Reviewerly/exHarmony huggingface dataset:
Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

The unprecedented growth of unstructured data poses many challenges in semantic computing, which is an active research area for many years. While unearthing interesting patterns such as entities, relationships, and other metadata are important, it is equally important to represent them in an efficient, easy to access manner. Knowledge Graphs (KGs) are one such mechanism to represent facts extracted from unstructured text. KGs represent entities as nodes and relationships as edges. Such a representation may find applications in many meaning-aware computing applications such as question answering, summarization, etc., to name a few. Very recently, knowledge graph-based recommendation systems have become popular which has many advantages over traditional recommendation engines. This survey is an attempt to summarize and critically evaluate some of the very recent approaches to knowledge graph-based recommendation approaches.
Robustness Certification of Generative Models

Generative neural networks can be used to specify continuous transformations between images via latent-space interpolation. However, certifying that all images captured by the resulting path in the image manifold satisfy a given property can be very challenging. This is because this set is highly non-convex, thwarting existing scalable robustness analysis methods, which are often based on convex relaxations. We present ApproxLine, a scalable certification method that successfully verifies non-trivial specifications involving generative models and classifiers. ApproxLine can provide both sound deterministic and probabilistic guarantees, by capturing either infinite non-convex sets of neural network activation vectors or distributions over such sets. We show that ApproxLine is practically useful and can verify interesting interpolations in the networks latent space.
LibGuides: Accessing and Using NIC Digital Resources: Using a Mobile Device for Classes

This guide provides how-to's, links, and relevant information for accessing and utilizing the various software, internet programs, and more that are provided by NIC and used in class instruction. How to best utilize the Canvas app and attempt to complete assignments using your mobile device.
USING SCANNING TECHNIQUE IN READING AT THE ELEVENTH GRADE OF SMK TIWIKRAMA MAMUJU

The objective of the research is to find out the use scanning technique effective in improving the Students’ Reading Ability at the eleventh grade students’ of SMK Tiwikrama Mamuju. This research took place at SMK Tiwikrama Mamuju which the researcher used pre-experimental. Population of research was the eleventh grade students of SMKTiwikrama in academic year 2019/2020. Sample of the research was 30 students that take by purposive sampling technique. The researcher used reading test as instrument of the research. Result of data analysis showed that t-table value at n-1 = 30-1=29 (at significant level α = 0,05) was 2,045 and t-test value was 25,57. It showed t-test was higher than t-table, it meant that null hypothesis was rejected and alternative hypothesis was accepted. Therefore, the researcher concluded that the use of Scanning technique could improve the students’ reading ability at the eleventh gradeof SMK Tiwikrama. It was supported result of students’ mean score at pre-test and post-test. Which mean score of pre-test was 49,17(very poor score) improved to 75 (good score) at the post-test.
Brazil Round 17 — Great opportunities in the near-field and the frontier

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Multiple clustering solutions using FK-means Algorithm

Most of the clustering algorithms try to find a single efficient clustering solution. However, data can be grouped and interpreted in many different ways. Moreover, different efficient clustering solutions are interesting for different views. This is particularly true in the high dimensional, where different views reveal different possible groupings of the data. In this paper, we apply multivariate mutual information to find multiple views for the data by grouping features. Then we apply the Fast K-means (FK-means) algorithm to these different views to find multiple clustering solutions. Our experiments on synthetic and real data show that our method can discover multiple views of the data that correspond to different solutions
Sistem Pemantauan Kendaran Rental Berbasis Global Positioning System (GPS) dengan Kendali Jarak Jauh

The transportation service supply industry is growing rapidly lately. Starting from online motorcycle taxis, online taxis, to vehicle rental service providers, both motorbikes and cars. Car service providers are usually used for personal and public use with a large capital so security needs to be added to the car. This safeguard is in anticipation of customers who intentionally want to commit criminal acts by escaping the vehicle they rent. Therefore, in this study, a rental vehicle monitoring system was made that is able to send its location if there is an indication of fraud by the customer. This system is equipped with the Global Positioning System (GPS) uBlox Neo 6 and is integrated with the Arduino Uno microcontroller and GSM SIM900A module so that it is able to update location data through Short Message Service (SMS). In addition, this system is equipped with an internal storage module (data logger) so that it can record the trip of the rental vehicle according to the owner's order. From the test results that have been done the system is able to provide location information with an average distance difference of 12.184 meters from the actual position. In addition, the system is able to send its position via SMS with an average delivery time of 6.194 seconds.
Inexact and Stochastic Generalized Conditional Gradient with Augmented Lagrangian and Proximal Step

In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in the authors' previous paper, which we denote ICGALP, that allows for errors in the computation of several important quantities. In particular this allows one to compute some gradients, proximal terms, and/or linear minimization oracles in an inexact fashion that facilitates the practical application of the algorithm to computationally intensive settings, e.g. in high (or possibly infinite) dimensional Hilbert spaces commonly found in machine learning problems. The algorithm is able to solve composite minimization problems involving the sum of three convex proper lower-semicontinuous functions subject to an affine constraint of the form $Ax=b$ for some bounded linear operator $A$. Only one of the functions in the objective is assumed to be differentiable, the other two are assumed to have an accessible prox operator and a linear minimization oracle. As main results, we show convergence of the Lagrangian to an optimum and asymptotic feasibility of the affine constraint as well as weak convergence of the dual variable to a solution of the dual problem, all in an almost sure sense. Almost sure convergence rates, both pointwise and ergodic, are given for the Lagrangian values and the feasibility gap. Numerical experiments verifying the predicted rates of convergence are shown as well.
Predictive Uncertainty Estimates in Batch Normalized Neural Networks

Recent developments in Bayesian Learning have made the Bayesian view of parameter estimation applicable to a wider range of models, including Neural Networks. In particular, advancements in Approxi ...
Deep reinforcement learning collision avoidance using policy gradient optimisation and Q-learning

Usage of trust region policy optimisation (TRPO) and proximal policy optimisation (PPO) 'children of policy gradient optimisation method' and deep Q-learning network (DQN) in Lidar-based differential robots are proposed using Turtlebot and OpenAI's baselines optimisation methods. The simulation results proved that the three algorithms are ideal for obstacle avoidance and robot navigation with the utter advantage for TRPO and PPO in complex environments. The used policies can be used in a fully decentralised manner as the learned policy is not constrained by any robot parameters or communication protocols.
Making the Dead Alive : Dynamic Routines in Risk Management

Risk management in information security is relevant to most, if not all, organizations. It is perhaps even more relevant considering the opportunities offered by the digitalization era, where relia ...
Prosody in General American English / Prosody in GA

Intercampus session held on April 30th at 20:15 / Intercampus webconferernce held on April 30th 2020.
AUTOMATED APPROVAL SYSTEM USING SERVICENOW

The project Automated Approval System includes students account registration in the portal where they can apply for certificates like No Fee Due,Bonafide etc and also for ID Card.Each one has a different process and requires approvals of respective authorities to generate them.The software is accessed by the admin and also users can access by username and a password given by the admin. The data can be added, updated, deleted and retrieved easily. A user-friendly interface has been designed and the data is also protected. This software is build using ServiceNow platform. ServiceNow is a cloud-based software platform which supports ITSM. It enables to automate IT Business Management (ITBM). It offers power, flexibility and dependability to achieve the objectives of the incident and problem management.
Efficient 4-way Vectorizations of the Montgomery Ladder.

We propose two new algorithms for 4-way vectorization of the well known Montgomery ladder over elliptic curves of Montgomery form. The first algorithm is suitable for variable base scalar multiplication. In comparison to the previous work by Hisil et al. (2020), it eliminates a number of non-multiplication operations at the cost of a single multiplication by a curve constant. Implementation results show this trade-off to be advantageous. The second algorithm is suitable for fixed base scalar multiplication and provides clear speed improvement over a previous vectorization strategy due to Costigan and Schwabe (2009). The well known Montgomery curves Curve25519 and Curve448 are part of the TLS protocol, version~1.3. For these two curves we provide constant time assembly implementations of the new algorithms. Additionally, for the algorithm of Hisil et al. (2020), we provide improved implementations for Curve25519 and new implementation for Curve448. Timings results on the Haswell and Skylake processors indicate that in practice the new algorithms are to be preferred over previous methods for scalar multiplication on these curves.
FIU Libraries: Rhetoric and Composition Research Guide: Process: Evaluating Information

This is a reference list of resources so that you can conduct research within the field of writing and rhetoric.
Leveraging Topic Models with Novel Word Embeddings for Effective Document Clustering

A large amount of data is stored in flat files. They are documents that are subjected to machine learning for clustering, classification and host of other operations. In all such use cases of text mining, there is need for representing documents for efficient processing. One such model which is widely used is known as latent Dirichlet allocation (LDA). This model is the basis for deriving many other models. For instance, topic models came into existence to perform document clustering. Many existing solutions suffer from the lack of sufficient word embeddings. To overcome this problem, in this paper, we propose a generative process model derived from LDA. Its novelty is that it exploits Word2vec for latent feature vector representations. These are from pre-trained word embeddings obtained from large document corpus. The proposed model leverages topic model and improves the performance of document clustering. It is evaluated with a prototype that shows utility of the proposed model.
Extraction of Tumor Chunk Using Image Segmentation: Thresholding and HSV Color Space

Hue–saturation–value (HSV) color space has proved efficiently by extracting a colored object using image segmentation. The study involves extracting the image, tumor chunk from brain tumor X-ray, or MRI for further analysis—brain tumor type, size, and duration of the tumor. Applying image blur and erosion segments the tumor in the image. The segmentation deals with the intensive study of the image dataset where convolution and thresholding take place. The more number of epochs is applied for training the brain tumor image; the better, accurate, and efficient results are obtained. In this study, the results showed the difference for the epochs applied as 20 and 50 with steps per epochs as 2000 and 8000. Hence, HSV color space thresholding produced 99% accuracy in extracting the image. The least loss: 0.0032 and best accuracy: 0.9993 were obtained on training the image dataset for 50 epochs.
THE FACTORS INFLUENCING GROUP COHESION AT HUMANITARIAN AND TECHNICAL STUDENTS’ GROUPS

The article studies the factors influencing group cohesion at humanitarian and technical students’ groups. The survey included 324 students of Vasily Stus National University in Donetsk and Vinnitsa State Pedagogical University. The study was carried out using the following methods: the method estimating development of small groups, Rogers-Diamond techniques estimating social and psychological adaptation, Schwartz Value Survey, Leontiev’s test of life-meaningful orientations, Guildford's “Social Intelligence” test, and Danilchenko’s subjective well-being technique. These methods allowed measuring and exploring the features of group cohesion, as well as the characteristics of a group and personal characteristics of its members. SPSS statistical package was used for data processing, in particular, frequency, correlation and regression analyses. We found out from the performed research that groups of student in different specialties had different characteristics. Humanitarian students were characterized by high group development, adaptation, self-acceptance, acceptance of others, emotional comfort, internality, high awareness and emotional intelligence, they assessed the psychological climate in their groups as good. Humanitarian students were more successful communicators. They could use the maximum information about people’s behaviour, understood the non-verbal communicative language, and successfully predicted behaviour of others. In turn, students in technical specialties were characterized by a strong desire to dominate, escapism and a desire for security. The performed regression analysis showed that the factors of group cohesion of humanities students and technical students were also different. Group cohesion of humanitarian students was determined exclusively by group motivation. Technical students needed group motivation, a tendency toward affiliation and a positive assessment by their group. These factors will determine in the future an individual’s social status in a group. &nbsp
Earthquake rupture through a step-over fault system: A case study of the Leech River Fault, southern Vancouver Island

The Leech River fault (LRF) zone located on southern Vancouver Island is a major regional seismic source. We investigate potential interactions between earthquake ruptures on the LRF and the neighboring Southern Whidbey Island fault (SWIF), which can be interpreted as a step-over fault system. Using a linear slip-weakening frictional law, we perform 3D finite element simulations to study rupture jumping scenarios from the LRF (source fault) to the SWIF (receiver fault), focusing on the influences of the offset distance, fault initial stress level, and fault burial depth. We find a smaller offset distance, a higher initial stress level on either fault or a shallower fault burial depth will promote rupture jumping. Jumping scenarios can be interpreted as the response of the receiver fault to stress perturbations radiated from the source fault rupture. We demonstrate that the final rupture jumping scenario depends on various parameters, which can be collectively quantified by two keystone variables, the time-averaged Over Stressed Zone (where shear stress exceeds static frictional strength on the receiver fault) size $\overline{R_e}$ and the receiver fault initial stress level. Specifically, a smaller offset distance, a higher initial shear stress level, or a shallower burial depth will lead to a larger $\overline{R_e}$. The seismic moment on the receiver fault increases with increasing $\overline{R_e}$. When $\overline{R_e}$ reaches the threshold dependent on the receiver fault initial stress level, the rupture becomes break-away.
Extract value from your unstructured geoscience data

Preview this conference paper: Extract value from your unstructured geoscience data, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2020/restech2020/202RESTECH10-1.gif
국내 외국인 유학생의 역선교 전략

This study is on the reverse dispatch mission strategy of foreign students studying at the graduate school of Seoul Christian University (SCUGS). First of all, this study examines the factors affecting missionary environment and focuses on the correlation between psychological factor, social factor, faith factor and personal vision factor on mission strategy. It is also meaningful that it can be a virtuous paradigm for missionary strategy to maximize the point of contact by establishing a Korean language school in Southeast Asian countries, establishing a link system with SCUGS, seeking situational awareness and long-term missionary strategies, supplementing and correcting problems of local missionary work, and sending back to the country.
Authentication for Computer Communication &gt; An Overview of Authentication for Computer Communications

Authentication is the first and foremost security principle that involves validation of identity of an user or a machine. The successful authentication is required for authorization and secure data exchanges. A common classification of authentication system based on factors – something you know, something you own, something you did and something you are – is explained. Well-known and used password schemes employed in practice is described along with standards. A description Kerberos of authentication system based on symmetric key encryption and SSL based authentication which is based on public key encryption is given. Related Single-Sign-On technologies are explained. A brief overview of authentication for cloud computing, IoT and UIDAI is presented.
A STUDY ON SOCIAL MEDIA MARKETING AMONG STUDENTS IN CHENNAI

Technology is been helpful in many ways for a human but there are problem faced by the current generation, which everyone neglect to look up on. The objectives of the paper is to identity the variable which are used for social media marketing and their relationship among students in Chennai. The data used for the study is primary source of collecting data from in and around Chennai region. The 153 respondent were answered the questionnaire through online collection method. The respondent were selected based on convenient sampling method. The tools used for the study is cross tabulation for to understand the variables and Garrett Ranking Techniques. The results shows that Facebook plays a major role in marketing of products followed by twitter and Instragram. The students are uses mobile mostly for entertainment purpose so the companies are used YouTube to market their products.
Combining SDLC Method And ITIL Framework by Involving Auditors

The framework that provides best practice in IT service management is called IT Infrastructure Library (ITIL). The IT service management leads to the management of a service, this service is generated through the system development process. Each system has a different scale, characteristics, requirements, types, and properties but has the same stage in its development, that is by implementing the System Development Life Cycle (SDLC)Software developers on IT projects must implement SDLC by selecting a particular model process. But many IT development teams do not pay attention to software management frameworks or standards so that they will experience difficulties in planning, reporting work results, monitoring, controlling, etc., especially when all personnel in the team don’t have sufficient experience. It would be better if every software house company and IT Consultant applies the general framework and standardization so that the software development process will be more directed and produced high quality services.In conducting the SDLC process, an accountant is needed, in this case the auditor who has a role as an information provider in describing business processes in a company activity, the auditor also plays a role as controller that ensuring the system is in accordance with accounting regulations, and accountants also act as checker of the system so that the auditors must involved in the initial design of a system. The application of ITIL in the SDLC process will have an impact on the ease of the auditor in carrying out the duties.
Intelligent Tutoring Systems for Generation Z's Addiction

As generation Z's big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting intelligent-like behavior, being capable learn from the learner, will become the next educational superstars. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Z's addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.
Steganalysis of Reversible Data Hiding in Color Image Using Grayscale Invariance

Reversible data hiding in images is the technique for retrieving back the original image, from an image on which some data hiding has been performed. The retrieved image can be subjected to further image processing without any loss or compromising the data. The technique of grayscale invariance means to keep the gray values unchanged throughout the message embedding and extracting process, and with the help of the unchanged grayscale values, the original image is retrieved. The message and other information are embedded into the RGB channels of the image. The message is embedded into the R channel and B channel, and the G channel is adjusted so that the grayscale value does not change. The auxiliary parameters are also embedded into the B channel of the image. We have analyzed the limit of embedding rate and performed histogram attack.