Reviewerly/exHarmony huggingface dataset:
Analisis Perbandingan Performansi Kontroler RYU Dan POX Berbasis Software Defined Network (SDN) Pada Routing OSPF

Abstrak— Routing adalah proses proses pencarian lintasan yang akan di lalui oleh paket data. Software Defined Network (SDN) adalah sebuah konsep konfigurasi jaringan computer baru yang dimana data plane dan control plane dipisahkan. Kelebihan dari Software Defined Network dibandingkan dengan jaringan konvensional yaitu, pemusatan konfigurasi pada kontroler Software Defined Network. Pada penelitian ini, skenario jaringan Software Defined Network di bangun menggunakan software mininet untuk pembentukan topologinya dengan menerapkan algoritma Dijkstra pada kontroler RYU dan POX untuk pemilihan jalur terpendeknya. Pada pengujian dibentuk 6 topologi dengan pemberian bobot yang berbeda, tujuan pembedaan bobot untuk memastikan bahwa algoritma Djikstra benar benar dapat di terapkan pada kontroler RYU dan POX. Pemberian bobot juga dilakukan agar kontroler dapat membentuk tabel routing untuk mengidentidikasi jalur tercepat yang akan di lalui oleh paket data tersebut. Hasil pengujian pada semua topologi diambil rata-ratanya untuk membandingkan performansinya. Hasil rata-rata dari semua pengujian adalah 1) pengujian waktu konvergensi pada RYU 0.4143s dan POX 0.5231s, 2) pengujian throughput yang di hasilkan RYU 12781.79kb/s dan POX 11412.24kb/s, 3) pengujian delay yang di hasilkan RYU 0.223ms dan POX 0.247ms, 4) pengujian jitter yang di hasilkan RYU 0.08ms dan POX 1.21ms. 5) rerata pengujian Packet Loss yang di hasilkan RYU 0 dan POX 0.2%.Kata Kunci—SDN, Mininet, RYU, POX.&#x0D; &#x0D; Routing is the process of finding the path to be traversed by data packets. Software Defined Network (SDN) is a new computer network configuration concept in which the data plane and control plane are separated. The advantage of a Software Defined Network compared to a conventional network is that the configuration is centralized on the Software Defined Network controller. In this study, the Software Defined Network network scenario was built using mininet software for the formation of the topology by applying the Dijkstra algorithm to the RYU and POX controllers for the selection of the shortest path. In the test, 6 topologies were formed with different weights, the purpose of the weight difference is to ensure that the Djikstra algorithm can really be applied to the RYU and POX controllers. Weighting is also done so that the controller can form a routing table to identify the fastest path to be traversed by the data packet. The test results on all topologies are averaged to compare their performance. The average results of all tests are 1) testing the convergence time at RYU 0.4143s and POX 0.5231s, 2) throughput testing the resulting RYU 12781.79kb/s and POX 11412.24kb/s, 3) delay testing the resulting RYU 0.223 ms and POX 0.247ms, 4) jitter test which resulted in RYU 0.08ms and POX 1.21ms. 5) the average Packet Loss test produced is RYU 0 and POX 0.2%.Keywords— SDN, Mininet, RYU, POX.
Biblical Scholarship in the GitHub Jungle

Clear Bible's MACULA project is a major data integration challenge. Clear creates freely licensed linguistic datasets for the entire Bible in the original Hebrew, Aramaic, and Greek languages. We integrate these datasets with high quality datasets created by others and align them with translations in many languages. Language is complex and individuals have different ways of organizing and understanding texts, so integrating these diverse datasets is not straightforward. Few texts have been analyzed as thoroughly as the Bible, from many different perspectives. A great deal of Biblical analysis is available on GitHub under open licenses, including well-established reference systems for the verses in a Bible and the words used in the original languages. But data integration is still problematic since there are different traditions, with different sets of books and different ways of dividing up individual books. For instance, Psalm 23 in a Protestant Bible is called Psalm 22 in a traditional Catholic Bible. Even a concept as simple as "what is a word" becomes complicated, since there is no clear universal distinction between a word and a morpheme — linguists employ a range of different criteria, which are not uniformly applicable across contexts and languages. In this article, we would like to illustrate some of the challenges we have encountered in the first year of our work on MACULA. We will also discuss the approach we have taken in response to each of these challenges.
Fact Vs. Fiction: Teaching Critical Thinking Skills in the Age of Fake News

"Fact Vs. Fiction: Teaching Critical Thinking Skills in the Age of Fake News." Journal of Electronic Resources in Medical Libraries, 19(3), pp. 112–113
PeerConnect: Live Virtual Event Platform by using Web Server

Peerconnect is a great solution for virtual event management. Peerconnect is an all-in-one event management platform for promoting and conducting online events on the same platform. The point of virtual event management software is to allow users to create hosts and manage the events at ease without having to depend on many software to accomplish different tasks. So, the event management software Peerconnect has established the pipeline for people who want to promote, manage and conduct virtual events. Organizers can create the events on the platform and can promote and sell the tickets to other users on the same platform. Organizers can host live chat rooms and live discussions with video calling capabilities as well. Peerconnect is a web application using which the user can get to know about the events which are conducted virtually. Once the user selects the event, then he has to register for it. Once the registration is successful, user can attend the event on the same platform. Once the registration is successful, users can successfully login and they can search for an event that they are interested in and filter them according to their interests. Once the user selects the event then they have to register for it. Once registration for the event is successful, user can join the event in just one click and attend the event on the same platform and can download the participation certificate as well. If a registered user misses an event, he can still watch the recorded session of that event anytime after its completion. The event organizer can create an organization and later post the event. During the event, the organizer can send files and use the chat feature to engage and clear doubts of the attendees. They can also share their screen so that it is visible to all the attendees. Organizers can also capture the attendance of those who are attending, based on which the attendees can be able to download their participation certificates.
Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm

Latent Dirichlet Allocation (LDA) is an approach to unsupervised learning that aims to investigate the semantics among words in a document as well as the influence of a subject on a word. As an LDA-based model, Joint Sentiment-Topic (JST) examines the impact of topics and emotions on words. The emotion parameter is insufficient, and additional parameters may play valuable roles in achieving better performance. In this study, two new topic models, Weighted Joint Sentiment-Topic (WJST) and Weighted Joint Sentiment-Topic 1 (WJST1), have been presented to extend and improve JST through two new parameters that can generate a sentiment dictionary. In the proposed methods, each word in a document affects its neighbors, and different words in the document may be affected simultaneously by several neighbor words. Therefore, proposed models consider the effect of words on each other, which, from our view, is an important factor and can increase the performance of baseline methods. Regarding evaluation results, the new parameters have an immense effect on model accuracy. While not requiring labeled data, the proposed methods are more accurate than discriminative models such as SVM and logistic regression in accordance with evaluation results. The proposed methods are simple with a low number of parameters. While providing a broad perception of connections between different words in documents of a single collection (single-domain) or multiple collections (multidomain), the proposed methods have prepared solutions for two different situations (single-domain and multidomain). WJST is suitable for multidomain datasets, and WJST1 is a version of WJST which is suitable for single-domain datasets. While being able to detect emotion at the level of the document, the proposed models improve the evaluation outcomes of the baseline approaches. Thirteen datasets with different sizes have been used in implementations. In this study, perplexity, opinion mining at the level of the document, and topic_coherency are employed for assessment. Also, a statistical test called Friedman test is used to check whether the results of the proposed models are statistically different from the results of other algorithms. As can be seen from results, the accuracy of proposed methods is above 80% for most of the datasets. WJST1 achieves the highest accuracy on Movie dataset with 97 percent, and WJST achieves the highest accuracy on Electronic dataset with 86 percent. The proposed models obtain better results compared to Adaptive Lexicon learning using Genetic Algorithm (ALGA), which employs an evolutionary approach to make an emotion dictionary. Results show that the proposed methods perform better with different topic number settings, especially for WJST1 with 97% accuracy at |Z| = 5 on the Movie dataset.
Comparison of RNN, LSTM, and GRU Methods on Forecasting Website Visitors

Forecasting is the best way to find out the number of website visitors. However, many researchers cannot determine which method is best used to solve the problem of forecasting website visitors. Several methods have been used in forecasting research. One of the best today is using deep learning methods. This study discusses forecasting website visitors using deep learning in one family, namely the RNN, LSTM, and GRU methods. The comparison made by these three methods can be used to get the best results in the field of forecasting. This study used two types of data: First Time Visits and Unique Visits. The test was carried out with epoch parameters starting from 1 to 500 at layers 1, 3, and 5. The test used first-time visit data and unique visit data. Although tested with different data, the test results obtained that the smallest MSE value is the LSTM method. The value of each MSE is 0.0125 for first-time visit data and 0.0265 for unique visit data. The contribution of this research has succeeded in showing the best performance of the three recurrent network methods with different MSE values.
A ViT-based lightweight method for the UAV platform object detection tasks

Lightweight design is a key way to realize the engineering application of deep learning algorithms on Unmanned Aerial Vehicle (UAV) platform. Aiming at the low detection accuracy of the current real-time object detection algorithms of the UAV platform, a lightweight model based on Vision Transformer (ViT) is designed in this paper. Firstly, a small Convolutional Neural Network (CNN) is used to extract primary features for reducing the number of parameters and computation amount of ViT network in this model, and using window modeling to replace part of the global modeling. Then, a feature-level mask self-supervised training method is applied to pre-train the ViT structure, which helps to accelerate the convergence and avoid a lot of labeling work. Finally, the result compared with other UAV lightweight object detection algorithms in the visdrone2018 dataset shows that this model has higher average accuracy on ensuring real-time speed, and verifies the effectiveness and reference value of the lightweight design method proposed in this paper.
Pruning threshold search algorithm combined with PDARTS

With the continuous improvement of network accuracy, the depth and width of the network also increase, so the requirements for storage and operation equipment are also higher and higher, which limits the application scenarios of the network. To solve this problem, many network acceleration methods have been proposed, among which thresholdbased pruning algorithm and differentiation-based network architecture search algorithm are two commonly used methods. The optimal pruning threshold is found by searching through a differentiation-based network architecture search algorithm using an incremental approach. Experiments were carried out on CIFAR-10/100 using VGG16, ResNet-56 and ResNet-18 respectively. The experimental results show that the algorithm in this paper can help the network to maintain better classification accuracy after pruning with a higher compression rate.
Dissociation Between Electrophysiology and Behavior—A Disaster or a Challenge?

This chapter points that some of the more interesting results in cognitive psychophysiology derive from instances in which the “behavioral” and the “physiological” observations appear to conflict. It describes studies of the latency of the P300 component of the event-related brain potential. The chapter fulfills, in part, the tutorial mission of the discussion by highlighting some of the methodological problems encountered during the acquisition, and analysis, of event-related brain potentials. The P300, on the other hand, allows a direct demonstration that the subject continues to process the stimulus sufficiently to recognize it as a rare stimulus. Moreover, it turns out that the commission of an error leads to a substantial delay in the emission of P300. By the time the P300 process in invoked the stimulus has been categorized as rhyming or not rhyming. The overt response is, however, withheld for yet another 300 msec, and the reason must be anchored in “criterion-,” not in “sensitivity”-related processes.
Collisional-model quantum trajectories for entangled-qubit environments

We study the dynamics of quantum systems interacting with a stream of entangled-qubit pairs in the weak coupling limit. For a large class of two-qubit bath states, we present a detailed framework describing conditional dynamical maps for the system, known as quantum trajectories, that arise when the qubits are measured. Depending on the measurement basis, these quantum trajectories can be jump-type or diffusive-type, and can successively transfer entanglement from the bath qubits other quantum systems. They also exhibit features not present in standard quantum optical trajectories due to the fact that collisional models are not confined by the standard white-noise limit for optical-mode baths. As an example of this formalism, we consider the case of two remote two-level systems, where jump-type quantum trajectories herald the birth and death of entanglement.
The Development of Computer Network Learning Media to Support Online Learning

In the new normal era, the learning system is carried out online. The lack of innovation in the use of learning media during the covid-19 pandemic, resulted in students being less motivated and having low learning enthusiasm in the computer network learning process. To overcome these problems, one alternative is to develop learning media during the pandemic. This research intends to improve computer network learning media as a support for online learning in the new normal era. The method used in this development research is Research and Development (R&amp;D) using the Instractional Development Institute (IDI) model approach. The IDI approach model has several stages of implementation including define, develop, and evaluate. The results of the research on the development of learning media are declared valid, practical and effective to be applied as a computer network learning media as a media to support online learning.
Ability-Based Methods for Personalized Keyboard Generation

This study introduces an ability-based method for personalized keyboard generation, wherein an individual’s own movement and human–computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user’s movement abilities through capturing directional constraints and preferences. We evaluated our approach in a study involving 16 participants using inertial sensing and facial electromyography as an access method, resulting in significantly increased communication rates using the personalized keyboard (52.0 bits/min) when compared to a generically optimized keyboard (47.9 bits/min). Our results demonstrate the ability to effectively characterize an individual’s movement abilities to design a personalized keyboard for improved communication. This work underscores the importance of integrating a user’s motor abilities when designing virtual interfaces.
Peachy Parallel Assignments (EduPar 2022)

The presentation of Peachy Parallel Assign-ments in several workshops on parallel and distributed computing education aims to promote the reuse of high-quality assignments, both saving precious faculty time and improving the quality of course assignments. Presented assignments are selected competitively- they must have been successfully used in a real classroom, be easy for other instructors to adopt, and be “cool and inspirational” to encourage students to spend time on them and talk about them with others. Winning assignments are also archived on the Peachy Parallel Assignments website. In this installment of Peachy Parallel Assignments, we present three new assignments. The first assignment is to simulate an Abelian Sandpile, with grains of sand moving from tall piles to shorter ones. This is a discrete simulation that creates colorful and intricate images. The second assignment is a Big Data problem in which students use the MapReduce paradigm to recreate “Warming Stripes”, a visualization of climate data that highlights climate change. The third assignment introduces climate-oriented optimization by asking students to schedule distributed workflows to minimize their carbon footprint.
A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-Implementation

In communication systems, Autoencoder (AE) refers to the concept of replacing parts of the transmitter and receiver by artificial neural networks (ANNs) to train the system end-to-end over a channel model. This approach aims to improve communication performance, especially for varying channel conditions, with the cost of high computational complexity for training and inference. Field-programmable gate arrays (FPGAs) have been shown to be a suitable platform for energy-efficient ANN implementation. However, the high number of operations and the large model size of ANNs limit the performance on the resource-constrained devices, which is critical for low-latency and high-throughput communication systems. To tackle his challenge, we propose a novel approach for efficient ANN-based demapping on FPGAs, which combines the adaptability of the AE with the efficiency of conventional demapping algorithms. After adaption to channel conditions, the channel characteristics, implicitly learned by the ANN, are extracted to enable the use of optimized conventional demapping algorithms for inference. We validate the hardware efficiency of our approach by providing FPGA implementation results and by comparing the communication performance to that of conventional systems. Our work opens a door for the practical application of ANN-based communication algorithms on FPGAs.
Extended TLS: Masking Server Host Identity on the Internet Using Encrypted TLS Handshake

The Internet is a common platform for sharing information. It is required to preserve every user’s privacy and security of information on the Internet. While data security is primarily taken care of by the TLS protocol and broader adaptation of HTTPS, FTPS, and SMPTS protocol, some fields of TLS expose the type of activity a user is performing, thus violating user privacy. One such protocol information is Server Name Indication (SNI) in the TLS ClinetHello message that goes in plaintext. Anyone intercepting the message thus identifies the service host type. We present a method named Extended TLS (ETLS) to mask the server host identity by encrypting the SNI without requiring any change in the existing protocols. In ETLS, a connection is established over two handshakes - the first handshake establishes a secure channel without sharing SNI information, and the second handshake shares the encrypted SNI. ETLS requires no modification in the already proven TLS encryption mechanism and retains all security benefits of the existing secure channel establishment. We demonstrate the feasibility of ETLS over live Internet with scripts that implement our methodology. Using a customized client-server and a commercial traffic shaper, we also demonstrated that the host identity is not exposed under ETLS, thus demonstrating its privacy-preserving property.
Strategic maneuver and disruption with reinforcement learning approaches for multi-agent coordination

Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provide windows of opportunity in various military tasks. Technologically advancing adversaries pose substantial risks to a friendly nation’s interests and resources. Superior resources alone are not enough to defeat adversaries in modern complex environments because adversaries create standoff in multiple domains against predictable military doctrine-based maneuvers. Therefore, as part of a defense strategy, friendly forces must use strategic maneuvers and disruption to gain superiority in complex multi-faceted domains, such as multi-domain operations (MDOs). One promising avenue for implementing strategic maneuver and disruption to gain superiority over adversaries is through coordination of MAS in future military operations. In this paper, we present overviews of prominent works in the RL domain with their strengths and weaknesses for overcoming the challenges associated with performing autonomous strategic maneuver and disruption in military contexts.