Reviewerly/exHarmony huggingface dataset:
Optimization of Cyber Defense Exercises Using Balanced Software Development Methodology

Cyber defense exercises (CDXs) represent an effective way to train cybersecurity experts. However, their development is lengthy and expensive. The reason lies in current practice where the CDX life cycle is not sufficiently mapped and formalized, and then exercises are developed ad-hoc. However, the CDX development shares many aspects with software development, especially with ERP systems. This paper presents a generic CDX development method that has been derived from existing CDX life cycles using the SPEM standard meta-model. The analysis of the method revealed bottlenecks in the CDX development process. Observations made from the analysis and discussed in the paper indicate that the organization of CDXs can be significantly optimized by applying a balanced mixed approach with agile preparation and plan-driven disciplined evaluation.
DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains

We address the issue of having a limited number of annotations for stance classification in a new domain, by adapting out-of-domain classifiers with domain adaptation. Existing approaches often align different domains in a single, global feature space (or view), which may fail to fully capture the richness of the languages used for expressing stances, leading to reduced adaptability on stance data. In this paper, we identify two major types of stance expressions that are linguistically distinct, and we propose a tailored dual-view adaptation network (DAN) to adapt these expressions across domains. The proposed model first learns a separate view for domain transfer in each expression channel and then selects the best adapted parts of both views for optimal transfer. We find that the learned view features can be more easily aligned and more stance-discriminative in either or both views, leading to more transferable overall features after combining the views. Results from extensive experiments show that our method can enhance the state-of-the-art single-view methods in matching stance data across different domains, and that it consistently improves those methods on various adaptation tasks.
Layer-Wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees.

To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers. However, the speedup brought by multiple workers is limited by the communication overhead. Two approaches, namely pipelining and gradient sparsification, have been separately proposed to alleviate the impact of communication overheads. Yet, the gradient sparsification methods can only initiate the communication after the backpropagation, and hence miss the pipelining opportunity. In this paper, we propose a new distributed optimization method named LAGS-SGD, which combines S-SGD with a novel layer-wise adaptive gradient sparsification (LAGS) scheme. In LAGS-SGD, every worker selects a small set of significant gradients from each layer independently whose size can be adaptive to the communication-to-computation ratio of that layer. The layer-wise nature of LAGS-SGD opens the opportunity of overlapping communications with computations, while the adaptive nature of LAGS-SGD makes it flexible to control the communication time. We prove that LAGS-SGD has convergence guarantees and it has the same order of convergence rate as vanilla S-SGD under a weak analytical assumption. Extensive experiments are conducted to verify the analytical assumption and the convergence performance of LAGS-SGD. Experimental results on a 16-GPU cluster show that LAGS-SGD outperforms the original S-SGD and existing sparsified S-SGD without losing obvious model accuracy.
Jaringan Syaraf Tiruan Dalam Menganalisa Bentuk Sel Darah Putih Dengan Menggunakan Metode Radial Basis Function (RBF)

White blood cells are cells that form components in the blood. White blood cells have a life span of between six to eight days. Analyzing white blood cells is still carried out through a series of laboratory tests which can take a while. This test covers the shape of the morphology of blood cells, which until now is generally still done manually. This manual inspection certainly has a low level of accuracy and accuracy because it is carried out humanely and has a possible degree of difference in analysis. This causes a lack of accuracy and accuracy performed by doctors and health laboratory personnel because the physical condition, knowledge, accuracy and concentration of doctors and health laboratory personnel can influence the identification of diseases so that there may be differences in analysis between one doctor and another. On hematology examination can be overcome manually by creating an automatic system with the help of a computer. Therefore, in this study the author tries to provide alternative solutions to the problem with the Radial Basis Function learning method, which according to his theory will produce higher accuracy values and faster iteration times
An Improved Cost Function for Hierarchical Cluster Trees

Hierarchical clustering has been a popular method in various data analysis applications. It partitions a data set into a hierarchical collection of clusters, and can provide a global view of (cluster) structure behind data across different granularity levels. A hierarchical clustering (HC) of a data set can be naturally represented by a tree, called a HC-tree , where leaves correspond to input data and subtrees rooted at internal nodes correspond to clusters. Many hierarchical clustering algorithms used in practice are developed in a procedure manner. In [9], Dasgupta proposed to study the hierarchical clustering problem from an optimization point of view, and introduced an intuitive cost function for similarity-based hierarchical clustering with nice properties as well as natural approximation algorithms. There since has been several followup work on better approximation algorithms, hardness analysis, and general understanding of the objective functions. We observe that while Dasgupta's cost function is effective at differentiating a good HC-tree from a bad one for a fixed graph, the value of this cost function does not reflect how well an input similarity graph is consistent to a hierarchical structure. In this paper, we present a new cost function, which is developed based on Dasgupta's cost function, to address this issue. The optimal tree under the new cost function remains the same as the one under Dasgupta's cost function. However, the value of our cost function is more meaningful. For example, the optimal cost of a graph $G$ equals $1$ if and only if $G$ has a perfect HC-structure in the sense that there exists a HC-tree that is consistent with all triplets relations in $G$; and the optimal cost will be larger than $1$ otherwise. The new way of formulating the cost function also leads to a polynomial time algorithm to compute the optimal cluster tree when the input graph has a perfect HC-structure, or an approximation algorithm when the input graph almost has a perfect HC-structure. Finally, we provide further understanding of the new cost function by studying its behavior for random graphs sampled from an edge probability matrix.
Revised polyhedral conic functions algorithm for supervised classification

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that the modifications provide %12 better generalization on average and up to 10x faster training. This paper also contributes to the literature by providing detailed comparisons of the other classification algorithms that use polyhedral conic functions for the first time.
Multidimensional ontology-based visual ranking

Evangelia Triperina obtained her PhD in Computer Sciences from the University of Limoges, France in 2020. Her thesis focuses on the interdisciplinary research in multidimensional decisions support in the ranking problematic aided by visual analytics and semantic web technologies. Interactive visual analytics exploit the increased visual perceptual abilities of the decision makers creating a more efficient decision support procedure, while semantic web technologies, and more specifically ontologies, structure data, make the system dynamic and add several needed capabilities to the decision support system. Furthermore, the multidimensional decision support method relies not only on evaluating an alternative based on multiple criteria, but also on important dimensions of the domain 1 .
Solution of Test Problems with Grey Wolf Optimization Algorithm and Comparison with Particle Swarm Optimization

In this study, Grey Wolf Optimization (GWO), which is a new method with swarm intelligence is compared with another metaheuristic optimization method, Particle Swarm Optimization (PSO), using optimization benchmark functions. Simulation studies on test functions are presented as a table by obtaining mean, standard deviation, best and worst values. In addition, the effects of population and iteration number change on the GWO algorithm are presented in separate tables. The GWO algorithm has establish a good balance between exploration and exploitation. Simulation studies have shown that GWO has better convergence performance and optimization accuracy.
Detection of Vulnerabilities in Smart Contracts Specifications in Ethereum Platforms.

Ethereum is the principal ecosystem based on blockchain that provides a suitable environment for coding and executing smart contracts, which have been receiving great attention due to the commercial apps and among the scientific community. The process of writing secure and well performing contracts in the Ethereum platform is a major challenge for developers. It consists of the application of non-conventional programming paradigms due to the inherent characteristics of the execution of distributed computing programs. Furthermore, the errors in the deployed contracts could have serious consequences because of the immediate linkage between the contract code and the financial transactions. The direct handling of the assets means that the errors can be more relevant for security and have greater economic consequences than a mistake in the conventional apps. In this paper, we propose a tool for the detection of vulnerabilities in high-level languages based on automatized static analysis.
Toward Explainable Automatic Classification of Children’s Speech Disorders

Early and adequate diagnosis of speech disorders can contribute to the quality of the treatment and thus to treatment success rates. Using acoustic analysis of the speech of children with speech disorders may aid therapists in the diagnostic process by identifying the acoustic characteristics that are unique to a specific disorder and that distinguish it from normal speech development. The purpose of this work is to investigate the feasibility of the automatic detection of speech disorders based on children’s voices. In this preliminary study, using a dataset of utterance recordings of 24 children whose mother tongue is Hebrew, we propose an automatic system that may facilitate accurate speech assessment by therapists by providing a preliminary diagnosis and explainable insights about the model’s predictions. We built a serial, two-step network that is both powerful and possibly interpretable. The first step can model the complex relations between acoustic features and the speech disorder while the second can shed light on the utterances that make the greatest contribution to the final classification. Our preliminary results focus on the broad spectrum of speech disorders. In future work, we plan to design a system that will be able to detect childhood apraxia of speech (CAS) specifically and shed light on the differences in the speech of individuals with CAS and those with other speech disorders.
Implementation of Smart Contracts Ethereum Blockchain in Web-Based Electronic Voting (e-voting)

&lt;p&gt;The increasing of digital technology today has helped many people to fulfill their needs. But the election system, still conventionally using paper in its implementation. Elections in general still use a centralized system, where there is an organization that manages it. Some of the problems that may occur in traditional electoral systems are that there are organizations that have full control over the database and system, so the possibility of hacking the database is quite a big opportunity.&lt;/p&gt;Blockchain innovation is one arrangement that can be utilized in light of the fact that it has a decentralized framework and the whole database is duplicated by all clients. Blockchain itself has been used by Bitcoin and Ethereum cryptocurrency which is known as a decentralized system. By using the blockchain in database recording on an e-voting system can reduce one source of fraud that is database manipulation. This study discusses the recording of voting data using blockchain technology. The implementation of Smart Contracts contained in the Ethereum Blockchain will be implemented to create this voting system
Navigating Forest Straight-Line Programs in Constant Time

We present a data structure of linear size that allows to perform navigation steps and subtree equality checks in grammar-compressed forests in constant time. Navigation steps include going to the parent, to the left/right neighbor or to the first/last child.
LSE_Lex40_UVIGO: una base de datos específicamente diseñada para el desarrollo de tecnología de reconocimiento automático de LSE

Automatic speech recognition can be considered a viable and mature technology. Many applications facilitating communication between people, and between humans and machines, are based on it. However, the automatic recognition of sign languages is not as advanced as that of spoken languages; if it were, Deaf people could communicate with someone who does not know sign language without an interpreter, gaining privacy and independence. And they could make use of automated video-controlled systems that recognize their instructions in an inclusive, quick and mobile way, similarly to how hearing people access voice-controlled systems. The GRADES and GTM research groups at the University of Vigo intend to advance in the development of an automatic recognizer for Spanish sign language (LSE) based on image recognition. From the review of the state of the art, it is concluded the need to develop an LSE database specifically designed for this purpose. The complexity of this task makes it advisable to approach it incrementally, for which we propose the goal of developing a recording methodology that allows the database to grow in size and complexity over time. This methodology includes the selection of the lexicon, the design of the recording station, the data storage structure, the computer programs for managing the video database and the associated metadata, and the protection of personal data of the signers. An initial version of the database, called LSE_Lex40_UVIGO, is made up of multiple repetitions of 40 isolated signs in LSE, carried out by different signers. This first version of the database will be useful for us to develop an isolated sign recognizer in diverse environments and independently of the user, and to demonstrate the usefulness of the acquisition methodology described in this contribution.
A study On : Confidentiality Approach to Prevent Features Disclosure in IoT Situations

This paper proposes an approach which safeguards confidentiality to avoid disclosures of features within a multiple IoT situation, that is, a setup of objects in networks that communicate with each other. Two ideas derived from the theory of databases, namely k-anonymity and t-certitude, form our basis. They are used to cluster the objects to provide a unitary view of them and their characteristics. In fact, the use of anonymity and t-closeness robustly ensures privacy for derived groups. Furthermore, description of the object grouping scheme that preserves privacy, which represents the core of our approach was studied. Eventually, we illustrated the corresponding security model and analyzed the associated properties. The study also provided important advantages for the protection of user privacy in all those situations where knowledge of object features may help an attacker to obtain information about user habits and behavior. This study prevents not only the disclosure of information but also the divulgation of features. This is a major strength of our approach as malicious analyzes of the characteristics of objects can interfere with the privacy of people.
Composite Framework of Artificial Neural Network and Decision Tree Algorithm for Prediction of Fraudulent Firm Using Audit Dataset

A Decision tree is a tree-like structure, which works in a transparent manner. But when we talk about exponential data, the decision tree is not recommended, as there may be exceptional situation in which the tree could be forced to fall apart and need to be constructed again. The poor statistical efficiency of decision tree can be nullified using Artificial Neural Networks. But due to hidden layers, Neural Nets do not have the ability to explain how it came to a particular outcome. If decision tree could be used in place, this limitation could be overcome. This paper proposes an idea of removing the limitation of both the models by using them together. In this, first, the Artificial Neural Network was constructed on the dataset, and then, decision tree was constructed. This composite framework provides more efficiency and clarity in solution.
Information Security Policy And Compliance In Oil And Gas Organizations-A Pilot Study

Purpose- Oil and gas organizations considered as the backbone of every country’s economy. Informationsecurity attacks on these organizations have been increased rapidly in the last decade. Oil and gasorganizations often invest in technical solutions to mitigate information security risks. Whereas, mostinformation security attacks occur due to internal employees’ negligence towards information securitypolicy. This paper based on the pilot study to analyze appropriate information security governance andsocial bonding effects on oil and gas organizations employee’s behavior towards information security policycompliance.Research methodology- This paper is survey-based research, and a self-administered questionnaire was usedfor data collection. Survey items were adapted from different authentic studies. All items were measuredthrough a 1 to 5 Likert-scale from 1 strongly disagree to 5 strongly agree. The survey was conducted in anoil refinery’s IT department.Findings- This research indicates that appropriate information security governance and information securityawareness improves social bonding, and good social bonding between employees can enhance informationsecurity policy compliance in oil and gas organizations.Conclusion- Oil and gas organizations must invest in employee’s education and training regardinginformation security. Moreover, the alignment of security policies and procedures are needed in oil and gasorganizations. Our paper shows that information security policy compliance can be improved with theeffectiveness of governance towards security education training and awareness, security policy andprocedures, physical security monitoring, and risk assessment and analysis.
Innovating and adapting to teach cytopathology online during a pandemic

We read the article by Kwon et al with great interest.1 As the authors pointed out, effective remote teaching not only requires faculty access to appropriate software and hardware but also knowledge and experience to troubleshoot while working with both tools. Although there has been tremendous advancement in image acquisition and broadcast, it comes with a cost that might be difficult for many teaching centers to incorporate into their budget, especially with cost-cutting measures occurring during the coronavirus disease 2019 (COVID-19) pandemic. In our experience, teaching via video conferencing in surgical pathology was not that difficult. However, cytology was different, as was highlighted by Kwon et al.1 We incorporated Canvas, which already was site-licensed by Emory University for online instruction, as a teaching tool in cytopathology. We used Studio (an application within the Canvas interface) to make high-quality videos (akin to scans of the slides), and important clusters were focused at ×10, ×20, and ×40 magnification (to overcome problems with the z-axis). Videos can be made with virtually any microscope camera that currently is available. Interactive questions can be added to these videos for active learning (as opposed to passive learning in didactic recorded video lectures). Videos are stored on the Canvas server; they do not consume space on the “C” drive, the local hard drive of the personal computer of the course designer. There is no need for a whole-slide imager with this application. No additional costs for licensing imaging software are incurred by the department because the program is site-licensed to the university. An entire cytopathology course with separate chapters was created with videos for separate cases to simulate scanning at the microscope. The videos also were used to create assignments and questions were stored in item banks for future use. Resident physicians and fellows can access these at any time, even when they are not on a cytology rotation, which also is helpful for keeping them accustomed to reviewing cytopathology material. The authors of the original article were given the opportunity to respond but declined. No specific funding was disclosed. The authors made no disclosures.
Asymptotically Good Additive Cyclic Codes

ZpZps-additive cyclic codes have been proved to be asymptotically good by Yao and Zhu. For binary Hamming scheme, we introduce a type of Z2(Z2 + uZ2)-additive cyclic codes generated by pairs of polynomials. Let R be the chain ring Z2 + uZ2, where u2 = 0. The asymptotic rates and relative distances of this class of codes are presented by establishing the relationship between the random Z2R-additive cyclic code and random binary quasi-cyclic code of index 2. We show that Z2R-additive cyclic codes are asymptotically good.