Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI HOTSPOT DENGAN USER MANAGERUNTUK INTERNET WIRELESS MENGGUNAKANMIKROTIK Rb 941-2nd DI MTsN 1SUMBAWA BESAR

This study aims to implement a wifi hotspot at MTsN 1 Sumbawa Besar to minimize network disturbances. This research is implemented using network devices such as Mikrotik Router, Windows Server 2007 and other supporting network devices. The network development method uses the Network Development Life Cycle (NDLC) approach. It is starting with the process of analysis, design, simulation to implementation. The instrument of data collection techniques in the study used observation, interviews, documentation and literature study. The final result of this research is to implement a hotspot network that can cover the entire madrasa area. MTsN 1 Sumbawa Besar canAccess the internet without any network disturbances.
Thriving Places, Thriving People: A Look at USM's Master of Library and Information Science

Article in Arete (Winter 2020/21, p. 9-11) featuring two recent MLIS graduates, Carrie Pennington Mastley and Laura Valliant. Reprinted with permission from USM Graduate School.
LibGuides: Criminal Justice LibGuide: Websites

This guide was created by Benedict College Librarian Brian Crawley to offer basic library and web resources for those studying Criminal Justice. The LibGuide offers suggested books, database, webpages and more for beginning research in the field of Crimin
Assistive Interaction Design for Early Phase Satellite Assembly in Immersive Augmented Reality

Planning a spacecraft mission is an intricate process, requiring expertise in a variety of domains. Space agencies use specially built Concurrent Engineering Facilities (CEF) to conduct the design studies for missions. Recently, AR-basedimmersive environments have been proposed for the early phase assembly of satellites in CEFs. Assistive interactions can support the design process and facilitate better user interactions in suchenvironments.In this thesis, we explore assistive interactions for early phase satellite assembly in immersive augmented reality environments. We identify the key requirements for assistive interactions for early phase satellite assembly. Based on our observations, we propose an intelligent AR-based interface for early phase satellite assembly that could be used for visualizing, planning, and reconfiguring early phase satellite designs. Oursystem uses simulation optimization to automatically calculate the configurations from the user-specified mission requirements and assists the user in completing the assembly by providing intelligent suggestions. We empirically evaluate the intelligent suggestions of our system and discuss the findings. Our work shows that intelligent interactions that assist the user in performing the assembly can result in enhanced usability and could be a valuable addition in AR-based environments for virtual satellite assembly.
Facial Expression Recognition Using VGG16 and LSTM

Facial expression recognition has made significant progress using deep learning, which has received increasing attention across all fields. Mainly, conventional facial expression recognition systems require constrained datasets for optimal performance, making them unsuitable for use on real-time data. Such real-time sequences limit the efficiency and accuracy of the traditional system. An innovative deep learning framework is proposed in this work that combines dual VGG16 and long short-term memory (LSTM) cells to recognize facial expressions in real-time. Three main aspects of the novel framework are: (i) To enhance each image's edge detail and resolve illumination variances, edge enhancement              pre-processing techniques are utilized; (ii) In order to extract spatial features from                        pre-processed images, the VGG16 model is used, which extracts them quite effectively; (iii) An LSTM layer is used in conjunction with VGG16 for extracting temporal relationships between successive frames along with the spatial feature maps. Comparing the experimental data to existing implementations, facial expression recognition has improved considerably in terms of robustness and accuracy.
A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Due to the extreme insistence for digital image processing, plentiful modern noise suppressing techniques are embodied of dissimilarity process and suppressing process. One of the extreme capability dissimilarity is hard decision threshold (HDT) dissimilarity, which has been recently declared in 2012, for suppressing the impulsive noisy photographs thus the computer experimental statement attempts to investigate the capability of the noise suppressing technique that is stand on HDT dissimilarity for the processed photographs, which are corrupted by fixed-intensity impulse noise (FIIN). This paper proposes the noise suppressing technique stand on HDT dissimilarity for FIIN. There are 3 primary contributions of this paper. The first contribution is the statistical average of the HDT dissimilarity of noise-free elements, which are computed from plentiful ground-truth photographs by varying window size for the best HDT window size. The second contribution is the statistical average of the HDT dissimilarity of corrupted elements, which are computed from plentiful corrupted photographs by varying outlier density for the best HDT window size. The final contribution is the statistical interrelation of the capability of the noise suppressing technique and hard consistent of HDT dissimilarity are investigated by varying the outlier denseness for the best HDT hard consistence.
Power-Based Attacks on Spatial DNN Accelerators

With proliferation of DNN-based applications, the confidentiality of DNN model is an important commercial goal. Spatial accelerators, that parallelize matrix/vector operations, are utilized for enhancing energy efficiency of DNN computation. Recently, model extraction attacks on simple accelerators, either with a single processing element or running a binarized network, were demonstrated using the methodology derived from differential power analysis (DPA) attack on cryptographic devices. This paper investigates the vulnerability of realistic spatial accelerators using general, 8-bit, number representation. We investigate two systolic array architectures with weight-stationary dataflow: (1) a 3 $\times$ 1 array for a dot-product operation, and (2) a 3 $\times$ 3 array for matrix-vector multiplication. Both are implemented on the SAKURA-G FPGA board. We show that both architectures are ultimately vulnerable. A conventional DPA succeeds fully on the 1D array, requiring 20K power measurements. However, the 2D array exhibits higher security even with 460K traces. We show that this is because the 2D array intrinsically entails multiple MACs simultaneously dependent on the same input. However, we find that a novel template-based DPA with multiple profiling phases is able to fully break the 2D array with only 40K traces. Corresponding countermeasures need to be investigated for spatial DNN accelerators.
Deep Learning based Trusted Model for Pervasive Computing using a LSTM-based Classification Model

Pervasive computing is the rising trend of embedding computational capability into each day objects to make them successfully communicate and perform helpful tasks in a approach that minimizes the end user's need to cooperate with computers. Pervasive computing's role is particularly prominent in the field, where it allows for the distribution of computational capabilities to the areas where people labour, raising issues like as religion, privacy, and uniqueness. The suggested research work intends to put into reality a deep learning-based wireless communication architecture to handle these generic problems in order to provide an optimum solutions. During the development of the proposed trustworthy model, long short-term memory construction is applied. The projected model's application is confirmed by comparing its effectiveness to that of a generic back propagation neural network. The LSTM-based model had a substantially higher accuracy rate of 93.87 percent than the back propagation-based deep model, which had an accuracy rate of 85.88 percent.
Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning

Humanitarian challenges, including natural disasters, food insecurity, climate change, racial and gender violence, environmental crises, the COVID-19 coronavirus pandemic, human rights violations, and forced displacements, disproportionately impact vulnerable communities worldwide. According to UN OCHA, 235 million people will require humanitarian assistance in 2021 . Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations. Scattered data science efforts exist to address these challenges, but they remain isolated from practice and prone to algorithmic harms concerning lack of privacy, fairness, interpretability, accountability, transparency, and ethics. Biases in data-driven methods carry the risk of amplifying inequalities in high-stakes policy decisions that impact the livelihood of millions of people. Consequently, proclaimed benefits of data-driven innovations remain inaccessible to policymakers, practitioners, and marginalized communities at the core of humanitarian actions and global development. To help fill this gap, we propose the Data-driven Humanitarian Mapping Research Program, which focuses on developing novel data science methodologies that harness human-machine intelligence for high-stakes public policy and resilience planning. The proceedings of the 1st Data-driven Humanitarian Mapping workshop at the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, August 24th, 2020.
WUTR: Wheel-Inspired Unified Theory of Reasoning

For ages, philosophers and logicians thought that there were 3 different inference methods. They are abduction, deduction, and induction. The same methods are considered modes of argumentation. This research paper is based on recent relevant studies, then, inspired by the wheel as a metaphor, it proposes a unified theory of reasoning . Just as water, steam and snow are three manifestations of one thing, but in different phases. The proposed wheel is then scrutinized for validity and usability
THE EFFECT OF USING BAMBOO DANCING COOPERATIVE LEARNING STRATEGY TOWARDS STUDENTS ENGLISH SPEAKING SKILL IN SENIOR HIGH SCHOOL 3 JAMBI CITY

The purpose of this study was to find out the effect or not there was any effect of using Bamboo Dancing Cooperative Learning towards Student English Speaking Skill. The problem in this study referred to the learning strategy English speaking skills. The study was conducted at the MIPA 6 and MIPA 7 in senior high school. The population of the study was 71 student in the second semester of MIPA 6 and MIPA 7. The sample of this study was all of the 36 is MIPA 6, 22 female and 14 male students. The types of the data were quantitative. The data were obtained by using tell the recount text from the picture to test the speaking of students, and analyzed by using manual effect analysis with speaking table score, oral proficiency scoring categories. Furthermore based on statistical compulation t-test was found that the coefficient of  = 4,05 where the coefficient of = 1,994. It means that there significant effect of using bamboo dancing method toward students’ english speakig skill. It also indicated that there significant effect of using Bamboo Dancing Cooperative Strategy Towards Students English Speaking Skill in Senior High School 3 Jambi City in 2020-2021 Academic Year. Keywords: Speaking, Bamboo Dancing, Cooperative Learning Strategy
A Declarative Framework for Mining Top-k High Utility Itemsets

The problem of mining high utility itemsets entails identifying a set of items that yield the highest utility values based on a given user utility threshold. In this paper, we utilize propositional satisfiability to model the Top-k high utility itemset problem as the computation of models of CNF formulas. To achieve our goal, we use a decomposition technique to improve our method’s scalability by deriving small and independent sub-problems to capture the Top-k high utility itemsets. Through empirical evaluations, we demonstrate that our approach is competitive to the state-of-the-art specialized algorithms.
Detection Of Malicious Urls Using Machine Learning Techniques

The net has become an essential portion of our everyday life for facts connection and knowledge diffusion. It helps in order to transact information regular, rapidly and quickly. Identifying theft in addition to identity fraud usually are referred as two sides of cyber-crime through which hackers in addition to malicious users get the personal information of current legitimate users to attempt fraud or deception determination for profit. Harmful URLs host unsolicited content (spam, phishing, drive-by exploits, and so forth. ) and attract unsuspecting users in order to become victims regarding scams (monetary reduction, theft of private information, and malware installation), and cause losses of billions of dollars each year. To find such crimes techniques should be quickly and precise along with the ability in order to discover new malicious content. Traditionally, this specific detection is carried out mostly with the usage of blacklists. However, blacklists should not be inclusive, and lack typically the ability to discover newly produced malicious URLs. To enhance the generality of malicious URL detectors, machine learning techniques have been explored along with increasing attention inside recent years. Inside this paper, I actually use a basic algorithm to discover and predicting Web addresses it truly is good or perhaps bad and in contrast to two other methods to know (SVM, LR).
Design of Wheeled Football Robot Coordination System at Base Station Using TCP / IP

Wheel Soccer Division, Indonesian Robot Contest is an annual competition held by the Ministry of Education. One team in this division consists of 3 robots connected in wireless communication coordinated by base station. Besides that, base station computer also connects the robots with referee computer or called Refbox (Referee Box). Refbox as server for base station computer and base station computer as server for robots. The problem arises in base station has to play double roles, as server and client. In testing had two the results of communication from the referee box to basestation and basestation to the client so the data from the referee box can be accepted by the client with a success rate of 93%.
A Comparative study of Andra bhasha bhushanam and Tolkappiyam

This review examines the subject under five sub-headings: writing intent, writing structure, Type of techniques, vocabulary, and spelling observation. Of these, the structure of the text is examined under two sub-headings, external structure and internal structure. Among the 32 writing techniques mentioned by Tolkappiyar, 1. Writing purpose, 2. Section order, 3. Codification expression, 4. Section expression, 5. Semantic coordination, 6. Fictional gestures, 7. Meaningful reference, 8. Alternative formula entry, 9. Future reference, 10. Sutra and examples are examined in bilingual grammars. In formulaic construction methods such as sutrartham, sutra structure, relation between sutras, gana, nipatam are taken into consideration. In the alphabet section, topics such as distribution of letters, creation of letters, conjugation, and word usage are examined. Finally, at the end of the character the 10 things identified in the bilingual grammars examined comparatively are revealed and the table of reference is compiled.
Unsupervised Open-Domain Question Answering

Open-domain Question Answering (ODQA) has achieved significant results in terms of supervised learning manner. However, data annotation cannot also be irresistible for its huge demand in an open domain. Though unsupervised QA or unsupervised Machine Reading Comprehension (MRC) has been tried more or less, unsupervised ODQA has not been touched according to our best knowledge. This paper thus pioneers the work of unsupervised ODQA by formally introducing the task and proposing a series of key data construction methods. Our exploration in this work inspiringly shows unsupervised ODQA can reach up to 86% performance of supervised ones.
Towards Learning a Vocabulary of Visual Concepts and Operators using Deep Neural Networks

Deep neural networks have become the default choice for many applications like image and video recognition, segmentation and other image and video related tasks.However, a critical challenge with these models is the lack of explainability.This requirement of generating explainable predictions has motivated the research community to perform various analysis on trained models.In this study, we analyze the learned feature maps of trained models using MNIST images for achieving more explainable predictions.Our study is focused on deriving a set of primitive elements, here called visual concepts, that can be used to generate any arbitrary sample from the data generating distribution.We derive the primitive elements from the feature maps learned by the model.We illustrate the idea by generating visual concepts from a Variational Autoencoder trained using MNIST images.We augment the training data of MNIST dataset by adding about 60,000 new images generated with visual concepts chosen at random.With this we were able to reduce the reconstruction loss (mean square error) from an initial value of 120 without augmentation to 60 with augmentation.Our approach is a first step towards the final goal of achieving trained deep neural network models whose predictions, features in hidden layers and the learned filters can be well explained.Such a model when deployed in production can easily be modified to adapt to new data, whereas existing deep learning models need a re training or fine tuning. This process again needs a huge number of data samples that are not easy to generate unless the model has good explainability.
The influence of student motivation on student achievement in junior high school

This study aims to determine and analyze the effect of student motivation on student achievement in Junior High School 40 OKU. This type of research is quantitative. The sample in this study was 96 students of Junior High School 40 OKU. Data were collected using a questionnaire, documentation, and observation. Data were analyzed using the SPSS program. The results of this study suggest that there is an effect of student motivation on student achievement. This paper is expected to be useful for school policymakers in increasing student motivation and student achievement.
DIPS at CheckThat! 2021: Verified claim retrieval

This paper outlines the approach of team DIPS towards solving the CheckThat! 2021 Lab Task 2 - a semantic textual similarity problem for retrieving previously fact-checked claims. The task is divided into two subtasks, where the goal is to rank a set of already fact-checked claims based on their relevance to an input claim. The main difference between the two is the data sources, i.e., Task 2A's claims are tweets, while Task 2B - debates and speeches. For solving the task, we combine variety of algorithms - BM25, S-BERT, a custom classifier, and RankSVM into a claim retrieval system. Moreover, we show that data preprocessing is critical for such tasks and can lead to significant improvements in MRR and MAP. We have participated in the English edition of both subtasks and our system was ranked third in Task 2A, and first in Task 2B. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression

Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight clustering-based DNN model compression. DKM casts k-means clustering as an attention problem and enables joint optimization of the DNN parameters and clustering centroids. Unlike prior works that rely on additional regularizers and parameters, DKM-based compression keeps the original loss function and model architecture fixed. We evaluated DKM-based compression on various DNN models for computer vision and natural language processing (NLP) tasks. Our results demonstrate that DKM delivers superior compression and accuracy trade-off on ImageNet1k and GLUE benchmarks. For example, DKM-based compression can offer 74.5% top-1 ImageNet1k accuracy on ResNet50 DNN model with 3.3MB model size (29.4x model compression factor). For MobileNet-v1, which is a challenging DNN to compress, DKM delivers 63.9% top-1 ImageNet1k accuracy with 0.72 MB model size (22.4x model compression factor). This result is 6.8% higher top-1accuracy and 33% relatively smaller model size than the current state-of-the-art DNN compression algorithms. Additionally, DKM enables compression of DistilBERT model by 11.8x with minimal (1.1%) accuracy loss on GLUE NLP benchmarks.
Flexible Code Using Polymorphism

Ask a developer, “What are the fundamental characteristics of object-oriented programming (OOP)?” and you will get an immediate reply saying, “Classes (and objects), inheritance, abstraction, encapsulation, and polymorphism are the most important characteristics in OOP. In addition, when you analyze enterprise code that is based on OOP, you’ll find different forms of polymorphism.” But the truth is, a novice programmer rarely uses the power of polymorphism. This chapter focuses on this topic. It shows you some simple but powerful code examples using this principle.
Chi-square Loss for Softmax: an Echo of Neural Network Structure

Softmax working with cross-entropy is widely used in classification, which evaluates the similarity between two discrete distribution columns (predictions and true labels). Inspired by chi-square test, we designed a new loss function called chi-square loss, which is also works for Softmax. Chi-square loss has a statistical background. We proved that it is unbiased in optimization, and clarified its using conditions (its formula determines that it must work with label smoothing). In addition, we studied the sample distribution of this loss function by visualization and found that the distribution is related to the neural network structure, which is distinct compared to cross-entropy. In the past, the influence of structure was often ignored when visualizing. Chi-square loss can notice changes in neural network structure because it is very strict, and we explained the reason for this strictness. We also studied the influence of label smoothing and discussed the relationship between label smoothing and training accuracy and stability. Since the chi-square loss is very strict, the performance will degrade when dealing samples of very many classes.