Reviewerly/exHarmony huggingface dataset:
Texts and Tasks in Indonesian Language Textbooks: Do They Support Indonesian Students in the International Reading Literacy Test?

Several international tests on reading literacy consistently showed poor scores for Indonesian students. Textbooks are one of the teaching tools that can contribute to the development of students’ reading literacy. However, no research has examined textbooks’ capacity to develop students’ reading literacy skills, especially those assessed on international tests. The current study investigated the carrying capacity of texts and tasks in Indonesian language textbooks to develop students’ reading literacy skills, particularly those evaluated on international tests. The data were obtained from six Indonesian language textbooks used by junior and senior high school students in Indonesia. These textbooks are officially recommended by the government and are widely used in middle schools in Indonesia. The content analysis combined with the performance framework in the International Student Assessment was used to reveal the text and task dimensions of the Indonesian Language textbooks. The results of the study show that in the aspect of text, textbooks have conformity with the international test, but are weak in infographics which are not problematic. Likewise, the underlying contexts of the texts are not evenly distributed. In the task aspect, the dominant cognitive processes are to search for and locate information, but are limited in understanding, evaluating, and reflecting on the contents of the text. The response format is dominated by essay-type tests that do not adequately account for objective tests. From this, it can be concluded that the texts and tasks of the Indonesian textbooks do not correspond to the texts and tasks of the international reading literacy tests. This study recommends the need for adjustments in establishing guidelines for developing and evaluating textbooks to develop students’ reading literacy at the international level.
Towards a lightweight security framework using blockchain and machine learning

Cyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these attacks. In this paper, we propose an integrated security framework using Blockchain (BC) and Machine Learning (ML) to protect IoT sensor networks. The framework consists of two modules: a BC prevention module and a ML detection module. The BC prevention module has two lightweight mechanisms: identity management and trust management. Identity management employs a lightweight Smart Contract (SC) to manage the node registration and authentication, ensuring that unauthorized entities are prohibited from engaging in any tasks, while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network's lifetime and tracking historical node behaviors. Consensus and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance (VBFT) mechanism to ensure network reliability and integrity. The ML detection module utilizes LightGBM algorithm to classify malicious nodes and notify the BC network if it must make decisions to mitigate their impacts. We investigate the performance of several off-the-shelf ML algorithms, including Logistic Regression, Complement Naive Bayes, Nearest Centroid, and Stacking, using the WSN-DS dataset. LightGBM is selected following a detailed comparative analysis conducted using accuracy, precision, recall, F1-score, processing time, training time, prediction time, computational complexity, and Matthews Correlation Coefficient (MCC) evaluation metrics.
Cybersecurity Standards and Policies for CPS in IoE

Cybersecurity standards and policies are always one of the top priorities of any organization and network for smooth services and operations. The Internet of Everything (IoE) is one of the new and developing areas where the traditional standards and policies implementation is a significant challenge. ISO-27001 is an international standard that lays out a specification for an Information Security Management System (ISMS). This standard aims to address data security by focusing on people and processes and also technology. The standard has a heavy focus on its risk-assessment approach which stipulates that a risk assessment must be carried out before any controls can be selected and implemented. Some other well-known standards such as ISO 27002, ISO 38500, COBIT/COBIT 5, PRINCE2, and NIST CSF are under discussion, especially for IoE networks. This chapter discusses the existing standards and policies designed for Cyber-Physical Systems (CPS) in IoE networks. This chapter also discusses the security vulnerabilities and privacy threats of Cyber-Physical Systems (CPS) in IoE networks. This chapter also presents security and privacy solutions/architectures that improve the security and privacy of CPS in IoE networks.
Notes on correctness of p-values when analyzing experiments using SAS and R

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p -values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p -values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p -values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.
A hybrid Cycle GAN-based lightweight road perception pipeline for road dataset generation for Urban mobility

One of the major problems that cause continual trouble in deep learning networks is that training a large network requires massive labelled datasets. The preparation of a massive labelled dataset is a cumbersome task and requires lot of human interventions. This paper proposes a novel generator network ‘Sim2Real’ transfer is a recent and fast-developing field in machine learning used to bridge the gap between simulated and real data. Training with simulated datasets often converges due to its size but fails to generalize real-world applications. Simulated datasets can be used to train and test deep learning models, enables the development and evaluation of new algorithms and architectures. By simulating road dataset, researchers can generate large amounts of realistic road-traffic dataset that can be used to study and understand several problems such as vehicular object tracking and classification, traffic situation analysis etc. The main advantage of such a transfer algorithm is to use the abundance of a simulated dataset to generate huge realistic-looking datasets to solve data-intense tasks. This work presents a novel, robust sim2real algorithm that converts the labels of a semantic segmentation map to a realistic-looking street view using the Cityscapes dataset and aims to achieve robust urban mobility for smart cities. Further, the generalizability of the Cycle Generative Adversarial Network (CycleGAN) architecture was tested by using an origami robot dataset for sim2real transfer. We show that the results were found to be qualitatively satisfactory for different traffic analysis applications. In addition, road perception was done using a lightweight SVM pipeline and evaluated on the KITTI dataset. We have incorporated Cycle Consistency Loss and Identity Loss as the metrics to evaluate the performance of the proposed Cycle GAN model. We inferred that the proposed Cycle GAN model provides an Identity loss of less than 0.2 in both the Cityscapes dataset and KITTI datasets. Also, we understand that the super-pixel resolution has a good impact on the quantitative results of the proposed Cycle GAN models.
Entropic Uncertainty for Biased Measurements

Entropic Uncertainty relations are powerful tools, especially in quantum cryptography. They typically bound the amount of uncertainty a third-party adversary may hold on a measurement outcome as a result of the measurement overlap. However, when the two measurement bases are biased towards one another, standard entropic uncertainty relations do not always provide optimal lower bounds on the entropy. Here, we derive a new entropic uncertainty relation, for certain quantum states, which can provide a significantly higher bound even if the two measurement bases are no longer mutually unbiased. We evaluate our bound on two different quantum cryptographic protocols, including BB84 with faulty/biased measurement devices, and show that our new bound can produce substantially higher key-rates under several scenarios when compared with prior work using standard entropic uncertainty relations.
Future Video Prediction from a Single Frame for Video Anomaly Detection

Video anomaly detection (VAD) is an important but challenging task in computer vision. The main challenge rises due to the rarity of training samples to model all anomaly cases. Hence, semi-supervised anomaly detection methods have gotten more attention, since they focus on modeling normals and they detect anomalies by measuring the deviations from normal patterns. Despite impressive advances of these methods in modeling normal motion and appearance, long-term motion modeling has not been effectively explored so far. Inspired by the abilities of the future frame prediction proxy-task, we introduce the task of future video prediction from a single frame, as a novel proxy-task for video anomaly detection. This proxy-task alleviates the challenges of previous methods in learning longer motion patterns. Moreover, we replace the initial and future raw frames with their corresponding semantic segmentation map, which not only makes the method aware of object class but also makes the prediction task less complex for the model. Extensive experiments on the benchmark datasets (ShanghaiTech, UCSD-Ped1, and UCSD-Ped2) show the effectiveness of the method and the superiority of its performance compared to SOTA prediction-based VAD methods.
Exploring the Teaching Growth and Development of College Teachers in the Context of Informatization

This paper explores the issue of teaching growth and development of college teachers under the background of information technology. It analyzes and discusses five aspects, namely, education and teaching concept, teacher and teaching association, the growth path of college teachers, the direction of teaching development of college teachers, and strategies and suggestions for teaching innovation of college teachers. This paper argues that the growth and development of college teachers' teaching is a complex and dynamic process, which requires college teachers to constantly update and optimize their education and teaching concepts, pay attention to and satisfy students' learning needs and interests, select and use suitable teaching contents and methods, design and produce creative teaching forms and methods, explore and practice new or effective teaching models, and demonstrate and evaluate their teaching effects and quality, to realize the novelty or effectiveness of teaching and to improve or break through the level or level of teaching. This paper hopes to provide some useful methods and opinions for the growth and development of college teachers' teaching, as well as some useful bases and references for the research and evaluation of education.
TALUS: Reinforcing TEE Confidentiality with Cryptographic Coprocessors

Platforms are nowadays typically equipped with trusted execution environments (TEEs), such as Intel SGX or ARM TrustZone. However, recent microarchitectural attacks on TEEs repeatedly broke their confidentiality guarantees, including the leakage of long-term cryptographic secrets. These systems are typically also equipped with a cryptographic coprocessor, such as a TPM or Google Titan. These coprocessors offer a unique set of security features focused on safeguarding cryptographic secrets. Still, despite their simultaneous availability, the integration between these technologies is practically nonexistent, which prevents them from benefitting from each other’s strengths. In this paper, we propose TALUS, a general design and a set of three main requirements for a secure symbiosis between TEEs and cryptographic coprocessors. We implement a proof-of-concept of TALUS based on Intel SGX and a hardware TPM. We show that with TALUS, the long-term secrets used in the SGX life cycle can be moved to the TPM. We demonstrate that our design is robust even in the presence of transient execution attacks, preventing an entire class of attacks due to the reduced attack surface on the shared hardware.
Eagle: Efficient Privacy Preserving Smart Contracts

The proliferation of Decentralised Finance (DeFi) and Decentralised Autonomous Organisations (DAO), which in current form are exposed to front-running of token transactions and proposal voting, demonstrate the need to shield user inputs and internal state from the parties executing smart contracts. In this work we present “Eagle”, an efficient UC-secure protocol which efficiently realises a notion of privacy preserving smart contracts where both the amounts of tokens and the auxiliary data given as input to a contract are kept private from all parties but the one providing the input. Prior proposals realizing privacy preserving smart contracts on public, permissionless blockchains generally offer a limited contract functionality or require a trusted third party to manage private inputs and state. We achieve our results through a combination of secure multi-party computation (MPC) and zero-knowledge proofs on Pedersen commitments. Although other approaches leverage MPC in this setting, these incur impractical computational overheads by requiring the computation of cryptographic primitives within MPC. Our solution achieves security without the need of any cryptographic primitives to be computed inside the MPC instance and only require a constant amount of exponentiations per client input.
Relational practices in Norwegian students’ e-mail requests in English A focus on openings and closings

The paper explores relational practices in openings and closings in Norwegian students’ e-mail requests in English. It investigates the range of opening and closing sequences and the frequency of their occurrence as well as their variation depending on the level of request imposition and social distance between the e-mail writer and the lecturer. The very high frequency of occurrence of openings and closings, considered to be optional e-mail elements, in a small corpus of 109 authentic e-mails demonstrates a clear orientation to interpersonal aspects of communication. An orientation to familiarity was identified in the openings while a slight preference for deference was found in the closings. Social distance appeared to affect relational practices to a greater extent than the level of imposition.
An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT

Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers’ needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing gateway peer (GWP) in the blockchain network to address scalability, ledger optimization, and secure model transmission issues. In the architecture, we replace the centralized aggregator with the blockchain network, while GWP limits the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we incorporated differential privacy and advanced normalization techniques into ML processes. These approaches enhance the cybersecurity of end-users and promote the adoption of technological innovation standards by service providers. The proposed approach has undergone extensive testing using the well-respected Stanford (CARS) dataset. We experimentally demonstrate that the proposed architecture enhances network scalability and significantly optimizes the ledger. In addition, the normalization technique outperforms batch normalization when features are under DP protection.
Quantum Algorithm Cards: Streamlining the Development of Hybrid Classical-Quantum Applications

The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations implies better performance and efficiency for certain algorithmic tasks than current computers provide. However, to gain benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward. In this paper, we investigate challenges that emerge when building larger hybrid classical-quantum computers and introduce the Quantum Algorithm Card (QAC) concept, an approach that could be employed to facilitate the decision making process around quantum technology.
To Memorize or to Document: A Survey of Developers’ Views on Knowledge Availability

When developing, maintaining, or evolving a system, developers need different types of knowledge (e.g., domain, processes, architecture). They may have memorized (but potentially not documented) the knowledge they perceive important, while they need to recover knowledge that they could not memorize. Previous research has focused on knowledge recovery, but not on what knowledge developers consider important to memorize or document. We address this gap by reporting a survey among 37 participants in which we investigated developers’ perspectives on different types of knowledge. Our results indicate that the developers consider certain types of knowledge more important than others, particularly with respect to memorizing them—while all of them should be documented, using specific means. Such insights help researchers and practitioners understand developers’ knowledge and documentation needs within processes, thereby guiding practices and new techniques.
Penerapan Data Mining Dalam Mengelompokkan Kunjungan Wisatawan Mancanegara Di Prov. Sulawesi Selatan Dengan K-Means Dan SVM

Indonesia's exchange rate can rise due to foreign tourist visits, which can also benefit the local economy. The provincial capital. South Sulawesi is Makassar which is one of the locations for tourist visits. There are 11 main tourist attractions in Prov. South Sulawesi according to sulselprov 1) Maritime Tourism, 2) Losari Beach, 3) Rotterdam Fort, 4) Somba opu Fort, 5) Takabonerate Marine Park, 6) Bantimurung National Park, 7) Malino, 8) Tanjung Bira Beach, 9) Kesu Tourism, 10) Londa Tourism, 11) Pallawa Tourism. The purpose of this study is to analyze the application of data mining in classifying the number of foreign tourists visiting the prefecture. South Sulawesi uses k-means. The data used comes from BPS Prov. South Sulawesi. The data is grouped into two clusters. That is, the most tourists as C1 with results from Malaysia, and low tourist arrivals as C0 with results from Singapore, Japan, South Korea, Taiwan, China, India, the Philippines, Hong Kong, Thailand, Australia, USA, UK, Netherlands, Germany, France, Russia, Saudi Arabia, Egypt, United Arab Emirates, Pearl of the Persian Gulf, and Switzerland then I use and process this data again with SVM to look for precision, precision and recall values and get 100.00% accuracy in the RapidMiner application.
Methodological approach for developing online courses: a case study

Online courses are becoming more popular and accessible, but they require careful design and development of their structure and content. This paper presents a methodological approach for creating online courses by analysing existing practices and learner feedback. The paper focuses on the case study of the online course “Methods for Teaching Mathematics to Students in Technical Universities”, developed and tested by the authors. The paper discusses the main aspects of online course development, such as weekly planning, testing subsystem, video and PDF content, forum and survey subsystems, and assessment of learning outcomes. The paper also evaluates the quality of the online course and identifies some areas for improvement. The paper concludes that online course quality depends on various factors and provides recommendations for online course developers.
Detecting Threats from Live Videos using Deep Learning Algorithms

Threat detection is an important area of research, particularly in security and surveillance applications. The research is focused on developing a threat detection system using DL techniques. The system aims to detect potential threats in real-time video streams, enabling early identification and timely response to potential security risks. The study uses two state-of-the-art DL models, MobileNet and YOLOv5, to train the object detection system. The TensorFlow object detection API is employed for training and evaluating the models. The results of the study indicate that MobileNet outperforms YOLOv5 in terms of detection accuracy, speed, and overall performance. The justification for selecting MobileNet over YOLOv5 is based on several factors. First, MobileNet has a lightweight architecture, making it suitable for real-time applications where processing speed is critical. Second, it is efficient in terms of memory usage, enabling it to operate effectively on low-resource devices. Third, MobileNet provides high accuracy in detecting objects of different sizes and shapes. The study evaluated the performance of the threat detection system using various evaluation metrics, including mean average recall (mAR), mean average precision (mAP) and Intersection over union (IoU). The results show that the system achieved high accuracy in detecting threats, with an overall mAP (mean average precision) of 0.9125, mAR (mean average recall) of 0.9565 and Intersection over union (IoU) of 0.9045. In this study, researchers present a highly efficient and successful method for identifying threats through the utilization of deep learning methods. The research demonstrates the superiority of MobileNet over YOLOv5 in terms of performance, and the results obtained validate the effectiveness of the proposed system in detecting potential threats in real-time video streams.