework designed for semantic segmentation and tested in a synthetic-to-real adaptation scenario. First, we propose a pixel-level adaptation strategy based on a cross-domain image mapping to transfer visual attributes from an unlabelled target dataset to a fully annotated source one. In a second moment, we introduce an additional feature-level adaptation to enforce feature distribution alignment between source and target domains.
Information Losses in Neural Classifiers With Applications to Training Data Selection Strategies and Cyber Physical Systems

Author(s): Foggo, Brandon James | Advisor(s): Yu, Nanpeng | Abstract: This dissertation considers the subject of information losses arising from finite datasets used in the training of neural classifiers. It proves a relationship between such losses and the product of the expected total variation of the estimated neural model with the information about the feature space contained in the hidden representation of that model. It then bounds this expected total variation as a function of the size of randomly sampled datasets in a fairly general setting, and without bringing in any additional dependence on model complexity. It ultimately obtains bounds on information losses that are less sensitive to input compression and much tighter than existing bounds. It then uses these bounds to explain some recent experimental findings of information compression in neural networks which cannot be explained by previous work. The dissertation goes on to provide analytical derivations for the relationship between neural architectures and the mutual information contained in their representations, which can be useful for guided architecture selection schemes. It then uses these developments to propose and illustrate a new framework for analyzing training data selection methods. The dissertation use this framework to prove that facility location methods reduce these losses, and then derive a new data dependent bound on them. This bound can be used to evaluate datasets and acts as an additional analytical tool for the study of data selection techniques. The dissertation then applies this theory to the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by exploiting some of the problem's information theoretic properties. This focus, along with the advances developed earlier in this work, helps us create two new Phase Identification techniques. The first transforms the bound on information losses into a data selection technique. This is important because phase identification data labels are difficult to obtain in practice. The second interprets the properties of distribution systems in the terms of the information losses developed earlier in the dissertation. This allows us to obtain an improvement in the representation learned by any classifier applied to the problem. Furthermore, since many problems in cyber-physical systems share similarities to the physical properties of phase identification exploited in this dissertation, the techniques can be applied to a wide range of similar problems.
An Interactive Perspective Scene Completion Framework Guided by Complanate Mesh

This paper presents an efficient interactive framework for perspective scene completion and editing tasks, which are available largely in the real world but rarely studied in the field of image completion.Considering that it is quite hard to extract perspective information from a single image, this work starts from a friendly and portable interactive platform to obtain the basic perspective data.Then, in order to make this interface less sensitive, easier and more flexible, a perspective-rectification based correction mechanism is proposed to iteratively update the locations of the initial points selected by users.At last, a complanate mesh is generated by the geometry calculations from these corrected initial positions.This mesh must approximate the perspective direction and the structure topology as much as possible so that the filling process can be conducted under the constraint of the perspective effects of the original image.Our experiments show the results with good qualities and performances, and also demonstrate the validity of our approaches by various perspective scenes and images.
An Implicit Attention Mechanism for Deep Learning Pedestrian Re-identification Frameworks.

The \emph{receptive fields} of deep learning classification models determine the regions of the input data that have the most significance for providing correct decisions. The primary way to learn such receptive fields is to train the models upon masked data, which helps the networks to ignore any unwanted regions, but has two major drawbacks: 1) it often yields edge-sensitive decision processes; and 2) augments the computational cost of the inference phase considerably. This paper describes a solution for implicitly driving the inference of the networks' receptive fields, by creating synthetic learning data composed of interchanged segments that should be \emph{apriori} important/irrelevant for the network decision. In practice, we use a segmentation module to distinguish between the foreground (important)/background (irrelevant) parts of each learning instance, and randomly swap segments between image pairs, while keeping the class label exclusively consistent with the label of the deemed important segments. This strategy typically drives the networks to early convergence and appropriate solutions, where the identity and clutter descriptions are not correlated. Moreover, this data augmentation solution has various interesting properties: 1) it is parameter-free; 2) it fully preserves the label information; and, 3) it is compatible with the typical data augmentation techniques. In the empirical validation, we considered the person re-identification problem and evaluated the effectiveness of the proposed solution in the well-known \emph{Richly Annotated Pedestrian} (RAP) dataset for two different settings (\emph{upper-body} and \emph{full-body}), observing highly competitive results over the state-of-the-art. Under a reproducible research paradigm, both the code and the empirical evaluation protocol are available at \url{this https URL}.
Sualtı Kurbağa Yüzüşünün Bazı Kinematic Özellikleri

Under liquid swimming for the robots is extremely interesting. In this context one can think of deep sea beds, oil deposits, acid tanks, etc. The next generation of robots will be based on animals rather than humans. If we consider the underwater swimming robots, swimming tecniques of frogs are as worthy as fishes. Their underwater motion is trust-drag based. By using the hydrodynamic equations of experimantal results of frogs’ underwater swimming, we obtain the speed and the distance for such a motion.
NER Models Using Pre-training and Transfer Learning for Healthcare.

In this paper, we present our approach to extracting structured information from unstructured Electronic Health Records (EHR) [2] which can be used to, for example, study adverse drug reactions in patients due to chemicals in their products. Our solution uses a combination of Natural Language Processing (NLP) techniques and a web-based annotation tool to optimize the performance of a custom Named Entity Recognition (NER) [1] model trained on a limited amount of EHR training data. This work was presented at the first Health Search and Data Mining Workshop (HSDM 2020) [26]. We showcase a combination of tools and techniques leveraging the recent advancements in NLP aimed at targeting domain shifts by applying transfer learning and language model pre-training techniques [3]. We present a comparison of our technique to the current popular approaches and show the effective increase in performance of the NER model and the reduction in time to annotate data.A key observation of the results presented is that the F1 score of model (0.734) trained with our approach with just 50% of available training data outperforms the F1 score of the blank spaCy model without language model component (0.704) trained with 100% of the available training data. We also demonstrate an annotation tool to minimize domain expert time and the manual effort required to generate such a training dataset. Further, we plan to release the annotated dataset as well as the pre-trained model to the community to further research in medical health records.
Efficient Multi-party Computation Algorithm Design for Real-World Applications

Secure Multi-Party Computation (MPC) is a promising privacy-preserving technology to enable multiple trustless parties to compute a function jointly without revealing private inputs to each other. With the fast development of MPC protocols, software implementation, and underlying computation infrastructure, MPC has developed from purely theoretical interest to tangible platform implementations for real-world applications. In this paper, we investigate multiple mechanisms to design efficient MPC algorithms by avoiding costly MPC operations and leveraging parallel operations. In order to speed up database table searching, a machine learning-based approach is proposed to completely avoid equality-check operations, playing the trade-off between efficiency and accuracy. According to our experimental results, a well-designed MPC algorithm could improve performance and scalability significantly, and thus make MPC technology practicable.
RGB - based Obstacle Detection and Avoidance for an EMG - Controlled Wheelchair

Many people suffer from limb functionality impairment due to neuromus-cular disease, stroke or trauma. This strong reduction of independencymakes 24-hour care necessary. Various assistive devices have been devel-oped which provide support in every-day tasks and enhance mobility. Fo-cusing on wheelchairs, many different steering methods are provided in-cluding joysticks and human-machine interfaces (HMI). However, control-ling a mobile platform through an HMI can be highly dif�cult and inaccu-rate. Shared control strategies support steering through HMIs with assis-tance during dif�cult maneuvers.This work enhances the shared control approach of the mobile wheelchair'EMG-controlled Daily Assistant' (EDAN). An obstacle detection and avoid-ance facilitates the steering to the user. The Obstacle Detection segmentsobjects from a depth image provided by an RGB-D camera and creates a2D obstacle map. The Obstacle Avoidance adjusts the steering commandsof the user to circumnavigate obstacles. Based on an Arti�cial PotentialField repulsive forces are computed which push the robot away from ob-stacles. Narrow passages are traversed with the support of a suction forcewhich pulls the robot through them. Obstacles outside the �eld of vieware kept in memory to prevent the wheelchair from rotating into them.The Obstacle Detection and Avoidance offers support for avoiding obsta-cles and traversing corridors. Further work is required to improve the re-quired parameter set and the robustness of the algorithm so that it canbe utilized in the daily life of a user. Different scenarios, like bypassing anobstacle, traversing a corridor and approaching a wall, are evaluated insimulation and on EDAN.
Ethical Climate and Organizational Citizenship Behaviors: the Mediating Role of Organizational trust

In this paper, we study the effects of ethical climate on Organizational Citizenship Behaviors (OCB) and the mediation role of organizational trust.The empirical verification of the conceptual model was conducted through a survey of 283 employees in telecommunication services in Morocco.The structural equation modeling approach was used to test the proposed hypotheses.The main results of this paper indicate the existence of a positive and significant relationship between ethical climate and OCB.Also, organizational trust fully mediates the relationship between ethical climate and OCB.Research results were discussed and managerial implications were outlined.
Web Search Personalization Using Semantic Similarity Measure and Exploring Annotation based Web to Enhance Information Retrieval

Over the past few decades, search engines have turned out to be the dominant source to retrieve the information online. However, despite the most successful search engines, approximately fifty percent of search sessions fail to find the relevant results. Thus we emphasize on storing the data on every users’ search activities: The queries they submit and results they select. By using these data, we implement a relevance model that guides the promotion of relevant results during regular Web search. We demonstrate how to devise and implement such a system in practice which retrieves result in semantically personalized set of recommendations.
Use of mobile phones for library assistance

Sweeping changes in the knowledge search behavior of users are triggered with the rapid growth and use of the internet. Libraries are increasingly reliant on providers of server and library technology to provide mobile software solutions and digital content pre-packaged access. The user base of mobile devices (mobile phones, palmtops, PDAs, etc.) also increases the demand for access to internet services for such devices. On the other hand, university library services must concentrate at material that is most used for off-site users, such as library hours, an online public database, learning guides, and instruction aids. Mobile library services thus allow users to download and study materials published on user mobile devices at anytime and anywhere by the university library.
Face forgery detection using conditional GAN

Face forgery in videos is an increasing threat due to an easy access to manipulation tools. This work propose a Deep Learning approach to generate a mask showing the region of a tampered face. A Conditional Generative Adversarial Network is used,a total of 16 features are extracted from the video and used as input of the network. This method is tested on videos from YouTube and a dataset from Google and Jigsaw. The results are extremely promising, although there is still room for improvements.
Measuring Classifier Performance with Risk and Error Matrix Charts

Measuring classifier performance is important in machine learning. Risk charts and error matrix charts have been developed for this purpose. The strengths and weak-nesses of using these charts are outlined. Challenges with using these charts are dis-cussed including how base rates and using prevalence data for building models and incidence data for evaluating models affect model performance. A number of solu-tions for overcoming these challenges are covered.
Guides: Computer Science: Tips for finding books

Explore library and Internet resources for any topics related to Computer Science and Computer Information Systems.
Libraries: MODfest 2020: MODFEST 2020: INTRODUCTION

Celebrating its 18th season, Modfest 2020 is Vassar College’s annual exploration of the arts of the 20th and 21st centuries. January 30-February 9, 2020