Reviewerly/exHarmony huggingface dataset:
Threat Modeling in Cloud Computing - A Literature Review

Cloud computing has significantly changed the operational models of companies. This adoption has consequently caused impact on security, resulting in a wider attack surface. Due to the diverse deployment models of the cloud computing architecture, securing the environment has become a challenging task. This paper provides a narrative review of threat modeling approaches in cloud computing. It seeks to identify research challenges and gaps that new research potentially needs to address. It reviews 10 recent related studies and identifies two main types of approaches. Findings show that the next-generation threat modeling needs to introduce more formal methodologies, including a quality assessment of the threat modeling process and its output. Furthermore, automation-enabled methods are vital for advancing the threat modeling process and enabling live integration with cyber threat intelligence for developing threat identification, management, and mitigation.
Self-Organizing Maps in High Energy Physics

Abstract The Self-Organizing-Map (SOM) is a widely used neural network for dimensional reduction and clustering. It has yet to find its use in high energy physics. This paper discusses two applications of SOM: first, we find map regions with a high relative content of a rare process ( H → WW *). Second we obtain Monte Carlo normalization factors for different physics processes by fitting the dimensionally reduced representation. Analysis and training are performed on ATLAS open data.
An Adversarial Sample Defense Method Based on Saliency Information

In recent years, the development of deep neural networks is in full gear in the fields of computer vision, natural language processing, and others. However, the existence of adversarial examples brings risks to these tasks, which is also a huge obstacle to implement deep learning applications in the real world. In order to solve the aforementioned problems and improve the robustness of neural networks, a novel defense network based on generative adversarial networks (GANs) and saliency information is proposed. First, the generator is utilized to eliminate disturbances of adversarial samples and clean samples, at the same time, the distance between these two distributions is minimized by loss function. Then, salient feature extraction model is used to extract salient maps of both clean examples and adversarial samples, thus improving the denoising effect of the generator by reducing the difference between salient maps. The proposed method can guide the generation networks to accurately remove the invisible disturbance and to restore the adversarial samples to clean samples, which not only improves the success rate of classification but also achieves the defense effect. Extensive experiments are conducted to compare the defense effect of our proposed method with other defense methods against various attacks. Experimental results show that our method has strong defensive capabilities against these attack methods.
Pyrate: a novel system for data transformations, reconstruction and analysis

Abstract The Pyrate framework provides a dynamic, versatile, and memory-efficient approach to data format transformations, object reconstruction and data analysis in particle physics. The system is implemented with the Python programming language, allowing easy access to the scientific Python ecosystem and commodity big data technologies. Developed within the context of the SABRE South experiment for dark matter direct detection, Pyrate relies on a blackboard design pattern where algorithmic trees are dynamically generated throughout a run where root nodes are managed by a central control unit. The system guarantees an economical usage of memory allocated by algorithms where individual algorithmic instances can be reused for multiple objects. The framework is intended to improve upon the user experience, portability and scalability of offline software systems currently available in the particle physics community with particular attention to medium to small-scale experiments.
Anomaly detection of I/O behaviours in HEP computing cluster based on unsupervised machine learning

Abstract Problematic I/O pattern is the major cause of low efficient HEP jobs. In a cluster with tens of thousands of job slots, locating the source of an anomalous workload is a nontrivial task. Automatic anomaly detection can largely shorten the recovery time of these situations and reduce manpower for problem diagnoses. This paper provides a data-driven approach to solve this problem. We design and implement an anomaly detection system based on Isolation Forest, a very efficient and scalable machine learning algorithm for spatial anomaly detection in high dimension space. Historical monitoring I/O patterns collected from the Lustre file system provides a sufficient statistical basis for model training and updating. Routine model updates and job tagging ensures adaptability and promptness. Web-based visualization and sorting tools facilitate the validation of model prediction. With the detection system, time spent on tracing the source of a problematic workload can be reduced from hours to minutes.
Inference of astrophysical parameters with a conditional invertible neural network

Abstract Conditional Invertible Neural Networks (cINNs) provide a new technique for the inference of free model parameters by enabling the creation of posterior distributions. With these distributions, the mean parameter values, their uncertainties and the correlations between the parameters can be estimated. In this contribution, we summarize the functionality of cINNs, which are based on normalizing flows, and present the application of this new method to a scenario from astroparticle physics. We show that it is possible to constrain properties of the currently unknown sources of ultra-high-energy cosmic rays and compare the posterior distributions obtained with the network to those acquired using the classic Markov Chain Monte Carlo method.
Encryption Proxies in a Confidential Computing Environment

With the increasing adoption of cloud native applications, security and privacy are among the pressing concerns hindering a wider adoption of cloud services. One of the main challenges in this context is providing reliable protection of customer data from unauthorized usage, including the cases when adversary gains root access to host systems. Using transparent data encryption, tokenizataion and pseudonymization by deploying an encryption proxy between the end user and cloud provider are some of the established approaches to protect sensitive data in the cloud. In this paper, we extend the advantages of an encryption proxy by deploying and securing it using a shielded execution framework based on Confidential Computing. Our Proof of Concept (PoC) guarantees the necessary security requirements needed to run in an untrusted computing infrastructure. The experimental evaluations show that the PoC achieves reasonable performance results while providing strong security properties with small Trusted Computing Base.
COLORS ANALYSIS AND TECHNOLOGICAL ERRORS IDENTIFIED IN PRINTING OF ROMANIAN POSTAL STAMPS

Papers of value such as banknotes, stamps, stocks etc. are typographical products which are executed using special technologies which should ensure their protection against forgery. In order to verify their security elements, a digital comparative instrument has been designed and manufactured. This contains two digital microscopes mounted in parallel which can move simultaneously on the Oz axis and an adjustable and graded support for positioning the samples on the Ox and the Oy axes. The microscopes are calibrated from an optical and spectral standpoint at the beginning of the study based on the imposed tolerances. The visual field of the microscopes is illuminated in a controlled manner according to a standard light source, to properly ensure the correctness and consistency of the measurements. In this article, the experimental devise is used to record and analyze comparatively the colors and dimensions of the Romanian postal stamps to identify the technological printing errors.
CLUE: a clustering algorithm for current and future experiments

Abstract CLUE (CLUstering of Energy) is a fast parallel clustering algorithm for High Granularity Calorimeters in High Energy Physics. In these types of detectors, the standard clustering algorithms using combinatorics are expected to fail due to large number of digitized energy deposits (hits) in the reconstruction stage bringing to a consequent memory/timing explosion. This innovative algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. Initially CLUE was developed in a standalone repository that allows performance benchmarking with respect to its CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing. CLUE has been successfully used in simulation and beam tests of the High Granularity Calorimeter to be installed for the upgrade of the CMS detector in Phase-2 of the HL-LHC. Recently CLUE was also imported in the key4hep framework and first results will be shown for detectors proposed in future collider projects.
Game-Theoretic Model for the Interaction of the Online Course Design Process Participants within the University

The market of digital educational resources and, in particular, online courses for the higher education system is actively developing. A significant role in creating a high-quality competitive online course is played by the organizational and legal system of interaction between authors and the university. This research article aims at developing a game-theoretical model of interaction between the participants of online course creating process in order to identify optimal (mutually beneficial) options and conditions for partnership interaction between the authors of online courses and the university. To represent the interaction between the authors and the university in terms of game theory, there are analyzed the specific features of creating an online course. The authors are the first to construct a mathematical game model for a typical situation of commercializing an online course within the university-and-author interaction. This situation is interpreted as a dynamic finite paired non-antagonistic non-cooperative game with complete perfect information. The Nash equilibrium, perfect on subgames, is found by the method of backward induction; the profile of optimal strategies is determined. The analysis of the results can help to substantiate, form and improve the strategy of mutually beneficial cooperation between the authors of the online course and the university.
Data Augmentation Method for Pedestrian Dress Recognition in Road Monitoring and Pedestrian Multiple Information Recognition Model

Road intelligence monitoring is an inevitable trend of urban intelligence, and clothing information is the main factor to identify pedestrians. Therefore, this paper establishes a multi-information clothing recognition model and proposes a data augmentation method based on road monitoring. First, we use Mask R-CNN to detect the clothing category information in the monitoring; then, we transfer the mask to the k-means cluster to obtain the color and finally obtain the clothing color and category. However, the monitoring scene and dataset are quite different, so a data augmentation method suitable for road monitoring is designed to improve the recognition ability of small targets and occluded targets. The small target mAP (mean average precision) recognition ability is improved by 12.37% (from 30.37%). The method of this study can help find relevant passers-by in the actual monitoring scene, which is conducive to the intelligent development of the city.
A Novel Approach for Contextual Clustering and Retrieval of Behavior Trees to Enrich the Behavior of Social Intelligent Agents

Recently, many works have been carried out to find effective ways that can allow for plausibly effective interactions of social intelligent agents (SIAs) in unpredictable environments in a reasonable time. Behavior trees (BTs) allow for knowledge to be modeled as a graph representation and provide a way for SIAs to effectively interact with the received information. BTs can store past social experiences that can then be used by SIAs to provide adequate human-like interactions when facing new social situations (query). One challenge appears when a social agent with vast past experiences—represented as a forest of BTs—tries to retrieve a similar BT to learn from in order to provide plausible interactions in the current situation in a cost-effective manner. Cognitive scripts with their inherent temporal structure can address this challenge where they can facilitate the use of contextual retrieval techniques on BTs represented as cognitive scripts. This paper introduces novel hybrid retrieval techniques that use agglomerative hierarchical clustering (H-clustering) and similarity-based algorithms: map-and-reduce and least common parent (LCP) to effectively retrieve similar BTs to a specific query BT in a reasonable time. The model groups BTs, represented as cognitive scripts, into compact clusters that can then be used to retrieve the most similar BT to a query one in real time without noticeable delay. A comparison was done between the performance of the proposed hybrid-retrieval techniques using a semi-structured dataset of cognitive scripts. The results showed that H-clustering-map-and-reduce is more cost-effective than H-clustering-LCP as it allowed for a low average retrieval time of 8 × 10−3 s compared to 3.1 s, respectively.
Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model

This work presents a Bayesian statistical approach to the saliency map estimation problem. More specifically, we formalize the saliency map estimation issue in the fully automatic Markovian framework. The major and original contribution of the proposed Bayesian–Markov model resides in the exploitation of a pixel pairwise modeling and a likelihood model based on a parametric mixture of two different class-conditional likelihood distributions whose parameters are adaptively and previously estimated for each image. This allows us to adapt our saliency estimation model to the specific characteristics of each image of the dataset and to provide a nearly parameter-free—hence dataset-independent—unsupervised saliency map estimation procedure. In our case, the parameters of the likelihood model are all estimated under the principles of the iterative conditional estimation framework. Once the estimation step is completed, the MPM (maximum posterior marginal) solution of the saliency map (which we show as particularly suitable for this type of estimation), is then estimated by a stochastic sampling scheme approximating the posterior distribution (whose parameters were previously estimated). This unsupervised data-driven Markovian framework overcomes the limitations of current ad hoc or supervised energy-based or Markovian models that often involve many parameters to adapt and that are finely tuned for each different benchmark database. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods and turns out to be particularly stable across a wide variety of benchmark datasets.
On the Exact Evaluation of Integrals of Wavelets

Wavelet expansions are a powerful tool for constructing adaptive approximations. For this reason, they find applications in a variety of fields, from signal processing to approximation theory. Wavelets are usually derived from refinable functions, which are the solution of a recursive functional equation called the refinement equation. The analytical expression of refinable functions is known in only a few cases, so if we need to evaluate refinable functions we can make use only of the refinement equation. This is also true for the evaluation of their derivatives and integrals. In this paper, we detail a procedure for computing integrals of wavelet products exactly, up to machine precision. The efficient and accurate evaluation of these integrals is particularly required for the computation of the connection coefficients in the wavelet Galerkin method. We show the effectiveness of the procedure by evaluating the integrals of pseudo-splines.
Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors (FPHD). The design uses the hash method to quickly extract entity features, and uses a deep neural network to learn the implicit association between entity features. This design solves two main problems in large-scale dynamic data addition: (1) The linear growth of the size of the embedded vector table and the size of the vocabulary table leads to huge memory consumption. (2) It is difficult to deal with the problem of adding new entities to the retraining model. Finally, taking the movie data as an example, this paper introduces the encoding method and the specific algorithm flow in detail, and realizes the effect of rapid reuse of dynamic addition data model. Compared with three existing embedding algorithms that can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has significantly improved in time complexity and space complexity.
Relationships between human resource management practices, employee satisfaction, service quality, and employee service behavior in the hotel industry

The hotel industry is critical in developing the economy. Moreover, it is the largest and most rapidly growing industry in Jordan. Employee satisfaction is a crucial element for the success of any organization, particularly in the hotel industry. Therefore, this study aims to examine the influence of human resource management (HRM) practices since these constructs could influence different outcomes at the workplace, such as employee satisfaction, service quality, and employee service behavior in the hotel industry. The study has undertaken five primary HRM practices – recruitment, capability, compensation, performance appraisal, and training and development (T&amp;amp;amp;D) – to measure their impact on employee satisfaction, service quality, and employee service behavior. Data were collected from 290 employees and 290 customers of Jordan’s hotels across all categories in the four main tourist attractions: Amman, Petra, Aqaba, and the Dead Sea. A quantitative approach was employed using various statistical tools such as mean, tabulation of data, correlation, and ANOVA by SPSS software. The results indicated that HRM practices positively affect service quality, employee service behavior, and employee satisfaction. It was also found that when employees in Jordan’s hotel industry demonstrate excellent service behavior, the customer perceptions of service quality increase. Finally, effective human resource management strategy systematically organizes all individual human resource management measures to directly influence employee satisfaction, service behavior, and service quality in a way that leads hotels to achieve organizational success.
Geo-Spatial Mapping of Hate Speech Prediction in Roman Urdu

Social media has transformed into a crucial channel for political expression. Twitter, especially, is a vital platform used to exchange political hate in Pakistan. Political hate speech affects the public image of politicians, targets their supporters, and hurts public sentiments. Hate speech is a controversial public speech that promotes violence toward a person or group based on specific characteristics. Although studies have been conducted to identify hate speech in European languages, Roman languages have yet to receive much attention. In this research work, we present the automatic detection of political hate speech in Roman Urdu. An exclusive political hate speech labeled dataset (RU-PHS) containing 5002 instances and city-level information has been developed. To overcome the vast lexical structure of Roman Urdu, we propose an algorithm for the lexical unification of Roman Urdu. Three vectorization techniques are developed: TF-IDF, word2vec, and fastText. A comparative analysis of the accuracy and time complexity of conventional machine learning models and fine-tuned neural networks using dense word representations is presented for classifying and predicting political hate speech. The results show that a random forest and the proposed feed-forward neural network achieve an accuracy of 93% using fastText word embedding to distinguish between neutral and politically offensive speech. The statistical information helps identify trends and patterns, and the hotspot and cluster analysis assist in pinpointing Punjab as a highly susceptible area in Pakistan in terms of political hate tweet generation.
AutoGAN: An Automated Human-Out-of-the-Loop Approach for Training Generative Adversarial Networks

Generative Adversarial Networks (GANs) have been used for many applications with overwhelming success. The training process of these models is complex, involving a zero-sum game between two neural networks trained in an adversarial manner. Thus, to use GANs, researchers and developers need to answer the question: “Is the GAN sufficiently trained?”. However, understanding when a GAN is well trained for a given problem is a challenging and laborious task that usually requires monitoring the training process and human intervention for assessing the quality of the GAN generated outcomes. Currently, there is no automatic mechanism for determining the required number of epochs that correspond to a well-trained GAN, allowing the training process to be safely stopped. In this paper, we propose AutoGAN, an algorithm that allows one to answer this question in a fully automatic manner with minimal human intervention, being applicable to different data modalities including imagery and tabular data. Through an extensive set of experiments, we show the clear advantage of our solution when compared against alternative methods, for a task where the GAN outputs are used as an oversampling method. Moreover, we show that AutoGAN not only determines a good stopping point for training the GAN, but it also allows one to run fewer training epochs to achieve a similar or better performance with the GAN outputs.
Kişiselleştirilmiş Haber Tavsiye Sistemi

Recommendation Systems are the methods that suggest the next choices of the user in a predictable way, based on the preferences made by the user before. This method is become even more popular nowadays and it can be applied to any topic or field that needs future estimation evaluating the data at hand. It is a kind of information extraction study. Furthermore, the fact that Amazon receives about 35% of its revenue from referral systems is an indication of how important this method is. However, news recommendation system which is a similar application area, is not also widely used as others. In this study, it is aimed to design a news recommendation system by taking into account the sites the user enters, the words that they searched for and bookmarks. The machine learning model has been trained with a data set that includes news categories and news content in order to present the news to the user as interested. By giving the data from the user environment to the trained model, the found interested categories of the user is processed instantly by the RSS. These news selected from RSS are shown to the user in order of priority regarding the daily news agenda. The real user test showed impressive accuracy as 89%. This solution presents a content-based recommendation system as nature of the problem.