Reviewerly/exHarmony huggingface dataset:
Two Novel Performance Improvements for Evolving CNN Topologies

Convolutional Neural Networks (CNNs) are the state-of-the-art algorithms for the processing of images. However the configuration and training of these networks is a complex task requiring deep domain knowledge, experience and much trial and error. Using genetic algorithms, competitive CNN topologies for image recognition can be produced for any specific purpose, however in previous work this has come at high computational cost. In this work two novel approaches are presented to the utilisation of these algorithms, effective in reducing complexity and training time by nearly 20%. This is accomplished via regularisation directly on training time, and the use of partial training to enable early ranking of individual architectures. Both approaches are validated on the benchmark CIFAR10 data set, and maintain accuracy.
A Study Proposal of Individualized Educational Program Development Strategies Based on Single Subject Studies

The problem of underachieved students in vocational education is a painful point in improving the quality of higher vocational education.By introducing a small sample and non-statistical data research method of single subject research into the process of teaching reform in our school, we are able to use the natural science method of behavioral science to guide the practice of teaching reform through the construction of a detailed observation system and data analysis system.This paper presents the ideas, methods, steps, and technical approaches for the implementation of this study proposal, and provides a practical reference for practitioners of teaching reform.
Cataloging and Classification: Back to Basics: Introduction

An idea was born at the 2019 Technical Services Education Special Interest Group session of the Association for Library and Information Science Education (ALISE) annual conference. Presenters and a...
Photonic perceptron based on a Kerr microcomb for high-speed, scalable, optical neural networks

&lt;p&gt;&lt;b&gt;Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning — have significant potential for ultra-high computing speed and energy efficiency. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking. &lt;/b&gt;&lt;/p&gt;
Peningkatan Kompetensi guru Akuntansi dalam Pengajaran Komputer Akuntansi

The development of technology has been very rapid and the curriculum has lagged far behind. This requires a curriculum change. SMK has made this change but in its development it is no longer relevant. Teachers as the spearhead of successful learning in schools must have competencies that are relevant to the development needs of the industry. This activity aims to improve the ability of Accounting MGMP teachers in West Sumatra province in learning computer accounting (Accurate). The approach taken in this research is a case study. The training process starts from application installation, creation of new company files, input transactions, and preparation of financial reports. The activity went well, but some teachers still needed further training and a whatsapp group was formed for further discussion. The obstacles faced were participants who were not familiar with computers.
DEVELOPING THE MANAGER'S SENSE OF TIME THE MAIN FACTORS INFLUENCING

The article is devoted to the formation of one of the most important personal competencies of a Manager, namely, the development of a sense of time. The methods tested by the author are presented, which significantly improve the quality of daily planning. Specific professions that require the active development of a sense of time are identified
PARALLELIZATION OF DIGITAL IMAGE WATERMARKING IN THE FREQUENCY DOMAIN WITH DCT

In today's digital world, in each area, there is the massive use of digital content.A huge amount of data can be transmitted or scattered easily with no cost, there are more chances of the copying of such digital information.So, there is a need to restrict such illegal copies of digital content.One of the solutions to this problem is digital watermarking.Frequency domain watermarking techniques use different transformation approaches like Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Discrete Wavelet Transform (DWT), etc.The parallelization techniques are used for DCT as it consists of various time consuming mathematical computations.The DCT transformation work on blocks of an image.This helps for parallelization as it does not contain dependency within work.In this paper, we have demonstrated the parallel execution of image watermarking, also the comparative analysis of parallel execution and sequential execution of image watermarking.We used parallel MATLAB for the parallel programming and then listed out all the parameters and their execution cost to be specifically determined.
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms

Convolutional Neural Networks (CNNs) have proven to be a powerful state-of-the-art method for image classification tasks. One drawback however is the high computational complexity and high memory consumption of CNNs which makes them unfeasible for execution on embedded platforms which are constrained on physical resources needed to support CNNs. Quantization has often been used to efficiently optimize CNNs for memory and computational complexity at the cost of a loss of prediction accuracy. We therefore propose a method to optimally quantize the weights, biases and activations of each layer of a pre-trained CNN while controlling the loss in inference accuracy to enable quantized inference. We quantize the 32-bit floating-point precision parameters to low bitwidth fixed-point representations thereby finding optimal bitwidths and fractional offsets for parameters of each layer of a given CNN. We quantize parameters of a CNN post-training without re-training it. Our method is designed to quantize parameters of a CNN taking into account how other parameters are quantized because ignoring quantization errors due to other quantized parameters leads to a low precision CNN with accuracy losses of up to 50% which is far beyond what is acceptable. Our final method therefore gives a low precision CNN with accuracy losses of less than 1%. As compared to a method used by commercial tools that quantize all parameters to 8-bits, our approach provides quantized CNN with averages of 53% lower memory consumption and 77.5% lower cost of executing multiplications for the two CNNs trained on the four datasets that we tested our work on. We find that layer-wise quantization of parameters significantly helps in this process.
Recommending Accurate and Diverse Items Using Bilateral Branch Network

Recommender systems have played a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key factor in recommendation to broaden user's horizons as well as to promote enterprises' sales. However, the trading-off between accuracy and diversity remains to be a big challenge, and the data and user biases have not been explored yet. In this paper, we develop an adaptive learning framework for accurate and diversified recommendation. We generalize recent proposed bi-lateral branch network in the computer vision community from image classification to item recommendation. Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network. We also capture user level diversity using a two-way adaptive metric learning backbone network in each branch. We conduct extensive experiments on three real-world datasets. Results demonstrate that our proposed approach consistently outperforms the state-of-the-art baselines.
WITHDRAWN: Monitoring and control of various process using distributed control system

Distributed Control System (DCS) is dined as a system of controllers connected by a data network which appears like a single system. The functions, the locations, the configuration and the specifications may be different but they all communicate using a single language and hence they are knitted together. DCS is preferred in applications where large number of input output modules are involved in the communication, such as chemical plant or oil refineries. They are multitasking systems able to handle large common databases and allows for various control loops, using graphical representation of function blocks. Monitoring and control of process from a remote station is the need of the hour. DCS is one such provision implemented in most of the process control industries. This proposal is based on controlling the existing process stations in the lab (level and flow process) by using DCS and implementing the concept of Supervisory Control and Data Acquisition (SCADA) in the stations. A user-friendly Front Panel is designed through which the process control panel can be monitored and controlled for the respective stations. Functional blocks are used for programming the Proportional Integral Derivative (PID) Controller for the level and flow process.
Regulating Misandry

Internet and social media became a significant weapon to disseminate and share information around the globe. These digital tools are also being misused for hate-crime activities. Hate crimes are criminal offences motivated by prejudice and committed against someone's identity. Women, children, disabled people, and refugees are often being targeted because of their different characteristics. A type of hate crime can also be online hate speech, or “cyberhate.” A quite recent research revealed that men can also be considered as victims of cyberhate. In particular, an Australian survey demonstrated that 54% of the victims of online abuse were men. The survey also pointed out that men were often subjected to abuse and insults, trolling, and malicious gossip and/or rumours. The present contribution reviews the notion of “misandry” and how it is expressed in the digital world and emphasises the need for protective measures not only for women but also for men.
Library Research Guides: OpenAthens: FAQs

This guide provides information for Kaufman Library's transition to OpenAthens authentication of electronic resources with GALILEO. Frequently Asked Questions About OpenAthens
Deliberative and Conceptual Inference in Service Robots

Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot's knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem space that is explored through heuristic search, and the latter is based on conceptual knowledge including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot's communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.
Gatekeepers on the Internet: the Role of Intermediaries in Local Enforcement

The Paper examines the use of communication service providers as gatekeepers for the regulation of the internet. It analyses the obligations of social media companies to deal with online abuse and places this discussion in the context of online harms and the UK government's plans to establish a duty of care.
Respirare il tempo: Re-Azioni di superamento COVID-19

The current work retraces the experience of 'Mirabilia', an online project originated at the beginning of the Italian lockdown (March 2020) by a spontaneous group made up of researchers from different disciplines, which had the purpose of showing human creativity and their ability to find strategies for survival and ways to cope with adversity. In particular, the paper illustrates the virtual exhibition created by Mirabilia "The breath of time": through a public call, many images and narrations of personal and particularly significant objects were collected during the quarantine.
Exploración y modificación de la actitud de los estudiantes de pre-universitario hacia el estudio de las matemáticas/ Exploring and changing senior high school students’ attitude towards mathematics

ABSTRACTObjective: This article aims at exploring senior high school students’ attitude towards mathematics before and after of introducing a virtual learning environment (VLE).Methods: The exploring of students’ attitude towards mathematics was completed by surveying a sample of students of “Republica Bolivariana de Venezuela” High School at the town of Florida in Camaguey province.  After the introduction of VLE tools in teaching the topic of statistics, the initial exploration was repeated.Results: The findings include a proposal of attitude descriptors and the description of the impact of VLE in motivation.Conclusions: The use of VLE supported on personal cellphones in teaching mathematics fosters motivation and learning.
3D Brain Image Segmentation Model using Deep Learning and Hidden Markov Random Fields

In this paper, we present a new brain image segmentation model (DL-HMRF) using deep learning and hidden Markov random fields (HMRFs). That segments the brain images into different tissue types (Grey Matter, White Matter, and Cerebro Spinal Fluid) in order to help physicians to determine easily the final decision. The underlying method is based on hidden Markov random fields to extract image features for using them as inputs of our neural network model. IBSR images with their ground-truth are used to train and evaluate our model. The quality of segmentation is measured by using the Dice coefficient metric. Setting the parameters of the DL-HMRF model is a task in itself. We have generated six models with different parameters to select the one that gives the best segmentation. Through tests, the selected model shows excellent results in terms of the quality of segmentation and execution time.
Blended Learning Model Trough Kanji Hyoushi App On Shokyuu Hyouki Class

Shokyuu Hyouki is a compulsory subject that is given to third-semester students on Japanese Department Andalas University.The main purpose of Shokyuu Hyouki course is to improve student's capability in understanding Kanji suited to N4.This research is part of a descriptive study classified as true experimental, it explains the application of Blended learning method on Shokyuu Hyouki subject.As a learning method, Blended learning combines lecture and online class model at the same time by adding Kanji Hyoushi app as media.The result shows that the practice of Blended learning model in Shokyuu Hyouki subject has been successful.It is shown on the increasing percentage of students whose marks are ranged from A to B+.Through observation and interview, the finding shows that Blended learning model helps the students in mastering Kanji at ease.
IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks

The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples, the legitimate input added with imperceptible and well-designed perturbations that can fool DNNs easily in the DNN testing/deploying stage. Previous adversarial example generation algorithms for adversarial white-box attacks used Jacobian gradient information to add perturbations. This information is too imprecise and inexplicit, which will cause unnecessary perturbations when generating adversarial examples. This paper aims to address this issue. We first propose to apply a more informative and distilled gradient information, namely integrated gradient, to generate adversarial examples. To further make the perturbations more imperceptible, we propose to employ the restriction combination of $L_0$ and $L_1/L_2$ secondly, which can restrict the total perturbations and perturbation points simultaneously. Meanwhile, to address the non-differentiable problem of $L_1$, we explore a proximal operation of $L_1$ thirdly. Based on these three works, we propose two Integrated gradient based White-box Adversarial example generation algorithms (IWA): IFPA and IUA. IFPA is suitable for situations where there are a determined number of points to be perturbed. IUA is suitable for situations where no perturbation point number is preset in order to obtain more adversarial examples. We verify the effectiveness of the proposed algorithms on both structured and unstructured datasets, and we compare them with five baseline generation algorithms. The results show that our proposed algorithms do craft adversarial examples with more imperceptible perturbations and satisfactory crafting rate. $L_2$ restriction is more suitable for unstructured dataset and $L_1$ restriction performs better in structured dataset.
Neural Network Framework

In this section, we will see how to train a neural network model in the Wolfram Language, how to access the results, and the trained network. We will review the basic commands to export and import a net model. We end the chapter with the exploration of the Wolfram Neural Net Repository and the review of the LeNet network model.
The Mind's Eye: Visualizing Class-Agnostic Features of CNNs

Visual interpretability of Convolutional Neural Networks (CNNs) has gained significant popularity because of the great challenges that CNN complexity imposes to understanding their inner workings. Although many techniques have been proposed to visualize class features of CNNs, most of them do not provide a correspondence between inputs and the extracted features in specific layers. This prevents the discovery of stimuli that each layer responds better to. We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Exploring features in this class-agnostic manner allows for a greater focus on the feature extractor of CNNs. Our method uses a dual-objective activation maximization and distance minimization loss, without requiring a generator network nor modifications to the original model. This limits the number of FLOPs to that of the original network. We demonstrate the visualization quality on widely-used architectures.
FREEDOM OF SPEECH AND THE PANDEMIC

Freedom of speech has always been debated as critical. A lot of people misconceived them as total rights which permits them to exercise them without limitations. Many have exceeded the limit by expressing themselves inappropriately. Freedom of speech is important to maintain human dignity. But some quarters have misunderstood it so much so that this right has been distorted and deformed to suit some private agenda. There has been prosecution made under the anti-fake news law relating to the dissemination of Covid 19 news to instil panic in the people. This paper is important as it provides some insights on how laws are made to restrict freedom and how such rights should be exercised with responsibility and accountability to bring about overall peace and harmony.
METODE MYER BRIGGS TYPE INDICATOR (MBTI) UNTUK TES KEPRIBADIAN SEBAGAI MEDIA PENGEMBANGAN DIRI

The MBTI (Myer Briggs Type Indicator) is the most accurate and easy to use inventory personality test. The Personality Test Expert System using the MBTI method is designed to be a guide for information about professions that are in accordance with students' talents and interests so that they can help in determining the appropriate choice of higher education majors. The MBTI Test Expert System application was successfully implemented using the Forward Chaining Method with the PHP programming language. The test results show the application runs in accordance with predetermined specifications. The percentage of eligibility for testing by experts is 92.5% which means good.
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers

Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset. That is, in general, freezing the trained feature extractor (the lower layers) and retraining the classifier (the upper layers) on the same dataset leads to worse performance. In this paper, for the first time, we show that the frozen classifier is transferable within the same dataset. We develop a novel top-down training method which can be viewed as an algorithm for searching for high-quality classifiers. We tested this method on automatic speech recognition (ASR) tasks and language modelling tasks. The proposed method consistently improves recurrent neural network ASR models on Wall Street Journal, self-attention ASR models on Switchboard, and AWD-LSTM language models on WikiText-2.