Reviewerly/exHarmony huggingface dataset:
Analysis of Students’ Understanding of Adapted Topic using The WhatsApp Group (WAG), Learning Video, and Assisted Module

This study aims to determine the extent of students' understanding of online learning by using instructional videos and WAG-assisted modules. The method used in this study is a pre-experimental method with one group pretest-posttest design. The research subjects were sixth-grade students in a public school in Bandung who were selected by purposive sampling technique. The data analysis used in this research is descriptive quantitative. The instrument used to explore the conceptual understanding was a test with two tiers of 20 questions. The first level questions are in the form of multiple choices with four choices to determine the answer, while the second level questions ask for reasons for choosing the answers on the first level. The research findings show that after implementing online learning using instructional videos and modules, students' understanding has increased quite significantly as indicated by the n-gain value from the low category to the high category. Analysis of the level of understanding of students shows changes towards the better at each level.
Hedge ratio estimation: A note on the Bitcoin future contract

This paper investigates the hedging effectiveness of Bitcoin (BTC) future contract using daily settlement prices for the period of 1 January 2018 until 26 March 2021. Standard OLS regressions, Error Correction Model (ECM), as well as GARCH and EGARCH models are used to estimate the optimal hedge ratio which is necessary for trading and risk management. The findings indicate that the time varying hedge ratios, if estimated through the Error Correction Model (ECM), are more efficient than the fixed hedge ratios in terms of risk minimization.
Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion

One of the most difficult jobs in remote sensing is dealing with traffic bottlenecks at airports. This fact has been confirmed by several studies attempting to resolve this issue. Among a wide range of approaches employed the most successful methods have been based on airplane object recognition using satellite images as datasets for deep learning models. Airplane object identification is not a viable method for resolving traffic congestion. There are several types of airplanes at the airport each with its own set of requirements and specifications.Utilizing satellite pictures will require the use of complex equipment which is a financial burden. In this work a universal low-cost and efficient solution for airport traffic congestion is proposed. Drone-captured aerial pictures are used to train and assess a Mask Region Convolution Neural Network model. This model can detect the presence of any aircraft in a photograph and pinpoint its location. It also includes mask estimations to properly identify each detected airplane type based on the estimated surface area and cabin length which are crucial variables in distinguishing planes. The study is conducted using Microsoft Common Object in Context COCO metrics average accuracies and a confusion matrix all of which demonstrate the approach's potential in providing valuable aid for dealing with traffic congestion at airports.
Modeling Performance and Energy trade-offs in Online Data-Intensive Applications

We consider energy minimization for data-intensive applications run on large number of servers, for given performance guarantees. We consider a system, where each incoming application is sent to a set of servers, and is considered to be completed if a subset of them finish serving it. We consider a simple case when each server core has two speed levels, where the higher speed can be achieved by higher power for each core independently. The core selects one of the two speeds probabilistically for each incoming application request. We model arrival of application requests by a Poisson process, and random service time at the server with independent exponential random variables. Our model and analysis generalizes to today's state-of-the-art in CPU energy management where each core can independently select a speed level from a set of supported speeds and corresponding voltages. The performance metrics under consideration are the mean number of applications in the system and the average energy expenditure. We first provide a tight approximation to study this previously intractable problem and derive closed form approximate expressions for the performance metrics when service times are exponentially distributed. Next, we study the trade-off between the approximate mean number of applications and energy expenditure in terms of the switching probability.
Implementasi Algoritma Hybrid dan Metode Least Significant Bit Untuk Keamanan Data

Computer networks can improve the quality of communication; however, it also allows accessing data illegally. Unauthorized access to people’s data can cause harm. In order for the data to remain safe, the cryptographic algorithm encryption process is needed. Nevertheless, there is also a disadvantage of encryption which can lead to suspicions. To get rid of unnecessary suspicion, there is a need for steganography algorithms, and one way of making sure that it is hidden is by using the least significant bit method. This research uses a hybrid algorithm and the least significant bit technique. Hybrid algorithm models are used for better security before being concealed under the image, and the only way of doing that is by using least significant bit method. The types of image files used are PNG and JPG or JPEG. The success rate of this experiment is 100%, with eight tests that have been done with a maximum insertion of 5000 characters.
Digital Currencies for Central Banks

Along with the strong development of the Industrial Revolution 4.0, the digital currency was born and is used as a means of payment increasingly popular in many countries worldwide. As a result, many central banks worldwide have begun studying and implementing projects to develop their digital currencies. Moreover, they have increasingly collaborated to implement the actual evaluation. However, the development of a central bank’s digital currency should be done with the consideration of many aspects, in which security is the most critical factor. Therefore, this research paper presents the fundamentals of central bank digital currencies such as their definitions, functions, roles, legal issues, different formats, and whether central banks should issue a digital currency?
Best lower bound on the probability of a binomial exceeding its expectation

Let X be a random variable distributed according to the binomial distribution with parameters n and p . It is shown that P ( X > E X ) ⩾ 1 / 4 if 1 > p ⩾ c / n , where c ≔ ln ( 4 / 3 ) , the best possible constant factor.
MUSIQ: Multi-scale Image Quality Transformer

Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often compromised by the fixed shape constraint in batch training. To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation. To address this, we design a multi-scale image quality Transformer (MUSIQ) to process native resolution images with varying sizes and aspect ratios. With a multi-scale image representation, our proposed method can capture image quality at different granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale embedding is proposed to support the positional embedding in the multi-scale representation. Experimental results verify that our method can achieve state-of-the-art performance on multiple large scale IQA datasets such as PaQ-2-PiQ, SPAQ and KonIQ-10k.
Age Detection based on Facial Image using Fusion Extreme Learning Machine Classifier

Age Estimation from the human facial image is a very difficult process since age is influenced by many factors gender, life style, working environment, mental state of the person etc. For age estimation LBP (Local Binary Pattern) histogram are generated and wrinkle estimation is performed using the Gabor filter resulting with the Gabor feature vectors based on the wrinkle levels. Training and testing are done with Fusion Extreme Learning Classifier which is proposed. The proposed fusion classifiers combine the Gabor feature vectors and LBP histograms and estimates the age based on the classification results and manual voting results. The testing is performed with FG-Net database and achieves higher accuracy levels with ease of computation.
NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function

Coherent imaging systems like synthetic aperture radar are susceptible to multiplicative noise that makes applications like automatic target recognition challenging. In this paper, NeighCNN, a deep learning-based speckle reduction algorithm that handles multiplicative noise with relatively simple convolutional neural network architecture, is proposed. We have designed a loss function which is an unique combination of weighted sum of Euclidean, neighbourhood, and perceptual loss for training the deep network. Euclidean and neighbourhood losses take pixel-level information into account, whereas perceptual loss considers high-level semantic features between two images. Various synthetic, as well as real SAR images, are used for testing the NeighCNN architecture, and the results verify the noise removal and edge preservation abilities of the proposed architecture. Performance metrics like peak-signal-to-noise ratio, structural similarity index, and universal image quality index are used for evaluating the efficiency of the proposed architecture on synthetic images.
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment

Contrastive learning has been widely used to train transformer-based vision-language models for video-text alignment and multi-modal representation learning. This paper presents a new algorithm called Token-Aware Cascade contrastive learning (TACo) that improves contrastive learning using two novel techniques. The first is the token-aware contrastive loss which is computed by taking into account the syntactic classes of words. This is motivated by the observation that for a video-text pair, the content words in the text, such as nouns and verbs, are more likely to be aligned with the visual contents in the video than the function words. Second, a cascade sampling method is applied to generate a small set of hard negative examples for efficient loss estimation for multi-modal fusion layers. To validate the effectiveness of TACo, in our experiments we finetune pretrained models for a set of downstream tasks including text-video retrieval (YouCook2, MSR-VTT and ActivityNet), video action step localization (CrossTask), video action segmentation (COIN). The results show that our models attain consistent improvements across different experimental settings over previous methods, setting new state-of-the-art on three public text-video retrieval benchmarks of YouCook2, MSR-VTT and ActivityNet.
KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks

Fruit flies are established model systems for studying olfactory learning as they will readily learn to associate odors with both electric shock or sugar rewards. The mechanisms of the insect brain apparently responsible for odor learning form a relatively shallow neuronal architecture. Olfactory inputs are received by the antennal lobe (AL) of the brain, which produces an encoding of each odor mixture across ~50 sub-units known as glomeruli. Each of these glomeruli then project its component of this feature vector to several of ~2000 so-called Kenyon Cells (KCs) in a region of the brain known as the mushroom body (MB). Fly responses to odors are generated by small downstream neuropils that decode the higher-order representation from the MB. Research has shown that there is no recognizable pattern in the glomeruli--KC connections (and thus the particular higher-order representations); they are akin to fingerprints~-- even isogenic flies have different projections. Leveraging insights from this architecture, we propose KCNet, a single-hidden-layer neural network that contains sparse, randomized, binary weights between the input layer and the hidden layer and analytically learned weights between the hidden layer and the output layer. Furthermore, we also propose a dynamic optimization algorithm that enables the KCNet to increase performance beyond its structural limits by searching a more efficient set of inputs. For odorant-perception tasks that predict perceptual properties of an odorant, we show that KCNet outperforms existing data-driven approaches, such as XGBoost. For image-classification tasks, KCNet achieves reasonable performance on benchmark datasets (MNIST, Fashion-MNIST, and EMNIST) without any data-augmentation methods or convolutional layers and shows particularly fast running time. Thus, neural networks inspired by the insect brain can be both economical and perform well.
Binomial Transform Technique For Solving Linear Difference Equations

Our goal in this paper is to find a new transformation technique for solving linear difference equations, as in the case of Z-transformation. And we were able to find the binomial transform, and this transform is one of the most common transformations. The linear difference equations can be solved by the binomial transformation.
Programming and Debugging with Semantically Lifted States

We propose a novel integration of programming languages with semantic technologies. We create a semantic reflection mechanism by a direct mapping from program states to RDF knowledge graphs. This mechanism enables several promising novel applications including the use of semantic technology, including reasoning, for debugging and validating the sanity of program states, and integration with external knowledge graphs. Additionally, by making the knowledge graph accessible from the program, method implementations can refer to state semantics rather than objects, establishing a deep integration between programs and semantics. This allows the programmer to use domain knowledge formalized as, e.g., an ontology directly in the program’s control flow. We formalize this integration by defining a core object based programming language that incorporates these features. A prototypical interpreter is available for download.
The Magic of Patterns: Ornamental Art of the South-East Asia

An overview of the exhibition «The Magic of Patterns. Ornamental Art of the South-East Asia» which took place from March 23 to June 24, 2021, at the Novosibirsk State Art Museum provides a description of the main types of arts and crafts in Southeast Asia — textiles, weapons, jewellery and toreutics, theatre puppets, adorned by intricate decor. The article is illustrated with examples from the Moscow State Museum of Oriental Art, which were shown at the exhibition.
The Effect of Courses of the Preparation and Training Department to Developing Imagined Fluency According to (VTS) for Some Teachers and Female Teachers of Physical Education in the Karbala Education Directorate

The research problem and its importance were dealt with while mentioning the imposition of the researchand its objectives in which the researchers sought to measure imagined fluency with the latest technologicaltechnologies, some physical education teachers and teachers have knowledge of the effect of courses of thepreparation and training department to developing this important mental variable. The researchers also dealtwith the concept and types of fluency, the executive functions of the brain, cognitive flexibility, and othertopics related to the research problem. Then the researchers mentioned the research methodology, its fieldprocedures, the most important tools, devices and tests used, along with a detailed explanation of the ViennaTest System (VTS) abbreviated to (Vienna Test System). To conclude with the research results that werepresented, analyzed and discussed. To come up with conclusions, the most important of which is the positiveimpact of the training courses held by the Preparation and Training Department, recommendations andscientific and practical proposals, most notably the call to establish a contemporary psychological laboratoryin the Iraqi education directorates to measure and develop complex psychological, mental and personalcapabilities. With documentation of Arab and foreign sources and an appendix to a sample for the resultof the (5 point) test, which measures the imagined fluency and cognitive flexibility of the research sample
Enhancing Students’ Metacognition and Reading Comprehension Using Graphic Organizers

The study aimed to reveal interesting insights on the effective use of Graphic Organizers (GOs) in the teaching of reading comprehension. It also intended to identify the metacognitive strategies frequently used by selected Grade 11 students using the Metacognitive Awareness Reading Strategy Inventory (MARSI) Version 1.0 by Mokhtari and Reichard (2002). This study used the experimental research design using quantitative approach with one group pre-test and post-test to determine the effectiveness of the GOs, paired samples t-test and independent samples t-test to check the significant differences in the results of the pre-test and post-test, as well as sex; while descriptive method was used for the results of MARSI. Findings of the study showed that the use of graphic organizers in enhancing the reading comprehension has a positive impact on the students, and that sex does not affect the reading comprehension scores of the respondents. The results of MARSI revealed that the most commonly used reading strategy is the Problem Solving Strategy (PROB). Both male and female respondents mostly use the Problem Solving Strategy (PROB) when reading. With this result, teachers are encouraged to consider utilizing graphic organizers in class. The students should also be given the opportunity to explore the other reading strategies that would help them enhance their comprehension skills through differentiated activities.
Tree Decomposed Graph Neural Network

Graph Neural Networks (GNNs) have achieved significant success in learning better representations by performing feature propagation and transformation iteratively to leverage neighborhood information. Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks. Furthermore, most deep GNNs only recognize the importance of higher-layer neighborhoods while yet to fully explore the importance of multi-hop dependency within the context of different layer neighborhoods in learning better representations. In this work, we first theoretically analyze the feature smoothing between neighborhoods in different layers and empirically demonstrate the variance of the homophily level across neighborhoods at different layers. Motivated by these analyses, we further propose a tree decomposition method to disentangle neighborhoods in different layers to alleviate feature smoothing among these layers. Moreover, we characterize the multi-hop dependency via graph diffusion within our tree decomposition formulation to construct Tree Decomposed Graph Neural Network (TDGNN), which can flexibly incorporate information from large receptive fields and aggregate this information utilizing the multi-hop dependency. Comprehensive experiments demonstrate the superior performance of TDGNN on both homophily and heterophily networks under a variety of node classification settings. Extensive parameter analysis highlights the ability of TDGNN to prevent over-smoothing and incorporate features from shallow layers with deeper multi-hop dependencies, which provides new insights towards deeper graph neural networks. Code of TDGNN: http://github.com/YuWVandy/TDGNN
Applying Machine Learning in Self-adaptive Systems: A Systematic Literature Review

Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.
Bitcoin ATMs to be tested out at Sheetz convenience stores

Sheetz announced Tuesday it will be adding bitcoin ATMs to six stores in Pennsylvania and North Carolina, giving customers the opportunity to sell and buy cryptocurrency using U.S. dollars.
On the Effectiveness of English Reading Teaching in Junior Middle School

As an important part of English teaching,English reading is an essential link in the process of students’ learning English.Teachers should actively guide students to fully understand the contents of reading articles,master the language characteristics of English subjects,improve and innovate reading methods and teaching strategies,and help students gradually develop good reading habits,under the influence of a good reading environment,constantly improve the effectiveness of junior high school English reading teaching.