Reviewerly/exHarmony huggingface dataset:
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search

Neural Architecture Search (NAS) has shown great potentials in automatically designing neural network architectures for real-time semantic segmentation. Unlike previous works that utilize a simplified search space with cell-sharing way, we introduce a new search space where a lightweight model can be more effectively searched by replacing the cell-sharing manner with cell-independent one. Based on this, the communication of local to global information is achieved through two well-designed modules. For local information exchange, a graph convolutional network (GCN) guided module is seamlessly integrated as a communication deliver between cells. For global information aggregation, we propose a novel dense-connected fusion module (cell) which aggregates long-range multi-level features in the network automatically. In addition, a latency-oriented constraint is endowed into the search process to balance the accuracy and latency. We name the proposed framework as Local-to-Global Information Communication Network Search (LGCNet). Extensive experiments on Cityscapes and CamVid datasets demonstrate that LGCNet achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, LGCNet achieves the new best performance of 74.0\% mIoU with the speed of 115.2 FPS on Titan Xp.
Quantum algorithms for geologic fracture networks

Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.
Determination of Platelet Count Estimation Factor on Peripheral Blood Smear Confirmation Using Field Number 22 Microscope

The automatic platelet count sometimes requires confirmation on the peripheral blood smear. Platelet count estimation can also be used for reporting platelet count if an automatic cell counter is not available, with an estimation factor according to the Field Number (FN) of the microscope used. This study aimed to determine the platelet count estimation factor based on peripheral blood smear confirmation using an FN 22 microscope. An observational cross-sectional study was carried out in patients who had routine hematological and peripheral blood smear examinations during September 2021 by determination of platelet count using the automatic cell counter method and an average number of platelet counts per field of view with 100x objective magnification. The estimation factor is the total ratio divided by sample size. The total ratio of 254 samples was 4.086. The platelet count estimation factor was 16, indicating that 1 platelet per field of view was equivalent to 16x103/µL. There was a very strong significant correlation between mean platelet count per field of view and platelet count using the automatic cell counter (p&lt;0.001, R&gt;0.750). The field number is the image diameter of the microscope eyepiece. The latest generations of microscope use FN 20 or more, which provides a wider field of view, enabling the observation of more platelets. Factor estimation was used to determine the estimated platelet count on a peripheral blood smear. A big difference between automatic cell counter and peripheral blood smear might indicate pre-analytic, analytic, and post-analytic errors. The platelet count estimation factor based on peripheral blood smear confirmation using the FN 22 microscope was 16. Each laboratory needs to determine the estimation factor according to the FN microscope used.
Features of the Transformation of Russian Universities into Digital Universities

Traditionally, specialists with multilingual competences have been more highly valued in the labor market in any field of activity. However, the term “multilingualism” is losing original meaning, which implies mastery of several foreign languages in modern universal digital transformation’s world. Today, multilingual competences imply not only a high level of foreign language proficiency, but also the ability to quickly perceive digital languages, i.e. to intuitively use various digital services, software packages and applications, understanding the principles of algorithm and computer program development. The global digitalization of the economy, accelerated by pandemic restrictions, has accelerated the digitalization of higher education institutions. Digitalization has forced students, faculty, and university administrators to rapidly adopt new digital services. The article examines the features of the digital transformation of universities that won the competition of the Ministry of Science and Higher Education held within the framework of the national program “Digital Economy in the Russian Federation” to develop a digital university model. The analysis revealed general trends in the digital transformation of universities, showing that the development of a digital university model is a complex task aimed at re-engineering business processes with the capabilities of modern IT technologies by introducing digital services in all areas of university activities: administrative, educational, scientific and innovative. The authors conclude that it is necessary to intensify the replication of the developed model of the digital university and the possibility of disseminating the best practices of digitalization of business processes by universities around the world.
Noise effects on purity and quantum entanglement in terms of physical implementability

Quantum decoherence due to imperfect manipulation of quantum devices is a key issue in the noisy intermediate-scale quantum (NISQ) era. Standard analyses in quantum information and quantum computation use error rates to parameterize quantum noise channels. However, there is no explicit relation between the decoherence effect induced by a noise channel and its error rate. In this work, we propose to characterize the decoherence effect of a noise channel by the physical implementability of its inverse, which is a universal parameter quantifying the difficulty to simulate the noise inverse with accessible quantum channels. We establish two concise inequalities connecting the decrease of the state purity and logarithmic negativity after a noise channel to the physical implementability of the noise inverse, which is required to be decomposed as mutually orthogonal unitaries or product channels respectively. Our results are numerically demonstrated on several commonly adopted two-qubit noise models. We believe that these relations contribute to the theoretical research on the entanglement properties of noise channels and provide guiding principles for quantum circuit design.
PERFORMANCE REFINEMENT OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR SOLVING BIG DATA PROBLEMS

Two of the most well-liked neural network frameworks, Theano and TensorFlow, will be compared in this study for how well they perform on a given problem. The MNIST database will be used for this specific problem, which is the recognition of handwritten digits from one to nine. It is a good idea to use more examples than contrasted ones to compare these frameworks because this database is the subject of active research at the moment and has produced excellent results. However, in order to be trained and deliver accurate results, neural networks need a sizeable amount of sample data, as will be covered in more detail later. Because of this, big data experts frequently encounter problems of this nature. As the project description implies, we won't just present a standard comparison because of this; instead, we'll work to present a comparison of these networks' performance in a Big Data environment using distributed computing. The FMNIST or Fashion MNIST database and CIFAR10 will also be tested (using the same neural network design), extending the scope of the comparison beyond MNIST. The same code will be used with the same structure thanks to the use of a higher-level library called Keras, which makes use of the aforementioned support (in our case, Theano or TensorFlow). There has been a surge in open-source parallel GPU implementation research and development as a result of the high computational cost of training CNNs on large data sets. However, there aren't many studies that have been done to assess the performance traits of those implementations. In this study, we compare these implementations carefully across a wide range of parameter configurations, look into potential performance bottlenecks, and pinpoint a number of areas that could use more fine-tuning.
An Efficient Task Allocation Using Resource Shortest Scheduling and Fairness Firefly Algorithm In Cloud Computing

Abstract The cloud computing has become significant part of day to day life. It is assimilated the several resource for the user’s requirement. However, the scale and highly dynamic environment of cloud application executes significant new challenges to resource management and efficient resource scheduling are highly demanded. The problem of Resource scheduling and Task allocation is an important aspect on the cloud performance. The propose a systematic approach to denote the reliability, running time, and failure processing of resource scheduling in cloud computing. The Resource Shortest Scheduling and Fairness Fire Algorithm (RSSFFA) is enhance the resource scheduling and task allocation during the time of loading the server in polynomial time and consume an execution time.
Credit-card Fraud Detection System using Neural Networks

Recently, with the development of online transactions, the credit-card transactions begun to be the most prevalent online payment methods. Credit-card fraud refers to the use fake Credit-Cards to purchase goods without paying. With the fast research and development in the area of information technology and data mining methods including the neural networks and decision trees, to advanced machine learning and deep learning methods, researchers have proposed a wide range of antifraud systems. Mainly, the Machine Learning (ML) and Deep Learning (DL) methods are employed to perform the fraud detection task. This paper aims to explore the existing credit-card fraud detection methods, and categorize them into two main categories. In addition, we investigated the deployment of neural network models with credit-card fraud detection problem, since we employed the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). ANN and CNN models are implemented and assessed using a credit-card dataset. The main contribution of this paper focuses on increasing the fraud-detection classification accuracy through developing an efficient deep neural network model.
Novel Data Science Approach for Emotion Analytics: from Machine Learning to Quantum Cognition

In the field of People Intelligence, Emotion Analytics is one of the emerging and growing challenges. A prominent study field is analyzing an individual's emotional state from textual data, as well as recognizing emotions from audio and video recordings. Current Artificial Intelligence approaches for Emotion Analytics based on machine and deep learning and neural networks based on classic data science approaches assume rational people's decision-making process. People's decision-making is irrational. As a result of recent quantum cognition advancements, we show that emotional judgments from one modality may be incompatible with judgments from another, and they cannot be assessed together to produce a final judgment. As a result, the cognitive process exhibits "quantum-like" biases that classical AI approaches based on probability models challenged to analyze. As a result, we offer an emotion analytics approach based on the quantum data science method for predicting people's emotions by a fundamentally novel assessment method.
Dual Structural Knowledge Interaction for Domain Adaptation

Domain adaptation aims to transfer knowledge from a label-rich source domain to an unlabeled target domain. A common strategy is to assign pseudo-labels to unlabeled target samples for performing representation learning. However, most existing methods only apply the source-guided classifier to generate the source-biased pseudo-labels for self-training, leading to biased target representations. Moreover, the generated pseudo-labels ignore the manifold assumption that neighboring samples are likely to have the same labels. To address the above problem, we formulate a novel structural knowledge to assign target-oriented and manifold-guided pseudo-labels for unlabeled target samples. The structural knowledge consists of cluster-based knowledge and locality-based knowledge. The cluster-based knowledge denotes the label consistency between the target samples and the non-parametric target cluster centers, making the pseudo-labels target-oriented. The locality-based knowledge constrains the target sample and its neighbors to satisfy the manifold assumption. As the neighbors contain the source and target samples, the source and target locality-based knowledge are utilized to boost the descriptions. With the structural knowledge, we propose a novel Dual Structural Knowledge Interaction (DSKI) framework for domain adaptation. For generating aligned and discriminative features, knowledge consistency constraint and instance mutual constraint are proposed in DSKI. Evaluations on three benchmarks demonstrate the effectiveness of the Dual Structural Knowledge Interaction, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> 74.9%, 87.7%, and 90.8% for Office-Home, VisDa-2017, and Office-31, respectively.
ANALISIS PERBEDAAN KADAR BESI (Fe) MENGGUNAKAN SERBUK CANGKANG TELUR PADA AIR SUMUR GALI

Water is the most important substance for the human body after air. Water is used for many things such as washing, cooking, drinking and others. The aim of this research was to analyze Iron (Fe) Levels Differences by Using Egg Shells as Adsorbent. The type of this research was Experimental method by using Posttest Only Design approach. The results of the research found that giving egg shell powder of 5 grams, 7 grams and 9 grams to sample A, B and C (p-value 0,952 , 0,729 and 0,118) were able to reduce iron (Fe) levels in water. However, it did not meet the clean water requirements that have been set because the results obtained are iron (Fe) levels are still above 1 mg/l. It can be cocluded that giving 5 grams, 7 grams, and 9 grams of eggshell powder in well water can reduce iron (Fe) levels. It is recommended for the community to be able to utilize eggshell powder in reducing iron (Fe) levels in well water.
Demo: Hijacking Connected Vehicle Alexa Skills

Current voice assistant platforms allow users to interact with their cars through voice commands.However, this convenience comes with substantial cyber-risk to voice-controlled vehicles.In this demo, we show a "malicious" skill with unwanted control actions on the Alexa system could hijack voice commands that are supposed to be sent to a benign third-party connected vehicle skill.
Dissecting American Fuzzy Lop - A FuzzBench Evaluation

AFL is one of the most used and extended fuzzing projects, adopted by industry and academic researchers alike.While the community agrees on AFL's effectiveness at discovering new vulnerabilities and at its outstanding usability, many of its internal design choices remain untested to date.Security practitioners often clone the project "as-is" and use it as a starting point to develop new techniques, usually taking everything under the hood for granted.Instead, we believe that a careful analysis of the different parameters could help modern fuzzers to improve their performance and explain how each choice can affect the outcome of security testing, either negatively or positively.The goal of this paper is to provide a comprehensive understanding of the internal mechanisms of AFL by performing experiments and comparing different metrics used to evaluate fuzzers.This will prove the efficacy of some patterns and clarify which aspects are instead outdated.To achieve this, we set up nine unique experiments that we carried out on the popular Fuzzbench platform.Each test focuses on a different aspect of AFL, ranging from its mutation approach to the feedback encoding scheme and the scheduling methodologies.Our preliminary findings show that each design choice affects different factors of AFL.While some of these are positively correlated with the number of detected bugs or the target coverage, other features are related to usability and reliability.Most important, the outcome of our experiments will indicate which parts of AFL we should preserve in modern fuzzers.
The Employment of Antecedent Strategy for Managing Behavior Challenges in EFL Classroom

Antecedent strategy as a part of the evidence-based practice is obtained through the process of students interfering behaviour analysis. This part of evidence-based practice which also named as antecedent-based intervention has a purpose to help the teachers in managing student disruptive behavior as one of the biggest challenges in classroom management by making a significant change to the learning environment which trigger the problem behavior and expounding the behaviors that should be done by the students. The antecedent strategy is very helpful for the teacher in managing students' disruptive behavior as one of the biggest challenges in EFL classroom management. Therefore, this study aims to analyze the employment of antecedent strategy for managing behavioral challenges in EFL classroom management. The method used in this study was library research in which the data were collected through a reading process of some library sources then analyzed by integrating the obtained information and the researcher's idea to answer the purpose of this research in finding section and draw the conclusion. The findings of this study revealed that in employing the antecedent strategy in a classroom, there are three major steps that the teacher needs to follow namely conducting a Functional Behavioral Assessment (FBA), clearly expounding the proper behavior, and effectively deciding preventive action.
Access and Utilization of Online Learning Resources Among Undergraduate Students

Online learning resources have made a significant contribution to teaching and learning. It has been felt that many students are underutilizing online learning resources for various reasons. This study aimed to analyze the online learning resources available to undergraduate students, the level of use of online learning resources by undergraduate students, and examine the challenges faced when accessing and using online learning resources. This research is descriptive research with the type of survey research. 200 students were selected as the sample. Three questions were asked and answered. The research findings revealed that the respondents had heard of web tools for teaching and were familiar with web tools for learning. The institution provided free tablets and internet for students as well. There were enough computers in the school's electronic library with free internet. There were internet cafes in the school environment as well as Students used Smartphones to access and use online learning resources. The study results concluded that online learning resources encourage students in the academic field and assist students in future learning. Based on and conclusions, it is recommended that; Universities must support the distribution of mobile learning devices, the government must provide funds for the provision of bandwidth and e-libraries, and teachers must be involved in the use of online learning resources because this will facilitate the teaching process and encourage students.
Strategi Dosen Mengembangkan Media Pembelajaran Berbasis Teknologi Informasi Untuk Meningkatkan Keterampilan Berfikir Kritis Mahasiswa PAI IAIN Curup

The development of this increasingly advanced era requires educators to have new innovations in the use of Information Technology-based media in directing more interesting and conducive learning in the implementation of learning. The purpose of this study was to find out how the lecturer's strategy in developing information technology-based learning media to improve critical thinking skills of PAI IAIN Curup students. This study uses a qualitative approach, for data analysis using qualitative data analysis methods, the technique used is non-statistical data techniques. Data were collected from interviews, observations and documentation. This study found that the lecturer's strategy in developing information technology-based learning media to improve the critical thinking process of PAI IAIN Curup students was by independent learning, assignments, product creation and field research. In the use of IT learning media, the IT media used are computers or laptops, LCD projectors and then the use of applications such as Mindemaple, Mind Master, Kahoot, Canva, Videoscrabe, Mandele, Zotero, Spss, QR Code, Kinemaster, Flip PDF Professional. Supporting factors are the availability of wifi, available media applications that will be used. Then the factors that hinder the lack of student learning facilities.
The Inconvenient Truths of Ground Truth for Binary Analysis

The effectiveness of binary analysis tools and techniques is often measured with respect to how well they map to a ground truth.We have found that not all ground truths are created equal.This paper challenges the binary analysis community to take a long look at the concept of ground truth, to ensure that we are in agreement with definition(s) of ground truth, so that we can be confident in the evaluation of tools and techniques.This becomes even more important as we move to trained machine learning models, which are only as useful as the validity of the ground truth in the training.
AKUISISI BUKTI DIGITAL PADA APLIKASI TAMTAM MESSENGER MENGGUNAKAN METODE NATIONAL INSTITUTE OF JUSTICE

Smartphones are one of the tools used in carrying out daily work activities, the number of active social media users in Indonesia in January 2019 reached 150 million, 130 million of whom accessed social media using smartphones. With the development of smartphones in social media, many are misused using instant messaging applications, one of which is cybercrime such as cyberbullying, human trafficking systems, fraud and so on. Cyberbullying is one of the negative impacts in cyber crime through the use of applications that are often used, namely the TamTam Messenger social media. The problem that will be discussed in this study is the digital forensic process, especially mobile forensics in order to find digital evidence that was deleted on the TamTam Messenger application using the MobileEdit forensic tools and the National Institute of Justice method. The results of this study show that using the MobileEdit forensic tools and the National Institute of Justice method succeeded in recovering digital evidence that had been deleted by the perpetrator on the TamTam Messenger application.