Reviewerly/exHarmony huggingface dataset:
Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques

Traffic engineering (TE) is one of the most critical issues in networking, as it enables efficient and reliable network operations. With the advent of Machine Learning (ML) techniques, many ML-based TE methods have emerged in recent years, especially those employing Deep Neural Networks for future traffic prediction to enhance the performance of traditional approaches. However, current methods suffer from two major issues. Firstly, most prior works only solve the TE problem based on short-term traffic prediction, neglecting the network traffic dynamics over an extended time period. This oversight results in high network disturbance when numerous traffic flows need to be rerouted to adapt to traffic changes. Secondly, although traffic prediction models rely on historical traffic data to perform future prediction, ML-based TE studies often ignore the high overhead for network traffic monitoring. To address these issues, we propose a traffic prediction-based routing algorithm in which the routing rules can be applied to multiple time-steps without requiring changes, ultimately leading to reduced network disturbance. We employ the segment routing (SR) technique as the routing algorithm and formulate the multi-time-step segment routing method that incorporates future traffic prediction. To address the high monitoring overhead, we present an approach that combines partial traffic prediction and compressive sensing techniques to estimate unmeasured data. Through extensive experiments on real backbone network traffic datasets, we demonstrate that our proposal can achieve more than 80% of the optimal performance in reducing maximum link utilization while significantly reducing the number of routing changes and traffic monitoring cost.
Intelligent Requirements Engineering: Applying Machine Learning for Requirements Classification

The classification of requirements plays a crucial role in requirements engineering, enabling the differentiation between legally relevant requirements and auxiliary content. However, the manual labeling of each content element in a requirements specification as a “functional requirement” or “non-functional requirement” or “information” is a time-consuming and error-prone task. In this paper, we propose an approach that automates the classification of content elements in a natural language requirements specification as either “functional requirement” or “non-functional requirement” or “information” Our approach leverages a combination of Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) for the classification task. The CNN model is responsible for extracting meaningful features from the textual content, while the SVM classifier is employed to make the final classification decision. To train and validate our model, we utilized online datasets specifically designed for requirements classification. Additionally, we augmented these datasets by incorporating data from other projects. The performance of our model was measured using various evaluation metrics, including accuracy, F1 score, precision, recall, and confusion matrix analysis. The experimental results demonstrate promising performance with a precision of 80%, accuracy of 85%, F1 score of 88%, and recall of 90%. These results indicate that our approach successfully automates the classification process and significantly reduces the need for manual labeling, thereby saving time and reducing the potential for errors in requirements classification.
Keynote Speech 1 Who You Gonna Call? Unmasking AI Investigations Through AI Forensics

Machine Learning (ML) and Artificial Intelligence (AI) have become inescapable forces, permeating every facet of our society, from business and academia to public and private sectors. However, AI failures are an undeniable reality that demands urgent attention from forensic researchers and practitioners. When AI embarks on mischievous endeavors, an important question arises: Who you gonna call? While AI/ML/ are hailed as powerful tools to enhance digital forensics processing, it is imperative that we redirect our focus towards the forensics of AI. Join me in this keynote as we explore the emerging field of AI forensics, an essential sub-discipline within the realm of digital forensics. Through an overview of this evolving field and a spotlight on intriguing research problems, we will ignite understanding of the pressing need to address AI investigations.
Contract Grading and the Development of an Efficacious Writerly Habitus

Contract grading has been shown to reduce stress and anxiety, promote self-directed learning, and disrupt unjust educational norms (Cowan, 2020; Inoue, 2019; Medina &amp;amp; Walker, 2018). Yet, there is growing recognition of challenges associated with the approach, including the unintended effects of deemphasizing grades (Inman &amp;amp; Powell, 2018) and the possibility that labor-based contracts, in particular, may put some students at a disadvantage (Carillo, 2021). This article reports selected findings from an IRB-approved multi-semester, comparative study of labor-based and labor-informed contract grading in first-year writing courses at a large private research university. The study affirms several findings from existing research on contract grading. In particular, it shows the approach mitigates students’ stress and anxiety and increases their overall satisfaction with grading. Contract grading shifts the assessment ecology of the first-year writing classroom so that the challenges and rewards of writing take priority over the pressures and limitations of grades (see Inoue, 2015). Drawing on the literature of self-efficacy (Bandura, 1977, 1994, 1997; Pajares, 2003), the authors theorize that contract grading encourages students to develop an efficacious writerly habitus grounded in self-motivated effort, increased confidence, and heightened understanding of writing as a mode of thinking.&amp;nbsp;
Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching

Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve high recognition accuracy. However, these methods first require a large number of carefully crafted traffic sign datasets for the training process. Moreover, since traffic signs differ in each country and there is a variety of traffic signs, these methods need to be fine-tuned when recognizing new traffic sign categories. To address these issues, we propose a traffic sign matching method for zero-shot recognition. Our proposed method can perform traffic sign recognition without training data by directly matching the similarity of target and template traffic sign images. Our method uses the midlevel features of CNNs to obtain robust feature representations of traffic signs without additional training or fine-tuning. We discovered that midlevel features improve the accuracy of zero-shot traffic sign recognition. The proposed method achieves promising recognition results on the German Traffic Sign Recognition Benchmark open dataset and a real-world dataset taken from Sapporo City, Japan.
ATS-YOLOv7: A Real-Time Multi-Scale Object Detection Method for UAV Aerial Images Based on Improved YOLOv7

The objects in UAV aerial images have multiple scales, dense distribution, and occlusion, posing considerable challenges for object detection. In order to address this problem, this paper proposes a real-time multi-scale object detection method based on an improved YOLOv7 model (ATS-YOLOv7) for UAV aerial images. First, this paper introduces a feature pyramid network, AF-FPN, which is composed of an adaptive attention module (AAM) and a feature enhancement module (FEM). AF-FPN reduces the loss of deep feature information due to the reduction of feature channels in the convolution process through the AAM and FEM, strengthens the feature perception ability, and improves the detection speed and accuracy for multi-scale objects. Second, we add a prediction head based on a transformer encoder block on the basis of the three-head structure of YOLOv7, improving the ability of the model to capture global information and feature expression, thus achieving efficient detection of objects with tiny scales and dense occlusion. Moreover, as the location loss function of YOLOv7, CIoU (complete intersection over union), cannot facilitate the regression of the prediction box angle to the ground truth box—resulting in a slow convergence rate during model training—this paper proposes a loss function with angle regression, SIoU (soft intersection over union), in order to accelerate the convergence rate during model training. Finally, a series of comparative experiments are carried out on the DIOR dataset. The results indicate that ATS-YOLOv7 has the best detection accuracy (mAP of 87%) and meets the real-time requirements of image processing (detection speed of 94.2 FPS).
DeFi Auditing: Mechanisms, Effectiveness, and User Perceptions

Decentralized Finance (DeFi), a blockchain-based financial ecosystem, suffers from smart contract vulnerabilities that led to a loss exceeding 3.24 billion USD by April 2022 [67]. To address this, blockchain firms audit DeFi applications, a process known as DeFi auditing. Our research aims to comprehend the mechanism and efficacy of DeFi auditing. We discovered its ability to detect vulnerabilities in smart contract logic and interactivity with other DeFi entities, but also noted its limitations in communication, transparency, remedial action implementation, and in preventing certain DeFi attacks. Moreover, our interview study delved into user perceptions of DeFi auditing, unmasking gaps in awareness, usage, and trust, and offering insights to address these issues.
A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity

Sensor-based human activity recognition is now well developed, but there are still many challenges, such as insufficient accuracy in the identification of similar activities. To overcome this issue, we collect data during similar human activities using three-axis acceleration and gyroscope sensors. We developed a model capable of classifying similar activities of human behavior, and the effectiveness and generalization capabilities of this model are evaluated. Based on the standardization and normalization of data, we consider the inherent similarities of human activity behaviors by introducing the multi-layer classifier model. The first layer of the proposed model is a random forest model based on the XGBoost feature selection algorithm. In the second layer of this model, similar human activities are extracted by applying the kernel Fisher discriminant analysis (KFDA) with feature mapping. Then, the support vector machine (SVM) model is applied to classify similar human activities. Our model is experimentally evaluated, and it is also applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that the proposed approach achieves recognition accuracies of 97.69%, 97.92%, 98.12%, and 90.6%, indicating excellent recognition performance. Additionally, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier to assess the model’s generalization ability. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.
Electromagnetically induced transparency in many-emitter waveguide quantum electrodynamics: Linear versus nonlinear waveguide dispersions

We study single-photon-induced electromagnetically induced transparency (EIT) in many-emitter waveguide quantum electrodynamics (wQED) with linear, nonlinear, and quasilinear waveguide dispersion relations. In the single-emitter problem, in addition to the robustness of the EIT spectral features in the overcoupled regime of wQED, we find that the nonlinear dispersion results in the appearance of a side peak for frequencies smaller than the resonant EIT frequency, which turns into a plateau as the nonlinearity is reduced. The quasilinear case produces a similar spectrum to the purely linear scenario with a small observable difference in the far-detuned regime. Consequently, for many-emitter scenarios, our results indicate the formation of band structure for purely linear cases, which, for smaller values of nonlinearity, leads to a narrow band-gap-like structure compared to the corresponding linear and quasilinear dispersion cases. Long-distance quantum networking aided with quantum memories can serve as one of the targeted applications of this work.
Intelligent design of network multimedia using big data and virtual Artificial Intelligence technology

Abstract In recent years, with the rapid development of the Internet of Things and communication technology, the application of network multimedia has become increasingly popular. At present, network multimedia has become a new type of mass media. Due to its interchangeability and extensive dissemination, it creates favourable conditions for information dissemination and acceptance in terms of vision, hearing, touch etc. However, at the same time, there are also some problems. The network multimedia does not have enough original information and the information retrieval is not intelligent, so this has caused great trouble to users. In order to solve these problems, the authors attempted to use the combination of Big Data (BD) and virtual Artificial Intelligence (AI) technology to realise the intelligent design of network multimedia. Therefore, based on the theoretical analysis, the authors also verified the effect of BD and virtual AI technology on the intelligent design of network multimedia. It was found that the intelligent design method proposed by the authors had good application effect in network multimedia, and could effectively improve the information retrieval speed of network multimedia. The query time of this method was 1.87 s less than that of the traditional method when the amount of information was 1000 and 18.16 s less than that of the traditional method when the amount of information was 40,000. At the same time, this method could also improve the accuracy of network multimedia information recommendation, and make network multimedia have better sharing, so as to provide more services for users. In addition, the discussion of BD and virtual AI technology in network multimedia intelligent design could also broaden the application scope of the Internet of Things and promote its better development.
Recreating Sustainable Societies through Experiential and Peer Learning in Higher Education

Education System in India was based on culture, traditions and social practices that was passed on to the pupils through inherited values. It was learnt at homes as well in schools such as the Gurukul System, which was the centre of diverse learning. The cultural practices in the society, whether reading the scriptures or acquiring hoplology skills, were needed for all round development of the individual. Art and craft; sports and yoga; and also, daily chores were given the same importance, for holistic learning. To some extent, they were also taught to have restraint on unearthly desires. This exercise was influenced by spiritual enlightenment, awareness about nature and self-discipline. It prepared them for their future life struggles, as well as psychological wellbeing. The modern education system introduced during the colonial era was aimed at competitiveness and exploitation of resources. They destroyed the culture and created social divides amongst children; and encouraged a consumeristic lifestyle. Gradually, these societies divorced themselves from ethical principles and were responsible for destruction of nature. The urban milieu vitiated further in this process, becoming centres of environmental degradation and pollution; and diminished social values. Although one associates education with higher level of consciousness, many modern societies have failed to prevent the ecological devastation. In fact, numerous traditional societies have lived more sustainably despite the people there being less literate. Thus, existing education system requires a certain change geared towards human awakening. The present study elucidates the importance of reviving some practices of the past within the modern curriculum by adopting experiential tools for a sustainable living.
User Interface, User Experience, Layouts

User Interface (UI) and layouts play a pivotal role in shaping user experiences across digital platforms. As technology continues to evolve, the demand for intuitive and visually appealing interfaces becomes increasingly crucial. This abstract explores the key principles and considerations in designing effective user interfaces and layouts to enhance user engagement and satisfaction.
Writing Competency and Student Participation in Filipino Class: The Mediating Effect of Reading Strategy

Aims: To identify the mediating effect of reading strategy in the relationship between writing competency and student participation in Filipino class.&#x0D; Study Design: Quantitative non-experimental design.&#x0D; Place and Duration of Study: The study was conducted among Senior High School Students of Cor Jesu College, Inc., Digos City, Davao del Sur during the school year 2022-2023.&#x0D; Methodology: The respondents were 310 Senior High School students which were chosen using stratified random sampling. Mean, Pearson r, linear regression, and path analysis were the statistical tools used in the study. Moreover, the data were collected through a survey questionnaire.&#x0D; Results: Results revealed that the level of students’ writing competency is high. Also, the level of students’ participation in Filipino class and the mediating variable, which is the reading strategy are high. It shows that writing competency and the student’s participation in class have a significant relationship. Furthermore, writing competency and reading strategy, as well as reading strategy and student participation in class have a significant relationship. Therefore, the results show a partial mediation that happened in this study.&#x0D; Conclusion: It can be concluded that writing competency had a significant effect on students' participation in class, but with the help of a reading strategy, the significant relationship between the two variables mentioned was reduced. In short, the reading strategy helps to reduce the negative effect of writing competency on students' participation in Filipino class.
High Throughput FPGA-Based Object Detection via Algorithm-Hardware Co-Design

Object detection and classification is a key task in many computer vision applications such as smart surveillance and autonomous vehicles. Recent advances in deep learning have significantly improved the quality of results achieved by these systems, making them more accurate and reliable in complex environments. Modern object detection systems make use of lightweight convolutional neural networks (CNNs) for feature extraction, coupled with single-shot multi-box detectors (SSDs) that generate bounding boxes around the identified objects along with their classification confidence scores. Subsequently, a non-maximum suppression (NMS) module removes any redundant detection boxes from the final output. Typical NMS algorithms must wait for all box predictions to be generated by the SSD-based feature extractor before processing them. This sequential dependency between box predictions and NMS results in a significant latency overhead and degrades the overall system throughput, even if a high-performance CNN accelerator is used for the SSD feature extraction component. In this paper, we present a novel pipelined NMS algorithm that eliminates this sequential dependency and associated NMS latency overhead. We then use our novel NMS algorithm to implement an end-to-end fully pipelined FPGA system for low-latency SSD-MobileNet-V1 object detection. Our system, implemented on an Intel Stratix 10 FPGA, runs at 400 MHz and achieves a throughput of 2,167 frames per second with an end-to-end batch-1 latency of 2.13 ms. Our system achieves 5.3 × higher throughput and 5 × lower latency compared to the best prior FPGA-based solution with comparable accuracy.
ICT Role in Public Institutions toward the Oman Vision 2040: Specialists’ Perspectives

This study investigates the role of ICT in public institutions in achieving Oman’s Vision 2040 by exploring specialties’ perspectives. A 28-item quantitative, Likert-type attitude questionnaire was administered to 600 ICT specialists working in 34 governmental institutions, with items exploring experts’ awareness (n=4), perceived importance (n=16), and challenges (n=6) relating to ICT importance in achieving the Vision. The findings indicate high awareness and perceived importance but also signal a strong perception that major challenges face the development of ICT in governmental institutions. No significant differences were found in relation to gender, but it was found that those with 1-5 years’ experience had a stronger belief in the importance of ICT to achieve OV40 compared with those with over 21 years’ experience, and those with 11–15 and 16–20 years’ experience had stronger perceptions of the challenges to ICT use by governmental institutions to achieve the Vision. No significant experience-related differences were found regarding the awareness of OV40 priorities and indicators. This study highlights the importance of ICT in the achievement of the Oman Vision 2040 and the challenges faced that should be addressed regarding the development of ICT in governmental institutions. The results indicate the need for a coherent multi-stakeholder approach to the development of government ICT infrastructure and systems to empower ICT professionals to effectively contribute to the achievement of the national vision. Keywords: ICT, public institutions, Oman Vision 2040, specialist. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.62.9
Konsep Kreatif Video Live Streaming Ilusman 3 Medan

This study aims to explore and analyze the application of creative concepts at the 33rd Ilusman 3 Medan reunion event in 2023. This research method involves a qualitative approach with a descriptive approach. This research also involves analysis of documentation and direct observation of the reunion event that has been carried out. The results showed that the application of creative concepts in the 33rd Ilusman 3 Medan event in 2023 contributed positively to the diversity and creativity of the event. A more memorable and satisfying experience, with more dynamic visual aspects, content presentation, and reunion atmosphere. The application of creative concepts also strengthens the emotional and nostalgic bond of participants to the alma mater or alumni group of SMA Negeri 3 Medan, which is fully supported by live streaming on Youtube and Zoom Meetings. Thus the interaction between participants becomes more in-depth and active participation in creative activities takes place. In addition, creative concepts poured both offline or on the stage and online or into live streaming also create a positive impact on the image and identity of the reunion organizers, creating an impression of professionalism and innovation towards the response of modern dynamics that lead to the development of digital technology. By utilizing creative elements. The creative elements implemented have gone through the stages of the design process and neat infrastructure design. Thus the reunion event can be more dynamic, relevant, and able to create a unique and memorable experience for the participants. The results showed that using creative aspects visually can create an evocative atmosphere during the event. In addition, the creative aspects applied in live streaming can certainly increase the attractiveness of an event.