Reviewerly/exHarmony huggingface dataset:
QoS Accurate Matching Algorithm Based on Service Resource Scheduling on Cloud Platform and Its Application

Using Quality of Service (QoS) as the standard for resource scheduling is a common method for service resource scheduling on cloud platforms. However, there are both mutually supportive and restrictive multiple technical parameters based on software and hardware resource consumption, and the transparent introduction of a large number of third-party service resources leads to incomplete or even missing QoS attribute values, making service performance evaluation inaccurate and often resulting in problems such as inadequate resource allocation or excessive resource allocation. To address these issues, this paper defines software and hardware QoS attributes and their calculation methods, proposes a dynamic model training method based on FM, and an accurate matching algorithm for service resource scheduling. We design and implement test cases that are the same as the actual operating environment and achieve the expected results after testing.
IMPLEMENTASI ALGORITMA BLUM BLUM SHUB PADA KUIS SANDI SEMAPHORE BERBASIS AUGMENTED REALITY

Semaphore is a way to send and receive news by using a flag, paddle, trunk, bare hands or gloves. The semaphore learning at School at this time still uses the Scout's manual and it is only in the form of 2D images and it is also delivered by the Scout coach. So that students tend to get bored in learning the semaphore movement and lack of understanding and education in the learning process in this modern era. From the existing problems, the semaphore movement learning application and semaphore quiz were built using augmented reality technology based on android to provide interesting and interactive learning. This application was built by adding the Blum-Blum Shub (BBS) algorithm for the randomization process which functions to generate random numbers in a mathematical process with the resulting output is a series of binary numbers. Application created uses Unity 3D, and developed uses the RUP (Rational Unifed Process) method. With this application, it can make easier for students to understand the material in semaphore learning
協生農法環境における農作業支援ロボットの開発

In this paper, a compact telescopic mechanism with a high extension and retraction ratio is fabricated for use in the work arm of an agricultural robot that works on crops in densely vegetated areas. We confirmed that a high extension and retraction ratio can be achieved in a compact form by utilizing the deformation of a thin metal plate in the shape of a rain gutter, which is also the mechanism of a metal measuring tape. By using the attraction force of a magnet to bundle multiple pieces, high rigidity against compressive and bending deformations was verified.
Glozoo: a trilingual (ES/EN/DE) corpus-based glossary for the translation of manipulated zoologisms

This study aims to illustrate how the Glozoo glossary of zoologisms can assist trainee translators in one of the most difficult scenarios in the translation of phraseology: when an idiom has undergone any kind of manipulation in the source text, and it lacks a full equivalent in the target language.For the design of this glossary of idioms, which comprises zoologisms in Spanish, English, and German, a corpus-based methodology has been employed.In this regard, the present study examines the main components of Glozoo, as well as the multiple possibilities it offers for the creation of ad hoc phraseological equivalences in those cases in which the manipulation of idioms and the absence of one-to-one cross-linguistic correspondences may hinder the task of translation.Given the importance of conveying the pragmatic, semantic, and discursive load of an idiom accurately in the target text and, concomitantly, transferring the manipulation recreated in the source text, a detailed teaching proposal is presented in which translators are taught a set of strategies to be implemented with the help of the glossary in order to solve these difficulties.The teaching proposal, consisting of a trilingual glossary of idioms and a set
TESDAM: Neural Networks Dataflow Modeling Based on Spatiotemporal Migration Representation

Spatial architecture has emerged as a promising way to accelerate artificial neural networks (ANNs), but requires to model the ANN hardware dataflow to obtain the optimal dataflow choice to obtain ideal acceleration performance. Recently, many modeling methods exploit imperative instructions to depict ANN dataflow, but these methods lack the representation for the temporal and spatial migration of dataflow in spatial architecture, which leads to inaccurate metrics estimation. To address the above challenges, TESDAM is proposed as an ANN dataflow modeling framework based on spatiotemporal migration representation. TESDAM designs a spatiotemporal migration matrix and a PE adjacency matrix to depict loop instance mapping, tensor mapping, spatiotemporal migration path, and hardware PE interconnection. Moreover, a comprehensive dataflow metrics model is supported by TESDAM to evaluate PE utilization, latency, and data reuse. Expensive experiences demonstrate that TESDAM reduces the overhead by 23.83%, and improves the average metrics estimation accuracy by 21.6% compared with the state-of-the-art methods without considering spatiotemporal migration.
FedFingerprinting: A Federated Learning Approach to Website Fingerprinting Attacks in Tor Networks

Various website fingerprinting attacks (WF) have been developed to detect anonymous users accessing illegal websites in Tor networks by analyzing Tor traffic. These attacks consider several traffic features, such as packet length, number of packets, and time, to identify users who attempt to access prohibited content. Due to the advance of artificial intelligence (AI) technologies, machine learning or deep learning techniques have been widely adopted for WF to generate an accurate model to break the privacy of illegal users. Nevertheless, such state-of-the-art approaches to WF assumed that entire data from various Tor nodes are collected and trained in a centralized way to generate the model: However, training data sets from Tor nodes may contain sensitive information that the Tor nodes may not want to share. In addition, significant computing and network bottleneck at the centralized server is inevitable in collecting and training various data in a centralized manner. Correspondingly, this paper proposes a novel framework using federated learning (FL) for WF in the Tor network (denoted as FedFingerprinting), enabling Tor nodes to generate the global model collaboratively without exposing their local data sets. Specifically, to alleviate the burden for local training of selected Tor nodes in the FL process, the importance of various handcrafting features used for WF is firstly evaluated through the analysis of the accuracy of features under the ensemble of tree machine learning methods. Then, to balance the accuracy and training time, the combination of selected top-ranked features is trained using FL approaches rather than raw data in the model. Moreover, considering the local model accuracy of each Tor node, effective Tor node selection for the FL process is also designed. Finally, under closed-world settings with the real-world Tor data sets, we empirically demonstrate the comparisons of the proposed FedFingerprinting with raw data and feature selection compared to various benchmarks in terms of the training time and accuracy. Then, the superior performance of the FedFingerprinting with Tor node selection is evaluated in terms of convergence speed.
Classification of Leukocytes: Comparison of Different Feature Extraction and Machine Learning Approaches

Blood cells can be separated into three types: erythrocytes, leukocytes and platelets, and to evaluate the health of a patient, a Complete Blood Count (CBC) is necessary. CBC is amongst the most performed tests worldwide, and when evaluated manually by physicians is time-consuming and susceptible to errors. Recently there have been efforts in the scientific community to automate the evaluation of CBC. One of the challenges in automating CBC is the classification of leukocytes or White Blood Cells (WBC). These cells are part of the immune system and are responsible for protecting the body against infections. The most common types of WBC are: neutrophils, eosinophils, monocytes and lymphocytes. The four types of WBC have similarities, and most techniques have difficulties classifying them into the four types. There are several techniques in literature tackling this issue. This paper’s objective is to compare different techniques to improve the reliability and reduce the time spent evaluating CBC. This objective will be accomplished by testing two feature extraction techniques and then using machine learning (ML) techniques to classify the features into the four types of leukocytes. The methods for feature extraction tested in this paper are Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP). The ML techniques tested will be Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and a convolutional neural network (CNN). The main goal of this paper is to enable the proposal of new techniques and products to support laboratories, physicians, and patients.
A Fundamental Tradeoff Among Storage, Computation, and Communication for Distributed Computing Over Star Network

Coded distributed computing can alleviate the communication load by leveraging the redundant storage and computation resources with coding techniques in distributed computing. In this paper, we study a MapReduce-type distributed computing framework over star topological network, where all the workers exchange information through a common access point. The optimal tradeoff among the normalized number of stored files (storage load), computed intermediate values (computation load), and transmitted bits in the uplink and downlink (communication loads) is characterized. A coded computing scheme is proposed to achieve the Pareto-optimal tradeoff surface, in which the access point only needs to perform simple chain coding between the signals it receives, and information- theoretical bound matching the surface is also provided.
SLR: A million-scale comprehensive crossword dataset for simultaneous learning and reasoning

The progress of the natural language understanding (NLU) community has put forward higher demands for the knowledge reserve and reasoning ability of the model. However, existing schemes for improving model capabilities often split them into two separate tasks. The task of enabling models to learn and reason about knowledge simultaneously has not received sufficient attention. In this paper, we propose a novel crossword-based NLU task that imparts knowledge information to a model by solving crossword clues and simultaneously trains the model to infer new knowledge from existing knowledge. To this end, we construct a comprehensive crossword dataset SLR containing more than 4 million unique clue-answer pairs. Compared to existing crossword datasets, SLR is more comprehensive and contains linguistic knowledge, expertise in various fields, and commonsense knowledge. Meanwhile, to evaluate the reasoning ability of the model, we design clever details in the reasoning of the answers, most clues require the solver to reason through two or more pieces of knowledge to arrive at an answer. We analyze the composition of the dataset and the similarities and differences of the various types of clues via sampling and consider various data partitioning methods to enhance the generalization ability of the training set. Furthermore, we test the performance of several different advanced models and methods on this dataset and analyze the strengths and weaknesses of each. An interesting conclusion is that even powerful language models perform poorly on tasks that require reasoning.
ROMoSu: Flexible Runtime Monitoring Support for ROS-based Applications

ROS-based robotic applications are becoming increasingly common in various different application domains, performing diverse tasks. Examples include autonomous vehicles, small unmanned systems, as well as industrial applications of Cyber-Physical Production Systems. What all these systems have in common is their tight integration between hardware and software components, and close interactions with humans, e.g., on a shop floor, or autonomously driving robots as part of a warehouse system. This, in turn, requires monitoring the behavior of the system at runtime and ensuring that it behaves according to its specified requirements. However, establishing and maintaining runtime monitoring support is a non-trivial task, requiring significant up-front investment and extensive domain knowledge. To alleviate this problem, in this paper, we present ROMoSu, a flexible runtime monitoring framework for ROS-based systems that allows defining multiple scenarios, or application-specific configurations, taking into account different monitoring needs, and provides tool support for creating, maintaining, and managing configurations at runtime. As part of our evaluation, we have conducted experiments with three different use cases, of both physical and simulated applications. Results confirm that ROMoSu can be successfully used to create monitoring configurations with little effort, create efficient monitors, and perform constraint checks based on the collected runtime data.
EDDE: An Event-Driven Data Exchange to Accurately Introspect Cobot Applications

Commercialized collaborative robots (cobots) are typically programmed in proprietary languages and employ interfaces that gather data at predefined frequencies, such as the Real Time Data Exchange offered by Universal Robots (UR). This approach challenges the observability of program execution during runtime. To address this issue, the paper proposes applying software architectural knowledge from the introspection of Cyber-Physical Systems (CPSs) to the robotics domain. The proposed solution is to improve the classical protocol interfaces with an Event-Driven Architecture, enabling the acquisition of program events in relation to state variables and thus, richer information concerning runtime. The proposed architecture, called the Event-Driven Data Exchange, is implemented and evaluated on a UR e-Series cobot in a practical study. The study highlights the significance of implementing software architectural knowledge in CPSs, such as cobots to extend observability and data availability.
Augmenting Robot Software Development Process with Flexbot

Robot Operating System (ROS) and its successor ROS2 have significantly improved the state-of-the-art robot software development process. However, even with all the amenities offered by ROS2 to ease the development, research has shown that practitioners still encounter development issues making software a significant bottleneck. Therefore, in this paper, we discuss ROS-based software development challenges encountered during the development and identified in the existing literature, and introduce the new Flexbot framework that seeks to mitigate some of the identified challenges using model-driven engineering (MDE).
Survey on Robotic Systems Integration

Software integration is central to successfully developing, deploying, and operating robotics applications. Yet, the particular integration process and its challenges are poorly understood. The continuous evolution of the robotics sector, incorporating constantly a growing number of technologies, makes the unification of processes increasingly complicated. Nevertheless, current research on robotics software integration largely focuses on specific integration activities instead of considering the overall activity as a process. To provide some insight into the state of robotics software integration, we drove a survey among researchers and practitioners in the field. In this survey, we inquired how robotics software integration is currently performed in order to identify similarities with traditional software development methodologies. Through this study, we discovered commonalities in the phases of the process and potential directions of a future research to address the current challenges in the area.
Getting Started with ROS2 Development: A Case Study of Software Development Challenges

ROS2 has started to gain attention from the industry as it fosters robot software development. Companies seek to use ROS2 in real products, thus increasing the need for ROS2-related skills. We investigate what kind of challenges junior software developers – i.e. computer science majors on one of their last B.Sc study modules – encounter when learning robotics using ROS2. We conduct a case study with a group of five students with a project goal to develop a robotic application utilizing an agile software development process. We inquire about the challenges after the project using semi-structured interviews. By analyzing the interviews we identified 87 development challenge instances that can be roughly divided into challenges caused by insufficient or misinterpreted documentation and challenges encountered during the development process or usage of ROS2 and its packages. Hence, based on the results, we suggest that the ROS2 community should invest in an integrated resource to help with ROS2 development and patch up the currently fragmented documentation of individual packages in order for it to be adopted more easily as a technology by junior software developers.
AN ML BASED DIGITAL FORENSICS SOFTWARE FOR TRIAGE ANALYSIS THROUGH FACE RECOGNITION

Since the past few years, the complexity and heterogeneity of digital crimes have increased exponentially, making digital evidence & digital forensics paramount for criminal investigation and civil litigation cases.Some of the routine digital forensic analysis tasks are cumbersome and can increase the number of pending cases, especially when there is a shortage of domain experts.While the work is simple, the sheer scale can be taxing.With the current scenarios and future predictions, crimes will only become more complex, and the precedent of collecting and examining digital evidence will only increase.In this research, we propose an ML-based Digital Forensics Software for Triage Analysis called Synthetic Forensic Omnituens (SynFO) that can automate evidence acquisition, extraction of relevant files, perform automated triage analysis and generate a basic report for the analyst.This research shows a promising future for automation with the help of Machine Learning.
Quantum Classifiers for Video Quality Delivery

Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion state and the kernel method performs 6.1% and 10.1% better for the medium and high congestion states. Empirical evidence suggests that when the SVC is trained on a very low amount of data, the classification accuracy varies significantly depending on the quality of the training data, however, the variance in classification accuracy of quantum models is significantly lower. Classical video QoD classifiers benefit from the quantum data embedding techniques. They learn better decision regions using less training data.
Whose Data are Reliable: Sensor Declared Data Reliability

Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor’s historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.
Multivariate Polynomial Public Key Digital Signature Algorithm: Semi-covariance Analysis and Performance Test over 5G Networks

The arrival of quantum computers is imminent, with no room for uncertainty. It is only a matter of a few years before classical encryption algorithms face significant risks. NIST is leading the world to devise and standardize Post Quantum Cryptography (PQC) algorithms. In this study, we considered the Multivariate Polynomial Public Key (MPPK) Digital Signature (DS) algorithm as one of recent PQC algorithms and generated MPPK/DS key pairs with the true random number from the quantum computer. For each key pair, we calculated the semi-covariance correlation to measure its robustness against brute force attacks. The results show that, in average with the selected seed prime, MPPK has lower semi-covariance and thereby stronger robustness over traditional RSA and SPHINCS algorithms. Additionally, we measured the latency performance of MPPK/DS algorithm over 5G, WiFi, and local area networks, in comparison of RSA and SPHINCS algorithms. Finally, We made all developments available online for research community to carry out similar comparisons with other PQC algorithms.
Evaluating Machine Learning Techniques for Predicting Link Instability in Wireless Networks to Support Live Video Streaming

This paper evaluates various Machine Learning (ML) techniques for classifying or predicting link instability, which we define as sudden increases in application-level packet loss and latency, or decreases in available bandwidth over a short, pre-determined period of time. To train and evaluate these ML techniques, we first construct attributes from raw wireless LTE traces. These attributes include statistical summaries (e.g., average and standard deviation) and trends (e.g., slope and correlation) of various network metrics. We then determine the attributes that provide the greatest predictive ability to construct our ML examples for training and testing. Using these ML examples, we evaluate the predictive capability of various ML techniques. We find that we can classify 5%-46% of sudden bandwidth drops, 40%-100% of increases in application-level packet loss, and 40%-100% of spikes in latency while maintaining false positive rates below 0.1. In general, we find that Decision Trees and K-Nearest Neighbours techniques provide the highest classification rate while incurring low false positive rates.