Reviewerly/exHarmony huggingface dataset:
Cyber Attack Risk Estimation Technique in Intelligent Cyber Physical System for Pharmaceutical Care Services

A Cyber Physical System (CPS) is a hybrid of physical and cyber systems that are inextricably linked. It is widely employed in vital national infrastructure industries such as healthcare, electric power, transportation, petroleum, and chemical etc. If an attack on the CPS is successful, the consequences will be unthinkable in pharmaceutical care. A well-designed CPS risk assessment will offer an overall picture of CPS security status and will aid in the optimal deployment of safeguard resources in pharmaceutical care. Despite the numerous similarities between CPS and IT systems, they differ in many ways, most notably the demand for real-time. As a result, CPS cannot successfully utilize standard risk assessment techniques for IT systems. Creative ideas on CPS risk assessment are desperately needed. In this study a survey has been conducted for cyber risk estimation which can help in pharmaceutical care.
Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> ) parameters, are trained on O(10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> ) words and produce awe-inspiring results. However, there are good reasons to look for alternative training methods: With current algorithms and hardware constraints sometimes only half the available computing power is actually used. This is due to a complicated interplay between the size of the ANN, the available memory, throughput limitations of interconnects, the architecture of the network of computers, and the training algorithm. Training a model like the aforementioned GPT-3 takes months and costs millions. A different training paradigm, which could make clever use of specialized hardware, may train large ANNs more efficiently.
Daylight Quantum Key Distribution in a Free-Space Channel Using Entangled Photons Emitted by a Quantum Dot Device

Quantum key distribution (QKD) is at the heart of future secure quantum communication networks as it can enhance security of classical communication strategies. Although prepare-and-measure protocols are the most widely used in practical applications, entangled-based QKD is promising for further improving the degree of security and developing free-trusted node networks. However, free-space link implementations for single photon transmission suffer daylight operations and few works have been demonstrated in this regime. Furthermore, entangled-based QKD in air link has been demonstrated only using spontaneous parametric down-conversion (SPDC) sources and never tested using quantum dot devices, despite they potentially overcome SPDC performances in terms of effi-ciency and fidelity of the QKD protocol.
On the Improvement of the Reasoning Cycle in Case-Based Reasoning

Case-based reasoning (CBR) is undoubtedly by far one of the most intuitive artificial intelligence problem-solving approaches. It is inherent to the reuse of existing experience that includes solutions to problems or mechanisms to derive these solutions. Unfortunately, existing CBR systems are lacking in generality as the adaptation process is usually driven by the application domain and heavily based on the domain expert’s knowledge. Moreover, existing CBR systems perform poorly as they deal with each step of the CBR methodology separately and independently from the other steps. This work is an effort to lay a primary foundation for a generic case-based reasoning framework that relies on a domain-independent approach. Each step of the reasoning process is conceived to fulfil not only specific requirements of that stage but also to sustain other stages’ processes. The experimental results of the application of our approach in a sober consumption energy system in buildings show the effectiveness of our approach.
Low False Alarm and Narrow-Wide Band Compatible Signal Detection Algorithm Combining the Multiscale Wavelet Transform Extremum Detection With the Spectrum Energy Detection

A low false alarm and narrow-wide band compatible signal detection algorithm is proposed by combining the multiscale wavelet transform extremum detection with the spectrum energy detection. The signals with different bandwidths are detected at respective wavelet scales. Signal detection at a single scale adopts the detection strategy of combining the wavelet transform extremum detection with the spectrum energy detection to decrease the probability of detecting spurious signals caused by noises, and the noise floor estimation and spectrum modification are completed by morphological filtering to solve the problem of the noise floor fluctuation. Finally, the detection results at multiple scales are fused and eliminated selectively. The simulation results show that the proposed algorithm is able to achieve excellent narrow-wide band compatible detection performance while realizing a relatively low probability of detecting spurious signals.
Semi-Supervised Dimensional Media Sentiment Analysis via Exploring Sample Relationships

Dimensional sentiment analysis (DSA) aims to recognize continuous real-valued annotations in multidimensional spaces such as valence-arousal space. It can serve as a sentiment analysis that is more fine-grained than traditional polarity classification. Existing methods primarily focused on supervised learning, which requires a large number of training samples. To address this issue, recent studies have suggested the use of a generative model for semi-supervised DSA. However, these methods lack the exploration of relationships between samples, which may limit their effectiveness. In this study, we proposed a sample relationships (SRs) model for semi-supervised DSA. Compared with the conventional methods, the SR module can integrate the interactive information from different samples, effectively capturing their interdependencies. The results of experiments conducted on three datasets indicated that the performance of the proposed method was better than those of the existing models and that the performance was effective and competitive even with insufficient data.
PrivyNAS: Privacy-Aware Neural Architecture Search for Split Computing in Edge-Cloud Systems

Split Computing has become a prominent resource-efficient method to enable machine learning (ML) applications on user-constrained edge devices, where compute-intensive ML workloads can be delegated to remote cloud servers for processing. However, the exposure of data to the cloud service providers as such raises privacy alarms due to the possible leakage of sensitive user information. On a relevant note, typical deep neural network (DNN) design frameworks do not take into account model splitting and its complications at the early design stages of DNNs. Thus, a natural question arises on how to bridge this gap and optimize the DNN design process such that split computing operations can meet the requirements of accuracy, performance, and privacy. In this paper, we strive to address this question through adopting a privacy-by-design approach, where privacy is characterized either as a constraint or an objective to realize privacy-aware models tailored for split computing. Using the -differential privacy standard for our case study, we conduct intensive empirical analysis on the relation between architectural parameters and intrinsic privacy budgets, and propose PrivyNAS – a privacy-aware Neural Architecture Search framework for split computing. On the CIFAR-10 dataset, our approach has demonstrated promising results in providing DNN architectures that balance the required design trade-offs.
Spatially-Regularized Features for Vehicle Re-Identification: An Explanation of Where Deep Models Should Focus

Vehicle re-identification aims to identify vehicles from different cameras and has drawn much attention in the multimedia community. In recent years, significant achievements in vehicle re-identification have been made due to the development of neural networks and deep feature representations. However, existing deep models are regarded as “black box” methods considering the lack of explaining where they focus. With Class Activation Mapping (CAM), we can localize the discriminative image regions by utilizing convolutional feature maps. After answering the “where” question, the question of “how” to generate reasonable features appears to be substantial. In this paper, we propose the novel Spatially-Regularized Features (SRF) that can be extracted from discriminative regions in an explainable way. Specifically, we first provide an evaluation mechanism called Peak-to-Sidelobe Ratio (PSR) to measure the distribution of the convolutional feature maps. PSR outputs the strength of a matrix peak and can be used to indicate the attention intensity of a specific region. Moreover, we propose a spatially regularized loss to make the deep models focus on more reasonable and discriminative image regions. Note that no additional manual annotation data is involved in the training process, making the SRF an efficient and effective approach. Extensive subjective and objective experiments show that the proposed method significantly outperforms the state-of-the-art methods on three large-scale vehicle re-identification datasets.
An Exploration of Students' Engagement in EFL Asynchronous Class

Studentsâ€™ engagement is an ongoing concern for educators because of its positive association with deep learning, educational purpose, and educational outcomes. The purpose of this study is to reveal the studentsâ€™ engagement and to explore the factors of studentsâ€™ engagement in EFL asynchronous classes. This research was qualitative design with a case study approach. The participants of this study were 35 ten-grades students in one of the senior high schools in Majalengka. Survey and interview were used to gain the data from the participants. The survey was conducted to 35 ten-graders which mainly focused on their engagement during the asynchronous class as well as their reasons of being to or not engage with the lessons. Meanwhile, factors influenced their engagement in the asynchronous class were elaborated using the interview. The result showed that students gave positive responses in studentsâ€™ engagement on cognitive and behavioral engagement, most of the students both high and middle achievers always follow the teacher instructions and sometimes give opinions/ideas. The most prominent factors that influence studentsâ€™ engagement is teacher factors, among them are interesting teaching method and teachersâ€™ characteristic This study suggests that teachers need a variety of methods in which to use online platforms and conduct more creative activities in order to get the best learning outcomes.Keywords: Asynchronous Learning, EFL Class, Student Engagement.
Are Natural Kind Terms Ambiguous?

Abstract Recent experimental studies have claimed to find evidence for the view that natural kind terms such as “water” are ambiguous: that they have two extensions, one determined by superficial properties, the other by underlying essence. In an online experiment, we presented to 600 participants scenarios describing discoveries of novel samples that differ in deep structure from samples of a familiar kind but are superficially identical, such as a water‐like substance that is not composed of H 2 O. We used three different types of question sets to probe whether the participants considered the sample as a member of the kind or not. Our results did not confirm the predictions of the ambiguity view. They were, rather, consistent with views that take underlying essences to be the sole criterion for membership in a natural kind.
Featured Cover

The cover image is based on the Research Article Fault estimation for periodic piecewise T-S fuzzy systems by Chenchen Fan et al., https://doi.org/10.1002/rnc.5733.
EVA

Intelligent video analytics is resource-intensive, e.g. computation and bandwidth. In this paper, we improve resource efficiency for intelligent video analytics via proposing a distributed architecture named EVA. We noticed that timeliness of anomaly detection is crucial in many video analytics applications along with minimal resource utilization. Therefore, we study the overhead of profiling techniques which find optimal configurations for deep learning during video analytics to achieve good resource-accuracy trade-offs. To minimize this overhead, we propose 2-stage deep learning to build efficient and robust models, and two rate adaption algorithms: Adaptive Frame Rate (AFR) and Adaptive Resolutions (ARR) to achieve good resource-accuracy tradeoff. Evaluations using UCF-Crime dataset show that EVA considerably outperforms baseline Awstream with 71% improvements in resource consumption, 69% in CPU utilization whilst reducing the bandwidth consumption by 70% and storage by 61%.
Does School Gardening and Nutrition Education Improve Knowledge and Fruit-Vegetable Consumption Among Elementary School Students?

Background: Although scientific evidence has revealed the benefits of fruit and vegetable consumption (FVC) for children, 96.8% of age school children in Indonesia and 70.6% of primary school students in Jakarta did not have FVC as recommended. Aims: to assess how Muhammadiyah Elementary School Students’ knowledge and FVC were affected by school gardening and nutrition education. Methods: Following 6 monthly program, knowledge and FVC were assessed by using the Semi-Quantitative Food Frequency Questionnaire (SQ-FFQ) and knowledge questionnaire. Results: 1) There is a significant increase in students’ knowledge scores from 43.24 ± 14.24 to 70.31 ± 18.92 (P-value 0.000). 4); 2) The mean of FCV was elevated from 211.45±12.33 to 289.76±19.64, however, it was not statistically significant (P-Value of 0.058). Conclusion: Although knowledge has been improved following 6 months program, the FVC level was not dramatically enhanced. Future similar activities need to be done in a non-pandemic situation and should be lengthened into more than 6 months to portray a significant increase in FVC.
HyperBrain

We present HyperBrain, a hypermedia client that autonomously navigates hypermedia environments to achieve user goals specified in natural language. To achieve this, the client makes use of a large language model to decide which of the available hypermedia controls should be used within a given application context. In a demonstrative scenario, we show the client's ability to autonomously select and follow simple hyperlinks towards a high-level goal, successfully traversing the hypermedia structure of Wikipedia given only the markup of the respective resources. We show that hypermedia navigation based on language models is effective, and propose that this should be considered as a step to create hypermedia environments that are used by autonomous clients alongside people.
The Effect of Balanced Nutrition Educational Game Methods on The Behaviour of Consuming Healthy Food in Student of The State Elementary School

Health problems in school-age children, especially in terms of nutrition, are still a problem in Indonesia, such as being underweight and overweight, this is caused by physical activity, and vice versa, nutritional status affects children's physical activity. Therefore, dietary problems often experienced by school-age children, such as malnutrition and overnutrition, if these nutritional problems are not treated as early as possible, can affect children's health in the future.&#x0D; This study is a pre-experimental research design that used one group pre-test and posttest, namely the effect of the balanced nutrition education game method on healthy eating behavior in students of SDN 87 Jambi City, which was carried out one time, before and after giving a balanced nutrition educational game.&#x0D; The results showed effect of the educational game method on the Wilcoxon test results showed the Asymp Sig (2-tailed) value of 0.000. The balanced nutrition education game method influences the behavior of consuming healthy food at SD Negeri 87 Jambi City in 2022. This is hoped to continue to provide balanced nutrition education using media that researchers for students have created so they can consume healthy balanced nutrition food.
Deep Learning-Based Control Model for Electronic Evidence Access Rights

With the rapid development of digitalization and intelligence in smart grids, how to identify untrusted users in the electric power system and implement user access control based on electronic evidence has become an important research topic. A credible assessment needs to be made on the subject users accessing electronic evidence. In this paper, we propose a deep learning-based user multi-dimensional attribute feature extraction access control model, which considers multi-dimensional attribute features such as user identity, behaviour, and device information, and determines whether the user is a legitimate access user by using CNN-Transformer deep learning framework to extract user features and determine their trust value. Through simulation experiments, it is proved that the access permission control model proposed in this paper has high accuracy and reliability and can meet the access requirements of different electronic evidence users.
A Study on Curriculum Integration of Career Planning and Innovation and Entrepreneurship Education

Educational institutions and schools play key roles and responsibilities in the context of integrating career planning and innovation and entrepreneurship education. The development of innovation and entrepreneurship education promotes career planning and provides broad career development opportunities, practice opportunities, and the development of autonomous thinking. Educational institutions and schools should design curricula that integrate with innovation and entrepreneurship education, provide career counseling and guidance services, and encourage students to participate in hands-on and practical projects to develop innovation and entrepreneurial spirit and skills. Through students' participation and practice, they can develop practical work competencies, master key skills, and achieve self-development and growth. The efforts of educational institutions and schools will provide a solid foundation for students' career development and innovative and entrepreneurial paths.
THE IMPLEMENTATION OF LABIRIN BOARD GAME IN ENHANCING STUDENTS’ SPEAKING SKILL IN ELEVENTH GRADE AT SMA NEGERI 18 MAKASSAR

&#x0D; Tri Ayu Utami Ibrahim. 2022. The Implementation of Labirin Board Game in Enhancing Students' Speaking Skill in Eleventh Grade at SMA Negeri 18 Makassar, Skripsi for English Education Study Program Faculty of Teacher Training and Education University of Bosowa. (Supervised by Nurfaizah Sahib, and Restu Januarty).&#x0D; &#x0D; &#x0D; This research focuses on the students’ speaking skill through labirin board game. The aim of this study was to find out weather there was an enhancement of students’ speaking skill after implementing the labirin Board Game in eleventh grade at SMA 18 Makassar in the 2022/2023 academic year. The method used in this study was quantitative experimental research with one group pre-test-post-test research design. Speaking test used as the instruments of elicting the data. The procedure of collecting data was pre-test, treatment, and post-test. The samples of this research were the eleventh grade with 36 students. Simple random sampling was a method used to select a sample . The results of data analysis showed there was an enhancement in students' speaking skill of vocabulary, pronunciation, and fluency through the implementation of the abirin Board Game. The mean score of the students increased from 5.06 on the pre-test to 7.67 on the post-test The difference between the pre-test and the post-test was statistically significant (0.00&lt;0.05). Based on the results of data analysis, the writer conclude that there was an enhancement after implementing the labirin Board Game on speaking skill in eleventh grade at SMA 18 Makassar.
Smart Community Networks and Systems

Community networks and systems were started as non-profitable last-mile access networks that were operated within local communities, sometimes even without access to the public Internet. They are established and maintained by communities to enhance community life quality rather than for business purposes. Many projects have been carried out to develop various community networks and systems worldwide, such as in Asian-Pacific, South Africa, Argentina, Tibet, USA, Canada, Netherlands, Italy, Spain, and many others.
Cross-Loss Pseudo Labeling for Semi-Supervised Segmentation

Training semantic segmentation models requires pixel-level annotations, leading to a significant labeling cost in dataset creation. To alleviate this issue, recent research has focused on semi-supervised learning, which utilizes only a small amount of annotation. In this context, pseudo labeling techniques are frequently employed to assign labels to unlabeled data based on the model’s predictions. However, there are fundamental limitations associated with the widespread application of pseudo labeling in this regard. Since pseudo labels are generally determined by the model’s predictions, these labels could be overconfidently assigned even for erroneous predictions, especially when the model has a confirmation bias. We observed that the overconfident prediction tendency of the cross-entropy loss exacerbates this issue, and to address it, we discover the focal loss, known for enabling more reliable confidence estimation, can complement the cross-entropy loss. The cross-entropy loss produces rich labels since it tends to be overconfident. On the other hand, the focal loss provides more conservative confidence, therefore, it produces a smaller number of pseudo labels compared to the cross-entropy. Based on such complementary mechanisms of two loss functions, we propose a simple yet effective pseudo labeling technique, Cross-Loss Pseudo Labeling (CLP), that alleviates the confirmation bias and lack of pseudo label problems. Intuitively, we can mitigate the overconfidence of the cross-entropy with the conservative predictions of the focal loss, while increasing the number of pseudo labels marked by the focal loss based on the cross-entropy. Additionally, CLP also contributes to improving the performance of the tail classes in class-imbalanced datasets through the class bias mitigation effect of the focal loss. In experimental results, our simple CLP improves mIoU by up to +10.4%p compared to a supervised model when only 1/32 true labels are available on PASCAL VOC 2012, and it surpassed the performance of the state-of-the-art methods.