Reviewerly/exHarmony huggingface dataset:
Penetration Testing and Ethical Hacking: Risk Assessments and Student Learning

This full paper describes a semester-long graduate penetration testing course curriculum; and, discusses student leanings as reported from the final project over multiple years of IRB-approved coursework participation. The curriculum is designed to support career changers where students work in small teams of students in potentially different time zones. The graduate students spend the term learning technical skills to perform industry-based risk assessments. Over the term, the students build a risk assessment based on the National Institute of Standards and Technology (NIST) Risk Assessment guidance. Each week of the semester focuses on a different technical aspect of penetration testing. Weekly topics are constructed around the CompTIA PenTest+ and Certified Ethical Hacker (CEH) certifications. Topics include: Penetration Test Standards, Passive Reconnaissance, Active Reconnaissance, Exploiting Operating Systems, Exploiting Web Applications, Building Custom Exploits, Mobile Device Security, Networking Exploits, Physical Security, and Advanced Research Topics (i.e. SCADA, loT Pen Testing). Graduate students work on weekly assignments and build a semester-long written report to showcase how the different aspects of penetration testing integrate into a final deliverable to a customer. The students build their technical experience each week across multiple operating systems and web application. They document findings in a self-developed risk assessment report template built from open source industry risk assessment examples. The graduate students are expected to not only find weaknesses in systems while studying the different topics, they are asked to map findings to risk levels (e.g. MITRE's ATT&CK framework), missing controls (e.g. NIST 800–53 SP), and provide a remediation and/or mitigation discussion (e.g. mapping to Open Web Application Security Project (OWASP), MITRE's ATT&CK, etc.). The final deliverable for the course is a completed risk assessment encompassing each of the weekly topics, a presentation of their completed risk assessment, and a graduate research aspect on a specific tool in the aforementioned weekly pentest topics. This full report shares example projects from students who volunteered (n=7) to participate in our IRB-approved coursework study.
TEACHING WRITING THROUGH TELEGRAM BOT : THE IMPACT ON STUDENTS WRITING PERFORMANCE

The widespread use of mobile device technology and the release of new mobile device applications like Telegram offers a new innovation in integrating technology in language learning. Accordingly, this study intends to evaluate the Telegram BOT's efficacy in enhancing writing abilities at a high school in Palopo, South Sulawesi, Indonesia . It applied pre-experimental with a single group pre- and post-test design as its focal point. The population of this study is 36 first-grade students and took 14 students as the sample randomly. The data was analyzed by using SPSS 22 described statistically and evaluated. The result reveals that the use of telegram bot effective to enhance the students’ writing performance. It is proved that the student's mean score in post test (83, 79) was greater than in pre-test (64, 79) and the significance value (Sig. 2-tailed) is 0.000&lt;0.05. It indicated that using the Telegram BOT Application as a learning medium improves the writing performance. Students can learn independently via Telegram BOT because the material has been provided all with detailed instructions in one of its features called Bot API.
Resource Scheduling and Load Balancing Algorithms in Cloud Computing

A technology called cloud computing makes it possible to offer computing resources including storage, software, and services over the internet. Users can access these resources whenever they need them and just pay for what they actually use. There is a strict need to maintain track of all the resources which are being used by machines to perform certain activities such as scheduling of tasks. Resource scheduling and load balancing are important techniques used in modern computing systems to improve performance and resource utilization. Resource scheduling algorithms are used to allocate resources such as CPU, memory, and storage to different tasks or processes. Load balancing algorithms, on the other hand, distribute incoming traffic or workloads across multiple servers or resources to ensure that no single resource becomes overloaded. These techniques are essential for maintaining the stability and efficiency of large-scale systems, such as cloud computing environments, web services, and distributed systems. There are various kinds of load-balancing algorithms that differ in terms of their complexity, flexibility, and performance. Selecting the right algorithm for a specific system depends on the system's specific requirements and constraints. Overall, resource scheduling and load-balancing algorithms play a crucial role in ensuring the smooth operation of modern computing systems and are an active area of research and development.
Efficient information reconciliation for high-dimensional quantum key distribution using Cascade

The Information Reconciliation phase in quantum key distribution requires efficient methods to achieve high secret key rates. We explore this stage for high-dimensional implementations and introduce a simple modification of the Cascade algorithm that achieves efficiencies close to the Slepian-Wolf bound on q-ary symmetric channels.
Deep Learning Approach for Aspect-Based Sentiment Analysis on Indonesian Hospitals Reviews

Hospitals are individual health service facilities that provide inpatient and outpatient care, therefore, quality hospital services, facilities, and resources are a necessity and must be met by every hospital. To assess the quality of hospital services, facilities, and resources, we can see reviews from users through reviews available on Google Maps. Using natural language understanding, we can identify the sentiment associated with each particular aspect. In this research, we developed a deep learning model which can classify three aspects (services, human resources, and facilities) with four sentiments (positive, negative, neutral, and none) using a multiclass-multioutput deep learning model, ensuring both aspect and sentiment classification within a single model. Our research concludes that the BERT-LSTM model demonstrates promising performance with an F1-score of 0.597, but faces challenges in accurately classifying neutral sentiment across all aspects due to the lack of neutral sentiment labels.
Perspectives and Potentials of Open Data for the Sports Sciences

Abstract: Open Science practices have become well established in recent years. In this position paper, we argue that Open Data in particular holds great potential for empirical research in sports science, and sport and exercise psychology in particular, since it fosters the reintegration of scientific knowledge as primary research data in subsequent research life cycles. On that account, the sports science community has to develop a unified position on research data management, which supports the implementation of Open Science practices and standards. To this end, in this article we first define Open Science and research data management (RDM) and describe them in the context of sports science. We then present examples of existing, relevant RDM solutions, with a particular focus on sport and exercise psychology and neighboring disciplines. Finally, we derive perspectives for the development of a sustainable RDM structure and present current developments within the German sports science community.
Comparative Analysis of Various Face Detection and Tracking and Recognition Mechanisms using Machine and Deep Learning Methods

Recently, the security of individuals has become the prime concern for the human community. Various real-time security management systems are developed widely. Visual surveillance is considered as one of the promising technique to improve the security which helps to detect and recognize the objects. Numerous techniques have been developed for real-time video surveillance. Face detection, tracking and recognition is one of the important part of visual surveillance systems. The existing face detection schemes suffer from various challenging issues such as pose variations, illumination conditions and occlusion and many more. To overcome these issues, we have developed new schemes which includes Bayesian learning, Region Based Convolutional Neural Networks(RCNN) and GoogleNet based CNN model for face detection, tracking and recognition. In this work, we compare the performance of proposed schemes with existing schemes for different datasets to conclude the robustness of proposed approach.
THE ROLE OF FAIRY TALE IN THE FAMILY UPBRINGING

A fairy tale is an integral part of education and upbringing of the younger generation, because it helps a child to get information about the surrounding world, overcome certain psychological problems, develop spiritual and family values, humanistic and aesthetic feelings, etc. A fairy tale promotes the development of a child’s speech and imagination, reveals creative abilities and has a high educational potential. Vasyl Sukhomlynskyi, an outstanding Ukrainian educator noted, “A fairy tale is, figuratively speaking, a fresh wind that blows the fire of a child’s thought and speech. Children understand an idea only when it is embodied in vivid images” [2].
Commercial Bank IT Risk Evaluation Model Based on GA-BP Neural Network

To assess IT risk in commercial banks and reduce the probability of incidents, we constructed a set of IT risk evaluation indices based on determining the set of IT risk factors. To improve the accuracy of the evaluation model, the Genetic Algorithm (GA) was used to adjust the weights and thresholds of the BP neural network, thereby establishing a GABP-based IT risk evaluation model. By collecting data from a commercial bank for model testing, the IT risk evaluation was successfully implemented, and the weights of risk evaluation indicators were calculated. The correlation coefficient R values of the model’s training set, test set, validation set, and full sample set were greater than 0.95, demonstrating excellent predictive performance and effective IT risk evaluation. Based on the evaluation indicators, the weights of the indicators, and risk levels, commercial banks can develop corresponding targeted preventive and control measures. This comprehensive approach provides commercial banks with more reliable IT risk management and decision support.
TikTok and Vocabulary Learning: A Survey Study of Indonesian Students from English Departments

The present study intends to investigate the attitudes of English department students on the use of TikTok for English vocabulary learning. The participants of this study were 116 Indonesian students from the English Language Education and English Letters Departments from 26 different universities in 10 provinces. This study employed an online survey as the method of collecting data. It found that generally, the participants reported highly positive attitudes towards using TikTok for learning vocabulary. However, they seemed to have ambivalent attitudes towards the effectiveness of using the platform for vocabulary learning, perhaps attributed to the default nature of TikTok as a social media rather than a learning media. Informed by the findings, the study suggested teachers incorporate TikTok in skill-based and vocabulary-building classes in the first semesters of English department curricula. Limitations and contributions of this study are stated along with the suggested direction of future relevant studies in the field.
3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation

Regression-based methods for 3D human pose estimation directly predict the 3D pose parameters from a 2D image using deep networks. While achieving state-of-the-art performance on standard benchmarks, their performance degrades under occlusion. In contrast, optimization-based methods fit a parametric body model to 2D features in an iterative manner. The localized reconstruction loss can potentially make them robust to occlusion, but they suffer from the 2D-3D ambiguity. Motivated by the recent success of generative models in rigid object pose estimation, we propose 3D-aware Neural Body Fitting (3DNBF) - an approximate analysis-by-synthesis approach to 3D human pose estimation with SOTA performance and occlusion robustness. In particular, we propose a generative model of deep features based on a volumetric human representation with Gaussian ellipsoidal kernels emitting 3D pose-dependent feature vectors. The neural features are trained with contrastive learning to become 3D-aware and hence to overcome the 2D-3D ambiguity. Experiments show that 3DNBF outperforms other approaches on both occluded and standard benchmarks. Code is available at https://github.com/edz-o/3DNBF
Source-free Depth for Object Pop-out

Depth cues are known to be useful for visual perception. However, direct measurement of depth is often impracticable. Fortunately, though, modern learning-based methods offer promising depth maps by inference in the wild. In this work, we adapt such depth inference models for object segmentation using the objects’ "pop-out" prior in 3D. The "pop-out" is a simple composition prior that assumes objects reside on the background surface. Such compositional prior allows us to reason about objects in the 3D space. More specifically, we adapt the inferred depth maps such that objects can be localized using only 3D information. Such separation, however, requires knowledge about contact surface which we learn using the weak supervision of the segmentation mask. Our intermediate representation of contact surface, and thereby reasoning about objects purely in 3D, allows us to better transfer the depth knowledge into semantics. The proposed adaptation method uses only the depth model without needing the source data used for training, making the learning process efficient and practical. Our experiments on eight datasets of two challenging tasks, namely salient object detection and camouflaged object detection, consistently demonstrate the benefit of our method in terms of both performance and generalizability. The source code is publicly available at https://github.com/Zongwei97/PopNet.
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation

Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels. However, most approaches struggle to produce accurate localization maps and suffer from false predictions in class-related backgrounds (i.e., biased objects), such as detecting a railroad with the train class. Recent methods that remove biased objects require additional supervision for manually identifying biased objects for each problematic class and collecting their datasets by reviewing predictions, limiting their applicability to the real-world dataset with multiple labels and complex relationships for biasing. Following the first observation that biased features can be separated and eliminated by matching biased objects with backgrounds in the same dataset, we propose a fully-automatic/model-agnostic biased removal framework called MARS (Model-Agnostic biased object Removal without additional Supervision), which utilizes semantically consistent features of an unsupervised technique to eliminate biased objects in pseudo labels. Surprisingly, we show that MARS achieves new state-of-the-art results on two popular benchmarks, PASCAL VOC 2012 (val: 77.7%, test: 77.2%) and MS COCO 2014 (val: 49.4%), by consistently improving the performance of various WSSS models by at least 30% without additional supervision. Code is available at https://github.com/shjo-april/MARS.
Self-supervised Monocular Depth Estimation: Let’s Talk About The Weather

Current, self-supervised depth estimation architectures rely on clear and sunny weather scenes to train deep neural networks. However, in many locations, this assumption is too strong. For example in the UK (2021), 149 days consisted of rain. For these architectures to be effective in real-world applications, we must create models that can generalise to all weather conditions, times of the day and image qualities. Using a combination of computer graphics and generative models, one can augment existing sunny-weather data in a variety of ways that simulate adverse weather effects. While it is tempting to use such data augmentations for self-supervised depth, in the past this was shown to degrade performance instead of improving it. In this paper, we put forward a method that uses augmentations to remedy this problem. By exploiting the correspondence between unaugmented and augmented data we introduce a pseudo-supervised loss for both depth and pose estimation. This brings back some of the benefits of supervised learning while still not requiring any labels. We also make a series of practical recommendations which collectively offer a reliable, efficient framework for weather-related augmentation of self-supervised depth from monocular video. We present extensive testing to show that our method, Robust-Depth, achieves SotA performance on the KITTI dataset while significantly surpassing SotA on challenging, adverse condition data such as DrivingStereo, Foggy CityScape and NuScenes-Night. The project website can be found at https://kieran514.github.io/Robust-Depth-Project/.
E2E-LOAD: End-to-End Long-form Online Action Detection

Recently, feature-based methods for Online Action Detection (OAD) have been gaining traction. However, these methods are constrained by their fixed backbone design, which fails to leverage the potential benefits of a trainable backbone. This paper introduces an end-to-end learning network that revises these approaches, incorporating a backbone network design that improves effectiveness and efficiency. Our proposed model utilizes a shared initial spatial model for all frames and maintains an extended sequence cache, which enables low-cost inference. We promote an asymmetric spatiotemporal model that caters to long-form and short-form modeling. Additionally, we propose an innovative and efficient inference mechanism that accelerates extensive spatiotemporal exploration. Through comprehensive ablation studies and experiments, we validate the performance and efficiency of our proposed method. Remarkably, we achieve an end-to-end learning OAD of 17.3 (+12.6) FPS with 72.4% (+1.2%), 90.3% (+0.7%), and 48.1% (+26.0%) mAP on THMOUS’14, TVSeries, and HDD, respectively. The source code is available at https://github.com/sqiangcao99/E2E-LOAD.
The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation

Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset, without accessing the actual source data. The previous approaches have attempted to address SFVUDA by leveraging self-supervision (e.g., enforcing temporal consistency) derived from the target data itself. In this work, we take an orthogonal approach by exploiting "web-supervision" from Large Language-Vision Models (LLVMs), driven by the rationale that LLVMs contain a rich world prior surprisingly robust to domain-shift. We showcase the unreasonable effectiveness of integrating LLVMs for SFVUDA by devising an intuitive and parameter-efficient method, which we name Domain Adaptation with Large Language-Vision models (DALL-V), that distills the world prior and complementary source model information into a student network tailored for the target. Despite the simplicity, DALL-V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> achieves significant improvement over state-of-the-art SFVUDA methods.
HandR<sup>2</sup>N<sup>2</sup>: Iterative 3D Hand Pose Estimation Using a Residual Recurrent Neural Network

3D hand pose estimation is a critical task in various human-computer interaction applications. Numerous deep learning based estimation models in this domain have been actively explored. However, the existing models follow a non-recurrent scheme and thus require complex architectures or redundant parameters in order to achieve acceptable model capacity. To tackle this limitation, this paper proposes HandR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a compact neural network that iteratively regresses the hand pose using a novel residual recurrent unit. The recurrent design allows recursive exploitation of partial layers to gradually optimize previously estimated joint locations. In addition, we exploit graph reasoning to capture kinematic dependencies between joints for better performance. Experimental results show that the proposed model significantly outperforms the existing methods on three hand pose benchmark datasets in terms of both accuracy and efficiency. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandR2N2.
Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study

Membership inference attacks (MIAs) aim to infer whether a data point has been used to train a machine learning model. These attacks can be employed to identify potential privacy vulnerabilities and detect unauthorized use of personal data. While MIAs have been traditionally studied for simple classification models, recent advancements in multi-modal pre-training, such as CLIP, have demonstrated remarkable zero-shot performance across a range of computer vision tasks. However, the sheer scale of data and models presents significant computational challenges for performing the attacks.This paper takes a first step towards developing practical MIAs against large-scale multi-modal models. We introduce a simple baseline strategy by thresholding the cosine similarity between text and image features of a target point and propose further enhancing the baseline by aggregating cosine similarity across transformations of the target. We also present a new weakly supervised attack method that leverages ground-truth non-members (e.g., obtained by using the publication date of a target model and the timestamps of the open data) to further enhance the attack. Our evaluation shows that CLIP models are susceptible to our attack strategies, with our simple baseline achieving over 75% membership identification accuracy. Furthermore, our enhanced attacks outperform the baseline across multiple models and datasets, with the weakly supervised attack demonstrating an average-case performance improvement of 17% and being at least 7X more effective at low false-positive rates. These findings highlight the importance of protecting the privacy of multi-modal foundational models, which were previously assumed to be less susceptible to MIAs due to less overfitting. Our code is available at https://github.com/ruoxi-jia-group/CLIP-MIA.