Reviewerly/exHarmony huggingface dataset:
Learning Media Implementation Training At SDN No.101763 Klumpang Kampung

Learning media at this time are very much needed in the world of education both in schools and lectures, such as macromedia flash applications that are implemented in various types of animation, for example motion animation, sound, video and so on. The research discussed is using motion tweening animation, animation that changes shape and shifts objects. The implemented learning media provides benefits for teachers and students who take part in the training, where there are still many teachers and students who do not understand about making animation using Macromedia Flash. From the results of the training, the benefits of this service activity include improving the skills of teachers and students in being creative in making animations and being able to implement them in schools. The learning method is carried out in students' classrooms using an introduction to animation making tutorial with slides and presentations in front, accompanied by computers related to animation making training materials
On the Exponential Sample Complexity of the Quantum State Sign Estimation Problem

We demonstrate that the ability to estimate the relative sign of an arbitrary $n$-qubit quantum state (with real amplitudes), given only $k$ copies of that state, would yield a $kn$-query algorithm for unstructured search. Thus the quantum sample complexity of sign estimation must be exponential: $Ω(2^{n/2}/n)$. In particular, we show that an efficient procedure for solving the sign estimation problem would allow for a polynomial time solution to the NP-complete problem 3-SAT.
Digitalisasi Data Perpustakaan Dalam Proses Peminjaman dan Pengembalian Buku pada Perpustakaan Sekolah Islam ‘Uluwwul Himmah Medan.

'Uluwwul Himmah Medan School is one of the schools and kindergartens in Medan where one of the supporting teaching and learning activities is the school library as a medium of science development for children in the school. At 'Uluwwul Himmah Medan School library management is still done manually, for that it takes a computerized system in managing library books both the process of borrowing and returning books. With the system that has been built, the process of managing library books both the process of borrowing and returning can be done quickly and appropriately.
Breast tumour segmentation using thresholding and cannyedge detector / Fatin Rasyidah Rosli ... [et al.]

Mammogram acts as a screening tool is used to acquire images of the breast in order to detect early signs of breast cancer. However, the limitation of the mammogram images is it turn out to be too dark or too bright which endangers the loss of useful information. Numerous techniques have been introduced to improve the mammograms including quantitative evaluation. Unlike existing research that required additional hardware to be implemented in the segmentation process on the mammogram, this paper proposes an automated approach to segment breast tumours using image processing. The segmentation process is performed on the mammogram images using thresholding and canny edge detection algorithms. Thirty-three images are collected and tested. Qualitative evaluations showed that the proposed system outperformed segmented breast tumour at an acceptance rate of 52.09 percent, whereas quantitative evaluation using Area Overlap, False Positive Rate and False Negative Rate produced an acceptance rate 52.09 percent, 33.34 percent and 14.57 percent respectively. The findings could improve the quality of mammography images and help radiologists and doctors to detect breast tumours more accurate in a shorter period of time.
Automatically Mining Feature for Uncertainty from Search Results

We are addressing the issue of identifying question facets that are several word groups or sentences that describe the content of an inquiry and summarize it. We presume that the main features of a query are normally replicated in the top document in the lists format and that the questions facets can be deleted by aggregating these significant lists. In order to automatically collect and group regular lists from free text, html tags and repeat regions within top search results, we suggest a systems solution, referred to as QDMiner. There are several collections and helpful QDMiner question facets, as experimental findings indicate. We explore more the issue of list replication by modeling finely-grained comparisons and penalizing duplication lists. Strongerquestioning facets can be found.
Dynamical Phases and Resonance Phenomena in Information-Processing Recurrent Neural Networks

Recurrent neural networks (RNNs) are complex dynamical systems, capable of ongoing activity without any driving input. The long-term behavior of free-running RNNs, described by periodic, chaotic and fixed point attractors, is controlled by the statistics of the neural connection weights, such as the density $d$ of non-zero connections, or the balance $b$ between excitatory and inhibitory connections. However, for information processing purposes, RNNs need to receive external input signals, and it is not clear which of the dynamical regimes is optimal for this information import. We use both the average correlations $C$ and the mutual information $I$ between the momentary input vector and the next system state vector as quantitative measures of information import and analyze their dependence on the balance and density of the network. Remarkably, both resulting phase diagrams $C(b,d)$ and $I(b,d)$ are highly consistent, pointing to a link between the dynamical systems and the information-processing approach to complex systems. Information import is maximal not at the 'edge of chaos', which is optimally suited for computation, but surprisingly in the low-density chaotic regime and at the border between the chaotic and fixed point regime. Moreover, we find a completely new type of resonance phenomenon, called 'Import Resonance' (IR), where the information import shows a maximum, i.e. a peak-like dependence on the coupling strength between the RNN and its input. IR complements Recurrence Resonance (RR), where correlation and mutual information of successive system states peak for a certain amplitude of noise added to the system. Both IR and RR can be exploited to optimize information processing in artificial neural networks and might also play a crucial role in biological neural systems.
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack

Image classification must work for autonomous vehicles (AV) operating on public roads, and actions performed based on image misclassification can have serious consequences. Traffic sign images can be misclassified by an adversarial attack on machine learning models used by AVs for traffic sign recognition. To make classification models resilient against adversarial attacks, we used a hybrid deep-learning model with both the quantum and classical layers. Our goal is to study the hybrid deep-learning architecture for classical-quantum transfer learning models to support the current era of intermediate-scale quantum technology. We have evaluated the impacts of various white box adversarial attacks on these hybrid models. The classical part of hybrid models includes a convolution network from the pre-trained Resnet18 model, which extracts informative features from a high dimensional LISA traffic sign image dataset. The output from the classical processor is processed further through the quantum layer, which is composed of various quantum gates and provides support to various quantum mechanical features like entanglement and superposition. We have tested multiple combinations of quantum circuits to provide better classification accuracy with decreasing training data and found better resiliency for our hybrid classical-quantum deep learning model during attacks compared to the classical-only machine learning models.
A Review on Scheduling in Cloud Computing

Cloud computing is the requirement based on clients that this computing which provides software, infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve the accuracy and correctness on task completion. The scheduling in cloud environment which enables the various cloud services to help framework implementation. Thus the far reaching way of different type of scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling to minimize the energy cost, efficiency and throughput of the system.
The Specification Language Server Protocol: A Proposal for Standardised LSP Extensions

The Language Server Protocol (LSP) changed the field of Integrated Development Environments (IDEs), as it decouples core (programming) language features functionality from editor smarts, thus lowering the effort required to extend an IDE to support a language.The concept is a success and has been adopted by several programming languages and beyond.This is shown by the emergence of several LSP implementations for the many programming and specification languages (languages with a focus on modelling, reasoning, or proofs).However, for such languages LSP has been ad-hocly extended with the additional functionalities that are typically not found for programming languages and thus not supported in LSP.This foils the original LSP decoupling goal, because the move towards a new IDE requires yet another re-implementation of the ad-hoc LSP extension.In this paper we contribute with a conservative extension of LSP providing a first proposal towards a standard protocol decoupling the support of specification languages from the IDE.We hope our research attracts the larger community and motivates the need of a joint task-force leading to a standardised LSP extension serving the particular needs of specification languages.
Segmentation and Recognition of Bengali Characters from Document Images

In this paper, a method to segment the Bengali character from document images and recognition of each character in multiple categories has been proposed. The proposed algorithm works in two stages. Firstly, characters are segmented from the document page from horizontal and vertical projection profile. Secondly, characters are recognized by a support vector machine (SVM) classifier using different features extracted from the characters. The proposed methodology has been evaluated by a number of document images and the experimental results demonstrate the efficiency of the proposed method.
Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

Adversarial training based on the maximum classifier discrepancy between two classifier structures has achieved great success in unsupervised domain adaptation tasks for image classification. The approach adopts the structure of two classifiers, though simple and intuitive, the learned classification boundary may not well represent the data property in the new domain. In this paper, we propose to extend the structure to multiple classifiers to further boost its performance. To this end, we develop a very straightforward approach to adding more classifiers. We employ the principle that the classifiers are different from each other to construct a discrepancy loss function for multiple classifiers. The proposed construction method of loss function makes it possible to add any number of classifiers to the original framework. The proposed approach is validated through extensive experimental evaluations. We demonstrate that, on average, adopting the structure of three classifiers normally yields the best performance as a trade-off between accuracy and efficiency. With minimum extra computational costs, the proposed approach can significantly improve the performance of the original algorithm. The source code of the proposed approach can be downloaded from \url{https://github.com/rucv/MMCD\_DA}.
A way to reduce the time consumption effect of for-loops for training neural networks: optimized propagation

Abstract In this paper, an alternative way to backpropagation is introduced and tested, which results in faster convergence of the cost function.
Comments on Leo Breiman's paper: "Statistical Modeling: The Two Cultures" (Statistical Science, 2001, 16(3), 199-231)

Breiman challenged statisticians to think more broadly, to step into the unknown, modelfree learning world, with him paving the way forward.Statistics community responded with slight optimism, some skepticism, and plenty of disbelief.Today, we are at the same crossroad anew.Faced with the enormous practical success of model-free, deep, and machine learning, we are naturally inclined to think that everything is resolved.A new frontier has emerged; the one where the role, impact, or stability of the learning algorithms is no longer measured by prediction quality, but an inferential one -asking the questions of why and if can no longer be safely ignored.
TEST BENCH SOLAR POWER MEASUREMENT

Nowadays, Solar power is utilized more in &lt;i&gt;domestic&lt;/i&gt;, commercial, and industrial applications. Solar energy is renewable energy which is produced from sun heat. In 2020 India solar installed capacity was 34,627 MW. In this paper developed an experimental setup for solar panel power measurement. In this current, voltage and light intensity has been measured. Declination angle of sun to earth is +23.5°degrees, -23.5°degrees. The model of sun is represented by an incandescent bulb light and the angles set up manually. In this explained about solar power output at load side and see the variation between power and load resistance. Solar panel is generated power by light intensity applied by using incandescent light bulb. The light intensity changed by using a voltage regulator. The power is measured by using voltmeter and ammeter readings. Solar energy is to emulate the solar panel output power characteristics. The model of sun was taken as an incandescent bulb light and placed on the circular iron track to change east to the west. By changing the position of the bulb and changing the bulb light intensity with the help of the D.C speed regulator. Solar panel is placed in such a way that it freely moves to certain angles. By taking voltmeter and ammeter readings calculate the power. The graph is drawn between resistance versus power, the unique source of energy. The main aim of the paper is. Ensure that the maximum power output occurs at load which is taken as a resistor bank.
Toast ‘n’ Spread / Suhaila Athirah Mohd Yazid

Our company Ezylife Sdn Bhd always produce a product that will make your everyday life easier. We have come up with a new product which is an innovated toaster that can toast and spread fillings on bread or waffles at once. It is called Toast ‘n’ Spread machine. We produce this product because normally people would choose bread over something else as their breakfast or snacks since it will make them full for a longer time. Nowadays, there are many people skipped their breakfast or cannot enjoy a proper meal even its just a slice of bread due to their busy schedules and routine. To them it takes a lot of time to switch on the toaster, open the jar of a jam or butter and spread it on breads. The hassleness of these things make them skipped their breakfast which is not good for their health. As we all know breakfast is an important meal of the day. This Toast ‘n’ Spread machine can solve these problems. They can enjoy toasted bread or waffles with fillings just by a few clicks on their phone without taking much of their time. Next, our target market will be focusing on working people, family especially urban area which is in Kuala Lumpur that are busy but want to have a decent meal for their family and a time conscious person who wants to save time. We also target offices and company, they can place this machine in the pantry, so their employees can have a healthy meal or snacks if they are hungry. Based on the specification of this product, the price is considered reasonable which is RM 140. The management team of our company are divided into several section and each section were led by experienced and professional employee to ensure our company’s product and efficiency.
Accelerating Markov Random Field Inference with Uncertainty Quantification

Statistical machine learning has widespread application in various domains. These methods include probabilistic algorithms, such as Markov Chain Monte-Carlo (MCMC), which rely on generating random numbers from probability distributions. These algorithms are computationally expensive on conventional processors, yet their statistical properties, namely interpretability and uncertainty quantification (UQ) compared to deep learning, make them an attractive alternative approach. Therefore, hardware specialization can be adopted to address the shortcomings of conventional processors in running these applications. In this paper, we propose a high-throughput accelerator for Markov Random Field (MRF) inference, a powerful model for representing a wide range of applications, using MCMC with Gibbs sampling. We propose a tiled architecture which takes advantage of near-memory computing, and memory optimizations tailored to the semantics of MRF. Additionally, we propose a novel hybrid on-chip/off-chip memory system and logging scheme to efficiently support UQ. This memory system design is not specific to MRF models and is applicable to applications using probabilistic algorithms. In addition, it dramatically reduces off-chip memory bandwidth requirements. We implemented an FPGA prototype of our proposed architecture using high-level synthesis tools and achieved 146MHz frequency for an accelerator with 32 function units on an Intel Arria 10 FPGA. Compared to prior work on FPGA, our accelerator achieves 26X speedup. Furthermore, our proposed memory system and logging scheme to support UQ reduces off-chip bandwidth by 71% for two applications. ASIC analysis in 15nm shows our design with 2048 function units running at 3GHz outperforms GPU implementations of motion estimation and stereo vision on Nvidia RTX2080Ti by 120X-210X, occupying only 7.7% of the area.
Web Log Mining of Server Log Data of ‘Counselling Website’ Using Microsoft Excel and Implementation of Preprocessing Algorithm

Web log mining is essential for the generation of useful patterns from web log data. Web log mining is an indispensable tool to improve website services and servers performance. It gives valuable information to website administrators for the advancement of performance of a website. Web log data mining is possible from a wide variety of available tools. We have introduced a method for web log mining using Microsoft Excel. In this paper we have used different features of Microsoft Excel like macros, pivot tables, filtering tools, graphical tools for preprocessing of web log data. A preprocessing algorithm is proposed for data cleaning of web log data using these features. These preprocessed web log files used for pattern discovery and pattern analysis. After data preprocessing various usage patterns generated to reveal actual behaviour of visitors at the time of traversal at website ‘rajeducon.com’. Various patterns are also generated for raw web log data using Microsoft Excel.
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm

Recently, pre-trained language models such as BERT have been applied to document ranking for information retrieval, which first pre-train a general language model on an unlabeled large corpus and then conduct ranking-specific fine-tuning on expert-labeled relevance datasets. Ideally, an IR system would model relevance from a user-system dualism: the user's view and the system's view. User's view judges the relevance based on the activities of "real users" while the system's view focuses on the relevance signals from the system side, e.g., from the experts or algorithms, etc. Inspired by the user-system relevance views and the success of pre-trained language models, in this paper we propose a novel ranking framework called Pre-Rank that takes both user's view and system's view into consideration, under the pre-training and fine-tuning paradigm. Specifically, to model the user's view of relevance, Pre-Rank pre-trains the initial query-document representations based on large-scale user activities data such as the click log. To model the system's view of relevance, Pre-Rank further fine-tunes the model on expert-labeled relevance data. More importantly, the pre-trained representations, are fine-tuned together with handcrafted learning-to-rank features under a wide and deep network architecture. In this way, Pre-Rank can model the relevance by incorporating the relevant knowledge and signals from both real search users and the IR experts. To verify the effectiveness of Pre-Rank, we showed two implementations by using BERT and SetRank as the underlying ranking model, respectively. Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.
Involving Common Media to Export Product Recommendation Using Existing Data

There is an increasingly blurred line between e-commersce and social networking. Many e-commerce platforms support the social authentication process through which users can sign in using their social network identity, for example, on Facebook or Twitter. In addition, users can also post new items on microblogs with links to the website of the product for e-commerce. The purpose of this paper is to recommend goods for e-commerce web pages to users on social networking sites under cold-start, an issue that was scarcely investigated before, in an innovative approach to the cold-start product advice. One of the main challenges for the advice is how to use the information derived from social networking platforms. We suggest using connected users through social networking websites and e-commerce websites as a bridge to map the functionality of social networking users to another feature for product suggestion and for social networking. In particular, we suggest learning the user and product characteristics of data obtained from e-commerce sites using recurring neural networks (known as the user embedding and the goods embedding), and then implement a revamped system of gradients boosting trees to turn user social networking features into user embedding.
Best 40% Mechanical Keyboard

Apart from taking less space, you might be wondering why you should consider getting a 40% mechanical keyboard? No doubt it is smaller than most ...
High-Payoff/High-Risk Research Enables Full-Spectrum Dominance in the Battlespace

As the vice president of Advanced Concepts &amp; Technology for Raytheon Intelligence &amp; Space, Bradford Tousley leads an innovative technology incubator inside Raytheon Technologies, which focusses on high-reward/high-risk disruptive technologies for the defense industry. In this interview he discusses the history of Raytheon Technologies and some of the advancements, including quantum computing, ARAKNID, DyNAMO, directed energy weapons, digital engineering, and synthetic biology, which will enable US forces and allies to maintain full-spectrum dominance in the battlespace in the near future. See video at https://youtu.be/8BGtJSISKUs.
The Rights Portal: Tools and Resources Supporting Standardized Rights Statement Implementation

This poster introduces and overviews The Rights Portal (https://rights-portal.dp.la/), a website published by the Digital Public Library of America's Rights Statements Working Group. The poster was first presented and published as a part of the 2020 Miami University Copyright Conference.