Reviewerly/exHarmony huggingface dataset:
Hands-on Cyber Risk Management Scepticism

In a computer network, risk is understood by the security network engineer as exposing the corporate network to an activity/event with undesirable consequences. The approach proposed in this research is to analyse the cyber risk and then manage it. This paper presents the challenges needed to be undertaken when analysing risk from an organisational point of view and as well as the steps needed to be scrutinised in terms of protection from the IT perspective. Depending on the risk scenario, we choose to manage risk in our favour, either minimising it, accepting it or avoiding it. Despite the risk-based scenario, protection must always be in place. It is recommended to increase data back-up strategies and use an external Managed Detection and Response provider to combat the increasing ransomware-as-a-service cyber-attacks affecting all scales of networks. The conclusion is the use of AI with human intervention to ensure accurate and timely hands-on cyber risk management.
Evaluating Transformer's Ability to Learn Mildly Context-Sensitive Languages

Despite the fact that Transformers perform well in NLP tasks, recent studies suggest that self-attention is theoretically limited in learning even some regular and context-free languages. These findings motivated us to think about their implications in modeling natural language, which is hypothesized to be mildly context-sensitive. We test the Transformer's ability to learn mildly context-sensitive languages of varying complexities, and find that they generalize well to unseen in-distribution data, but their ability to extrapolate to longer strings is worse than that of LSTMs. Our analyses show that the learned self-attention patterns and representations modeled dependency relations and demonstrated counting behavior, which may have helped the models solve the languages.
Jacobi polynomials for first-order generalized Reed--Muller codes

In this paper, we give the Jacobi polynomials for first-order generalized Reed--Muller codes. We show as a corollary the nonexistence of combinatorial $3$-designs in these codes.
Leveraging Self-Supervised Vision Transformers for Neural Transfer Function Design

In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification. In this paper, we present a novel method to define transfer functions for volume rendering by leveraging the feature extraction capabilities of self-supervised pre-trained vision transformers. To design a transfer function, users simply select the structures of interest in a slice viewer, and our method automatically selects similar structures based on the high-level features extracted by the neural network. Contrary to previous learning-based transfer function approaches, our method does not require training of models and allows for quick inference, enabling an interactive exploration of the volume data. Our approach reduces the amount of necessary annotations by interactively informing the user about the current classification, so they can focus on annotating the structures of interest that still require annotation. In practice, this allows users to design transfer functions within seconds, instead of minutes. We compare our method to existing learning-based approaches in terms of annotation and compute time, as well as with respect to segmentation accuracy. Our accompanying video showcases the interactivity and effectiveness of our method.
Unlabelled Sensing with Priors: Algorithm and Bounds

In this study, we consider a variant of unlabelled sensing where the measurements are sparsely permuted, and additionally, a few correspondences are known. We present an estimator to solve for the unknown vector. We derive a theoretical upper bound on the $\ell_2$ reconstruction error of the unknown vector. Through numerical experiments, we demonstrate that the additional known correspondences result in a significant improvement in the reconstruction error. Additionally, we compare our estimator with the classical robust regression estimator and we find that our method outperforms it on the normalized reconstruction error metric by up to $20\%$ in the high permutation regimes $(>30\%)$. Lastly, we showcase the practical utility of our framework on a non-rigid motion estimation problem. We show that using a few manually annotated points along point pairs with the key-point (SIFT-based) descriptor pairs with unknown or incorrectly known correspondences can improve motion estimation.
Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration

Existing Video Restoration (VR) methods always necessitate the individual deployment of models for each adverse weather to remove diverse adverse weather degradations, lacking the capability for adaptive processing of degradations. Such limitation amplifies the complexity and deployment costs in practical applications. To overcome this deficiency, in this paper, we propose a Cross-consistent Deep Unfolding Network (CDUN) for All-In-One VR, which enables the employment of a single model to remove diverse degradations for the first time. Specifically, the proposed CDUN accomplishes a novel iterative optimization framework, capable of restoring frames corrupted by corresponding degradations according to the degradation features given in advance. To empower the framework for eliminating diverse degradations, we devise a Sequence-wise Adaptive Degradation Estimator (SADE) to estimate degradation features for the input corrupted video. By orchestrating these two cascading procedures, CDUN achieves adaptive processing for diverse degradation. In addition, we introduce a window-based inter-frame fusion strategy to utilize information from more adjacent frames. This strategy involves the progressive stacking of temporal windows in multiple iterations, effectively enlarging the temporal receptive field and enabling each frame's restoration to leverage information from distant frames. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance in All-In-One VR.
Generative-based Fusion Mechanism for Multi-Modal Tracking

Generative models (GMs) have received increasing research interest for their remarkable capacity to achieve comprehensive understanding. However, their potential application in the domain of multi-modal tracking has remained relatively unexplored. In this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking. In this paper, we delve into two prominent GM techniques, namely, Conditional Generative Adversarial Networks (CGANs) and Diffusion Models (DMs). Different from the standard fusion process where the features from each modality are directly fed into the fusion block, we condition these multi-modal features with random noise in the GM framework, effectively transforming the original training samples into harder instances. This design excels at extracting discriminative clues from the features, enhancing the ultimate tracking performance. To quantitatively gauge the effectiveness of our approach, we conduct extensive experiments across two multi-modal tracking tasks, three baseline methods, and three challenging benchmarks. The experimental results demonstrate that the proposed generative-based fusion mechanism achieves state-of-the-art performance, setting new records on LasHeR and RGBD1K.
A Note on the Union-closed Sets Conjecture

Let $M$ be a non-zero binary matrix with distinct rows where the rows are closed under certain logical operators. In this article, we investigate the existence of columns containing an equal or greater number of ones than zeros. Specifically, the existence of such columns when the rows of the matrix are closed under $\textit{material conditional}$ leads us to a weaker version of the $\textit{Union-Closed Set Conjecture}$.
Interpretable Sequence Clustering

Categorical sequence clustering plays a crucial role in various fields, but the lack of interpretability in cluster assignments poses significant challenges. Sequences inherently lack explicit features, and existing sequence clustering algorithms heavily rely on complex representations, making it difficult to explain their results. To address this issue, we propose a method called Interpretable Sequence Clustering Tree (ISCT), which combines sequential patterns with a concise and interpretable tree structure. ISCT leverages k-1 patterns to generate k leaf nodes, corresponding to k clusters, which provides an intuitive explanation on how each cluster is formed. More precisely, ISCT first projects sequences into random subspaces and then utilizes the k-means algorithm to obtain high-quality initial cluster assignments. Subsequently, it constructs a pattern-based decision tree using a boosting-based construction strategy in which sequences are re-projected and re-clustered at each node before mining the top-1 discriminative splitting pattern. Experimental results on 14 real-world data sets demonstrate that our proposed method provides an interpretable tree structure while delivering fast and accurate cluster assignments.
A method based on hierarchical spatiotemporal features for trojan traffic detection

Trojans are one of the most threatening network attacks currently. HTTP-based Trojan, in particular, accounts for a considerable proportion of them. Moreover, as the network environment becomes more complex, HTTP-based Trojan is more concealed than others. At present, many intrusion detection systems (IDSs) are increasingly difficult to effectively detect such Trojan traffic due to the inherent shortcomings of the methods used and the backwardness of training data. Classical anomaly detection and traditional machine learning-based (TML-based) anomaly detection are highly dependent on expert knowledge to extract features artificially, which is difficult to implement in HTTP-based Trojan traffic detection. Deep learning-based (DL-based) anomaly detection has been locally applied to IDSs, but it cannot be transplanted to HTTP-based Trojan traffic detection directly. To solve this problem, in this paper, we propose a neural network detection model (HSTF-Model) based on hierarchical spatiotemporal features of traffic. Meanwhile, we combine deep learning algorithms with expert knowledge through feature encoders and statistical characteristics to improve the self-learning ability of the model. Experiments indicate that F1 of HSTF-Model can reach 99.4% in real traffic. In addition, we present a dataset BTHT consisting of HTTP-based benign and Trojan traffic to facilitate related research in the field.
Deep supervised hashing for fast retrieval of radio image cubes

The shear number of sources that will be detected by next-generation radio surveys will be astronomical, which will result in serendipitous discoveries. Data-dependent deep hashing algorithms have been shown to be efficient at image retrieval tasks in the fields of computer vision and multimedia. However, there are limited applications of these methodologies in the field of astronomy. In this work, we utilize deep hashing to rapidly search for similar images in a large database. The experiment uses a balanced dataset of 2708 samples consisting of four classes: Compact, FRI, FRII, and Bent. The performance of the method was evaluated using the mean average precision (mAP) metric where a precision of 88.5\% was achieved. The experimental results demonstrate the capability to search and retrieve similar radio images efficiently and at scale. The retrieval is based on the Hamming distance between the binary hash of the query image and those of the reference images in the database.
JoVE Video Dataset

The sensor network energy conservation method, which is a usage hybrid of a classical computer and quantum processor, has proved to perform better than the heuristic algorithm using a classical computer. In this manuscript, the technical context for the significance of the method is presented and justified. Then the experimental steps are demonstrated in an operational sequence with illustrations if ever needed. The method has been validated by positive results across a randomly generated sample set of network topologies. The successful experimental results of this method have provided a better approach for sensor network lifetime maximization problems and demonstrated that the current state of art quantum processor has been able to solve large practical engineering problems with merits that override current methods in the literature. In other words, quantum advantage can be exploited to best efforts. It has gone beyond the stage of proof of concept to proof of feasibility.
When self-supervision met Query Expansion

&lt;p&gt;The self-supervised training method can fully leverage the structural characteristics of unlabeled datasets, enhancing the technical prowess of natural language processing. We explore the application of self-supervision’s advantages to query expansion. To achieve this, we amassed a substantial query corpus and trained it using a generative model. Simultaneously, we introduced the SG-BERT/GPT2 extension method, leveraging two generators, BERT and GPT2. We also conducted an analysis of the impact on code search before and after generative model training, as well as the influence of the original query length on the expanded model. Experimental results consistently demonstrate the superiority of the trained model over the untrained one. Additionally, in deep learning-based code search methods, query expansion proves most effective for datasets with relatively short original query lengths, while longer expansions yield the opposite effect. Finally, when compared to various established query expansion methods, the SG-BERT/GPT2 expansion method, rooted in the self-supervised generative model, consistently outperforms them across multiple language test datasets. With ongoing self-supervised generation and continuous optimization of the query corpus, further enhancements in effectiveness can be achieved.&lt;/p&gt;
Controlling the Head of the School Administration Section in Managing archives at SMP N 24 Padang

This research was motivated because researchers found several problems related to the management of archives that are still not running properly. Problems related to archive management that are still not running as a whole. Overall. The role of the head of the Administration section in developing controlling archive management has actually been carried out, but there are still things that need to be improved and addressed again. Need to be improved and addressed again. Given that there are still employees Administration employees who still lack knowledge in archive management. Besides In addition, the role of the head of the Administration section must also be able to coordinate the Administration staff so that they carry out the stages of archive management in accordance with the stages of archive management. Administration staff so that they carry out the stages of archive management in accordance with the existing stages. stages that already exist. The purpose of this research was conducted to find out the stages and obstacles in controlling archive management at SMP N 24 Padang. This research uses descriptive qualitative methods. The results of this study indicate that the stages of archive management at SMP N 24 Padang have five stages in archive management. Padang has five stages in archive management. The stages are: a) Creation and Acceptance Stages, b) Distribution Stages, c) User Stages, d) Maintenance Stages, e) Destruction Stages. User, d) Maintenance Stages, e) Destruction Stages. Then the process of controlling the head of the Administration section in archive management has controlling such as: starting with the Standard Setting Stage, Determining Stage Measurement of Activity Implementation / Measurement of Implementation, Stage of Taking Action. The obstacles faced by the head of the Administration section in controlling archive management at SMP N 24 Padang, first, there are administrative staff who do not finish managing an archive. second, there are still Administration staff who do not understand how to manage archives correctly. do not understand how to properly manage archives.
Cost and Reliability Aware Scheduling of Workflows Across Multiple Clouds with Security Constraints

Abstract Many real-world scientific workflows can be represented by a Directed Acyclic Graph (DAG), where each node represents a task and a directed edge signifies a dependency between two tasks. Due to the increasing computational resource requirements of these workflows, they are deployed on multi-cloud systems for execution. In this paper, we propose a complete framework for scheduling workflows in a multi-cloud system. In particular, we focus on minimizing the total execution time, and cost, and maximizing the reliability subject to security constraints. First, we use a Gravitational Search Algorithm (GSA) based approach to assign resources to tasks. Next, for a given task-resource mapping, we propose a cipher assignment algorithm that assigns security services to edges responsible for transferring data in a time-optimal manner subject to given security constraints. The proposed algorithms have been analyzed to understand their time and space requirements. We implement the algorithms and experiment with many real-world scientific workflows of varying sizes. We compare the performance of the proposed method with the state-of-art methods. We observe that our method outperforms the others in terms of cost and reliability, and is inferior in terms of makespan in some cases.
Capturing drug use patterns at a glance: An n-ary word sufficient statistic for repeated univariate categorical values

Introduction The efficacy of treatments for substance use disorders (SUD) is tested in clinical trials in which participants typically provide urine samples to detect whether the person has used certain substances via urine drug screenings (UDS). UDS data form the foundation of treatment outcome assessment in the vast majority of SUD clinical trials. However, existing methods to calculate treatment outcomes are not standardized, impeding comparability between studies and prohibiting reproducibility of results. Methods We extended the concept of a binary UDS variable to multiple categories: “ + ” [positive for substance(s) of interest], “ – ” [negative for substance(s)], “ o ” [patient failed to provide sample], “*” [inconclusive or mixed results], and “_” [no specimens required per study design]. This construct can be used to create a standardized and sufficient representation of UDS datastreams and sufficiently collapses longitudinal records into a single, compact “word”, which preserves all information contained in the original data. Results We developed the R software package CTNote (available on CRAN) as a tool to enable computers to parse these “words”. The software package contains five groups of routines: detect a substance use pattern, account for a specific trial protocol, handle missing UDS data, measure the longest period of consecutive behavior, and count substance use events. Executing permutations of these routines result in algorithms which can define SUD clinical trial endpoints. As examples, we provide three algorithms to define primary endpoints from seminal SUD clinical trials. Discussion Representing substance use patterns as a “word” allows researchers and clinicians an “at a glance” assessment of participants’ responses to treatment over time. Further, machine readable use pattern summaries are a standardized method to calculate treatment outcomes and are therefore useful to all future SUD clinical trials. We discuss some caveats when applying this data summarization technique in practice and areas of future study.
Developing a new encryption algorithm for images transmitted through WSN systems

Wireless sensor networks (WSNs) have up until now faced many challenges because of their open, wide-ranging, and resource-limited nature, including security, efficiency, and energy consumption. In the military system, it is essential to provide high-level security to the significant data over wireless network which is proved to be unreliable public communications. To solve the above problem, high level of security with minimum complexity should be applied to be adequate to limited capabilities of transmission system. This paper presents a new algorithm named (SRS) for encrypting transmitted military images to keep them from getting hacked or broken by WSN. The SRS algorithm is designed to be lightweight, fast, and secure, taking into consideration the limited capabilities of the transmission system. It is implemented as a public key cryptosystem specifically designed for image encryption. The algorithm consists of two parts: encryption and decryption. The proposed system suggested some equations and calculations that are applied to the plain and coded images after being transmitted over the WSN. The results of testing the simulation model demonstrate the effectiveness of the system by evaluation using various metrics such as Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Overall, the paper presents a new encryption algorithm, SRS, specifically designed for securing military images transmitted over wireless sensor networks. The algorithm aims to provide a balance between security, efficiency, and energy consumption, considering the resource-limited nature of WSNs. The simulation results indicate the improvement of the proposed system by 13 %, 10 %, and 55 % in packet delivery ratio (PDR), throughput, and dropping ratio, respectively, and it shows that the suggested SRS method increased execution time by 67 % compared to RSA based algorithm
Emily Hund. The influencer industry: The quest for authenticity on social media

The Influencer Industry: The Quest for Authenticity on Social Media, written by American media researcher Emily Hund, takes the reader on a scouting expedition into the U. S. influencer industry. Hund is a well-established author in social media influencer research, where her works on gender dynamics in the influencer industry have been particularly notable (e. g., Duffy &amp; Hund, 2015, 2019). These works look at social media influence from a creative industry’s angle, and The Influencer Industry also stands in this lineage. Although the title does not make it clear, the book offers, first and foremost, a chronological history of the influencer industry in the United States. Beginning with the advent of political bloggers in the late 1990s, each chapter focuses on a specific period and the overarching developmental milestones Hund identifies as constituting the period. This way, Hund provides a much-needed historical perspective on the influencer industry. However, the book wants to be more than that – and it definitely is – in that it also reflects on how the influencer industry impacts contemporary forms of sociation.
Towards a Research Data Commons in the German National Research Data Infrastructure NFDI: Vision, Governance, Architecture

The concept of a "Research Data Commons" (RDC) established itself as an infrastructure ecosystem for science based on open standards and federated resources to facilitate the sharing of research data and services. The consortia of the German National Research Data Infrastructure (NFDI) have identified the collaborative provisioning of resources and services to be of key importance for a functioning and efficient RDC and are leveraging different corresponding measures to establish a sustainable concept in line with international developments.