Reviewerly/exHarmony huggingface dataset:
TiQSA: Workload Minimization in Convolutional Neural Networks Using Tile Quantization and Symmetry Approximation

Convolutional Neural Networks (CNNs) in the Internet-of-Things (IoT)-based applications face stringent constraints, like limited memory capacity and energy resources due to many computations in convolution layers. In order to reduce the computational workload in these layers, this paper proposes a hybrid convolution method in conjunction with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Particle of Swarm Convolution Layer Optimization (PSCLO)</i> algorithm. The hybrid convolution is an approximation that exploits the inherent symmetry of filter termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">symmetry approximation</i> and Winograd algorithm structure termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tile quantization approximation</i> . PSCLO optimizes the balance between workload reduction and accuracy degradation for each convolution layer by selecting fine-tuned thresholds to control each approximation’s intensity. The proposed methods have been evaluated on ImageNet, MNIST, Fashion-MNIST, SVHN, and CIFAR-10 datasets. The proposed techniques achieved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 5.28\text{x}$ </tex-math></inline-formula> multiplicative workload reduction without significant accuracy degradation (<0.1%) for ImageNet on ResNet-18, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 1.08\text{x}$ </tex-math></inline-formula> less multiplicative workload as compared to state-of-the-art Winograd CNN pruning. For LeNet, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 3.87\text{x}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 3.93\text{x}$ </tex-math></inline-formula> was the multiplicative workload reduction for MNIST and Fashion-MNIST datasets. The additive workload reduction was <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 2.5\text{x}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 2.56\text{x}$ </tex-math></inline-formula> for the respective datasets. There is no significant accuracy loss for MNIST and Fashion-MNIST dataset.
A Study on the Change of Ocean Education Policy and a Content Analysis on the Legal Basis in Korea

Ministry of Oceans and Fisheries(MoF) plays a key role in ocean education policy in Korea and has published plans for Ocean Education from 2005. However there are many problems including a lack of education system, low-level program contents, inefficient budgetary support and discontinuing school programs. In case of Japan, under the Ocean Development Basic Act, Japan developed supportive materials for ocean education targeting elementary, middle school and high school students. In the US, the Centers for Ocean Sciences Education Excellence(COSEE) under the National Oceanic and Atmospheric Administration(NOAA) is spreading marine scientific technology to the education field.BR Recently Korea has reorganized its Ocean Education System to enact the Ocean Education and Culture Promotion Act in 2020. Under the Ocean Education Basic Act, many institutional and legal changes are expected in the future. The purpose of this research is to analysis on the Ocean Education Policy by Ministry of Oceans and Fisheries and provide the trend of policy paradigm shift by comparing the vision and measures from 2005 to 2020. Moreover through the analysis of the legal contents, we searched for a complementary point and future development direction.
The Impact of Teacher’s Feedback in Increasing Student’s Self-Efficacy and Motivation

Generally, motivation is recognized as playing an important role in the learning process and academic success of students but-mobilizing and engaging in the task through the cognitive, behavioral and time effort investment is the product of several factors such as: factors related to personality (beliefs, emotions, attributions, etc.) and students personal abilities and others factors which is focusing on the type of interactions and climate that teachers is developing in the classroom.Everyone needs feedback to grow, especially the students because providing students engage with feedback, it should enhance learning and improve assessment performance.Formative feedback refers to that information communicated to the learner that is intended to modify his or her thinking or behavior for the purpose of improving learning and self-efficacy is the self-perceived ability to deal with specific situations.According to Bandura's theory, the judgments about self-efficacy influence the choice of situations in which we are involved, the amount of submitted effort in a given situation, the time we persist in a task, overcoming the obstacles and the resistence of pressure, emotional reactions during anticipation or involvement in that situation (for example the level of stress, anxiety).The aim of this study was to examine the effect of teachers feedback on student's self-efficacy and motivation in learning process.The participants were students aged 11-16 years (N=300).
An Adaptive Medical Ultrasound Images Despeckling Method Based on Deep Learning

Considering the shortage of traditional medical ultrasound image despeckle methods, an adaptive multi-exposure fusion framework and feedforward convolutional neural network model image despeckle method is proposed. Firstly, an ultrasound image training data set is produced. Then, a multi-exposure fusion framework with adaptive enhancement factors is proposed to enhance the image for effective feature extraction.Finally, a speckle model is trained through the network and a speckle image is obtained. Experimental results show that, compared with the existing methods, this paper can more effectively remove speckle noise in medical ultrasound images and retain more image details.
Block-Based Local Binary Pattern for Recognition of Handwritten Odia Numerals

In this paper, we exploit the texture feature of local binary pattern (LBP) for handwritten Odia numeral recognition. There are several challenges in the handwritten recognition due to the different writing style of the individuals. The histogram of the block-based LBP is computed considering the block sizes of (4 × 4), (8 × 8) and the whole image and these features are used to classify the numerals. For classification, SVM and single decision tree classifiers have been employed. In this experiment, we evaluated the performance of the block-based LBP on the ISI, Kolkata Odia numeral digital database. The performance of (4 × 4) block-based LBP shows high level of accuracy (97%) for both SVM and single tree classifiers. Single decision tree classifier shows promising results in terms of computational time and hence can be used for real-time applications.
Fully convolutional networks semantic segmentation based on conditional random field optimization

Each pixel can be classified in the image by the semantic segmentation. The segmentation detection results of pixel level can be got which are similar to the contour of the target object. However, the results of semantic segmentation trained by Fully convolutional networks often lead to the loss of detail information. This paper proposes a CRF-FCN model based on CRF optimization. Firstly, the original image is detected based on feature pyramid networks, and the target area information is extracted, which is used to train the high-order potential function of CRF. Then, the high-order CRF is used as the back-end of the complete convolution network to optimize the semantic image segmentation. The algorithm comparison experiment shows that our algorithm makes the target details more obvious, and improves the accuracy and efficiency of semantic segmentation.
A Dynamic Resource Allocation Algorithm in Cloud Computing Based on Workflow and Resource Clustering

Since the complexity of large-scale and scientific computation, workflow has been used for task decomposition in cloud computing. A dynamic resource allocation algorithm based on workflow and resource clustering is proposed in this paper. The workflow is described by a directed acyclic graph, which represents the precedence relations and communication cost of subtasks. Fussy clustering algorithm is used to group nodes by the features, which includes the computing capability, transmission capability, storage capacity, cost and reliability. Subtasks are mapped to different resource in cluster by multi-objective optimization model. Resource reputation is added to feature vector by feedback mechanism for evaluating resource reliability. Simulation results show the algorithm reduces the completion time and cost. It improves the resource utilization and load balance.
Implementasi Open Government Data oleh Pemerintah Kota Bandung

This article aims to describe and analyze how the implementation of Open Government Data by the Government of Bandung City is viewed from the theory of implementation of Open Government Data from research by Azmi Omar, Julian M. Bass, and Peter Lowit. The research method used is a descriptive method with a qualitative approach. Data collection techniques are carried out through literature study, observation, interviews, and documentation. The technique of determining informants was carried out purposively. The results showed that based on the four dimensions of the implementation of Open Government Data proposed by Azmi Omar et al, there are several things that need to be improved, such as community involvement in making datasets which is still minimal, public awareness of the importance of open data, the capacity of all Bandung City Government agencies to utilize manufacturing technology. data, as well as collaboration from parties in charge of observing the journey of Open Data Bandung. Researchers suggest that in order for the implementation of Open Government Data to run better, it is necessary to form a formal cooperation agreement with external parties as data producers and also to build special facilities for Open Data Bandung City so that the running of open government data in Bandung City can run effectively.
Multi-head Self-attention with Role-Guided Masks

The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms. Simply put, the attention mechanisms learn to attend to specific parts of the input dispensing recurrence and convolutions. While some of the learned attention heads have been found to play linguistically interpretable roles, they can be redundant or prone to errors. We propose a method to guide the attention heads towards roles identified in prior work as important. We do this by defining role-specific masks to constrain the heads to attend to specific parts of the input, such that different heads are designed to play different roles. Experiments on text classification and machine translation using 7 different datasets show that our method outperforms competitive attention-based, CNN, and RNN baselines.
A Meta-Learning approach to Model Uncertainty in Financial Time Series

Financial markets have experienced several negative sigma events in recent years; these eventsoccur with much more regularity than current risk models can predict. There is no guarantee thatthe training set's data generating process will be the same in the test set in finance. Mathematicalmodels are designed to operate with unlimited and changing data, and yet, actual events keepmaking life hard for most models. The assumption of independent and identically distributedrandom variables and a stationary time series do not hold in reality. Over-reliance on historical data and backtesting of models is not a sufficient approach to overcome these challenges.Reinforcement-learning faces similar challenges when applied to financial time series.Out-of-distribution generalization is a problem that cannot be solved without assumptions onthe data generating process. If the test data is arbitrary or unrelated to the training data, thengeneralization is not possible. Finding these particular principles could potentially help us buildAI and financial modeling systems. N-Beats, Oreshkin et al. [2020], is a deep neural architecturewith backward and forward residual links and a deep stack of fully-connected layers. N-Beats canbe considered as a meta-learning model for time series prediction. Meta-Learning is a machinelearning approach that intends to design models that can learn new skills or adapt to new environments rapidly with few training examples. We explore the performance of N-Beats and compareits performance with other deep learning models. The results are not conclusive in establishingN-Beats as a better model than the other models tested in this study. We show in this study thatother neural network-based models offer similar performance.
INTEGRATING PRIVACY PRESERVING IN INTERNET OF THINGS TO ENSURE DATA SECURITY USING VERTICAL PARTITIONING APPROACH

Guaranteeing security and data privacy are key issues in the Internet of Things (IoT). For the management of numerous security services, privacy plays a significant role. Devices which capable of being utilized anywhere any place, any device anything, any context anytime, anybody anytime, any business any service, in any network any path is the definition of IoT. Routing for IoT needs to be energy efficient. For multimodal problems, traditional evolutionary algorithms commonly result in population convergence towards a search spaces restricted area, hence, disregarding the local optimas remainder. The Ant Colony Optimization (ACO) algorithm has garnered much interest and has been researched extensively since it is one of the most efficient techniques for the resolution of optimization problems. This is a population-based heuristic evolution algorithm that is influenced by the results of research of actual ants natural collective behaviour. Multimodal optimizations Evolutionary algorithms can generally identify many optima within a single run and also retain their diversity of population throughout a run. Hence, for multimodal functions, these algorithms have the capability of global optimization. In this work, a hybrid ACO-Evolutionary Multimodal is proposed for energy-efficient routing. The data collected are stored in a vertically partitioned dataset to maintain the privacy of the data.
Six Levels of Contract Automation: The Evolution to Smart Legal Contracts - Further Analysis

The legal industry is undergoing digital transformation both in relation to legal processes and the form and functionality of legal instruments. Smart contracts or smart (& legal contracts), are an inevitable outcome. The purpose of this paper is to explore and clarify what is meant by ‘smart legal contracts’ (SLCs) and to orient them within the broader paradigm of digital transformation and automation of contracts, as the form and function of contracts become increasingly digitalised. In order to understand the implications of the various trends involved in digitalisation of contracts, it is important to distinguish between technology applied to different stages of the contract lifecycle. In particular, to identify at what point digitalisation of contracts begins to affect not only the form of the evidence of the contract, but also the contract’s inherent functionality. As contracts progress towards smart legal contracts, the form and functionality of a digital contract starts to depend on, and function with, the digital domain in which the contract operates, raising new legal and practical issues. Automation of contract performance is a field that is lacking in standard definitions and understanding of the incremental steps to contract digitalisation. This paper aims to provide a starting point from which to develop a framework and a taxonomy to assist discussion and development of legally enforceable automation of contract performance. This paper explores the different characteristics of contracts along a proposed spectrum of contract digitalisation by describing a conceptual model of levels of digitalisation and resulting automation of contracts. This model draws analogies with the SAE International J3016 “Levels of Automation” widely adopted for autonomous vehicles.SLCs as presented in this paper, allow parties to obtain legal certainty in the automation of contractual performance by bringing the agreed performance of formally coded contractual terms into the domain of the legally enforceable elements of the contract.
Etika Profesi Pustakawan dalam Praktik Pelayanan di Perpustakaan Daerah Kolaka Utara

The purpose of this study was to determine the application of the Librarian Profesional Ethics in Service Practice in The Library in The North Kolaka Area. The approach used in research is to use a qualitative descriptive approach. Which consisted of 8 informants, 3 librarians and 5 users. Data collection techniques used were observation, interviews, and documentation. The results of this study indicate that the application of the Librarian Profesional Ethics in Service Practice Library has been implemented by the Librarians properly in accordance with the rules written in the Indonesian Librarian Code of Ethics especially article 3 paragraph 6 regarding the polite and wise attitude of the Librarian in providing services. This was proven through direct observation and interviews at the North Kolaka Regional Library. Keywords: Library, librarian, librarian ethics
Using ART Surveillance Data in Clinical Research

Assisted reproductive technology (ART) surveillance data are important for assessing ART effectiveness and safety. Cycle-level and patient-level data allow the most detailed analysis. In using these data for research, it is important to recognize the strengths and limitations of surveillance data. The large sample size can facilitate precise estimation of the relationships between patient or treatment factors and outcomes, as well as permit detection of rare outcomes and trends over time. However, these databases may have limited detail compared with those designed specifically for research. Surveillance data allow the researcher to assess associations, but cannot prove causation. Appropriate strategies should be employed to address and communicate the limitations whenever possible. ART surveillance data should be available to interested and responsible researchers. Continued efforts to expand ART surveillance to additional countries will benefit the diverse population of women and men who are attempting to build their families using ART.
Basic Research on Teaching of Lesson Planning in Korean Practicum Class

Journal of the International Network for Korean Language and Culture 17-2, 1-28, In this study, primary research was conducted on how to teach lesson planning to Korean practicum classes. As suggested by the Korean language education academic community, the principles of drafting teaching plans were synthesized. The lesson plans of preliminary teachers of the credit bank system were analyzed for deficiencies. The lesson plans were divided into three categories: 1. Total Composition; 2. Teacher Language, and; 3. Interaction. Twenty-one specific items were prepared to analyze the lesson plans, and two appraisers analyzed 129 plans compiled by credit bank students. A Cohen's Kappa coefficient was performed using SPSS 25 to verify the reliability of the appraisers. An analysis of the results indicated that preliminary teachers were the most challenged with homework assignments, error correction, feedback, and presentations. Based on the problems revealed by each item, a specific teaching method for lesson planning was suggested.(Catholic University)
Blood Pressure, Heart Beat and Body Temperature Measurement by Using GSM and Low-Cost Microcontroller with Health Care Announcement

The movement of aged people or physically handicapped people and the people suffering from some serious diseases are usually confined to their homes and the other live with that chronic disease out of home, due to their health conditions. They are placed into a helpless or unable condition when they need to go out play with their friends and also not allowed for small tasks to do and totally placed in unworthy condition. This paper deals with The Design and Development of an Arduino based Microcontroller and GSM network Enabled Embedded System for Blood Pressure, Heart Beat and Body Temperature Monitoring, and Body balance and flexing system with ECG Signal Monitoring and The proposed system uses GPRS mobile communication system to check the patient’s abnormality within that of simulated biomedical parameters result. The normal or abnormal change in the values of reading in any of these parameters from their current reading values will be sensed immediately and health care help is given by the nearby people of family member or doctor. If there is no health care or help, the system sends a GPRS message directly to a family member, doctor, or caretaker’s mobile phone. It is a bi-directional communication system in which the caretaker/doctor, at any time, can send SMS to know the present status of a patient.
AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21.

Acronym identification focuses on finding the acronyms and the phrases that have been abbreviated, which is crucial for scientific document understanding tasks. However, the limited size of manually annotated datasets hinders further improvement for the problem. Recent breakthroughs of language models pre-trained on large corpora clearly show that unsupervised pre-training can vastly improve the performance of downstream tasks. In this paper, we present an Adversarial Training BERT method named AT-BERT, our winning solution to acronym identification task for Scientific Document Understanding (SDU) Challenge of AAAI 2021. Specifically, the pre-trained BERT is adopted to capture better semantic representation. Then we incorporate the FGM adversarial training strategy into the fine-tuning of BERT, which makes the model more robust and generalized. Furthermore, an ensemble mechanism is devised to involve the representations learned from multiple BERT variants. Assembling all these components together, the experimental results on the SciAI dataset show that our proposed approach outperforms all other competitive state-of-the-art methods.
Signal Briefings: Monitoring News Beyond the Brand

Public relations (PR) professionals are responsible for managing an organisation’s reputation through monitoring entities of interest and wider industry news. Monitoring and tracking wide news spaces such as industry news can cause a significant work load on PR professionals. We present Signal Briefings, a system which uses a combination of clustering and ranking to produce a small set of impactful articles distributed as a periodic email in a scalable and efficient manner.