Reviewerly/exHarmony huggingface dataset:
SoK: A Survey of Open-Source Threat Emulators

Threat emulators are tools or sets of scripts that emulate cyber attacks or malicious behavior. They can be used to create and launch single procedure attacks and multi-step attacks; the resulting attacks may be known or unknown cyber attacks. The motivation for using threat emulators varies and includes the need to perform automated security audits in organizations or reduce the size of red teams in order to lower pen testing costs; or the desire to create baseline tests for security tools under development or supply pen testers with another tool in their arsenal. In this paper, we review and compare various open-source threat emulators. We focus on tactics and techniques from the MITRE ATT&CK Enterprise matrix and determine whether they can be performed and tested with the emulators. We develop a comprehensive methodology for our qualitative and quantitative comparison of threat emulators with respect to general features, such as prerequisites, attack definition, cleanup, and more. Finally, we discuss the circumstances in which one threat emulator is preferred over another. This survey can help security teams, security developers, and product deployment teams examine their network environment or products with the most suitable threat emulator. Using the guidelines provided, a team can select the threat emulator that best meets their needs without evaluating all of them.
Automatic Differentiation Variational Inference with Mixtures

Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning. Generally approximate posteriors learned by ADVI are forced to be unimodal in order to facilitate use of the reparameterization trick. In this paper, we show how stratified sampling may be used to enable mixture distributions as the approximate posterior, and derive a new lower bound on the evidence analogous to the importance weighted autoencoder (IWAE). We show that this "SIWAE" is a tighter bound than both IWAE and the traditional ELBO, both of which are special instances of this bound. We verify empirically that the traditional ELBO objective disfavors the presence of multimodal posterior distributions and may therefore not be able to fully capture structure in the latent space. Our experiments show that using the SIWAE objective allows the encoder to learn more complex distributions which regularly contain multimodality, resulting in higher accuracy and better calibration in the presence of incomplete, limited, or corrupted data.
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification

In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it pro-vides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages. Third, for the very first time, it as-sesses the performance of various deep learning based methodologies for Urdu text document classification. In this regard, for experimentation, we adapt 10 deep learning classification methodologies which have pro-duced best performance figures for English text classification. Fourth, it also investigates the performance impact of transfer learning by utiliz-ing Bidirectional Encoder Representations from Transformers approach for Urdu language. Fifth, it evaluates the integrity of a hybrid approach which combines traditional machine learning based feature engineering and deep learning based automated feature engineering. Experimental results show that feature selection approach named as Normalised Dif-ference Measure along with Support Vector Machine outshines state-of-the-art performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and CLE Urdu Digest 1Million with a significant margin of 32%, and 13% respectively. Across all three datasets, Normalised Differ-ence Measure outperforms other filter based feature selection algorithms as it significantly uplifts the performance of all adopted machine learning, deep learning, and hybrid approaches. The source code and presented dataset are available at Github repository.
Approximation of noisy data using multivariate splines and finite element methods

We compare a recently proposed multivariate spline based on mixed partial derivatives with two other standard splines for the scattered data smoothing problem. The splines are defined as the minimiser of a penalised least squares functional. The penalties are based on partial differentiation operators, and are integrated using the finite element method. We compare three methods to two problems: to remove the mixture of Gaussian and impulsive noise from an image, and to recover a continuous function from a set of noisy observations.
Performance Optimization for Edge-Cloud Serverless Platforms via Dynamic Task Placement

We present a framework for performance optimization in serverless edge-cloud platforms using dynamic task placement. We focus on applications for smart edge devices, for example, smart cameras or speakers, that need to perform processing tasks on input data in real to near-real time. Our framework allows the user to specify cost and latency requirements for each application task, and for each input, it determines whether to execute the task on the edge device or in the cloud. Further, for cloud executions, the framework identifies the container resource configuration needed to satisfy the performance goals. We have evaluated our framework in simulation using measurements collected from serverless applications in AWS Lambda and AWS Greengrass. In addition, we have implemented a prototype of our framework that runs in these same platforms. In experiments with our prototype, our models can predict average end-to-end latency with less than 6% error, and we obtain almost three orders of magnitude reduction in end-to-end latency compared to edge-only execution.
Iterative Averaging in the Quest for Best Test Error

We analyse and explain the increased generalisation performance of iterate averaging using a Gaussian process perturbation model between the true and batch risk surface on the high dimensional quadratic. We derive three phenomena \latestEdits{from our theoretical results:} (1) The importance of combining iterate averaging (IA) with large learning rates and regularisation for improved regularisation. (2) Justification for less frequent averaging. (3) That we expect adaptive gradient methods to work equally well, or better, with iterate averaging than their non-adaptive counterparts. Inspired by these results\latestEdits{, together with} empirical investigations of the importance of appropriate regularisation for the solution diversity of the iterates, we propose two adaptive algorithms with iterate averaging. These give significantly better results compared to stochastic gradient descent (SGD), require less tuning and do not require early stopping or validation set monitoring. We showcase the efficacy of our approach on the CIFAR-10/100, ImageNet and Penn Treebank datasets on a variety of modern and classical network architectures.
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems

Face modification systems using deep learning have become increasingly powerful and accessible. Given images of a person's face, such systems can generate new images of that same person under different expressions and poses. Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined Deepfakes. In order to prevent a malicious user from generating modified images of a person without their consent we tackle the new problem of generating adversarial attacks against such image translation systems, which disrupt the resulting output image. We call this problem disrupting deepfakes. Most image translation architectures are generative models conditioned on an attribute (e.g. put a smile on this person's face). We are first to propose and successfully apply (1) class transferable adversarial attacks that generalize to different classes, which means that the attacker does not need to have knowledge about the conditioning class, and (2) adversarial training for generative adversarial networks (GANs) as a first step towards robust image translation networks. Finally, in gray-box scenarios, blurring can mount a successful defense against disruption. We present a spread-spectrum adversarial attack, which evades blur defenses. Our open-source code can be found at https://github.com/natanielruiz/disrupting-deepfakes.
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks

In Bayesian Deep Learning, distributions over the output of classification neural networks are often approximated by first constructing a Gaussian distribution over the weights, then sampling from it to receive a distribution over the softmax outputs. This is costly. We reconsider old work (Laplace Bridge) to construct a Dirichlet approximation of this softmax output distribution, which yields an analytic map between Gaussian distributions in logit space and Dirichlet distributions (the conjugate prior to the Categorical distribution) in the output space. Importantly, the vanilla Laplace Bridge comes with certain limitations. We analyze those and suggest a simple solution that compares favorably to other commonly used estimates of the softmax-Gaussian integral. We demonstrate that the resulting Dirichlet distribution has multiple advantages, in particular, more efficient computation of the uncertainty estimate and scaling to large datasets and networks like ImageNet and DenseNet. We further demonstrate the usefulness of this Dirichlet approximation by using it to construct a lightweight uncertainty-aware output ranking for ImageNet.
Using Image Captions and Multitask Learning for Recommending Query Reformulations

Interactive search sessions often contain multiple queries, where the user submits a reformulated version of the previous query in response to the original results. We aim to enhance the query recommendation experience for a commercial image search engine. Our proposed methodology incorporates current state-of-the-art practices from relevant literature -- the use of generation-based sequence-to-sequence models that capture session context, and a multitask architecture that simultaneously optimizes the ranking of results. We extend this setup by driving the learning of such a model with captions of clicked images as the target, instead of using the subsequent query within the session. Since these captions tend to be linguistically richer, the reformulation mechanism can be seen as assistance to construct more descriptive queries. In addition, via the use of a pairwise loss for the secondary ranking task, we show that the generated reformulations are more diverse.
A Study on Multimodal and Interactive Explanations for Visual Question Answering

Explainability and interpretability of AI models is an essential factor affecting the safety of AI. While various explainable AI (XAI) approaches aim at mitigating the lack of transparency in deep networks, the evidence of the effectiveness of these approaches in improving usability, trust, and understanding of AI systems are still missing. We evaluate multimodal explanations in the setting of a Visual Question Answering (VQA) task, by asking users to predict the response accuracy of a VQA agent with and without explanations. We use between-subjects and within-subjects experiments to probe explanation effectiveness in terms of improving user prediction accuracy, confidence, and reliance, among other factors. The results indicate that the explanations help improve human prediction accuracy, especially in trials when the VQA system's answer is inaccurate. Furthermore, we introduce active attention, a novel method for evaluating causal attentional effects through intervention by editing attention maps. User explanation ratings are strongly correlated with human prediction accuracy and suggest the efficacy of these explanations in human-machine AI collaboration tasks.
Learning More in Vehicle Re-Identification: Joint Local Blur Transformation and Adversarial Network Optimization

Vehicle re-identification (ReID) tasks are an important part of smart cities and are widely used in public security. It is extremely challenging because vehicles with different identities are generated from a uniform pipeline and cannot be distinguished based only on the subtle differences in their characteristics. To enhance the network’s ability to handle the diversity of samples in order to adapt to the changing external environment, we propose a novel data augmentation method to improve its performance. Our deep learning framework mainly consists of a local blur transformation and a transformation adversarial module. In particular, we first use a random selection algorithm to find a local region of interest in an image sample. Then, the parameter generator network, a lightweight convolutional neural network, is responsible for generating four weights and then as a basis to form a filter matrix for further blur transformations. Finally, an adversarial module is employed to ensure that as much noise information as possible is added to the image sample while preserving the structure of the training datasets. Furthermore, by updating the parameter generator network, the adversarial module can help produce more appropriate and harder training samples and lead to improving the framework’s performance. Extensive experiments on datasets, i.e., VeRi-776, VehicleID, and VERI-Wild, show that our method is superior to the state-of-the-art methods.
COUNTERING BULLYING IN SCHOOL ENVIRONMENT

&#x0D; In this study, an analysis of the problem of bullying in secondary schools has been carried out. To do so, comparison, generalization, observation, systemic analysis, theoretical cognition, and analogy methods are utilized. The necessity of pedagogical reacting to the stated problem has been proved. The authors present guidelines for countering the problem of bullying. The importance of humanization of relationships at school, beginning with leadership styles, goal-oriented formation of optimistic positions, and positive thinking of schoolchildren and teachers are emphasized. Particular attention is paid while working with the problem of bullying to the method of reliance on the strengths of an individual and the formation of its leadership qualities. The main components of socially significant knowledge: socio-psychological, socio-legal, and social-integrative, have been outlined. The formation of this knowledge will allow a person to avoid bullying or respond to it at the right moment. The need for a timely diagnosis of bullying has been accentuated.
Elicitation of Needs Using User Personas to Improve Software User Experience

In the development of a software, the elicitation of requirements in the software development process is a very important phase because at this stage it will determine how a system is made to work properly and according to the plan. User Persona is an HCI technique that works by gathering information about a user to understand the characteristics of that user. The purpose of this research is to solve the problem of uploading practicum modules which are sometimes late so that students or practitioners lack time to study and complete the given practicum assignments. The use of user personas applied at the elicitation stage of software requirements facilitates the needs analysis stage so that it is easier to understand what needs to be developed from the software according to the needs of the user.
The Privacy Limits of Transacting in Bitcoin

The Bitcoin blockchain, a prime example of disruptive technology, has fundamentally altered the way various industries approach remote transactions. Bitcoin grants privacy to its users by anonymizing public keys, provides autonomy by eliminating the need for trusted third parties, and maintains transparency through its public disclosure protocol. Bitcoin is an innovative manifestation of the Fourth Amendment ideals of security and autonomy. It is, thus, no surprise that the Bitcoin blockchain presents unprecedented Fourth Amendment challenges for courts to consider.&#x0D; In United States v. Gratkowski, the Fifth Circuit addressed the novel issue of whether Fourth Amendment protections extend to an individual’s Bitcoin transactions. Notably, the court was the first to find that an individual does not have a privacy interest in their information located directly on the Bitcoin blockchain. However, the Fifth Circuit applied inconsistent and flawed reasoning in reaching this decision, demonstrating a fundamental misunderstanding of Bitcoin and its users.&#x0D; Accordingly, this Note argues that the Gratkowski decision should be applied narrowly and with caution, especially considering the Supreme Court’s warnings against the incompatibility of current Fourth Amendment doctrine with the digital age. It then suggests implementing a modified reasonable expectation-of-privacy standard, supplementing the current standard with an additional inquiry into what information an individual disclosed when initiating a transaction. This modified standard would preserve the integrity of Bitcoin, while simultaneously articulating a proper framework for assessing privacy concerns in the context of Bitcoin and blockchain technology.
Improving Differentiable Neural Architecture Search with Sparse Connections and Model Pruning

Neural networks have contributed to many breakthroughs across several disciplines. Their ease of use and scalability have motivated the development of many techniques in computer vision, natural language processing, audio analysis, etc. The neural network architecture plays a dominant role in its performance, and there have been many advances on designs and strategies for defining efficient neural networks. However, manually tuning neural architectures requires a significant amount of time and expert knowledge. To overcome the difficulty of manually setting up the architecture for a neural network, Neural Architecture Search (NAS) has gained popularity. NAS methods involve three general dimensions, namely search space, search strategies, and performance estimation strategies [1]. Different approaches vary in these dimensions.
FEATURE MATCHING ENHANCEMENT USING THE GRAPH NEURAL NETWORK (GNN-RANSAC)

Abstract. Improving the performance of feature matching plays a key role in computers vision and photogrammetry applications, such as fast image recognition, Structure from Motion (SFM), aerial triangulation, Visual Simultaneous Localization and Mapping (VSLAM), etc., where the RANSAC algorithm is frequently used for outlier detection; note that RANSAC is the most widely used robust approach in photogrammetry and computer vision for outlier detection. It is known that the outlier ratio used in RANSAC primarily determines the number of trial runs needed, which eventually, determines the computation time. Over time, different methods have been proposed to reject the false-positive correspondences and improve RANSAC, such as GR_RANSAC, SuperGlue, and LPRANSAC. The specific objective of this study is to propose a filtering algorithm based on Graph Neural Networks (GNN), as a pre-processing step before RANSAC, which can result in improvements for rejecting the outliers. The research is based on the idea that descriptors of corresponding points, as well as their spatial relationship, should be similar in image sequences. In graph representation, built by the adjacency matrix of data (nodes features), there should be similarity for corresponding points that are close to each other in the image domain. From the many GNNs techniques, Graph Attention Networks (GATs) were selected for this study as they assign different importance to each neighbour’s contribution as anisotropic operations, so the features of neighbour nodes are not considered in the same way, unlike other GNNs techniques. In our approach, we build a graph in each image, because the similarity of the two-dimensional spatial relationships between points in the image domain of consecutive images should be similar. Then during processing, points with any significantly different neighbours are considered as outliers. Next, the points can be updated in the GNN layer. GNN-RANSAC is tested experimentally on real image pairs. Clearly, the proposed pre-filtering increases the inlier ratio and results in faster convergence compared to ordinary RANSAC, making it attractive for real-time applications. Furthermore, there is no need to learn the features.
Intellectual capital within the framework of governance and its impact on university performance, a case study of universities in the Sulaymaniyah of Iraq

Abstract - The research aimed to measure and evaluate the impact of intellectual capital and governance on organizational performance for a sample of universities in Sulaymaniyah. Where intellectual capital is considered one of the most important assets of the organization, it also helps in adding a competitive advantage and investing these assets in light of the mechanisms and principles of governance, which is reflected in achieving high levels of performance and providing services with high quality. In order to achieve the objectives of the research and test its hypotheses, the primary and secondary data were relied upon and a questionnaire was designed. The two researchers used the statistical program (SPSS) to analyze the data. During the research, it was found that there is a significant effect of intellectual capital on improving performance and raising the efficiency of organizations through the application of governance rules and the exploitation of intellectual resources and assets. The two researchers also made a set of recommendations, the most important of which is to help teachers overcome weaknesses in their performance and enhance strengths by urging the research organizations to develop the capabilities of teachers and improve their performance through their participation in conferences, scientific seminars, and efficient and distinguished training programs
Attention based Recurrent Neural Network for Nepali Text Summarization

Automatic text summarization has been a challenging topic in natural language processing (NLP) as it demands preserving important information while summarizing the large text into a summary. Extractive and abstractive text summarization are widely investigated approaches for text summarization. In extractive summarization, the important sentence from the large text is extracted and combined to create a summary whereas abstractive summarization creates a summary that is more focused on meaning, rather than content. Therefore, abstractive summarization gained more attention from researchers in the recent past. However, text summarization is still an untouched topic in the Nepali language. To this end, we proposed an abstractive text summarization for Nepali text. Here, we, first, create a Nepali text dataset by scraping Nepali news from the online news portals. Second, we design a deep learning-based text summarization model based on an encoder-decoder recurrent neural network with attention. More precisely, Long Short-Term Memory (LSTM) cells are used in the encoder and decoder layer. Third, we build nine different models by selecting various hyper-parameters such as the number of hidden layers and the number of nodes. Finally, we report the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score for each model to evaluate their performance. Among nine different models created by adjusting different numbers of layers and hidden states, the model with a single-layer encoder and 256 hidden states outperformed all other models with F-Score values of 15.74, 3.29, and 15.21 for ROUGE-1 ROUGE-2 and ROUGE-L, respectively.