Reviewerly/exHarmony huggingface dataset:
Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization

Training Convolutional Neural Network (CNN) models is difficult when there is a lack of labeled training data and no unlabeled data is available. A popular method for this is domain adaptation where the weights of a pre-trained CNN model are transferred to the problem setup. The model is pre-trained on the same task but in a different domain that has plenty of labeled data samples available. In a CNN model, we can rearrange the weights of a convolutional layer by permuting them along the input channel dimension. This work shows that certain weights that are learned in the pre-trained model work well in the problem setup when the weights are rearranged in this manner. Computing the set of all possible rearrangements of the weights is computationally intractable. This work proposes two algorithms to find a good rearrangement of the weights in reasonable computation time. The solutions from the algorithms perform equally well or better than fine-tuning in the domain adaptation between SVHN and MNIST data.
The Effectiveness of Using Think-Aloud Strategy to Improve Students’ Reading Comprehension Ability in Recount Text at The Eighth Grade Students Of SMP N 3 Gombong in The Academic Year of 2015/2016

The  objective  of  the  study  is  to  describe  the  effectiveness  of  using  think-aloud strategy to teach reading recount text to the eighth grade students of SMP N 3 Gombong. The  subject  of  the  study  is  the  eighth  grade  students  of  SMP  N  3  Gombong  in  the academic year 2015/2016. In this research, the researcher took 64 students of VIII C and VIII A as sample; they are experimental and control class. The result of the study showed that  the  students’  progress  of  experimental  group  in  mastering  reading  ability  is  really good. It was found the result of t-value was 4.89. Based on the 0.05 significant level, the value of t-table was 1.68. The computation showed that t- value was higher than t-table, which  was  4.89>1.68.  Moreover,  the  mean  of  control  class  (71.38)  is  included  into interval  70-79.  It  means  that  students  of  control  class  have  good  result  on  their reading ability. The mean of experimental class (77.13) is included into interval 70-79. It means that students of experimental class have good result on their reading ability. It shows that using think-aloud strategy on the teaching-learning process gives a positive contribution toward the students’ reading ability. So, the use of Think-Aloud Strategy was effective to improve students reading ability in recount text to eighth grade of SMP N 3 Gombong in Academic Year 2015/2016.
PENINGKATAN AKTIVITAS DAN PRESTASI BELAJAR MATEMATIKA MELALUI MODEL PEMBELAJARAN JIGSAW

The purpose of this study is to improve the activities and student learning outcomes through the Jigsaw learning model. This type of research is Class Action Research (CAR). The subjects of this study were teachers and eighth grade students of SMP N 2 Batujajar in the first semester which amounted to 34 students. The method of data collection is done through interviews, observation, tests and documentation. Data analysis techniques use methods that include data collection, data reduction, data presentation, and conclusion drawing. The results of this study indicate an increase in activity and learning achievement in mathematics. this can be seen through the following indicators: the ability to express thinking ideas during teaching and learning activities is 10 students (29.40%) after the action became 26 students (76.40%). activeness of asking the teacher in teaching and learning activities there were 9 students (26.40%) after the action became 28 students (82%). the ability to discuss with the study group there were 13 students (38.20%) after the action became 30 students (88.20%). students who have grades fulfilling KKM (≥75) there are 12 students (35.20%) after the action became 29 students (85.20%). The conclusion of this study is that the jigsaw learning model can improve the mathematics learning activities and achievements of class VIII students of SMP N 2 Batujajar Semester I.
Hand Printed Characters Recognition Using Wavelet Features and Neural Networks.(Dept.E)

Simplifying automatic recognition algorithms for hand-printed characters attracted immense research efforts [1-3]. Character recognition systems can improve the interaction between man and machine in many applications, including office automation, business and data entry applications. This paper introduces the use of bi-dimensional wavelet as features extractor that is feed to Artificial Neural Networks (ANNs) for recognition Latin hand-printed characters. An experiment to verify the efficiency of the system was performed. The proposed technique can be divided into three major steps: the first step is pre-processing in which the original image is transformed into a digitized image utilizing a 300 dpi scanner. Second, feature extraction using wavelets Finally, multilayer artificial neural network is used for characters recognition.
Evaluation of the Pedagogical Model Used in Virtual Reality Simulations for Training Portuguese Firefighters

This paper details the pedagogical model used in virtual reality simulations for training Portuguese fire officers in emergency operations management and the evaluation of students about the implementation of the model throughout the training courses. The data of this quantitative study were collected over two years (2018-2019) and involved the participation of 89 fire officers from all over the country, who attended 12 training courses delivered at the Portuguese National Fire Service School (Escola Nacional de Bombeiros). Each course included seven to eight students, all experienced incident commanders with at least five years of service in the role. Every student played as incident commander in two different virtual reality training scenarios and their performance was assessed using a standardized evaluation tool. Students evaluated the pedagogical model implementation in virtual reality simulations through a questionnaire applied at the end of each course. The study’s conclusions point out that most students consider the pedagogical model successfully implemented.
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations

We present the Topology Transformation Equivariant Representation (TopoTER) learning, a general paradigm of unsupervised learning of node representations of graph data for the wide applicability to Graph Convolutional Neural Networks (GCNNs). We formalize the TopoTER from an information-theoretic perspective, by maximizing the mutual information between topology transformations and node representations before and after the transformations. We derive that maximizing such mutual information can be relaxed to minimizing the cross entropy between the applied topology transformation and its estimation from node representations. In particular, we seek to sample a subset of node pairs from the original graph and flip the edge connectivity between each pair to transform the graph topology. Then, we self-train a representation encoder to learn node representations by reconstructing the topology transformations from the feature representations of the original and transformed graphs. In experiments, we apply the TopoTER to the downstream node and graph classification tasks, and results show that the TopoTER outperforms the state-of-the-art unsupervised approaches.
Sistem Keamanan Model Sidik Jari (Digital Key Security) Menggunakan Pemrograman Arduino Pada Sepeda Motor

Abstract: The fingerprint-type digital key model driving safety system offers an accurate security system in minimizing motorcycle units from potential theft crimes that are rampant. This study aims to develop a security system for a fingerprint model on a motorcycle using the arduino-based prototype development method. The test was carried out to determine the results of the arduino programming configuration on the board with a fingerprint scanner. Research results: 1) the design processed using the Arduino application shows that done compiling means that the configuration is successful. 2) The fingerprint scanning test results show that the ignition of the starter is faster when the palm of the finger is dry; that is, the average is activated 2.31 seconds. 3) The result of detection on unknown finger will be activate the alarm. Keywords: Security system, Fingerprint, Arduino application
A view on automated neural graph topology generation and a viable direction of innovation

Training deep neural networks requires knowledge and ample experimental time, as well as computational resources due to the search process of the architectural design. In this work, we review the current main research directions in the area of network architecture and finally propose in contrast a novel architecture, namely MultiGatedUnit, that uses directly learnable self-gating mechanisms for automated graph topology generation, currently in research and experimentation phase.
Multi-core and SIMD Architecture Based Implementation on SHA-256 of Blockchain

SHA-256 is a completely unpredictable pseudorandom function which generates unique output for a given input ensuring data authenticity and non-repudiation. It is the cornerstone for imparting security and privacy into Blockchain and its efficiency of calculation decides the performance of Blockchain. In this paper, we propose two novel methods to accelerate the calculation of SHA-256 in different situations. To eliminate the useless operations, we present pre-expanded padding blocks and hard coded into the software. On this basis, for a single message containing multiple 512-bit message blocks, we propose Interleaved Multi-Vectorizing Message Scheduling (IMV-MS) to optimize the message schedule stage of SHA-256, which utilize the interleaved multi-vectorizing (IMV) to combine single instruction multiple data (SIMD) vectorization with SHA-256. It splits a vectorized program into multiple states, then it interleaves the execution of vectorized states from those running instances which can make full use of the data parallelism in SIMD. On the other hand, in the situation where we hash several messages simultaneously, we propose the modified SHA-256 which employs SIMD instructions and thread-level parallelism technology together to realize parallel optimization on SHA-256. As experimental results show, IMV-MS and the proposed SHA-256 achieves up to 6.36X, 60.38X better performance compared with the pure SIMD vectorization and the pure scalar implementation, respectively.
Performance comparison of Bat search and Cuckoo search using software artefact infrastructure repository and regression testing

Regression testing is conducted to ensure that no new errors have been introduced into the software as a result of the maintenance activity performed. Re-executing all the existing test cases is however a highly expensive and time consuming approach to gain this confidence. Previous research revealed that nature inspired algorithms have vast application in this area. In this paper, Bat search algorithm is tested against Cuckoo search algorithm to solve regression test case selection problem. Two factors: number of faults covered and computational time are considered for the comparison. Extensive experiments have been conducted over the objects adopted from benchmarked software artifact infrastructure repository. Rigorous statistical tests are conducted to draw a conclusion that Cuckoo search is marginally advantageous over Bat search algorithm with respect to performance parameters. We believe that the results reported in this paper will enable researchers to develop more powerful algorithm for testing in near future.
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising

Sparse representation via a learned dictionary is a powerful prior for natural images. In recent years, unrolled sparse coding algorithms (e.g. LISTA) have proven to be useful for constructing interpretable deep-learning networks that perform on par with state-of-the-art models on image-restoration tasks. In this study we are concerned with extending the work of such convolutional dictionary learning (CDL) models. We propose to construct strided convolutional dictionaries with a single analytic low-pass filter and a set of learned filters regularized to occupy the complementary frequency space. By doing so, we address the necessary modeling assumptions of natural images with respect to convolutional sparse coding and reduce the mutual coherence and redundancy of the learned filters. We show improved denoising performance at reduced computational complexity when compared to other CDL methods, and competitive results when compared to popular deep-learning models. We further propose to parameterize the thresholds in the soft-thresholding operator of LISTA to be proportional to the estimated noise-variance from an input image. We demonstrate that this parameterization enhances robustness to noise-level mismatch between training and inference.
Burglars break into and steal merchandise at a Kitsap County pot shop

Kitsap County Sheriff's detectives are asking for the public's help to identify two burglary suspects caught on camera on Tuesday, Jan. 12th at about 2:30 a.m. after they tossed a tire jack and a rock through the front window of a marijuana shop in Silverdale.
Pembelajaran Daring Matematika Melalui Media Quizizz Berhadiah Dalam Meningkatkan Minat Belajar Pelajar SMPN 58 Surabaya

This research was conducted with the aim of knowing the impact of online learning methods, as well as the impact of using Quizizz as a learning media with prizes on student interest in learning at SMP Negeri 58 Surabaya. Data collection methods used by researchers in this study are observation, interviews, and documentation. The data analysis technique used is descriptive exploratory method which aims to describe and conclude the impact of using online learning methods for Mathematics, and the impact of using Quizizz media with prizes on student interest in learning.The results of this study can be seen: (1) in the use of online learning methods for Mathematics, student understanding has increased 58.3% from the previous condition. (2) the use of Quizizz learning media with prizes has an impact on student learning patterns, such as: level of concentration, memory, comprehension, durability of learning time, and an increase in student understanding, which is seen from the increase in the percentage level of student learning outcomes by 83. 3%.
Introduction of Technology of Social and Professional Self-determination of High School Students in the Conditions of Rural Institutions of General Secondary Education

This article substantiates the technology of social and professional self-determination of high school students in a rural institution of general Secondary Education, describes the developed stages and ways of implementing this technology in a meaningful way, shows the experience of implementation, and proves the effectiveness of the technology of social and professional self-determination of high school students. Today, there is an urgent need to create conditions for the formation of an individual educational request among high school students of general secondary education institutions, a personal need to choose professional activities, readiness for professional self-determination and further self-realization in new economic and socio-cultural conditions. The technology of socio-professional self-determination of high school students in the conditions of a rural institution of general secondary education in this study has all the signs of socio-pedagogical technology, which is defined as the most optimal sequence of socio-pedagogical activity, which allows to get a rational result in a specific situation. Work on the introduction of social and professional self-determination technology was carried out at 2 levels: internal school and external school.
Risks and Benefits of Initial Coin Offerings: Evidence from Impak Finance, a Regulated ICO

This study provides a better understanding of the business and legal environment surrounding Initial Coin Offerings (ICOs). Key stakeholders involved are the firms launching ICOs, the investors and the financial regulators. We conducted a case study of a firm that launched an ICO, impak Finance, the first regulated ICO in Canada. Based on the interviews of key respondents and documentation analyses, we developed a framework identifying the main risks and benefits for firms to perform an ICO and differentiating between unregulated and regulated ICOs.Our study makes a number of research and practical contributions. First, we document the case of the first legal ICO in Canada through the examination of documents and interviews with key stakeholders. The interviews conducted provided access to privileged insider information. Second, very few studies have been conducted on the economic impact of disruptive technologies, such as blockchains, as an important investment financing innovation vehicle. ICOs using blockchains is disruptive not only from a technology standpoint but also from a financial standpoint. While the possible applications of blockchains are unknown to us to date, we do know that blockchains have the potential to challenge the traditional financial system monitored by financial regulators. Lastly, the study identifies, through a framework, the risks and benefits of performing an ICO in an unregulated versus a regulated context, which has practical implications for firms operating in the FinTech space. We trust that this framework will be useful for future research, for firms operating in the crypto space and for financial regulators.
Occurrence and antimicrobial susceptibility of Staphylococcus aureus on automated teller machines in Kaduna Metropolis, Kaduna State, Nigeria

Background: The emergence of antimicrobial resistance has become a global public health menace with the increasing prevalence of Methicillin-Resistant Staphylococcus aureus. Methods and materials: The metallic keypads of Automated Teller Machines (ATMs) were examined for bacterial contamination, occurrence and antimicrobial susceptibility of Staphylococcus aureus. Surface swabs were collected from 235 Bank ATMs operating in Kaduna metropolis. The Laboratory analyses employed were the quantification of bacterial and coliform loads, identification of Staphylococcus species using biochemical tests and Microgen ID test system, antibiotic sensitivity test was also carried out on the isolates and minimum inhibitory concentration evaluation of Oxacillin and Vancomycin were conducted on the resistant isolates. Furthermore, Penicillin Binding Protein (PBP2a) Latex Agglutination Test was conducted to detect PBP2a in isolates of Staphylococcus aureus. Chi-square and Fisher's exact tests at 95% confidence were used to test for association. Value of p < 0.05 was considered statistically significant. Results: The results indicated the bacterial contamination of the ATM keypads in Kaduna metropolis with mean total aerobic bacterial counts ranged between 3.3 ± 1.04–4.4 ± 1.26 mean/log/cfu/cm2, and mean total coliform counts ranged between 1.0 ± 1.32–3.2 ± 1.75 mean/log/cfu/cm2. Conclusion: ATM keypads were contaminated with Staphylococcus aureus (38.3%), and 95.6% of the isolates were MRSA. The isolates showed a high frequency of resistance to front line antibiotics including Ciprofloxacin (91%) and Gentamicin (60%). Hand washing and proper cleaning regimens should be practised to reduce contamination in the course of using ATMs.
Utilization of Electronic Medical Records for Anggrek 7 Integrated Servant Post

Processing of medical record information at the Anggrek 7 Integrated Servant Post, Langenharjo Village, Sukoharjo Regency, is still experiencing problems including recording medical record data and history of health services for toddlers and pregnant women as well as reporting activities. Electronic medical records as an effort to overcome these obstacles. The research method is descriptive by following the system development life cycle method. Electronic medical records are capable of recording master data including data on toddlers, pregnant women and elderly data. The history of health services for children under five, pregnant women and the elderly can be done through the check and drug service feature. This application can help integrated service post volunteers to manage master data and services accurately and continuously. Integrated service postal service reports can be reported neatly and on time
Conference Keynote: Synthesizing Interpretable Behavior for Human-Aware AI Systems

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. As AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. This requires AI systems to exhibit behavior that is explainable to humans. Synthesizing such behavior requires AI systems to reason not only with their own models of the task at hand, but also about the mental models of the human collaborators. Using several case-studies from our ongoing research, I will discuss how such multi-model planning forms the basis for explainable behavior. I will also touch on the cognitive intelligence aspects of human-AI interaction by discussing how explicit shared knowledge and vocabularies are critical (and how it is important for AI researchers to resist Polanyi's revenge c.f. https://bit.ly/2ZdAXye).
GIS partial discharge defect diagnosis system and method based on Extreme Learning Machine

Abstract Partial discharge type identification is of great significance to the diagnosis of insulation faults in high-voltage power equipment. The partial discharge type recognition method based on deep learning map diagnosis has the disadvantages of large memory space and high hardware environment requirements. This paper presents a GIS discharge defect diagnosis system and method based on Extreme Learning Machine (ELM), which can be deployed on the edge layer equipment. The principal component analysis method is used to obtain the principal parameters that characterize the defects and form a sample feature data set. The sample feature data set is used to train the neural network model and continuously optimize it. Finally, the trained neural network model is used to identify the types of partial discharge defects. This method can effectively solve the problems that the partial discharge type identification method based on atlas diagnosis occupies large memory space and requires high hardware environment, which is convenient for the wide application of engineering.
Design of a Home/Office Automation for the Physically Handicapped Using Mobile Robots

In this paper, the design and implementation of an automated blind navigator for physically handicapped persons are presented. In this project, we have attempted to design an automation solution, keeping in mind the needs of the physically challenged, which in our context includes the elderly and the disabled.