Reviewerly/exHarmony huggingface dataset:
Establishment and application of intelligent evaluation system for scientific research performance

ObjectiveThe study built a scientific performance assessing model by using information technology to construct an intelligent reviewing system, in order to provide intelligent and normalized evaluation services for scientific research performance.MethodsA comprehensive and novel practical scientific performance reviewing system was established to provide objective evaluation of scientific output, which was based on accumulated experience in Fuwai Hospital and ample discussion by the experts of academic committee.ResultsScientific performance assessment service, which carried out by the collaboration of scientific research management department and informational technology center, were including senior professional title self-assessing system, annual scientific research performance assessing system on both the department basis and individual basis. All the service received satisfactory effect making the assessing service from manual to intelligent. It helps the assessment result turn into openness, fairness and justice. The establishment of the intelligent scientific performance assessing system broaden the mind and provide the basis for the further scientific reward self-evaluation, scientific project application conditions filtering, technology professionals intelligent screening, supervising qualification self-evaluation and scientific project self-reviewing etc.ConclusionsThe establishment of scientific performance reviewing system effectively avoided the subjective influence, provided timely, highly efficient and accurate evaluating service and comparative analysis, enabling the assessment result much more objective and fair. It merits the promotional value.Key words: Evaluation of scientific research performance; Intelligent evaluation; Information system
Quizlet and Vocabulary Mastery: A Case of Indonesian EFL Learners' Reading Comprehension

Mastering vocabulary is an essential aspect of becoming a proficient language user. However, learning vocabulary yields problems among EFL learners such as low participation, students’ difficulties to understand the vocabulary, and difficulties to keep students engaged with lessons being learned. It also affects students’ ability to comprehend the text because mastering vocabulary is a basic predictor of reading success. This study, thus, aims to explore the implementation of Quizlet in learning vocabulary and how it assists students’ reading comprehension. This study adopted case study design involving tenth-grade students of one public senior high school in Kabupaten Bandung Barat, Indonesia. The data were gained through observation, interview, and test. The data were triangulated to get a portrayal of what occurred during the learning process. The findings indicated that Quizlet to some extent helped students in learning vocabulary through its repetition, enjoyable activities during the learning process, building autonomous learning, encouraging collaboration among the students, as well as a competitive atmosphere that appears during learning, and fostering students’ persistence in learning vocabulary. Besides, the result showed that Quizlet can be a vocabulary learning tool as a pre-reading strategy because it assisted the students to prepare unfamiliar vocabulary and promote reading motivation and interest. Therefore, it is suggested that teachers implement Quizlet for vocabulary learning to provide students with a new way of learning that is interesting, innovative, and probably can improve students’ learning experience.
Exploring Human-Drone Interaction

Drones are becoming increasingly interactive and offer novel opportunities for mobile interactions. This hands-on course will present rapid prototyping techniques for designing drone interfaces. We will use physical prototyping to approach participatory design techniques when designing future interactions, and digital prototyping tools with small-sized drones using Python (with an option for visual programming) to implement part of the interaction. The course will include an introduction to the field of human-drone interaction and its methodologies, two hands-on prototyping sessions with physical materials and digital tools, and a presentation session where participants get to show off their prototypes!
LibGuides: Expanded Access to Digital Content: COVID-19 Resources

As a response to the global COVID-19 pandemic, many of the Library’s content providers have expanded our access to digital content in order to support remote research, learning, and teaching.
LibGuides: Readers' Advisory 11: Home

Use this guide for support in choosing independent reading books. Explore what to read next based on reviews, and databases of read-alikes and suggestions.
Swedish Interest Rate Curve Dynamics Using Artificial Neural Networks

This thesis is a comparative study where the question is whether a neural network approach can outperform the principal component analysis (PCA) approach for predicting changes of interest rate cur ...
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech

It is known that Automatic Speech Recognition (ASR) is very useful for human-computer interaction in all the human languages. However, due to its requirement for a big speech corpus, which is very expensive, it has not been developed for most of the languages. Multilingual ASR (MLASR) has been suggested to share existing speech corpora among related languages to develop an ASR for languages which do not have the required speech corpora. Literature shows that phonetic relatedness goes across language families. We have, therefore, conducted experiments on MLASR taking two language families: one as source (Oromo from Cushitic) and the other as target (Wolaytta from Omotic). Using Oromo Deep Neural Network (DNN) based acoustic model, Wolaytta pronunciation dictionary and language model we have achieved Word Error Rate (WER) of 48.34% for Wolaytta. Moreover, our experiments show that adding only 30 minutes of speech data from the target language (Wolaytta) to the whole training data (22.8 hours) of the source language (Oromo) results in a relative WER reduction of 32.77%. Our results show the possibility of developing ASR system for a language, if we have pronunciation dictionary and language model, using an existing speech corpus of another language irrespective of their language family.
Breast Cancer Prediction Using Machine Learning Algorithm

Breast cancer is often the most lethal diseases with a large mortality rate especially among women. Despite the severe effect of the disease, it is possible to pinpoint the genre of breast cancer using different machine learning algorithms. However, many of these algorithms perform differently depending on their types and complexities. In our work, I have analyzed and compared the classification results of various machine learning models and find out the best model to classify between different types of breast cancers. I have used Logistic Regression, SVM, Random Forest, , K neighbor classifier and Decision Tree classifiers for our comparative study.My task is to critically analysis different data and to classify them with respect to the efficancy of each algorithm in terms of accuracy, precision, recall and F1 Score.My best accuracy was 98.68% percent which was found using SVM. These techniques are coded in python and executed in Jupyter NoteBook, the Scientific Python Development Environment. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.48%.I infer from our study that SVM is the will suited algorithm for prediction and on the whole KNN presented Ill next to SVM.
NEAR REAL-TIME MONITORING AND ALERT SYSTEM FOR EARTHQUAKE AND SEISMIC SIGNALS

The main objective of this project was to design the Near Real-time Monitoring and Alert System for Earthquake as a safety net system which has the capability to detect earthquake as well as any geological seismic signals. The project aims to come out with both software and hardware framework. The software is used to develop the block diagram and graphic user interface (GUI) whereas the hardware is used as the prototype casing. The objective of this project is to create a simulation of the earthquake vibration and user interface by using LabVIEW based on control system by collaborating the MEMS accelerometer sensor with Arduino microcontrollers. Besides, the system will also able to store the data for the later use by professionals working in this sector and send the collected data through the email. Moreover, the system is also able to determine the vibration level as the Magnitude Richter Scale by using the mathematical modeling with exponential and logarithmic produced by made-up vibration. All the data and graph will be displayed in LabVIEW which can be a user interface. The use of collected data for predicting the next earthquake from the people who responsible in processing data. This system is designed to react as soon as the p-wave come or primary wave across the sensors where this wave is the fastest wave created by an earthquake and first wave to be recorded by a seismograph. The user also may able to know the severity of the earthquake by observe the virtual alarm that was created in the system. Moreover, installing the GSM module on the hardware design is one of the future plans for making this project efficient in terms of alarming people from the devices. Therefore, this project could give an automatic alarm all by itself without the needs of earthquake engineer to monitor the system.
Demonstrating Wearable Microphone Jamming

We engineered a wearable microphone jammer that is capable of disabling any microphones in the user's surroundings, including hidden microphones. Our device is based on a recent exploit that leverages the fact that when exposed to ultrasonic noise, commodity microphones will leak the noise into the audible range. This allowed us to build a novel wearable microphone jammer that is worn as a bracelet on the user's wrist and jams ubiquitously. We found that our device outperforms state-of-the-art jammers. (1) Existing jammers built from multiple transducers exhibit blind spots, i.e., locations in which transducers destructively interfere and where a microphone cannot be jammed; instead, our wearable jammer leverages natural hand gestures that occur while speaking, gesturing or moving around to blur out blind spots. (2) Existing jammers are directional, requiring users to point the jammer to a microphone; instead, our wearable jams in multiple directions. This is beneficial in that it allows our jammer to even protect against microphones out of sight, such as those hidden behind everyday objects.
LibGuides: Summer Reading .... In Person AND Virtually 2021: Beanstack!

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LibGuides: Collection Development & Analysis: CD Assignments

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Fuzzy Multi-Attribute Decision Making untuk Klasifikasi Potensi Kewirausahaan Berdasarkan Theory of Planned Behavior (Fuzzy Multi-Attribute Decision Making for Classifying Entrepreneurial Potential based on Theory of Planned Behavior)

Indonesia government has launched a program to encourage youth entrepreneurship as a strategy to improve national economy. This paper proposes a method to find an entrepreneurial potential based on academic behavior features that are extracted from the Higher Education Database PDDikti. The proposed approach applies the Fuzzy Multi-Attribute Decision Making (FMADM) technique. Rules for extracting features of student academic behavior were following Theory of Planned Behavior (TPB) and resulting in 14 features. The FMADM model combines Fuzzy Simple Additive Weighting and Fuzzy Technique for Order Preference by Similarity to Ideal Solution, which is called FSAW-TOPSIS. Friedman Test demonstrated that FSAW-TOPSIS gives more optimal solution with the highest Mean Rank of the potential entrepreneurial value of 2.96. Besides, through Hamming Distance Test, FSAW-TOPSIS results the best order with a 98% percentage and ranking of the smallest Squared Error of 0.3%, which makes the proposed model offered a better solution. It can be concluded that using TPB variables in PDDikti environment with FSAW-TOPSIS technique provides an optimal recommendation on student entrepreneurship potential, which can be used as a part of a decision-making system for higher education management.
Self-Organizing High-Assurance Privacy

When using a network, devices might have to securely communicate with oneanother. Issues arise when the devices are unaware of others in the network, orwhen the devices have to be manually updated ...
Design activism: catalysing communities of practice

Over the last decade, we have witnessed renewed interest in design as a socially engaged practice. Much of the debates around ‘social design’ point towards myriad approaches and disciplinary fields interwoven with grass-roots initiatives and social movements. Among these, design activism has gained traction as critical spatial practice that operates on the fringes of commercial and institutional spheres. The temporal, spatial and experimental nature of design activism is well delineated in scholarship but its long-term effect on everyday urban environments remains elusive. Moreover, the influence of design activism on socio-spatial dynamics is indeed largely under researched. By mobilising social practice theory, this paper proposes a novel theorisation of design activism that sheds light on the social formations and collective practices catalysed through the activist impulse. This ontological shift embraces an understanding of the socio-material world through practice. Such characterisation of design activism underscores collective moments of integration of the constitutive elements of practice, encapsulated by Shove, Pantzar and Watson as ‘material, competence and meaning’. The authors' own empirical research, funded by the Economic and Social Research Council (ESRC) in the UK, reveals design activism as necessarily intertwined with other everyday practices – gardening, celebrating, playing – that coalesce around a shared sense of citizenship. It also advances the role of design activism in forging communities of practice: mutually supportive and self-sustaining groups emerging out of the personal relations sustained and organised around a practice of place making.
The Approach of Paremias in Parallel Corpora

This article highlights the parallel corpus and the approach of paremias in parallel corpora. In parallel corpuses, paremias are given on the basis of translation and comparable corpus. The corpus-based approach provides an opportunity to gain valuable insight about the distribution of paremiasn and their role in a language community nWe investigated paremias on the basis of the Russian National Corps and Sketch Engine corpora.n
Design and Evaluation of Intelligent Commodity Market Information Monitoring System for Rural Farmers and Traders in Ethiopia

Market information in Ethiopia is not readily available to rural farmers and traders. Currently, the existing market system allows intermediaries to decide the market price of the product unfavourably to the farmers. On the other hand, traders cannot not access marketoriented commodities that can be sold at good prices. This paper attempts to design SMS (short message service) based intelligent market information monitoring system that acts as a platform where farmers and traders can share market information in various market domains.The development of the system followed a Rapid Application Development (RAD) approach where testing and evaluation is performed for accuracy, efficiency, usability, acceptance and performance on two crucial aspects of the system: SMS gateway and Query Understanding Engine (QUE). The evaluation and test result of the system is promising. It is found to be that 95% (Accuracy), 89.2% (efficiency), 90.21 (SUS score) and 3.9 seconds (Mean Average performance/speed) is registered. Moreover, User Acceptance Testing (UAT) evaluation showedthat it is ‘Accepted’ for use. It showed that such kinds of systems can solve the problems of lack of easily accessible market information by rural farmers and traders this shows that the system can contribute significantly to improving the accessibility of market information for rural farmers and traders in Ethiopia.Keywords: Commodity Market, Market Information, User Acceptance Testing (UAT), Rural Farmers
A Conditional One-Output Likelihood Formulation for Multitask Gaussian Processes

Multitask Gaussian processes (MTGP) are the Gaussian process (GP) framework's solution for multioutput regression problems in which the $T$ elements of the regressors cannot be considered conditionally independent given the observations. Standard MTGP models assume that there exist both a multitask covariance matrix as a function of an intertask matrix, and a noise covariance matrix. These matrices need to be approximated by a low rank simplification of order $P$ in order to reduce the number of parameters to be learnt from $T^2$ to $TP$. Here we introduce a novel approach that simplifies the multitask learning by reducing it to a set of conditioned univariate GPs without the need for any low rank approximations, therefore completely eliminating the requirement to select an adequate value for hyperparameter $P$. At the same time, by extending this approach with both a hierarchical and an approximate model, the proposed extensions are capable of recovering the multitask covariance and noise matrices after learning only $2T$ parameters, avoiding the validation of any model hyperparameter and reducing the overall complexity of the model as well as the risk of overfitting. Experimental results over synthetic and real problems confirm the advantages of this inference approach in its ability to accurately recover the original noise and signal matrices, as well as the achieved performance improvement in comparison to other state of art MTGP approaches. We have also integrated the model with standard GP toolboxes, showing that it is computationally competitive with state of the art options.
Tree path majority data structures

We present the first solution to finding τ-majorities on tree paths. Given a tree of n nodes, each with a label from [1..σ], and a fixed threshold 0<τ<1, such a query gives two nodes u and v and asks for all the labels that appear more than τ⋅|Puv| times in the path Puv from u to v, where |Puv| denotes the number of nodes in Puv. Note that the answer to any query is of size up to 1/τ. On a w-bit RAM, we obtain a linear-space data structure with O((1/τ)lg⁡lgw⁡σ) query time, which is worst-case optimal for polylogarithmic-sized alphabets. We also describe two succinct-space solutions with query time O((1/τ)lg⁎⁡nlg⁡lgw⁡σ). One uses 2nH+4n+o(n)(H+1) bits, where H≤lg⁡σ is the entropy of the label distribution; the other uses nH+O(n)+o(nH) bits. By using just o(nlg⁡σ) extra bits, our succinct structures allow τ to be specified at query time. We obtain analogous results to find a τ-minority, that is, an element that appears between 1 and τ⋅|Puv| times in Puv.
Cryptography: A Never Ending Technology

In today’s world, we see every person is having some personal or private data, which he/she wants to keep private and confidential so that noother person can access that data without his or her permission. Cryptography is a technique to implement this process. Cryptography is being used from veryancient times about thousands of years ago. It keeps on modernizing with the time. Let’s see in this article what cryptography is all about.
Search for Binary Black Holes in 10 years of Fermi/LAT Data with Information Field Theory

Blazars are powered by super-massive black holes at their centers and are known for extreme variability on timescales from minutes to years. In case of a binary black hole system, this duality is traceable as periodic modulation of their MeV to GeV emission. So far, no high-significance periodicity has been found with standard approaches. We developed a method to search for periodic patterns in Fermi/LAT light curves, using information field theory (IFT). IFT is a formulation of Bayesian statistics in terms of fields. Bayesian statistics is ideal for the problem at hand since the data is incomplete, irregularly sampled and obeys non-Gaussian statistics such that common least-squares methods do not apply. We present a proof of principle of this method, analyzing a sample of promising binary black hole candidates like PG 1553 + 113 and Mrk 501.
LibGuides: Writing Resource Hub: Grammar & Mechanics Resources

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LibGuides: Wegner Library Interlibrary Loan-ILLiad: Request Article with UGet It!

A how-to guide for creating, navigating and placing a request with Wegner's Interlibrary Loan System, ILLiad. How to create a request
Urgensi yang Harus diperhatikan dalam Dunia Pendidikan

ABSTRACTEducation has the right to be obtained by anyone especially those who excel but have not been able to finance education. Education can be obtained from various places from formal and non-formal. There are many interests that must be considered in education. Among them that must be considered are growth in education, must have motivation with good goals, psychology in education, factors that influence the quality of education. Facility costs and also friends also greatly influence the development of education. There are so many ways that can be done in gaining knowledge including methods such as discussions, groups, forums, questions and answers, assignments, demonstrations and so forth.
Facial Micro-Expression Spotting From Videos

Micro-expressions are involuntary facial expressions that last between 1/25th to 1/5th of a second and it usually happens when people try to mask their emotions in high-stake situations. As a result, it is hard to detect the occurrence of a spontaneous micro-expression because the limitation of human vision in spotting the brief and subtle change of facial expression. Nonetheless, the study of the micro-expressions has gaining more attentions lately because there are a number of impressive potential contribution can be derived from the application of micro-expressions recognition despite the difficulty in recognizing the micro-expression. For instance, individual who works in law enforcement can detect lies told by a criminal by reading the criminal’s micro-expression, health professional can produce a more accurate diagnosis of their mentally-ill patient by obtaining complete information through recognizing their patient’s micro-expression, and many more. In order to cope with the limitation of human vision in reading the micro-expressions, a computer-based micro-expression spotting system is suggested in this proposal. A micro-expression recognition system can be categorized into two major tasks which are the spotting of the fleeting change of facial expression and the classification of the emotion behind the spotted micro-expressions. In this thesis, the proposed system is solely focus on the spotting part because spotting of the micro-expression is the primitive procedure in the micro-expression recognition system. Last but not least, the detection of the change in the ratio of the Euclidean distance computed at the selected facial regions such as eyebrow and mouth between video frames that last for 8 consecutive frames will be indicated the presence of the micro-expressions. In a nutshell, the average accuracy rate and the highest accuracy rate achieved by this system are 64.77 % and 82.30 % respectively.