Reviewerly/exHarmony huggingface dataset:
A framework for automated evaluation of security metrics

Observation is the foundation of scientific experimentation. We consider observations to be measurements when they are quantified with respect to an agreed upon scale, or measurement unit. A number of metrics have been proposed in the literature which attempt to quantify some property of cyber security, but no systematic validation has been conducted to characterize the behaviour of these metrics as measurement instruments, or to understand how the quantity being measured is related to the security of the system under test. In this paper we broadly classify the body of available security metrics against the recently released Cyber Security Body of Knowledge, and identify common attributes across metric classes which may be useful anchors for comparison. We propose a general four stage evaluation pipeline to encapsulate the processing specifics of each metric, encouraging a separation of the actual measurement logic from the model it is often paired with in publication. Decoupling these stages allows us to systematically apply a range of inputs to a set of metrics, and we demonstrate some important results in our proof of concept. First, we determine a metric's suitability for use as a measurement instrument against validation criteria like operational range, sensitivity, and precision by observing performance over controlled variations of a reference input. Then we show how evaluating multiple metrics against common reference sets allows direct comparison of results and identification of patterns in measurement performance. Consequently, development and operations teams can also use this strategy to evaluate security tradeoffs between competing input designs or to measure the effects of incremental changes during production deployments.
Efficient Parameter Estimation of Truncated Boolean Product Distributions

We study the problem of estimating the parameters of a Boolean product distribution in d dimensions, when the samples are truncated by a set $$S \subseteq \{0, 1\}^d$$ accessible through a membership oracle. This is the first time that the computational and statistical complexity of learning from truncated samples is considered in a discrete setting. We introduce a natural notion of fatness of the truncation set S, under which truncated samples reveal enough information about the true distribution. We show that if the truncation set is sufficiently fat, samples from the true distribution can be generated from truncated samples. A stunning consequence is that virtually any statistical task (e.g., learning in total variation distance, parameter estimation, uniformity or identity testing) that can be performed efficiently for Boolean product distributions, can also be performed from truncated samples, with a small increase in sample complexity. We generalize our approach to ranking distributions over d alternatives, where we show how fatness implies efficient parameter estimation of Mallows models from truncated samples. Exploring the limits of learning discrete models from truncated samples, we identify three natural conditions that are necessary for efficient identifiability: (i) the truncation set S should be rich enough; (ii) S should be accessible through membership queries; and (iii) the truncation by S should leave enough randomness in all directions. By carefully adapting the Stochastic Gradient Descent approach of (Daskalakis et al., FOCS 2018), we show that these conditions are also sufficient for efficient learning of truncated Boolean product distributions.
Continuous optimization by hierarchical gaussian mixture with clustering embedded resource allocation

Continuous optimization problems, which are highly related to real-world problems, are difficult since they are multimodal and rugged. In this paper, we introduce a new algorithm called Global and Local Adaptation with Clustering Embedded Resource Allocation (GLACERA) to solve problems with relaxed conditions. We believe that continuous optimization problems are solvable as long as the local optima can serve as clues for finding the global optimum. To solve the problems more efficiently, a new clustering technique is proposed to help identify potential local optima. Then, a new multi-armed bandit technique, aiming to reach global optimum with greater probability, is presented to allocate resources for each local optimum. Finally, a new formula is proposed for balancing exploration and exploitation according to remaining number of function evaluations. We compare GLACERA with four milestone algorithms that are commonly used for continuous optimization, and show that it can solve a new class of functions while others failed, while remain competitive in solving benchmark problems of IEEE CEC2005 and IEEE CEC2013.
Improving Context-Aware Neural Machine Translation with Target-Side Context

In recent years, several studies on neural machine translation (NMT) have attempted to use document-level context by using a multi-encoder and two attention mechanisms to read the current and previous sentences to incorporate the context of the previous sentences. These studies concluded that the target-side context is less useful than the source-side context. However, we considered that the reason why the target-side context is less useful lies in the architecture used to model these contexts. Therefore, in this study, we investigate how the target-side context can improve context-aware neural machine translation. We propose a weight sharing method wherein NMT saves decoder states and calculates an attention vector using the saved states when translating a current sentence. Our experiments show that the target-side context is also useful if we plug it into NMT as the decoder state when translating a previous sentence.
Dynamic Hypergames for Synthesis of Deceptive Strategies With Temporal Logic Objectives

In this paper, we study the use of deception for strategic planning in adversarial environments. We model the interaction between the agent (player 1) and the adversary (player 2) as a two-player concurrent stochastic game in which the adversary has incomplete information about the agent’s task specification given as a temporal logic formula. During the interaction, the adversary can infer the agent’s intention from observations and adapt its strategy so as to prevent the agent from satisfying the objective. To plan against such an adaptive opponent, the agent must leverage its knowledge about the adversary’s incomplete information to influence the behavior of the opponent, and thereby be deceptive. To synthesize a deceptive strategy, we introduce a class of hypergame models that capture the interaction between the agent and its adversary given asymmetric, incomplete information. We develop a solution concept for this class of hypergames and show that the subjectively rationalizable strategy for the agent is deceptive and maximizes the probability of satisfying the task in temporal logic. Such a deceptive strategy is obtained by modeling the opponent’s evolving perception of the agent’s objective and integrating it into planning. This allows the agent to manipulate the opponent’s perception so as to induce the opponent into taking actions that benefit the agent. We demonstrate the effectiveness of our deceptive planning algorithm using robot motion planning examples with temporal logic objectives and design a detection mechanism to notify the agent of potential errors in modeling the adversary’s behavior. Note to Practitioners—Many security and defense applications employ deception mechanisms for strategic advantages. This work presents a game-theoretic framework for planning deceptive strategies in stochastic environments and shows that the opponent modeling plays a key role in the design of effective deception mechanisms. For applications to cyber-physical security, the practitioners can employ temporal logic for specifying security properties in the system and analyze defense with deception using the proposed methods.
Building Lexical Resources for Dialectical Arabic

The natural language processing of Arabic dialects faces a major difficulty, which is the lack of lexical resources. This problem complicates the penetration and the business of related technologies such as machine translation, speech recognition, and sentiment analysis. Current solutions frequently use lexica, which are specific to the task at hand and limited to some language variety. Modern communication platforms including social media gather people from different nations and regions. This has increased the demand for general-purpose lexica towards effective natural language processing solutions. This chapter presents a collaborative web-based platform for building a cross-dialectical, general-purpose lexicon for Arabic dialects. This solution was tested by a team of two annotators, a reviewer, and a lexicographer. The lexicon expansion rate was measured and analyzed to estimate the overhead required to reach the desired size of the lexicon. The inter-annotator reliability was analyzed using Cohen's Kappa.
An Overview of Classification Issues in Sensor-Based Activity Recognition

Human activity analysis and recognition tasks can be considered as classification problems in most of the cases. This chapter represents the overview of classification problems explaining their different types with examples. Binary classification, multilabel classification, multi-class classification, and hierarchical classification tasks are presented in this chapter. At the end, we have given a brief summary about the bias-variance trade-off problem.
General controllers evolved through grammatical evolution with a divergent search

In this work, we analyse the performance of Novelty Search (NS) in a set of generalization experiments in a navigation task with Grammatical Evolution. Agents are trained on a single, simple environment, and tested on a selection of related, increasingly more difficult environments. We show that agents discovered with NS, although using a tiny number (six) of training samples, successfully generalise to these more difficult environments.
ChoCo: a multimodal corpus of the Choctaw language

This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.
Word-Level Sign Language Gesture Prediction Under Different Conditions

With over 6% population suffering from hearingArora, Monika problems and relying on sign language to communicate with the masses and expressing their emotions through actions. It has beenMehta, Priyanshu an onerous task for theMittal, Divyanshu speech and hearing-impaired people to make people understand and thus it is necessity to build a systemBajaj, Prachi that can help anyone in understanding the gestures and generate its meaning. A system for sign language recognition can be a preliminary step to establish better communication. We used word-level Argentinian Sign Language (LSA) video dataset with 64 actions which are shot under different lights and with non-identical subjects. Video data accommodate both dimensional and sequential attributes, thus we used a deep convolutional neural network along with recurrent neural network with LSTM units to incorporate both together. We created two different test cases, that is, indoor lighting environment with single subject and a mix of both indoor and outdoor conditions with multiple subjects and have achieved accuracy of 93.75% and 90.625%, respectively.
Implementation matters, also in concurrent evolutionary algorithms

Concurrency in evolutionary algorithms allow researchers to leverage the performance of powerful multi-core desktop architectures by parallelizing tasks using a high-level interface. However, concurrency also introduces additional complexity at the model and the implementation level. In this paper we describe how using parallel execution monitoring tools to check the effective parallelism of the implementation, can help to work out some of said complexities at the implementation-level, which in turn, are translated into improvements at the algorithmic-level. The performance gain is noticeable from an evaluations/seconds point of view to the possible scaling that can be achieved in the running machine, up to a superlinear scaling in an off-the-shelf platfonn. We show that the design using communicating sequential processes implemented in the language Raku is the basis for these improvements.
Textureless Object Recognition Using an RGB-D Sensor

Object recognition is a significant task in an industrial assembly line, where a robotic arm should pick a small, textureless, and mostly homogeneous object to place it in its designated location. Despite all the recent advancements in object recognition, the problem still remains challenging for textureless industrial parts with similar shapes. In this paper, we propose an effective and real-time system using a single RGB-D camera to recognize the industrial objects placed at arbitrary viewing direction around the vertical axis. First, we segment the region of interest using an improved watershed segmentation approach. Then, we extract low-level geometrical features. Finally, we train five models and compare their accuracy based on different rotation strategies. Our experimental results highlight the efficiency as well as real-time suitability of our approach.
Differential Privacy Online Learning Based on the Composition Theorem

Privacy protection is becoming more and more important in the era of big data. Differential privacy is a rigorous and provable privacy protection method that can protect privacy for a single piece of data. But existing differential privacy online learning methods have great limitations in the scope of application and accuracy. Aiming at this problem, we propose a more general and accurate algorithm, named DPOL-CT, for differential privacy online learning. We first distinguish the difference in differential privacy protection between offline learning and online learning. Then we prove that the DPOL-CT algorithm achieves (∊, δ)-differential privacy for online learning under the Gaussian, the Laplace and the Staircase mechanisms and enjoys a sublinear expected regret bound. We further discuss the trade-off between the differential privacy level and the regret bound. Theoretical analysis and experimental results show that the DPOL-CT algorithm has good performance guarantees.
Archive-based swarms

The Particle Swarm Optimization (PSO) algorithm updates each individual's velocity and position using its own prior best position and the best position found so far by any particle. Effective search for global optima can benefit if each particle may utilize additional historical information regarding the quality of previously visited positions in its proximity. We present an approach for doing so, using an archive containing some prior particle positions, analogous to the archive used in some multi-objective optimization algorithms. A specific instance of this approach is the proposed Fitness-Distance-Ratio Archive-Based Swarm Optimization algorithm, shown to outperform PSO and three other variants, when applied to high-dimensional neural network training problems.
PROCESS GENRE BASED APPROACH MODEL IN LEARNING ACADEMIC WRITING SKILLS

Abstract: Academic writing is one of the skills that need to be developed among students. As a skill, writing is a process that requires continuous and systematic training. The writing process starts from pre-writing, writing, and post-writing. Some studies suggest that such writing processes need to be developed by strategies involving student activity. The activity can be done by text modeling. Students are asked to observe and construct text. The aim is to help students understanding text structure and its language.  The construction of the text can be done both through collaborative and independent. Through discussion activities, students enable to construct text together. The aim is for interaction between text and fellow readers. Learning model that combines writing process and approach to text is known as the Process Genre Based Approach Model.
Recognition of English spoken stressed syllables based on natural language processing and endpoint detection algorithm

The traditional stress evaluation method cannot describe the chaotic characteristics of speech signals well or cannot fully approximate the complex nonlinear relationship between features, so that the exact location of stress cannot be accurately determined. In order to improve the recognition of spoken English stressed syllables, this study established a word stress recognition model. The model can accurately recognize the stressed syllables in the words and calculate the fundamental frequency change trajectory of the language tones using the fundamental frequency scaling function. According to this trajectory and using the time domain fundamental frequency synchronization superposition algorithm to modify the fundamental frequency parameters in the DIVA model motion instruction, the spoken language learner can master the rhythm in the spoken language. In addition, this study sets up experiments to study the effects of the model. The results show that the stress recognition model established in this paper has good reliability and stability.
An Analysis on Incompetent Search Engine and Its Search Engine Optimization (SEO)

Search engine optimization (SEO) is a technical term that optimizes the website over the Internet to get the better rank compare to its competitor, the ultimate goal of SEO is to get the brand to its website. Earlier time there are few websites and therefore the competition among website are narrower, but at present the scenario of the websites is gigantic, and therefore the people are using so many atrocious ways to get the page rank. This paper consists of the lagging part of SEO and incompetent search engines. The paper shows an analysis that how the search engines are not effective while crawling the webpages, and also analyses how the SEO is been affected by futile work of search engines and its page rank to the website owner.
Generation of consistent sets of multi-label classification rules with a multi-objective evolutionary algorithm

Multi-label classification consists in classifying an instance into two or more classes simultaneously. Recently, the interest in interpretable classification models have grown, partially as a consequence of regulations such as the General Data Protection Regulation. In this context, we propose a multi-objective evolutionary algorithm that generates multiple rule-based multi-label classification models, allowing users to choose among models that offer different compromises between predictive power and interpretability. The most important contributions of this work are: the generated models are based on sets (unordered collection) of rules and the rule creation mechanism employs a conflict avoidance algorithm which guarantees that all rules within a model are consistent with each other. We conducted experiments on synthetic and real-world datasets and compared our results with state-of-the-art algorithms in terms of predictive performance (F-Score) and interpretability (model size), and demonstrate that our best models had comparable F-Score and smaller model sizes.
Multi-document Text Summarization Tool

In today’s world, there is a massive amount of data being continuously generated every minute. This data can be utilised to gain a large amount of information that can have numerous uses. However, it is difficult to obtain this information because of the speed and volume of data being generated. One of the tools that can be useful in extracting useful information from textual data is a text summarization and analysis tool. Many text summarization tools are being developed but largely focus on summarising a single document effectively. This project aims to create a text summarization tool using abstractive and extractive text summarization techniques that can extract the relevant and important information from multiple documents and present it as a concise summary. The tool also performs multiple analyses on the data to obtain more useful information and make inferences based on the contents of the input textual data. This tool has various use cases as it can greatly reduce the time spent in gathering information from a large number of different documents such as surveys and feedback forms from various sources by providing an effective summary and analysis of the relevant data in these text documents.
SoK

Information security metrics are used to measure the effectiveness of information security countermeasures. A large number of metrics and their technical nature creates difficulties when generating reports for the information security management level of an organization. Managers struggle with the usefulness and clarity of the metrics because they are not linked to the security management goals. Also, responsible managers with no technical information security background struggle to understand the metrics. Therefore, this study uses a state-of-the-art literature analysis together with the Goal-Question-Metric approach to investigate linking technical security metrics to management success factors. This study enables the management to design appropriate security reports for their organization and to direct the metrics toward making goal-oriented decisions. Furthermore, the study invites future research by revealing areas in which security metrics do not exist and create new solutions and studies to suggest a standardized information security dashboard.
Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

Abstract In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.
Design of Visualization System in Electric Vehicle Charge and Discharge Status Information

Traditional information visualization system has the defect of low monitoring information visualization, based on which charging and discharging state information visualization system of electric vehicles is designed. Moreover, information visual processor, AD data sampling board, RFID radio frequency card and other hardware structures are designed according to system function, and data acquisition function protocol is followed to show system visual function and introduce cloud data monitoring technology so that wind power can be predicted to manage the entire life operation and maintenance cycle. Meanwhile, real-time monitoring status and data of electric vehicles are released. Therefore, comparative experiment is designed to verify that the system can improve visualization of charge and discharge status information in practical applications and has certain social research value.