Reviewerly/exHarmony huggingface dataset:
LibGuides: Technology Gone Wild!: Home

New technologies can make our lives longer, easier, and more fun. But some technologies raise tough questions about ethics, privacy, inequality, and safety. Here, you will find links to all the information you'll need for your Lit & Society research. New technologies can make our lives longer, easier, and more fun. But some technologies raise tough questions about ethics, privacy, inequality, and safety. Here, you will find links to all the information you'll need for your Lit & Society research for M
Parameterized Convexity Testing

In this work, we develop new insights into the fundamental problem of convexity testing of real-valued functions over the domain $[n]$. Specifically, we present a nonadaptive algorithm that, given inputs $\eps \in (0,1), s \in \mathbb{N}$, and oracle access to a function, $\eps$-tests convexity in $O(\log (s)/\eps)$, where $s$ is an upper bound on the number of distinct discrete derivatives of the function. We also show that this bound is tight. Since $s \leq n$, our query complexity bound is at least as good as that of the optimal convexity tester (Ben Eliezer; ITCS 2019) with complexity $O(\frac{\log \eps n}{\eps})$; our bound is strictly better when $s = o(n)$. The main contribution of our work is to appropriately parameterize the complexity of convexity testing to circumvent the worst-case lower bound (Belovs et al.; SODA 2020) of $\Omega(\frac{\log (\eps n)}{\eps})$ expressed in terms of the input size and obtain a more efficient algorithm.
Automated Bridal Veni Making

The presented work is related to tying of flowers and more specifically it deals with knotting mechanism using Pneumatic actuator driven by a Servo motor. Veni making using different kinds of flowers are preferred by most of the women during their festival occasions. Each type of flower has different processes and methods for tying. Various types of flowers used for veni making are Sampangi, Jasmine and Roses. Here the flowers are tied one by one with gripper and servomotor unit using pneumatic actuator in order to make a bridal Veni. When it is done manually, a person has to tie each and every flower to produce Veni in different designs. There is no known mechanism in which the veni can be automated. Generally there are many automated mechanisms for tying a bunch of flowers for easier transportation or storage, whereas an automated mechanism for bridal veni making is not known. This proposed work provides an automated process for bridal veni making using pneumatic pistons, Servomotor with gripper unit and relay circuit.
Accessible Learning Sources: A Need Analysis on Maritime English Learning Apps

Maritime English is defined as the English language used by seafarers both at sea and in port and by individuals working in the shipping and shipbuilding industry. The teachers are required to make the students skillful and ready to work in maritime sectors once they graduate. The existing and relevant maritime English learning materials following STCW 2010 curriculum in Indonesia are still rare and limited. This study aims to conduct a need analysis of android and web-based Maritime English apps based on the students' perspectives. This study uses questionnaires shared and completed randomly by 318 students from different majors and at Sekolah Tinggi Ilmu Pelayaran (Maritime Higher Education Institute) Jakarta. The data is gathered and analyzed accordingly to create the essential concept of the MarEng apps design. The questionnaire result indicates that most of the participants need an ME learning platform in order for them to have better access to and to practice the required knowledge and skills. Explicit bilingual instructions and features like video, audio, and text are also preferred to be incorporated. It is suggested to start from the elementary level and cover the four skills, especially listening, speaking, and pronunciation. It is so suggested that the platform can be constructed in easy and fun UI/UX covering the required content.
Cloud Computing Governance Readiness Assessment: Profiling

Cloud Computing makes the dream of computing real as a tool and in the form of service. This internet - based ongoing technology which has brought flexibility, capacity and power of processing has realized service- oriented idea and has created a new ecosystem in the computing world with its great power and benefits. Cloud Computing is revolutionizing the Information Technology (IT) industry by enabling organizations to have flexible costs by buying a service instead of owning their assets. This will enable Universities to transfer their processing and storage to cloud for easy access by students. Nevertheless, decision makers in IT organizations have difficulties in evaluating Cloud services because there are no guidelines or any structured form to decide what Cloud services they should use. Cloud computing has emerged as an important platform to seeking innovative ways to save money and increase the trust and value of their information systems.
Activating the cognitive learning activity through “Dead” case

The authors investigated technologization of educational process in high school and formation of interactive communicative environment for professionally oriented learning. Analysis of the problems of cases use in modern teaching practice was conducted. Methods of questionnaires, empirical data generalization, theoretical analysis of case study, justification of approaches to its technological effectiveness, “dead case”, findings generalization, defining prospects of further problem consideration were used. The authors identified the features of case study technological implementation and developed the “dead” case-based study algorithm for development of future lecturers’ professional skills. The conditions of the effective use of case technology in high school were defined. The authors proved the integral character of the case technology.
Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles.

To enable safe and reliable decision-making, autonomous vehicles (AVs) feed sensor data to perception algorithms to understand the environment. Sensor fusion with multi-frame tracking is becoming increasingly popular for detecting 3D objects. Thus, in this work, we perform an analysis of camera-LiDAR fusion, in the AV context, under LiDAR spoofing attacks. Recently, LiDAR-only perception was shown vulnerable to LiDAR spoofing attacks; however, we demonstrate these attacks are not capable of disrupting camera-LiDAR fusion. We then define a novel, context-aware attack: frustum attack, and show that out of 8 widely used perception algorithms - across 3 architectures of LiDAR-only and 3 architectures of camera-LiDAR fusion - all are significantly vulnerable to the frustum attack. In addition, we demonstrate that the frustum attack is stealthy to existing defenses against LiDAR spoofing as it preserves consistencies between camera and LiDAR semantics. Finally, we show that the frustum attack can be exercised consistently over time to form stealthy longitudinal attack sequences, compromising the tracking module and creating adverse outcomes on end-to-end AV control.
Terlambatnya Proses Pendinginan Muatan Gas Polymer Grade Propylene di Kapal LPG/C Coral Millepora

The LPG Carrier is one of the ships that carries liquid gas cargoes below -48o C. The LPG Carrier has facilities in the form of a reliquefaction plant that uses seawater as a coolant for gas cargo. Therefore, the reliquefaction plant has an important role in the smooth loading and unloading process. If one of the reliquefaction plant particles is damaged, of course the cooling process of the polymer grade propylene gas load will be hampered. The purpose of this study is to describe the problem in the cooling process of polymer grade propylene load. The authors conducted research using a qualitative descriptive method through an interview approach, direct observation of the object of research, and supported by paper-based documents or photos. Based on the results of the study, it was found that the causes of delays in the cooling process of polymer grade propylene gas cargo, namely the influence of weather, sea water content, clogged sea water filters, clogged condenser tubes, and condenser shell tube leaks.
Factors affecting enrollment in Early Childhood Education: A case study of Punjab

This paper was aimed to examine the factors affecting enrollment in early childhood education in Punjab, Pakistan. The objectives of this paper were to find out the effect of early childhood education on enrollment in Punjab and to observe the implementations of early childhood education methodology in early childhood school. There were three categories of the literacy rate highest (47.4% to 70.5%), middle9 36.8% to 47%) and lowest (20.7 % to 36%) in Punjab. Stratified random sampling techniques were used and strata were made concerning the literacy rate. One district choose form each category of the literacy rate. 25 school heads were taken as a sample from each region. It was concluded from the results of the respondents that multiple factors affect the enrollments in these schools. It was recommended from the conclusions that the government of Punjab may provide facilities (classroom, instructional materials, and equipment) for enhancing enrollment at the primary level in Punjab.
MIND MAPPING AND SCIENCE PERFORMANCE OF GRADE 5 PUPILS

This study aimed in determining the effect of mind mapping to the science performance of Grade 5 Pupils of San Juan Elementary School.Two strategies were executed by the researchers in order analyze the performance of 40 pupils.For the direct teaching, the teacher delivered the topic on "Female Reproductive System" with proper procedurefrom motivation to evaluation.Another strategy being executed by the teacher is by integrating mind mapping so as to evaluate the students" understanding using the same topic.The result of evaluation was documented.Using direct teaching using traditional strategy and through multimedia presentation, the 40 pupils obtained a mean of 2.15 and sd 1.66 in a 5item quiz.On the other hand, peer teaching generated a mean of 3.55 and sd 1.38.Lower coefficient of variation was observed in the result of integrating mind mapping (0.39) than the result of direct teaching (0.77), showing that integrating mind mapping in the teaching process generated a less varied scores than direct teaching.Using paired t-test, the tcomputed was -4.11 while the t-critical was 1.99.These values showed that integrating mind mapping in the lesson proper has significant effect to the science performance of the pupils.The conclusion is supported by the p-value 0.00.Based on the result of this study, recommendations to Science teachers, school heads, district supervisors and future researchers were given at the end of the study.Promotion of integrating mind mapping in teaching should be one of the foci of educators so as to improve science performance of pupils.The teacher-researchers reflected also on the importance of involving the learners on the process of teaching-learning as they themselves are the center and not the teachers.
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems

In this paper, we investigate how to use limited code-switching data to implement a code-switching speech recognition system. We utilize the Transformer end-to-end model to develop our code switching speech recognition system, which is trained with the Mandarin dataset and a small amount of Mandarin-English code switching dataset, as the baseline of this paper. Next, we compare the performance of systems after adding multi-task learning and transfer learning. Character Error Rate(CER) is adopted as the criterion for the system. Finally, we combined the three systems with the language model, respectively, our best result dropped to 23.9% compared with the baseline of 28.7%.
Life Pharmacy Website Design with Model View Controller Method

Nowadays research and development of medicinal plants both at home and abroad is growing rapidly. Research is mainly developing in terms of pharmacology and phytochemicals based on indications of medicinal plants that have been used by some people with empirically proven efficacy. The results of this study, of course, further strengthen the users of medicinal plants for their efficacy and usefulness. Moreover, many researchers have also conducted toxicological tests to determine the safety of medicinal plants that are often used for long-term use, as well as incidental use. The traditional healers also have a lot of knowledge of poisonous medicinal plants, but still use them because they know how to process them.
Using Contextualized Representations For Biomedical Entity Recognition

Distributed representations are usually used as input features in text mining tasks. Previous works have shown its potential in encoding semantics. Generally, there existing two representation methods namely static and dynamic, which means they are context-free and context-dependent respectively. Many works have demonstrated that context based representations significantly improved performance in natural language processing field. Therefore, in this paper, we utilize contextualized representations to recognize biomedical entities and evaluate the results at entity-level on BC2GM and BC5CDR-disease datasets. Results show that we obtain a F1-score of 75.16% and 75.97%, which improving 2.54% and 3.96% respectively compared with context-free representations. It indicates that the method based on contextualized representations is promising for entity recognition tasks.
Study to Perform Opinion Mining on Motivation Factors Generating Cyber Crime by Twitter Analytics

Subsequently ages, researchers like Smith, Katherine T.; Smith, L. Murphy; Smith, Jacob L(2011), Cindy J. Smith; Sheldon X. Zhang (2011), Paternoster (2011), Schell and Melnychuk (2011), Alex Kigerl 2010, 2011, Bradford W. Reyns (2012), E. Gabriella (2012), Jean, Brody, Richard G.; Mulig, Elizabeth, Redins (2012), Ngo, Fawn T, McMahon, Richard; Bressler, Martin S.; Bressler (2016), Williams, Aditi Phadnis /Business Standard reports National Crime Records Bureau, Analysis: IndiaSpend (2016), Vivina Vishwanathan (2017), Teen Jose et al (2017) have talked the perceptions of cyber integrities, wrongdoings and sanctuary facets with the progression in the drifts of technology and traffic from outmoded to an online approach. Through the literature review from multiple magnitudes, it bounces a clear portrait that computer / cyber safekeeping or associated apprehensions are historic in nature. Over the literature assessment from several scopes, it gives a vibrant depiction that computer / cyber safekeeping or associated anxieties are historic in nature. It is also used to include outmoded crimes in which computers or networks are rummage-sale to empower the illegitimate commotion.This study is based on secondary data and primary data collected from the Twitter platform of social media and application on R package has been implemented to retrieve outcome. Thus, helps to understand the amount of discussion made on Twitter for motivations factors behind cybercrime.
Learning to Recommend Using Non-Uniform Data

Learning user preferences for products based on their past purchases or reviews is at the cornerstone of modern recommendation engines. One complication in this learning task is that some users are more likely to purchase products or review them, and some products are more likely to be purchased or reviewed by the users. This non-uniform pattern degrades the power of many existing recommendation algorithms, as they assume that the observed data are sampled uniformly at random among user-product pairs. In addition, existing literature on modeling non-uniformity either assume user interests are independent of the products, or lack theoretical understanding. In this paper, we first model the user-product preferences as a partially observed matrix with non-uniform observation pattern. Next, building on the literature about low-rank matrix estimation, we introduce a new weighted trace-norm penalized regression to predict unobserved values of the matrix. We then prove an upper bound for the prediction error of our proposed approach. Our upper bound is a function of a number of parameters that are based on a certain weight matrix that depends on the joint distribution of users and products. Utilizing this observation, we introduce a new optimization problem to select a weight matrix that minimizes the upper bound on the prediction error. The final product is a new estimator, NU-Recommend, that outperforms existing methods in both synthetic and real datasets. Our approach aims at accurate predictions for all users while prioritizing fairness. To achieve this, we employ a bias-variance tradeoff mechanism that ensures good overall prediction performance without compromising the predictive accuracy for less active users.
Investigating Interpersonal Meaning-Making in Indonesian EFL Learners' Writing: A Systemic Functional Perspective

This study presents an investigation of interpersonal meaning-making in students’ writing analytical exposition text. The objectives of this study are to describe the common mood types and to describe modality realization used in the text. Nine texts from 144 texts were collected to analyze. The theories from Systemic Functional Linguistics are adapted as the framework to analyze the clause of the text. The findings of this study showed that students have used mood types which can be seen in the use of subject and finite as mood elements, and the use of predicator, adjunct, and complement as residue. The arrangement of mood element ad residue shows the mood type they used. Regarding to SFL theories, the common mood type in this study is declarative as its function of analytical exposition text to discuss topic critically and deeply from one point of view. The result showed that students have attempted to provide information in the text. In the terms of modality, the median modality has a higher frequency in this text than that of high modality and low modality. It indicates that the students have an effort to position themselves and build the relationship to the reader in the text. Moreover, the zero frequency of low modality shows that the students did not provide opportunities for readers to doubt their position in the text. In conclusion, problem –solving approach as the pedagogical implication is appropriate for students to improve cognitive aspects in terms of communicative skills, so students have been able to create the effective analytical exposition text which is intended to discuss something critically from one point of view.
Separating Content and Style for Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples. Existing works focus on disentangling domain-invariant content code and domain-specific style code individually for multimodal purposes. However, less attention has been paid to interpreting and manipulating the translated image. In this paper, we propose to separate the content code and style code simultaneously in a unified framework. Based on the correlation between the latent features and the high-level domain-invariant tasks, the proposed framework demonstrates superior performance in multimodal translation, interpretability and manipulation of the translated image. Experimental results show that the proposed approach outperforms the existing unsupervised image translation methods in terms of visual quality and diversity.
2nd Place Solution for SODA10M Challenge 2021 -- Continual Detection Track

In this technical report, we present our approaches for the continual object detection track of the SODA10M challenge. We adapt ResNet50-FPN as the baseline and try several improvements for the final submission model. We find that task-specific replay scheme, learning rate scheduling, model calibration, and using original image scale helps to improve performance for both large and small objects in images. Our team `hypertune28' secured the second position among 52 participants in the challenge. This work will be presented at the ICCV 2021 Workshop on Self-supervised Learning for Next-Generation Industry-level Autonomous Driving (SSLAD).
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies in its message passing mechanism to aggregate neighborhood information. However, we observed that message passing largely slows down the convergence of GCNs during training, especially for large-scale recommender systems, which hinders their wide adoption. LightGCN makes an early attempt to simplify GCNs for collaborative filtering by omitting feature transformations and nonlinear activations. In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation. Instead of explicit message passing, UltraGCN resorts to directly approximate the limit of infinite-layer graph convolutions via a constraint loss. Meanwhile, UltraGCN allows for more appropriate edge weight assignments and flexible adjustment of the relative importances among different types of relationships. This finally yields a simple yet effective UltraGCN model, which is easy to implement and efficient to train. Experimental results on four benchmark datasets show that UltraGCN not only outperforms the state-of-the-art GCN models but also achieves more than 10x speedup over LightGCN. Our source code will be available at https://reczoo.github.io/UltraGCN.
Interactive Part Segmentation Using Edge Images

As more and more fields utilize deep learning, there is an increasing demand to make suitable training data for each field. The existing interactive object segmentation models can easily make the mask label data because these can accurately segment the area of the target object through user interaction. However, it is difficult to accurately segment the target part in the object using the existing models. We propose a method to increase the accuracy of part segmentation by using the proposed interactive object segmentation model trained only with edge images instead of color images. The results evaluated with the PASCAL VOC Part dataset show that the proposed method can accurately segment the target part compared to the existing interactive object segmentation model and the semantic part-segmentation model.
Bitcoin and Other Crypto-Assets in 2021 – What Does Regulation Hold for the Industry?

The price of bitcoin has continued on an upward trend since October 2020 and reaching new all-time highs in January 2021; but will this trend last and is this rise in price sustainable? This article considers factors that have contributed to this price rise and assesses the implications of looming regulation and other factors that could interrupt this trend.
ANALISA BENTUK PEMALSUAN TANDA TANGAN PADA DOKUMEN

Graphonomy is an interdisciplinary field that is directed at the scientific analysis of the process of writing and signatures, the results of scratches, and other graphic skills on documents that lead to the examination of writing for identification purposes to serve law enforcement and justice. Analysis of the form of signatures on documents using the forensic identification method is divided into 3 forms that produce differences in crossing, touch (pressure), ink, and pulling edges, where the principle of signature examination is to compare the evidence with the original comparison of the victim. The examination will produce a different analysis from each examiner's view seen from the form of the signature, so that the examiner must recognize all parts of the writing or signature that have a strong characteristic of permanent content in a document to be examined.