Reviewerly/exHarmony huggingface dataset:
An Efficient Scheduling Scheme of Swapping and Purification Operations for End-to-End Entanglement Distribution in Quantum Networks

Entanglement-based quantum networks provide unconditionally secure quantum communication by distributing end-to-end entangled pairs. To reach such goal, entanglement swapping and purification are two necessary operations, which can expand the entanglement distance and improve the fidelity of entangled pairs, respectively. During the end-to-end entanglement distribution process, the execution sequence of these two operations has a significant impact on the final performance, such as throughput and fidelity. Thus, how to schedule the sequence of swapping and purification becomes a critical problem in quantum networks. Our work focuses on such scheduling problem and we devise an efficient solution with fidelity maintenance. In our paper, we first propose a concurrent swapping method for any-length path to reduce delay. Secondly, by modeling the scheduling sequence as a permutation problem, we obtain an optimal scheduling principle called “prioritized purification (PP)”. Finally, to meet the fidelity requirement of the end-to-end entangled pairs, we propose a fidelity maintenance scheme based on PP (FMSPP). The simulation results show that the proposed FMSPP can achieve higher distribution rates and less resource overhead than other scheduling solutions, and the advantages are more significant when there are more hops on the path.
Mini-Workshop: Mathematical Foundations of Robust and Generalizable Learning

Machine learning has become an highly active field of research, but its mathematical underpinnings are still hardly understood. This workshop identified key challenges, and it discussed potential solutions. Bringing together a diverse group of researchers, the workshop established different views on the topic based on notions from statistics, probability theory, and optimization.
Pragmatic analysis of ECC based security models from an empirical perspective

This paper provides a detailed discussion about currently proposed Elliptic Curve Cryptography (ECC) models focusing on performance parameters such as Security, Complexity, Scalability, and cost of deployment. It was observed that Machine Learning optimizations including bio inspired computing, deep learning, and transformation models outperform other techniques. This discussion is extended via an empirical estimation of these models related to the performance metrics under different application scenarios. This paper also proposes the calculation of an ECC Performance Metric (EPM), which combines the evaluated parameter sets to identify ECC Models that can perform better under multiple operating scenarios.
UAV Inspection of Large Components: Determination of Alternative Inspection Points and Online Route Optimization

Automation is playing an increasing role in the field of quality assurance. For the visual inspection of larger assemblies such as aircraft fuselages or ship hulls, the use of UAVs is an option. This paper deals with one aspect of the UAV-supported inspection of assemblies in production. Here, newly added components have to be checked for correct assembly. The planning of the shortest possible route from which all components to be inspected can be viewed as well as the estimation of the UAV’s position relative to the component have already been presented in previous work. We propose strategies that can be used if an inspection point cannot be reached by the UAV or the component to be inspected cannot be seen by the UAV’s camera from the inspection point. For this purpose, we generate alternative inspection points that can be used if errors occur during the inspection from the original inspection point. To achieve this, we present a metric that can be used to select an alternative inspection point that is as suitable as possible. We conclude by demonstrating how this strategy works by evoking different failure cases in a simulated environment.
Serving DEI Needs from an Inclusive and Critical Cataloging Perspective: Report of the Cataloging and Classification Research Interest Group Meeting, ALA Core Interest Group Week, March 2023

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author.
Social Engineering Attack-Defense Strategies Based on Reinforcement Learning

Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity, as human vulnerabilities are often the weakest link in the entire network. Such vulnerabilities are becoming increasingly susceptible to network security risks. Addressing the social engineering attack defense problem has been the focus of many studies. However, two main challenges hinder its successful resolution. Firstly, the vulnerabilities in social engineering attacks are unique due to multistage attacks, leading to incorrect social engineering defense strategies. Secondly, social engineering attacks are real-time, and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions. This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning (IQL) reinforcement learning method on human vulnerability attributes. The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy. Furthermore, the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks. The experimental results demonstrate that the proposed algorithm outperforms the traditional Q-learning (QL) and deep Q-network (DQN) approaches in terms of time efficiency, taking 9.1% and 19.4% less time, respectively. Moreover, the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes. This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain.
A Survey on Acute Leukemia Expression Data Classification Using Ensembles

Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to classify new samples. Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers. Over the past few decades, researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields, including but not limited to disease diagnosis, finance, bioinformatics, healthcare, manufacturing, and geography. This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification. Moreover, a framework for classifying acute leukemia gene expression data is proposed. The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework. Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.
Consolidation and Grading

Chapter 7 advocates two reforms: unbundling and relabeling relevant statutes of the federal criminal code. The goal that propels these reforms – the production of more and more accurate information – remains the driving force of this chapter’s remaining two proposals; consolidation and grading of white-collar statutes.1
Efektivitas Penggunaan Modul Pencegahan Penyalahgunaan Napza untuk Meningkatkan Pengetahuan Pencegahan bagi Siswa SMP di Jakarta Timur

THE EFFECTIVENESS OF USING THE DRUG ABUSE PREVENTION MODULE TO INCREASE PREVENTION KNOWLEDGE FOR MIDDLE SCHOOL STUDENTS IN EAST JAKARTA. Learning resources basically need to contribute and be effective for students in the learning process. One of the learning resources that can be used is the module as a learning medium. included in the prevention of drug abuse and teachers as intermediary’s sahib have good knowledge of drugs. This research is an experimental study involving 50 junior high school teachers in East Jakarta. The results of this study are stated that the module which is the product of this research has a significant effectiveness in increasing knowledge of drug abuse for junior high school teachers in East Jakarta. Of course, this can be a consideration for the government and schools to use the module in this study as a befall for teachers in conveying knowledge to students about the dangers of drug abuse.
An Optimal Online Semi-connected PLA Algorithm with Maximum Error Bound (Extended Abstract)

Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of "semi-connection" that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution and achieves better performances than the state-of-art solutions.
A Five-Week Experiment to Elaborate on FAIR-Enabling Services

Yesterday, I proposed that a strategy for implementing the FAIR principles for research data management can focus on ensuring five FAIR-enabling services, which in turn will prompt tactical choices of FAIR-enabling resources that may satisfactorily address each question and thereby produce a comprehensive implementation profile. The purpose of such care in design is to de-risk one’s investment in “going FAIR”, as the cost of systems implementation and maintenance can easily...
Auto Parking System with LiDAR and Reflective Tape for Turtlebot3 Burger Robot

The Turtlebot3 Burger Robot is a standard platform of ROS Robotic cooperation, which is used as a learning media and as a prototype in a delivery robot project. This robot has been modified with the addition of the LiDAR sensor for obstacle avoidance and auto parking, as well as the addition of an i
Tular Nalar: Upaya Melahirkan Lingkar Epistemik Literasi Digital

Abstract. Traditional intellectuals only focus on theory and never place their investigations as an effort to solve existing social problems. On the other hand, the link between academic activities that answer problems and engage in concrete issues in society will foster what is known as organic intellectuals.&#x0D; Abstrak. Intelektual tradisional hanya berkutat pada teori dan tidak pernah menempatkan penyelidikannya sebagai upaya menyelesaikan persoalan sosial yang ada. Di lain pihak, keterkaitan kegiatan akademik yang menjawab persoalan dan terlibat dalam isu-isu konkret dalam masyarakat akan menumbuhkan apa yang disebut sebagai intelektual organik.
A Continuous Camera Placement Optimization Model For Surround View

Increasing use of Advanced Driver Assistance Systems (ADAS) in autonomous vehicles is rising the demand for advanced perception systems. With more sensors being placed on the vehicle than ever, a need arises to optimize the placement of the sensors on the vehicle's body to maximize coverage at minimal cost. Camera Placement Optimization (CPO) methods tailored for multi-camera networks to maximize a vehicle's surrounding view coverage are limited. While existing CPO methods tend to sample the simulation space to work in the discrete domain using integer programming-based problem formulation, this article proposes a novel approach to optimize for camera poses for vehicle surround-view in the continuous domain using gradient-free blackbox optimization techniques by defining a non-linear objective function. Experimental results on more than 100 instances of real world 3D models of vehicles of various shapes and sizes show that the proposed method is effective in maximizing coverage for vehicle surround-view by a multiple camera network in a reasonable amount of time. Comparisons against a discrete CPO formulation show that the proposed method significantly improves coverage accuracy by optimizing poses of multiple cameras for vehicle surround-view.
Generating Interpretable Play-Style Descriptions Through Deep Unsupervised Clustering of Trajectories

In any game, play style is a concept that describes the technique and strategy employed by a player to achieve a goal. Identifying a player's style is desirable as it can enlighten players on which approaches work better or worse in different scenarios and inform developers of the value of design decisions. In previous work, we demonstrated an unsupervised LSTM-autoencoder clustering approach for play-style identification capable of handling multi-dimensional variable length player trajectories. The efficacy of our model was demonstrated on both complete and partial trajectories in both a simulated and natural environment. Lastly, through state frequency analysis the properties of each of the play styles were identified and compared. This work expands on this approach by demonstrating a process by which we utilise temporal information to identify the decision boundaries related to particular clusters. Additionally, we demonstrate further robustness by applying the same techniques to MiniDungeons, another popular domain for player modelling research. Finally, we also propose approaches for determining mean play-style examples suitable for describing general play-style behaviours and for determining the correct number of represented play-styles.
Analisis Sistem Informasi Akuntansi Penggajian Dan Pengupahan Pada Sebuah Perusahaan

The payroll and wages system is made to make it easier for financial officers who have the responsibility to provide salaries or wages to employees. The data used in the process of providing employee work results includes the division of tasks and responsibilities in work as well as accounting records that must be owned by the company so that what employees receive is in accordance with their work results. The purpose of this study is to find out whether the payroll and wage system at this finance company is running well. This study uses data collection methods including: observation, interviews, and literature study. Data analysis in research is a descriptive analysis of the results of research that has been done, so it can be concluded that the finance company has run a very good system. The documents used in payroll and wages are sufficient so that the process of giving salaries and wages to employees runs well because the systems and documents really support company activities. The completeness of the documents made by the company is in accordance with the theory of the payroll and wage accounting system and the accounting records and financial bookkeeping that are used are appropriate so that when distributing payroll and wages the employees do not experience difficulties. The bookkeeping in the company, such as general journals and employee earning cards, is proof of giving employee work results according to the rules that apply to the company.
Various Approaches to Object Detection using Deep Learning

Abstract: In order to detect objects in actual scenarios, this research study examines the practical use of deep learning techniques, with a focus on convolutional neural networks (CNNs). The importance of object categorization as the fundamental building block for object detection is emphasized, and several learning strategies are investigated in order to extract features from challenging multidimensional data. In order to get accurate and effective object detection findings, the study studies various deep learning architectures, including R-CNN, Fast R-CNN, Faster R-CNN, and YOLO. In addition, this paper demonstrates the diverse uses of deep learning-based object detection, including its influence on automated vehicles, medical technology, and industrial automation, where it is changing several sectors of the economy. The analysis also foresees the future developments of deep learning models, predicting their paradigm-shifting effects across a variety of areas and their capacity to successfully address practical difficulties.
On the Privacy of Multi-Versioned Approximate Membership Check Filters

Approximate membership filters are increasingly used in many computing and networking applications and new filter designs are being continuously presented to improve one or more performance metrics. Therefore, understanding their security and privacy is an important issue. Previous works have considered attackers that only have access to an individual filter in isolation. For applications that generate many related filters, such as a filter for a deny list that evolves over time, that analysis is insufficient. This paper considers an attacker with access to several versions of a filter that share most of the same input elements. We find that for typical implementations of Bloom, cuckoo, and quotient filters, the attacker gains little or no advantage with access to multiple versions of a filter. However, typical xor filters do reveal more information about their input elements by querying multiple versions of a filter, and we propose techniques to enhance the privacy of xor filters and others.
Development of Nature Tourism Innovations with Cultural History Sites Through Sport Tourism (Kayaking) and Based Ecotourism ProgramsDigitization-Android in Simawang District

Another problem faced by the tourism management community in the Simawang district is the lack of knowledge and expertise in tourism management, both in terms of administration, organization and management of existing facilities and infrastructure. The lack of insight and knowledge of the tourism management community in IT development also has an impact on the lack of tourism promotion through social media that is currently developing. The solution to overcome these problems is by implementing a program to increase the insight and knowledge of the tourism management community related to management, administration, promotion, marketing and the ability to use IT. In addition, the solution provided is also in terms of increasing the quality and quantity of tourist objects from existing natural and regional potentials, namely through the development of sport tourism, such as water sports using kayaking. This community service or partnership activity includes; participant recruitment, implementation of training activities, coaching, guidance and development of activities using the Participation Actions Learning System (PALS) method, real development by tourism managers through partner participation in training and coaching and activity evaluation.
Retracted: Dynamic Analysis of College Physical Education Teaching Quality Evaluation Based on Network under the Big Data

[This retracts the article DOI: 10.1155/2021/5949167.].
Estimation of number of unmanned aerial vehicles in a scene utilizing acoustic signatures and machine learning

With the exponential growth in unmanned aerial vehicle (UAV)-based applications, there is a need to ensure safe and secure operations. From a security perspective, detecting and localizing intruder UAVs is still a challenge. It is even more challenging to accurately estimate the number of intruder UAVs on the scene. In this work, we propose a simple acoustic-based technique to detect and estimate the number of UAVs. Our method utilizes acoustic signals generated from the motion of UAV motors and propellers. Acoustic signals are captured by flying an arbitrary number of ten UAVs in different combinations in an indoor setting. The recorded acoustic signals are trimmed, processed, and arranged to create an UAV audio dataset. The UAV audio dataset is subjected to time-frequency transformations to generate audio spectrogram images. The generated spectrogram images are then fed to a custom lightweight convolutional neural network (CNN) architecture to estimate the number of UAVs in the scene. Following training, the proposed model achieves an average test accuracy of 93.33% as compared to state-of-the-art benchmark models. Furthermore, the deployment feasibility of the proposed model is validated by running inference time calculations on edge computing devices, such as the Raspberry Pi 4, NVIDIA Jetson Nano, and NVIDIA Jetson AGX Xavier.
Learning Hierarchical Spatial Tasks with Visiting Relations for Next POI Recommendation

Sparsity is an established problem for the next Point-of-Interest (POI) recommendation task, where it hinders effective learning of user preferences from the User-POI matrix. However, learning multiple hierarchically related spatial tasks, and visiting relations between users and POIs, can help to alleviate this sparsity problem. In this article, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we propose a selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Further, we propose a novel Joint Triplet Loss Learning (JTLL) module to learn visited and unvisited relations between users and POIs for the recommendation task. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our proposed approach significantly outperforms baseline and the state-of-the-art methods.