Reviewerly/exHarmony huggingface dataset:
Desain Perancangan Sistem Pakar Pemeriksaan Dimensi Dan Arah Pada Tanda Tangan

The signature is a fully reliable proof of identity to explain the authenticity of a document, which becomes a serious problem that the acceptance of a signature as evidence of a person's recognition, in this case, is not accompanied by its identification, this causes great danger to the possibility of forgery and various other crimes related to signatures. The lack of experts and the lack of application of knowledge about this, and the development of crimes in this field, it is necessary to overcome using investigations based on science and technology as a tool for the prevention and prosecution of crimes, especially in the field of signature examination. The design of the expert system in this study is designed to assist signature examiners in anticipating the increasing crime of signature forgery, with restrictions on the dimensions and direction of the signature.
Sistem Pembelajaran Berbasis Website Quizpper untuk SMA Yayasan Mekar Arum

This community service activity was carried out with the aim that the existing teaching and learning process at the Mekar Arum Foundation High School, Bandung Regency, West Java became more efficient and flexible. By providing an output in the form of an online learning website called Quizpper (quiz without paper), the Quizpper website is designed with the aim of improving the quality of teaching and learning activities between students and teachers in achieving learning objectives. Through the features available on the Quizpper learning website, the learning process also becomes more structured and organized, this is because Quizpper utilizes a database storage system. Design and development are carried out using the Research and Development (RD) method with a waterfall development model which consists of several stages, namely, requirements analysis, design, implementation or coding, and testing. The Quizpper website was developed using the PHP Codeigniter 3 framework and the mysql database as the storage tool. The arrangement and appearance of the front-end in the Quizpper learning system are adjusted based on teaching and learning activities in class.
Optimalisasi Data Tidak Seimbang Pada Data Nasabah Koperasi dalam Pemberian Pinjaman Menggunakan Random Oversampling

Cooperatives have developed from time to time, in providing services, credit cooperatives certainly have certain requirements as prospective customers to receive loans. Cooperatives need to check whether interested parties will receive loans. Loans to customers are the main source of income for cooperatives. In data mining, there are several classification algorithms that can be used for credit analysis, including the Random Forest and the C4.5 Algorithm. Data on prospective customers received from cooperatives as a condition for applying for credit is processed using Random Forest data mining and C4.5 Algorithm to support credit analysis in order to obtain accurate information on whether the prospect who applies for credit is feasible or not, this study was conducted to classify loans to prospective customers. cooperative customers using the Random Forest method and the C4.5 Algorithm which is optimized by Random Oversampling because the dataset is in an unbalanced condition. In testing the C4.5 Algorithm which is optimized with Random Oversampling, it gets an accuracy of 78.03%, where the accuracy increases by 7.89% from the previous 70.14%. Meanwhile, Random Forest with Random Oversampling has an accuracy value of 87.12%, an increase of 23.69% from the previous Random Forest test of 63.43
Analisis Hasil DoS SYN Flood Attack Pada Web Server

abstract - The development of technology and information certainly has many good impacts on human life, but behind all this it turns out that this also has a bad impact. The ease of accessing information, besides making us quickly know all things that are good and useful, turns out to be just as fast as making someone aware of things that are not good that can disrupt people's welfare, for example regarding information on how to exploit security holes in using the internet. With the amount of information that has been spread regarding the use of security holes on the internet, now someone can easily learn so that he can launch an attack on the internet that can harm other people. One of the attacks that is currently easy to learn and use is the DoS (Denial of Service) Attack, due to the large amount of information that has been spread about how to use out this attack coupled with computers that are now increasingly sophisticated and the many tools that can be obtained easily and for free, so it is not surprising that we often see many attempts of this attack on the internet. Therefore, for educational purposes, a DoS attack simulation will be carried out in this study to find out how dangerous the impact of this attack is.abstrak - Perkembangan teknologi dan informasi tentunya membawa banyak dampak yang baik bagi kehidupan manusia tetapi dibelakang itu semua ternyata hal ini juga membawa dampak yang buruk. Kemudahan dalam mengakses informasi, selain membuat kita cepat tahu akan segala seuatu hal yang baik dan berguna ternyata sama cepatnya dengan membuat seseorang mengetahui hal-hal yang tidak baik yang dapat membuat kesejahteraan masyarakat terganggu, contohnya mengenai informasi cara mengekspoitasi celah keamanan dalam menggunakan internet. Dengan banyaknya informasi yang telah tersebar mengenai pemanfaatan celah keamanan di internet, maka sekarang seseorang dapat dengan mudah mepelajari hingga ia dapat melancarkan suatu serangan di internet yang bisa merugikan orang lain. Salah satu serangan yang saat ini dapat dengan mudah dipelajari dan digunakan ialah DoS (Denial of Service) Attack, dikarenakan banyaknya informasi yang telah tersebar mengenai cara melakukan serangan ini ditambah dengan komputer yang kini semakin canggih serta banyaknya tools yang bisa didapatkan dengan mudah dan gratis, maka tidak mengherankan jika kita sering melihat banyaknya percobaan serangan ini di internet. Oleh karena itu demi tujuan edukasi maka akan dilakukan simulasi serangan DoS pada penelitian ini untuk mengetahui seberapa berbahaya dampak dari serangan ini.
Installing a Global Service Manager

In this chapter, we will show you how to download and install an Oracle global service manager (GSM) on Linux servers.
Exploring Raft Replication in Oracle 23c

In Chapter 2 , you learned what sharding is, as well as two sharding methods: user-defined sharding and composite sharding. Oracle has introduced a new feature called Raft replication in Oracle 23c. In this chapter, we will cover this new feature, and we will demonstrate a few scenarios using the Raft replication feature in sharding.
Configuring and Administering Oracle GDS

In this chapter, you will learn how to configure the various Global Data Services (GDS) components and learn how to administer GDS, which includes learning how to add a global service manager (GSM), how to create and connect to the catalog database, how to add a gdspool and regions to the GDS configuration, how to add databases to the GDS configuration, and how to manage the GDS configurations.
Comparison of feature-based sentence ranking methods for extractive summarization of Turkish news texts

Document summarization is the task of generating a shorter form of document with important information content.Automatic text summarization has been developed for this process and is still widely used.It is divided into two main parts as extractive summarization and abstractive summarization.In this study, we used sentence ranking methods for extractive summarization for Turkish news text within the scope of the experimental study.We used different summarization rates, 20%, 30%, 40%, 50% and 60%.Summarization results were evaluated with the ROUGE ve BLEU metrics.We proposed new methods based on major vowel harmony and minor vowel harmony features.We obtained high evaluation results in both ROUGE ve BLEU metrics with major vowel harmony and minor vowel harmony features.Additionally, we studied a hybrid model using major vowel harmony and minor vowel harmony rules together.We obtained the best results with major vowel harmony, minor vowel harmony, and hybrid model (major vowel harmony and minor vowel harmony together).We compared the three proposed methods with the BERTurk model prepared for Turkish based on Google BERT.The results obtained gave very close results to this state-of-the-art method and showed that it is worth developing.
Introduction to Oracle Global Data Services

One of the key challenges in database environments is ensuring seamless failover and transparent client connections. When a server that clients are connected to fails, both the clients and the application servers typically encounter errors, and new connections are not automatically redirected to an online database. This can result in disruptions and downtime for the application.
Monitoring Oracle GDS and Oracle Sharding

Most administrators prefer using Enterprise Manager to monitor and administer databases. The web user interface is a simplified way to monitor databases over using the command line. In addition, identifying bottlenecks using graphs is easier than checking text or XML versions of log files and comparing logged metrics and values. Graphs help you easily identify a maximum value for the specific threshold and switch to the tuning page.
Benefits and Challenges of the Educational Metaverse: Evidence from Quantitative and Qualitative Data

The metaverse, as a more advanced form of virtual reality, has great potential for education because of its unique affordances for enhancing immersion, interaction, and presence. However, because its development is still in its infancy, there are few empirical studies on the application of metaverse in education, with insufficient empirical evidence from the literature regarding its effectiveness and valued design features, as well as its advantages and disadvantages as a technology-enhanced learning environment. Utilizing the case study method, this study designed and implemented a 40-minute psychology course with 31 undergraduate students in the metaverse using the Virbela platform and collected both quantitative and qualitative data to empirically explore the benefits and challenges of the educational metaverse. The quantitative results showed that the students reported good learning experiences in the metaverse, but their learning outcomes were unsatisfactory, just over the passing level. The qualitative results revealed useful design features and common technical challenges of the educational metaverse. Based on the results, several implications for designing and developing effective courses in the educational metaverse were proposed.
Machine Learning Approaches for Attacking Behaviors in Robot Soccer

In this work, Machine Learning approaches were applied to attacking behaviors in RoboCup Small-Size League autonomous robot soccer.Neural networks were used in order to get a binary prediction of an attacking action's success, while deep reinforcement learning was leveraged to learn low level skills which control the robot's wheel speeds and kicker.A trained neural network was used to predict whether a shot would be successful, improving the number of goals scored by the attacking behavior by 84 to 186%.The reinforcement learning methodologies used in this work produced behaviors which were efficient in speed, beating manually programmed behaviors in time taken, but can benefit from future refinements to improve accuracy in shooting towards goal.
Secure Big Data Storage on Multi-Cloud Servers with Enhanced Attribute-Based Access Control

Abstract: Data outsourcing is now subject to additional safety and privacy concerns as a result of the rise in cloud computing demand. Because data management was moved to words an unstable cloud during the outsourcing stage of the data, data permit control identified a key problem in the cloud repository. This analysis uses the Attribute-Based Access Control method to control access to the cloud storage to solve the issue. The data is reserved in the cloud and protected by strict security before the user may access it. This model takes into account specific properties of the cloud data stored in the database authentication process in order to store the information of registered collections alongside the user-committed data. Each of the clusters has a unique set of benefits, including usernames, group names, and registry message authorization codes. Prior to sending the data to the service provider, it should be encrypted to ensure its security. However, there may occasionally be problems with the supplier’s security measures. In this outcome, a variety of metrics are analyzed, such as the amount of time used for encryption, decryption, key generation, and time consumption. The numerous existing techniques, like Nth Truncated Ring Units and Cipher text. Constraints on online storage access may make policy attribute-based encryption impossible. The time of encryption is reduced by 20% and 36%, respectively, via NTRU and CP-ABE. The proposed technique decrypts data 10.64% faster than the current approach, which decrypts data 19% more slowly.
Hierarchical variable clustering using singular value decomposition

In this work, we present a novel method for divisive hierarchical variable clustering. A cluster is a group of elements that exhibit higher similarity among themselves than to elements outside this cluster. The correlation coefficient serves as a natural measure to assess the similarity of variables. This means that in a correlation matrix, a cluster is represented by a block of variables with greater internal than external correlation. Our approach provides a nonparametric solution to identify such block structures in the correlation matrix using singular vectors of the underlying data matrix. When divisively clustering $p$ variables, there are $2^{p-1}$ possible splits. Using the singular vectors for cluster identification, we can effectively reduce these number to at most $p(p-1)$, thereby making it computationally efficient. We elaborate on the methodology and outline the incorporation of dissimilarity measures and linkage functions to assess distances between clusters. Additionally, we demonstrate that these distances are ultrametric, ensuring that the resulting hierarchical cluster structure can be uniquely represented by a dendrogram, with the heights of the dendrogram being interpretable. To validate the efficiency of our method, we perform simulation studies and analyze real world data on personality traits and cognitive abilities. Supplementary materials for this article can be accessed online.
Efficient learning of $t$-doped stabilizer states with single-copy measurements

One of the primary objectives in the field of quantum state learning is to develop algorithms that are time-efficient for learning states generated from quantum circuits. Earlier investigations have demonstrated time-efficient algorithms for states generated from Clifford circuits with at most $\log(n)$ non-Clifford gates. However, these algorithms necessitate multi-copy measurements, posing implementation challenges in the near term due to the requisite quantum memory. On the contrary, using solely single-qubit measurements in the computational basis is insufficient in learning even the output distribution of a Clifford circuit with one additional $T$ gate under reasonable post-quantum cryptographic assumptions. In this work, we introduce an efficient quantum algorithm that employs only nonadaptive single-copy measurement to learn states produced by Clifford circuits with a maximum of $O(\log n)$ non-Clifford gates, filling a gap between the previous positive and negative results.
Emergent communication for AR

Mobile augmented reality (MAR) is widely acknowledged as one of the ubiquitous interfaces to the digital twin and Metaverse, demanding unparalleled levels of latency, computational power, and energy efficiency. The existing solutions for realizing MAR combine multiple technologies like edge, cloud computing, and fifth-generation (5G) networks. However, the inherent communication latency of visual data imposes apparent limitations on the quality of experience (QoE). To address the challenge, we propose an emergent semantic communication framework to learn the communication protocols in MAR. Specifically, we train two agents through a modified Lewis signaling game to emerge a discrete communication protocol spontaneously. Based on this protocol, two agents can communicate about the abstract idea of visual data through messages with extremely small data sizes in a noisy channel, which leads to message errors. To better simulate real-world scenarios, we incorporate channel uncertainty into our training process. Experiments have shown that the proposed scheme has better generalization on unseen objects than traditional object recognition used in MAR and can effectively enhance communication efficiency through the utilization of small-size messages.
Quantum secure non-malleable randomness encoder and its applications

"Non-Malleable Randomness Encoder"(NMRE) was introduced by Kanukurthi, Obbattu, and Sekar~[KOS18] as a useful cryptographic primitive helpful in the construction of non-malleable codes. To the best of our knowledge, their construction is not known to be quantum secure. We provide a construction of a first rate-$1/2$, $2$-split, quantum secure NMRE and use this in a black-box manner, to construct for the first time the following: 1) rate $1/11$, $3$-split, quantum non-malleable code, 2) rate $1/3$, $3$-split, quantum secure non-malleable code, 3) rate $1/5$, $2$-split, average case quantum secure non-malleable code.
Using Text Injection to Improve Recognition of Personal Identifiers in Speech

Accurate recognition of specific categories, such as persons' names, dates or other identifiers is critical in many Automatic Speech Recognition (ASR) applications. As these categories represent personal information, ethical use of this data including collection, transcription, training and evaluation demands special care. One way of ensuring the security and privacy of individuals is to redact or eliminate Personally Identifiable Information (PII) from collection altogether. However, this results in ASR models that tend to have lower recognition accuracy of these categories. We use text-injection to improve the recognition of PII categories by including fake textual substitutes of PII categories in the training data using a text injection method. We demonstrate substantial improvement to Recall of Names and Dates in medical notes while improving overall WER. For alphanumeric digit sequences we show improvements to Character Error Rate and Sentence Accuracy.
Classical Non-Relativistic Fractons

We initiate the study of the classical mechanics of non-relativistic fractons in its simplest setting - that of identical one dimensional particles with local Hamiltonians characterized by by a conserved dipole moment in addition to the usual symmetries of space and time translation invariance. We introduce a family of models and study the $N$ body problem for them. We find that locality leads to a ``Machian" dynamics in which a given particle exhibits finite inertia only if within a specified distance of at least another one. For well separated particles this leads to immobility, much as for quantum models of fractons discussed before. For two or more particles within inertial reach of each other at the start of motion we get an interesting interplay of inertia and interactions. Specifically for a solvable ``inertia only" model of fractons we find that $N=2$ particles always become immobile at long times. Remarkably $N =3$ particles generically evolve to a late time state with one immobile particle and two that oscillate about a common center of mass with generalizations of such ``Machian clusters" for $N > 3$ . Interestingly, Machian clusters exhibit physical limit cycles in a Hamiltonian system even though mathematical limit cycles are forbidden by Liouville's theorem.
Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineering

The final frontier for simulation is the accurate representation of complex, real-world social systems. While agent-based modeling (ABM) seeks to study the behavior and interactions of agents within a larger system, it is unable to faithfully capture the full complexity of human-driven behavior. Large language models (LLMs), like ChatGPT, have emerged as a potential solution to this bottleneck by enabling researchers to explore human-driven interactions in previously unimaginable ways. Our research investigates simulations of human interactions using LLMs. Through prompt engineering, inspired by Park et al. (2023), we present two simulations of believable proxies of human behavior: a two-agent negotiation and a six-agent murder mystery game.
Energy Efficiency of Quantum Statevector Simulation at Scale

Classical simulations are essential for the development of quantum computing, and their exponential scaling can easily fill any modern supercomputer. In this paper we consider the performance and energy consumption of large Quantum Fourier Transform (QFT) simulations run on ARCHER2, the UK’s National Supercomputing Service, with QuEST toolkit. We take into account CPU clock frequency and node memory size, and use cache-blocking to rearrange the circuit, which minimises communications. We find that using 2.00 GHz instead of 2.25 GHz can save as much as 25% of energy at 5% increase in runtime. Higher node memory also has the potential to be more efficient, and cost the user fewer CUs, but at higher runtime penalty. Finally, we present a cache-blocking QFT circuit, which halves the required communication. All our optimisations combined result in 40% faster simulations and 35% energy savings in 44 qubit simulations on 4,096 ARCHER2 nodes.
Symphony: Optimized Model Serving using Centralized Orchestration

The orchestration of deep neural network (DNN) model inference on GPU clusters presents two significant challenges: achieving high accelerator efficiency given the batching properties of model inference while meeting latency service level objectives (SLOs), and adapting to workload changes both in terms of short-term fluctuations and long-term resource allocation. To address these challenges, we propose Symphony, a centralized scheduling system that can scale to millions of requests per second and coordinate tens of thousands of GPUs. Our system utilizes a non-work-conserving scheduling algorithm capable of achieving high batch efficiency while also enabling robust autoscaling. Additionally, we developed an epoch-scale algorithm that allocates models to sub-clusters based on the compute and memory needs of the models. Through extensive experiments, we demonstrate that Symphony outperforms prior systems by up to 4.7x higher goodput.