Reviewerly/exHarmony huggingface dataset:
Router forensics: Navigating the digital crossroads

As the digital landscape continues to evolve, routers have become central gatekeepers, governing the flow of information in networks. This study delves deep into the realm of router forensics, focusing on the methodologies and techniques employed to extract and analyze forensic data from these pivotal devices. Drawing upon both traditional and contemporary approaches, our research underscores the significance of router logs, volatile data, and the challenges that arise in their forensic analysis. We highlight the pressing need for standardized forensic protocols, especially in the face of diverse router architectures and rapidly emerging cyber threats. Our study also emphasizes the potential of leveraging advanced technologies, such as machine learning, in enhancing forensic capabilities. By providing a comprehensive overview of the current state of router forensics and shedding light on potential future trajectories, this research aims to fortify the cybersecurity community's arsenal against escalating cyber threats, ensuring a more secure and resilient digital ecosystem.
IDOS: A Unified Debiasing Method via Word Shuffling

Recent studies show that advanced natural language understanding (NLU) models may exploit dataset biases to achieve superior performance on in-distribution datasets but fail to generalize to out-of-distribution datasets that do not contain such biases. Previous works have made promising progress in mitigating dataset biases with an extra model to estimate them. However, these methods rely on prior bias knowledge or tedious model-tuning tricks which may be hard to apply widely. To tackle the above problem, we propose to model biases by shuffling the words of the input sample, as word shuffling can break the semantics that relies on correct word order while keeping the biases that are unaffected. Thanks to word shuffling, we further propose IDOS, a unified debiasing method that enables bias estimation and debiased prediction by one single NLU model. Experimental results on three NLU benchmarks show that despite its simplicity, our method improves the generalization ability of NLU models and achieves a comparable performance to previous debiasing methods.
Annotation Quality Measurement in Multi-Label Annotations

Annotation quality measurement is crucial when building a supervised dataset for either general purpose research or domain applications. Inter-rater agreement measure is one of the most vital aspects in terms of establishing annotation quality. The traditional inter-rater agreement measures cannot address the issue in multi-label scenario. To adapt to multi-label annotations, the recent research has developed a bootstrapping method to measure the level of agreement between two raters. In this paper we propose a fine-grained multi-label agreement measure MLA, which attends to discover slight differences in inter-rater agreement across different annotations when multiple raters are involved. We demonstrate its compatibility with traditional measures through mathematics and experiments. The experimental results show it can interpret the agreement more accurately and consistently with intuitive understanding. In addition, a toolset is provided to enable users to generate the multi-label annotations that mimic different annotators, and calculate various agreement coefficients for several scenarios.
The Impact of Business Entrepreneurship on Marketing Performance at Jordanian Commercial Banks

The aim of this research was to explore how various facets of entrepreneurship (including innovation, proactivity, and risk management) influence different dimensions of marketing performance (such as customer satisfaction, loyalty, and acquisition) within commercial banks in Jordan. The study society consists of (644) managers in Jordanian commercial banks, and the sample using random sample, in (13) banks. The study devised a questionnaire data collection as a tool, and the Quantitative approach (analytical descriptive) was used, and Numerous statistical methodologies employed were. The research results indicate that there's a strong correlation between effective marketing performance and its perceived significance. Both marketing performance and entrepreneurial tendencies scored high in terms of their perceived importance. Furthermore, there's a significant positive link between entrepreneurial facets and marketing outcomes in Jordan's commercial banks. To capitalize on this, the study advises bank managers to harness the evident synergy between entrepreneurial actions and marketing achievements. By doing so, banks can bolster their reputation and position in the marketplace. There's an emphasis on the need for banking leaders to prioritize creativity and innovation since these are integral to the bank's marketing efficacy and service enhancement. The study also underscores the importance of proactivity in offering banking services, given its crucial role in strengthening the bank's competitive edge.
Label-Guided Compressed Prototypical Network for Incremental Few-Shot Text Classification

Incremental few-shot text classification (IFSTC) involves the sequential classification of new classes with limited instances while preserving the discriminability of old classes, which can effectively support downstream tasks such as information extraction and knowledge graph construction. The primary objective of IFSTC is to achieve a balance between plasticity and stability, where classification models should be plastic enough to learn patterns from new classes, while also being stable enough to retain knowledge learned from previously seen classes. In previous work of incremental few-shot learning, the popular approach is to compress the base classes space to enhance model plasticity. However, the application of current space compression methods to text classification presents challenges due to the difficulty in simulating latent data through text data synthesis. Moreover, freezing all model parameters to maintain model stability is not a viable solution as it fails to incorporate new knowledge. To solve the problems above, we propose Label-guided Compressed Prototypical Network (LGCPN) for IFSTC, which consists of two parts. Firstly, label-guided space compression is proposed to improve model plasticity with text data through leveraging the information carried by labels to assist in the space compression. Secondly, few-params tuning is designed to maintain model stability and enable it to learn new knowledge by selectively fine-tuning few parameters. We experimented on two public datasets to evaluate the performance of our proposed method for IFSTC. The experimental results demonstrate that our method can significantly improve the accuracy of each round in the incremental stage compared to two baselines.
Episode-Based Prompt Learning for Any-Shot Intent Detection

Emerging intents may have zero or a few labeled samples in realistic dialog systems. Therefore, models need to be capable of performing both zero-shot and few-shot intent detection. However, existing zero-shot intent detection models do not generalize well to few-shot settings and vice versa. To this end, we explore a novel and realistic setting, namely, any-shot intent detection. Based on this new paradigm, we propose Episode-based Prompt Learning (EPL) framework. The framework first reformulates the intent detection task as a sentence-pair classification task using prompt templates and unifies the different settings. Then, it introduces two training mechanisms, which alleviate the impact of different prompt templates on performance and simulate any-shot settings in the training phase, effectively improving the model’s performance. Experimental results on four datasets show that EPL outperforms strong baselines by a large margin on zero-shot and any-shot intent detection and achieves competitive results on few-shot intent detection.
Could the Technology for Adaptive Learning Systems Come out of GBL?

Games based learning (GBL) has a related field that it can draw upon: intelligent tutoring systems (ITS). Acommon concern in both fields is adaptivity, whereby the system can automatically adapt to the user. In order to supportthis adaptation, an ITS will generally include a user model and may also have a formal domain model. Work in this areastarted optimistically some years ago, but seems to have either lost some of that initial enthusiasm or been diverted intoother directions. We scan recent ITS literature to help consider why this might be, and suggest how GBL may be the fieldbest placed to take the work forward again. Learner models have long been seen as useful for adaptive learning systems.They include information about the learner which allows the system to adapt the course of learning materials andexercises to the learner's particular characteristics. In order to achieve a good quality of adaptation to the user, a detailedmodel of the required domain knowledge is typically added. The user and domain models then have to be broughttogether to lay out a course of exercises for the learner to do, and to track progress as the knowledge is learned. It's anattractive research programme, but recent work has moved to new issues, such as MOOCs. The reasons for that are partlyopportunistic and economical, but also suggest a deeper problem with the research programme. It is a costly task todevelop a domain model, and a suitable learner model that can take advantage of it. We suggest that GBL is in a goodposition to push through this cost barrier, because much of the effort is already implicitly involved in the game designprocess, which typically has to be more rigorously planned out in order to make the game a good one. One might thusexpect the next breakthroughs in adaptive learning systems to come from GBL. We further argue that the advantages toresearch, offered by the ITS framework, are also potentially beneficial to the way we teach the subject of GBL to ourstudents on game development courses.
Design and Manufacture of Rainfall Measuring Instruments Based on Android Smartphone and ATmega328P Microcontroller Using Hall Effect Sensor

Aims: To produce low-cost measuring instruments with high accuracy, which are expected to be used as a reference in developing rainfall observation instruments to reduce observation costs.&#x0D; Study Design: Design of Rainfall measuring instruments based on Android Smartphone and ATmega328P microcontroller using hall effect sensor.&#x0D; Place and Duration of Study: Department of Physics, Udayana University, and Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), Region-III Badung, from June 2022 to August 2022.&#x0D; Methodology: Calibration is done by comparing the output value of the rainfall measuring instruments using hall effect sensor and the BMKG calibration standard tool. The calibrated parameter is rainfall. The relationship between the measurement values of the design tool and the standard tool is determined using linear regression method to obtain a correction equation.&#x0D; Results: Rainfall measuring instruments based on Android Smartphone and ATmega328P microcontroller using hall effect sensor have been produced. The measurement accuracy obtained for the rainfall parameter is 97.97% with uncertainty (U95) of 0.06 mm/min. The accuracy value indicates that the resulting tool has a good level of accuracy to the standard tool.&#x0D; Conclusion: In this research, has been produced a rainfall measuring instrument at a low-cost with high accuracy and precision, which has been calibrated with BMKG standard tool.
DECISION TREE PADA PENILAIAN PERFORMA LAYANAN DRIVER GOJEK DENGAN ALGORITMA C4.5

Transportation is an important means to support daily activities and operations, be it private transportation or public transportation. This study will analyze the level of customer satisfaction as a public vehicle user, in this case the author utilizes data on customers who use and utilize the gojek application as a means of transportation, using a questionnaire data collection technique, the writer will determine client satisfaction levels using the variables Tangible, Reliability, Responsiveness, Assurance, Empathy. The analytical Data mining with the is a technique employed in this investigation. concept of the Algorithm C4.5 classification which can show the results of the highest Entrophy and gain values modeled as a decision tree with the aim that the results obtained can be a guide for gojek partners to service and increase customers. Considering the outcomes of the classicization It is demonstrated by the C4.5 algorithm that the accuracy achieves 75.33% where the factors that become the assessment of satisfaction are Tangible, Reliability.
Multi-Scale Feature Refined Network for Human Pose Estimation

Occlusive keypoints has been a challenge for human pose estimation, especially the mutual occlusion of human bodies. One possible solution to this problem is to utilize multi-scale features, where small scale features are capable of identifying keypoints, while large-scale features can capture the relationship between keypoints. Feature fusion among multi-scale features allows for the exchange of information between keypoints, facilitating the inference of occluded keypoints based on the identified keypoints. However, it’s found that there are invalid features in feature fusion which will interfere valid feature. In this paper, we propose multi-scale feature refined network (MSFRNet) based on HRNet and a new attention module namely multi-resolution attention module (MRAM). The proposed MRAM is designed to strengthen the effective information while suppressing redundant information. It has multiple inputs and outputs and can learn the relationships between keypoints while retaining detailed information. The proposed MSFRNet outperforms HRNet, achieving a 1.4[Formula: see text]AP improvement on the COCO dataset with only a marginal computational increase of 0.35 GFLOPs. Additionally, it demonstrates superior performance with a 0.9[Formula: see text]AP, 0.7[Formula: see text]AP, and 1.8[Formula: see text]AP improvement on the MPII, CrowdPose and OCHuman datasets, respectively. Furthermore, compared with the latest attention mechanism PSA, the MSFRNet exhibits lower computational cost while maintaining the same pose-estimation accuracy.
A - 151 Assessing the Role of Executive Function in the Memory Performances of Retired National Football League Players

We evaluated the relationship between retired National Football League players executive functioning (EF) abilities on verbal memory performance and subprocesses.Fifty-eight participants were divided into two groups: intact executive functioning (IEF) and deficit executive functioning (DEF). Participants completed the California Verbal Learning Test, Second Edition (CVLT-II) to evaluate verbal memory performance. Additionally, the Item Specific Deficit Approach (ISDA) was applied to the CVLT-II to quantify verbal memory subprocesses (i.e., encoding, consolidation, & retrieval). Next, we determined which ISDA indices predicted long-delayed free recall (LDFR) for both groups. We then computed hierarchal regressions to determine which ISDA indices were predictive of LDFR for each group. Next, we retained significant predictors from the ISDA and correlated them with measures of executive function in both groups with and without partialling out cognitive reserve (CR).We found the IEF group outperformed the DEF group on the CVLT-II learning trials and LDFR, and demonstrated better encoding abilities. Hierarchical regression revealed that the ISDA was predictive of LDFR in both groups. The DEF group LDFR issues were only predicted by encoding problems. In contrast, LDFR deficits in the IEF were primarily driven by consolidation problems. The ISDA encoding index correlated with Trail Making Test part B and Phonemic Fluency Test. However, after partialling out the variance accounted for by CR, the associations between the encoding index and executive function were nonsignificant.Our results suggest that greater executive function results in better memory performances in retired football players. Lastly, improved executive function is related to greater CR.
Securing Confidential Information in Big Data Based on Cognitive Artificial Intelligence and Machine Learning

The high demand for handling big data along with the current vulnerabilities of contemporary computer systems has made the world in need of a better cybersecurity system with high performance. Furthermore, confidential information is, most likely, combined with bulky non-confidential data. Yet, the encryption process of big data consumes a lot of resources and severely burdens the system's performance. This paper presents the architecture of a novel approach that encompasses Cognitive Artificial Intelligence and Machine Learning Methodology for Securing Sensitive Information (CAIMLSSI) in Big Data to overcome both vulnerabilities and performance issues. We describe the details of each module of the architecture, discuss validation issues, and present the mathematical analysis that shows the robustness of CAIMLSSI.
Modal Fusion Sign Language Recognition Based on Swin Transformer Architecture

Sign language plays an important role in information transmission and emotional communication between the hearing-impaired and the outside world. It is expressed through a rapid and complex combination of gestures, body postures and facial expressions. In this paper, Google's MediaPipe framework is used to extract and process key point information of hands. Depth features are used to supplement the RBG diagram. A framework of skeleton sensing multi-modal recognition suitable for complex scenes is proposed. This can be used to deal with the problem of sign language recognition of multi-modal information in different scenes. However, most of the existing sign language recognition methods are traditional convolutional networks and LSTM. Although they have excellent achievements in image recognition and video recognition, their application in sign language recognition does not conform to the characteristics of sign language. In this paper, a Swin Transformer model is used to process both depth information and RGB information. Finally, statistical data fusion method was used to fuse the depth prediction results with RGB prediction results. Experimental analysis shows that this method performs well in sign language recognition in complex scenes compared with existing methods.
Identification of Child Physical Abuse Based on Openpose and ST-GCN

In order to safeguard the physical and mental health of children, this article proposes a behavior recognition method based on the spatial temporal graph convolutional neural network using skeleton data after customizing mild, moderate, and severe physical abuse behaviors. OpenPose algorithm is introduced to extract human skeleton data for training and recognition tasks, improving the robustness of the model in complex environments. Experimental results show that the recognition accuracy of OpenPose+ST-GCN for mild physical abuse behavior reaches up to 98%, and the average accuracy for recognizing all three levels of physical abuse behavior is 64.13%. The average recognition accuracy for recognizing the three levels of physical abuse behavior using OpenPose+2s-AGCN is 65.52%, which is 1.39% higher than that of OpenPose+ST-GCN.
Developing a Generic Focus Modality for Multimodal Interactive Environments

In human communication we need to establish the target of our message and understand if someone is addressing us. Such mechanisms facilitate communication in environments where several potential interlocutors exist. With the advances of speech technologies supporting interaction with computers, the establishment of a device as an interlocutor has often been performed resorting to wake-up words. While this addresses the issue, it is far from the naturalness and efficiency of what we can accomplish in human-human communication. In this regard, research has considered alternatives, such as the visual focus of attention, but the implementations are often scenario specific and not easily available for a generalized use. In this paper, we argue that the establishment of a machine as an interlocutor, particularly for speech interaction, should consider a wide range of verbal and nonverbal aspects, and we conceptualize and present a first proof-of-concept of its integration as a core feature in a multimodal interactive framework.
The development trend of artificial intelligence

This paper aims to discuss the subject concept, progress and future development trend of artificial intelligence( AI ), summarize the application prospect of AI technology, and put forward the problems and contradictions existing in the development of AI, It summarizes the ways of man-machine combination to solve this contradictory problem.
Monocular 3D Object Detection Utilizing Auxiliary Learning With Deformable Convolution

In autonomous driving systems, the monocular 3D object detection algorithm is a crucial component. The safety of autonomous vehicles heavily depends on a well-designed detection system. Therefore, developing a robust and efficient 3D object detection algorithm is a major goal for institutes and researchers. Having a 3D sense is essential in autonomous vehicles and robotics, as it allows the system to understand its surroundings and react accordingly. Compared with stereo-based and Lidar-based methods, monocular 3D Object detection is a challenging task as it only utilizes 2D information to generate complex 3D features, making it low-cost, less computationally intensive, and with great potential. However, the performance of monocular methods is impaired due to the lack of depth information. In this paper, we propose a simple, end-to-end, and effective network for monocular 3D object detection without the use of external training data. Our work is inspired by auxiliary learning, in which we use a robust feature extractor as our backbone and multiple regression heads to learn auxiliary knowledge. These auxiliary regression heads will be discarded after training for improved inference efficiency, allowing us to take advantage of auxiliary learning and enabling the model to learn critical information more conceptually. The proposed method achieves 17.28% and 20.10% for the moderate level of the Car category on the KITTI benchmark test set and validation set, respectively, which outperforms the previous monocular 3D object detection approaches.