Reviewerly/exHarmony huggingface dataset:
Fractals: the missing link between certainty and uncertainty

The paper is approaching the concept of fractal, as being the explanation for both, uncertainty and certainty. The certainty is expressed by the fact that one can create structures and concepts going out from simple instruments such as distances and rules that are generating the respective fractals. On the other side some other fractal forms are ruled by uncertainty, in the sense that the rules are completely unknown or their formula cannot be established exactly. One very special type of rule that is the base for many fractals encountered in the nature is given by the Fibonacci series. It is so special because the deterministic rule it is so simple, but the results and the shapes resulting from it are so complicated and it seems to be like the nature is telling us: “Look at all the complicated things I can create only based on just one simple rule” The paper deals with the shapes of fractals by analyzing them using specific tools of analysis in both, the time domain and the spectral domain.
THE IMPLEMENTATION OF CONTEXTUAL TEACHING AND LEARNINGMETHOD TO INCREASE STUDENTS’ VOCABULARY MASTERY OF THE SEVENTH GRADE AT MTs NEGERI 02. TOLITOLI

The objective of this research is to find out whether the using of contextual teaching and learning can increase students’ vocabulary mastery. It is a pre-experimental research. The study was taken by the seventh grade of MTs Negeri 02. Tolitoli. It was conducted in one class (VII D). The data were obtained through a pre-test and post-test.The mean score of pre-test was (65.6) while in the post-test improved up to (74.07). Moreover the Minimum Mastery Criterion of English lesson was 75 (seventy-five). Then the data of both test were analyzed by using T-test, and the result showed that the T-count value was 5.48 greater than T-table value was 1.701 It can be inferred that the using of contextual teaching and learning method can increase students’ vocabulary mastery. Keywords : Increase, Vocabulary, Contextual Teaching Learning.
STUDENTS’ ANXIETY IN SPEAKING ENGLISH AT SMP NEGERI 2 MARTAPURA TIMUR.

English is one of the lessons to be learned in school, especially in Junior high school. In learning English, the ability to speak English is one of the skills that students need to master, but there are still many students who feel anxious when speaking in front of many people. This research was conducted to see student anxiety in speaking English among junior high school students in 8th grade SMP Negeri 2 Martapura Timur. This study aims to determine the causes of student anxiety when speaking English and how students deal with anxiety. Researchers use qualitative methods, interviews as a type of research, and two methods are used to collect the required data, namely: observation and interviews. Three students were selected as participants. From these results, it was found that fear of being ridiculed by others, limited vocabulary, fear of mistakes and lack of preparation are factors that contribute to students' anxiety in speaking English. Students’ ways to overcome their anxiety were preparation, relaxation, positive thinking, and resurrection.
actual aspects of planning and development of training programs

The key aspects of the Sustainable Development Goals in the context of the quality of education are identified, formulated in the framework of the UN Conference «2030 Agenda for Sustainable Development», which are the basis for ensuring an effective process of planning and development of educational programs; The importance of the design and development of curriculum oriented to students based on a problem-oriented approach is substantiated; The key advantages of the modular step-by-step structure of the curriculum are revealed, which allow you to select the appropriate level of complexity of tasks and not overload the classes.
LibGuides: Teach Yourself Tech: Tech-Talk

Free online computer and technology courses, quick tutorials, tips from Tech-Talk and more, on computer basics, popular software programs, Internet security, social media, . . .
Engineering Education Examples for Electric Power Energy after the 2011 Large Disaster, and Power Energy Situation of Japan

In Japan, the March 11 2011, large disasters of East Japan's record tremendous Earthquake M9 · Tsunami etc. have changed conventional sense of electric power energy. The author investigates power and energy technologies on electric power engineering education in 2011-March 2019. This paper reviews its results in Japan and power energy situation in 2020.
Usability study on Chatbots in Swedish Business to Consumer market

Chatbots are software applications that are built to interact with users in text. Even if the first chatbot was developed more than 50 years ago the interest for chatbots is today constantly growin ...
Top 5 reasons why fresh content is important for your website

Your website is a unique identity over the internet just like any other website. Each fresh content update or amount of change that you make on your ...
LibGuides: Business and Accountancy : E-Resources

A comprehensive guide to the Library's resources and services to support with your course. How to find electronic resources for your course
Adaptable Semi-Automated 3D Segmentation Using Deep Learning with Spatial Slice Propagation

Even with the recent advances of deep learning pushing the field of medical image analysis further than ever before, progress is still slow due to limited availability of annotated data. There are ...
LibGuides: Engaging in Science, Engineering, and Math (SEM) PD 126: Find Books/eBooks

Research information for students enrolled in PD 126: Engaging in Science, Engineering, and Math
Research on the Creative Practice of College Efficient Classroom Teaching in the Mixed Teaching Mode of "Rain Classroom"

The wide spread of long-distance communication and smart phones provides new development ideas for modern education, and it also impacts the traditional teaching model. Rain Classroom is a mixed wisdom teaching tool that combines modern information technology and traditional teaching organically. It can realize real-time and effective interaction between teachers and students and improve the learning effect. This article analyzes and discusses the construction of the mixed teaching mode of Rain Class and the innovative practice path of college physical education under this mode.
Design of DES encryption algorithm as bundleddata asynchronous pipeline using FPGA

Currently, digital systems that are able to meet major security restrictions are increasingly being demanded, both in the military and in commercial areas. Data security can be achieved by cryptographic algorithms. An important encryption algorithm known as data encryption standard (DES) was implemented in field programmable gate array (FPGA) in different synchronous architectures. In this paper, we have proposed the implementation of the DES algorithm in FPGA, in the asynchronous pipeline style. Compared to the implementation in FPGA using two different project styles, the proposed asynchronous obtained the average increase of 14.9% in throughput and the average reduction of 66.3% in latency.
PENGGUNAAN METODE DEBAT (STUDENT DEBATE CLUB)UNTUK MENINGKATKAN KETERAMPILAN ARGUMENTASI SISWA DALAM PEMBELAJARAN SEJARAH (Penelitian Tindakan Kelas di Kelas XI IPS 1 SMA Negeri 9 Bandung)

This thesis is a research result entitled Application of the Debate Method (Student Debate Club) to Improve Students Argumentation Skills in Learning History (Class Action Research at XI IPS 1 SMA Negeri 9 Bandung). This research was motivated by the findings of researchers at XI IPS 1 Class in SMA Negeri 9 Bandung which showed that students were still lacking in argumentation skills. This can be seen from the majority of students who just quiet when the teacher given the opportunity to ask questions or make some arguments, and when asked by the teacher these students didn’t understand. Therefore, the researchers conducted a class action research conducted in three cycles. Each of the cycles is performed one action using the Class Action Research (CAR) model of Kemmis and McTaggart designs, the process of which consists of planning, implementing, observing, and reflecting. Indicators of the argumentation skills used are courage in argumentation, sound regulation when arguing, attitude in argumentation, and the argumentation skills. Sub-indicators of the argumentation skills are used confidence in expressing arguments, not questioning the correctness of the arguments, sounding clear, fluent when speaking, appreciating the arguments of others, criticizing the arguments of others wisely, delivering arguments logically and analytically, and conveying arguments based on argumentation. The acquisition of argumentation skills scores has increased in each cycle. Increasing the percentage of each cycle shows the effectiveness of the application of the debate method (Student Debate Club) to improve students' argumentation skills in learning history.
Query processing over secure perturbed data over hybrid cloud

Purpose Hybrid cloud composing of public and private cloud is seen as a solution for storage of health care data characterized by many private and sensitive data. In many hybrid cloud-based solutions, the data are perturbed and kept in public cloud, and the perturbation credentials are kept in private cloud. Design/methodology/approach Hybrid cloud is a model combing private and public cloud. Security for the data is enforced using this distribution in hybrid clouds. However, these mechanisms are not efficient for range query and retrieval of data from cloud. In this work, a secure and efficient retrieval solution combining K -mean clustering, geometric perturbation and R -Tree indexing is proposed for hybrid clouds. Findings Compared to existing solution, the proposed indexing on perturbed data is able to achieve 33% reduced retrieval time. The security of indexes as measured using variance of differences was 66% more than existing solutions. Originality/value This study is an attempt for efficient retrieval of data with range queries using R -Tree indexing approach.
Tamper Protection for Cryptographic Hardware : A survey and analysis of state-of-the-art tamper protection for communication devices handling cryptographic keys

This master's thesis was conducted at Sectra Communications AB, where the aim of the thesis was to investigate the state of the art of physical hardware tampering attacks and corresponding protecti ...
College Libraries: Self-Quarantine Activities: Music and Radio

Here are a few activities and resources to student, faculty and staff to cope while self-quarantining
[postdoc] Advanced static analysis

The ERC project “STATOR” (2012-2017) focuses on discovering new algorithms for automatic proofs of program properties through static analysis. Techniques under consideration include combinations of abstract interpretation, automated theorem proving, and operation research. The project considers both theoretical advances and practical validation of these advances on real-world software. The postdocs will conduct research within the project's goals, and may help in the supervision of (...)
PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX DENGAN VARIABEL EKSOGEN COVID-19

The Covid-19 pandemic is spreading rapidly throughout the world, one of which is Indonesia. The outbreak of the Covid-19 outbreak can affect the Indonesian economy, one of which is from the Jakarta Composite Index (JCI). JCI from before the Covid-19 pandemic on December 30, 2019 amounted to 6299.54. After the Covid-19 pandemic broke out widely in Indonesia from March 2, 2020, the JCI figure became 5361.25 and increasingly unstable until March 24, 2020, amounting to 3937.63. The Covid-19 pandemic caused the Jakarta Composite Indeks (JCI) to become unstable. The data used are daily data on the Jakarta Composite Index (JCI) except for holidays, totaling 388 data, from January 2, 2019 to July 30, 2020. In this study the ARIMAX method will be applied to forecast the Jakarta Composite Index (JCI) with the exogenous covid variable. 19. Jakarta Composite Index (JCI) modeling using exogenous covid-19 variables was carried out with multiple regression and produced a deterministic trend ARIMAX model with ARIMA model residuals (1,1,2). Keywords: Covid-19, Jakarta Composite Index (JCI), forecast, ARIMAX
LibGuides: Hold/request/renew an item in OCtopus: Holds and Requests Using OCtopus

This LibGuide provides step-by-step instructions on how to place a hold or request, and how to renew an item using OCtopus.
PENGARUH PENAMBAHAN LOGAM TRANSISI NIKEL TERHADAP SIFAT FISIS NANOROD ZnO

Nickel-doped ZnO nanostructures have been successfully grown using seed mediated hydrothermal method. The growth of ZnO nanostructures has been done by concentration of the Ni-doped solution, is 8 mM with the growth temperature of 90°C in 7 hours. The grown ZnO nanostructures were characterized by using UV-Vis Spectroscopy, FESEM, and XRD. The UV-Vis spectra of the samples showed that the ZnO nanorod is hexagonal in shape with a strong absorption occured in the wavelength range of 300-380 nm. The FESEM images showed that geometrical shape of Ni-doped ZnO nanostructures are nanorod with a hexagonal and nanoflower faced shapes. XRD patterns observed show five diffraction peaks at 2θ: 32,09°; 34,76°; 36,65°; 47,95° and 56,97° for Ni doped ZnO nanostructures with crystal orientation of (100), (002), (101), (102) and (110) respectively. The strongest line was found in the crystal plane of (101).
Improved knowledge distillation by utilizing backward pass knowledge in neural networks

Knowledge distillation (KD) is one of the prominent techniques for model compression. In this method, the knowledge of a large network (teacher) is distilled into a model (student) with usually significantly fewer parameters. KD tries to better-match the output of the student model to that of the teacher model based on the knowledge extracts from the forward pass of the teacher network. Although conventional KD is effective for matching the two networks over the given data points, there is no guarantee that these models would match in other areas for which we do not have enough training samples. In this work, we address that problem by generating new auxiliary training samples based on extracting knowledge from the backward pass of the teacher in the areas where the student diverges greatly from the teacher. We compute the difference between the teacher and the student and generate new data samples that maximize the divergence. This is done by perturbing data samples in the direction of the gradient of the difference between the student and the teacher. Augmenting the training set by adding this auxiliary improves the performance of KD significantly and leads to a closer match between the student and the teacher. Using this approach, when data samples come from a discrete domain, such as applications of natural language processing (NLP) and language understanding, is not trivial. However, we show how this technique can be used successfully in such applications. We studied the effect of the proposed method on various tasks in different domains, including images and NLP tasks with considerably smaller student networks. The results of our experiments, when compared with the original KD, show 4% improvement on MNIST with a student network that is 160 times smaller, 1% improvement on a CIFAR-10 dataset with a student that is 9 times smaller, and an average 1.5% improvement on the GLUE benchmark with a distilroBERTa-base student.
Case-based reasoning with precedent models: Preliminary report

Formalizing case-based reasoning is an important topic in AI and Law, which has been discussed using various approaches, such as formal dialogue games, abstract dialectical frameworks. In this paper we model case-based reasoning by using the formal argument semantics of case models. With the precedent models we present, the validity of legal arguments in the case-based reasoning process can be shown formally. We also present a case study of precedent models in a real legal domain and evaluate the validity of arguments in case-based reasoning.
The Effectiveness of Using Crossword Puzzle to Improve Students’ Vocabulary Mastery

The aim of this study was to get empirical evidence about the use of Crossword Puzzle in improving students’ vocabulary mastery. The method used in this study was quantitative method. Thisstudy was an experimental study with pre and posttest group design which involved 45 seventh grade students at SMPN 2 Pringgasela. The findings of this study showed that there was a significantimprovement of students’ vocabulary mastery after being taught by using Crossword Puzzle in which t-test score at the significant degree of 95% was higher than t-table. Thus, it can be stated that the use of Crossword Puzzle was effective in improving the students’ vocabulary mastery.
Jocurile didactice online la chimie – factor motivant în instruirea bazată pe concepția stem

The educational ideal of the modern school aims at training personalities with creative and innovative potential. Online teaching games are attractive in the organization and development of the educational process, requiring the manifestation of an original, innovative thinking. They offer  the possibility of integrating information technologies with the field of Natural Sciences, presenting a form of STEM training.
A rotation based regularization method for semi-supervised learning

In manifold learning, the intrinsic geometry of the manifold is explored and preserved by identifying the optimal local neighborhood around each observation. It is well known that when a Riemannian manifold is unfolded correctly, the observations lying spatially near to the manifold, should remain near on the lower dimension as well. Due to the nonlinear properties of manifold around each observation, finding such optimal neighborhood on the manifold is a challenge. Thus, a sub-optimal neighborhood may lead to erroneous representation and incorrect inferences. In this paper, we propose a rotation-based affinity metric for accurate graph Laplacian approximation. It exploits the property of aligned tangent spaces of observations in an optimal neighborhood to approximate correct affinity between them. Extensive experiments on both synthetic and real world datasets have been performed. It is observed that proposed method outperforms existing nonlinear dimensionality reduction techniques in low-dimensional representation for synthetic datasets. The results on real world datasets like COVID-19 prove that our approach increases the accuracy of classification by enhancing Laplacian regularization.
MVP: Multivariate polynomials for conditional generation

Conditional Generative Adversarial Nets (cGANs) have been widely adopted for image generation. cGANs take i) a noise vector and ii) a conditional variable as input. The conditional variable can be discrete (e.g., a class label) or continuous (e.g., an input image) resulting into class-conditional (image) generation and image-to-image translation models, respectively. However, depending on whether the conditional variable is discrete or continuous, various cGANs employ substantially different deep architectures and loss functions for their training. In this paper, we propose a novel framework, called MVP, for conditional data generation. MVP resorts to multivariate polynomials of higher-order and treats in a unified way both discrete and continuous conditional variables. MVP is highly expressive, capturing higher-order auto- and cross-correlations of input variables (noise vector and conditional variable). Tailored sharing schemes are designed between the polynomial’s parameter tensors, which result in simple recursive formulas. MVP can synthesize realistic images in both class-conditional and image-to-image translation tasks even in the absence of activation functions between the layers.
A dynamic configuration with a shared knowledge centre for multi-objective ant colony optimisation algorithms

This paper proposes a dynamic configuration approach to the ant colony optimisation algorithm configuration, applied to multi-objective optimisation problems. Indeed, the inertia of the static vision of the pheromone or visibility preferences values makes our dynamic approach desired. We propose a model based on a collective knowledge centre shared by the colony members, storing the best configurations based on the old colony's experiments during the learning phase for random problems. The construction of this centre is based on statistical and qualitative studies of the evaluation criteria that will be explained over the paper. Our model gives results that show a rise in quality of the outputs, as well as a proof of concept for the artificial learning approach.
Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and 2) preference elicitation. It highlights a number of promising directions for Natural Language Generation research for mitigating cognitive bias including: the need for personalization, as well as for transparency and control.
THE EFFECT OF SERVICE AND COSTUMERS LOYALTY ON COSTUMERS SATISFACTION OF ONLINE TRANSPORTATION (Study Case: Students of IIB Darmajaya for MAXIM Users)

The purpose of this study was to determine the effect of service and costumers loyalty on costumers satisfaction. The scope of this study was students of IIB Darmajaya and user MAXIM application. This study used two variables. Service (X1) and Costumers Loyalty (X2) were dependent variables and Costumers Satisfaction (Y) was the independent variables. The research methods of this study used mix methods and the data for this study was able to be collected using questionnaire. Keyword:  Service, Costumers Loyalty, Costumers Satisfaction, MAXIM
Streamlining Management in the Agri-Food Sector through Blockchain Technology

Industry 4.0, innovations and digital technologies in agricultural development are considered the main criteria meant to increase the efficiency of production activity in this branch and to propel economic development to a higher level. The researches in the area of Agriculture 4.0 alongside Industry 4.0 increasingly address digital technologies such as cloud, platform, robotics, artificial intelligence, Internet of things and blockchain to the sustainable development strategies and growth policies of many countries around the world. Sustainable development in agriculture through digitization involves the development of new management models based on information technologies to improve efficiency on farms, soil conservation and biodiversity by minimizing the negative impact on the environment, ensuring a stable level of production and profit. The present research aims to investigate the notion of blockchain technology and capitalize on this technology to streamline the management in the agricultural sector. The paper is a study of international experience and good practices of digital technologies tenders in the agri-food sector.