Reviewerly/exHarmony huggingface dataset:
Ontology-driven Security Testing of Web Applications

Vulnerabilities in existing software systems represent a great challenge for security assurance, where well known attacks like cross-site scripting (XSS) or SQL injections (SQLI) still represent a common threat for today's web applications. Failure to cover these issues in verification might result in unforeseen consequences for users of such software systems. For this reason, we have to come up with a rigorous testing approach should that should combine knowledge about common attacks and the system under test. Ontologies, which is a concept originating from philosophy and also considered in AI research, provide means for formalizing such knowledge from which we want to obtain test cases in an automated fashion. In this paper, we follow this idea and present a security testing approach that relies on ontologies of attacks and the system under test. In particular, the used ontology depicts information from the domain of web applications as well as their communication protocol. Actually, such a model represents an attack ontology that serves as the initial step in a test generation process. In turn, the inferred output is used in order to test a SUT for vulnerabilities. The test case generation process converts ontologies into input models for combinatorial testing (CT), from which we obtain abstract test cases that can be automatically mapped to concrete ones. Besides outlining the foundations behind this approach, we also show its applicability considering case studies from the domain of web applications.
On D-ary Fano Codes

We define a D-ary Fano code based on a natural generalization of the splitting criterion of the binary Fano code to the case of D-ary code. We show that this choice allows for an efficient computation of the code tree and also leads to a strong guarantee with respect to the redundancy of the resulting code: for any source distribution p = p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,... p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> 1) for D = 2, 3,4 the resulting code satisfies L̅ - H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> (p) ≤ 1 - p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> , where L̅ is the average codeword length, p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> = min <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> (p) = Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">=</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> 1/p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> (the D-ary entropy);2) inequality (1) holds for every D ≥ 2 whenever every internal node has exactly D children in the code tree produced by our construction.We also formulate a conjecture on the basic step applied by our algorithm in each internal node of the code tree, that, if true, would imply that the bound in (1) is actually achieved for all D ≥ 2 without the restriction of item 2.
A practical application for sentiment analysis on social media textual data

With the amount of data that is available today in textual form, it is essential to be able to extract as much useful information as possible from them. While some textual documents are easy to be understood, other textual documents may need extra processes to discover the hidden information within it. For instance, how the author was feeling while writing this piece of text, or what emotions authors are expressing in this piece of text. The idea of discovering what emotions are expressed in a textual document is known as sentiment analysis. The interest in sentiment analysis has been steadily growing in the past decade. Being able to accurately detect and measure the different emotions present in a text has become more and more useful as the availability of online resources has increased. These resources can range from product reviews to social media content. Each of these resources presents their own distinct challenges while still sharing the core techniques and procedures. In this paper, we introduce an application that can detect four distinct emotions from social media posts. We will first outline the techniques we have used as well as our outcomes, then discuss the challenges that we faced, and finally, our proposed solutions for the continuation of this project.
Information Security Behavior: Development of a Measurement Instrument Based on the Self-determination Theory

Employee information security behaviour is important in securing an organisation’s information technology resources. Employees can act in a risky or secure manner. Improving employee information security behaviour is important for organisations and should follow an assessment of their behaviour. A robust measuring instrument is a necessity for effectively assessing information security behaviour. In this study, a questionnaire was developed based on the Human Aspects of Information Security Questionnaire and self-determination theory and validated statistically. Data obtained through a quantitative survey (N = 263) at a South African university was used to validate the questionnaire. The result is a questionnaire that has internally consistent items, as shown by the results of the reliability analysis. Universities can use the questionnaire to identify developmental areas to improve information security from a behaviour perspective.
Sequential Monte Carlo and Probability Hypothesis Densities for Underwater Multitarget Tracking in Active Sonobuoy Systems

The problem of underwarter multitarget localization and tracking in active sonobuoy systems essentially belongs to the category of multi-sensor data fusion and parameter estimation. The random sets theory derived sequential monte carlo and probability hypothesis densities(SMC-PHD) filter, which can be used to obtain the approximate solution of bayesian posterior recursion for multisensor data fusion and multi-target tracking. In this paper, a clear mathematical theoretical framework and state estimation model has been constructed, and therefore the time-delay and doppler measurements obtained by each node in the active sonobuoy system are used as the input of SMC-PHD filter to output reliable estimation of unknown and time-varying number of underwater targets and their states under clutter environment. Simulation results show that the proposed method can achieve high precision tracking of time-varying number of targets in clutter environment.
Exploring Unique Relevance for Mutual Information based Feature Selection

Mutual Information (MI), a measure from information theory, is widely used in feature selection. Despite its great success, a promising feature property, namely the unique relevance (UR) of a feature, remains unexplored. In this paper, we improve the performance of mutual information based feature selection (MIBFS) by exploring the utility of unique relevance (UR). We provide a theoretical justification for the value of UR and prove that the optimal feature subset must contain all features with UR. Since existing MIBFS follows the criterion of Maximize Relevance with Minimum Redundancy (MRwMR) which ignores UR of features, we augment it to include the objective of boosting unique relevance (BUR). This leads to a new criterion for MIBFS, called MRwMR-BUR. We conduct experiments on six public datasets and the results indicate that MRwMR-BUR consistently outperforms MRwMR when tested with three popular classifiers. We believe this new insight can lead to new optimality bounds and algorithms.
Implementation of Automatic Face Detection System Based on ARM

The embedded automatic face detection system is to locate the face region in the real scene. In order to reach a real-time performance, We adopt a cascaded structure of the convolution neural network model to solve face detection problem. In hardware implementation, the ARM Cortex-A9 processor is used to achieve the algorithm. And the Neon unit in ARM is used to achieve the parallel operation of the basic data of the algorithm, so the detection algorithm is accelerated to achieve the real-time operation of the automatic face detection system.
Gesture-Based Human-Machine Interface System by Using Omnidirectional Camera

There has been extensively a huge amount of research on gesture-based human-machine interface so far. It is used not only for computer operations but also for virtual reality operations and computer game operations as applications in which gesture input is effective. In general, a dedicated device for such a system is developed; however, a gesture-based human-machine interface by using a generic device is more convenient and scalable than a dedicated device. In this paper, we propose a gesture-based human-machine interface system by using a generic device, an omnidirectional camera. We can input some simple operations for computer games by gestures, and the system can recognize a series of successive gestures. In addition, the system can simultaneously recognize gestures of several persons who stand along the surrounding of an omnidirectional camera.
Jointly Learning the Discriminative Dictionary and Projection for Face Recognition

Recently, dictionary learning has become an active topic. However, the majority of dictionary learning methods directly employs original or predefined handcrafted features to describe the data, which ignores the intrinsic relationship between the dictionary and features. In this study, we present a method called jointly learning the discriminative dictionary and projection (JLDDP) that can simultaneously learn the discriminative dictionary and projection for both image-based and video-based face recognition. The dictionary can realize a tight correspondence between atoms and class labels. Simultaneously, the projection matrix can extract discriminative information from the original samples. Through adopting the Fisher discrimination criterion, the proposed framework enables a better fit between the learned dictionary and projection. With the representation error and coding coefficients, the classification scheme further improves the discriminative ability of our method. An iterative optimization algorithm is proposed, and the convergence is proved mathematically. Extensive experimental results on seven image-based and video-based face databases demonstrate the validity of JLDDP.
A Study of Population Diversity Using an Enhanced Brain Storm Optimization

In evolutionary algorithm, population diversity is a vital factor for solving problems. To avoid the premature convergence, it is imperative to preserve the population diversity during the evolution. The population diversity ensures avoiding the premature convergence. Population diversity is a measure that has been used extensively in studies to measure premature convergence. Population diversity is convenient for measuring and dynamically adjusting an algorithm's ability of exploration or exploitation. Brain Storm Optimization (BSO) is a new kind of swarm intelligence method inspired by the cooperative behavior of human beings in the problem-solving process. BSO suffers the premature convergence which happens partly due to the solutions getting clustered. The solution set clustered after a few iterations which indicate that the diversity level decreases rapidly during the search. In order to enhance the computational efficacy of the original BSO algorithm and maintain the population diversity, we propose two ways to re-cluster the original BSO. We introduce a BSO in objective space with a Cauchy distribution with the knowledge that Cauchy distribution infers a faster rate. In addition, we introduce a new step size equation as a parameter to balance the exploration and exploitation in the search space. A good algorithm maintains population diversity in both spaces (objective and parameter spaces). Our goal is to investigate why the enhanced BSO performs efficiently from the perspective of population diversity on a set of five benchmark functions. Experimental figures show that the performance of the proposed algorithms performs better from the population diversity measurement.
Robot Service for Elderly to Find Misplaced Items: A Resource Efficient Implementation on Low-Computational Device

Elderly people often forget to put the items they need due to decreased memory. In this study, we developed an Integrated platform assistance robot providing support to elderly people. We developed a robot assistant platform that was equipped with an indoor positioning system that can help the elderly find misplaced items. Deep learning already has good accuracy in detecting the object but requires great computation resources. When applied to devices that have limited computing and memory capabilities such as robots, the computation time becomes slow or not applicable. We built a lightweight CNN that could run on a single board computer. To improve the accuracy of the network, we apply knowledge distillation by using an extensive network (YOLOv3) as a teacher. To increase computational speed, we do it by reducing the number of layers by implementing batch normalization fission. After being tested on the YOLO, knowledge distillation method can be used to increase accuracy, batch normalization fission will increase computation speed. From the experiment results using the VOC dataset on YOLO architecture with MobileNet feature extractor, the knowledge distillation method can increase accuracy by 9.4% from 0.3850 mAP to 0.4215 mAP and batch normalization fission can speeds up the computation time to 100.7% from 8.3 FPS to 16.66 FPS on CPU i7. The Knowledge Distillation successfully increase the model's accuracy, reducing the model's size, and batch normalization fusion method can speed up the detection process.
Real-Time Full-Field Photogrammetry Implementation During the Mars 2020 Heat Shield Acceptance Test

This paper describes the first known attempt of implementing full-field photogrammetry during the acceptance test of critical flight hardware the size ( D=4.4 m) of the Mars 2020 heatshield carrier structure. Mars 2020, NASA's next science mission to Mars, uses a similar entry vehicle as the Mars Science Laboratory (MSL). In fact, the intention was that Mars 2020 would use the spare MSL heat shield (HS) structure. Unfortunately, the spare HS failed during proof-loading. The redesigned heat shield was proof loaded using a simpler load application method that enabled the use of photogrammetric measurements to be made on the entire outer mold line of the HS structure. In essence, full-field photogrammetry was used as real-time inspection technique during loading to guard against failure. Examples of how the real-time results were used to assess the health of the hardware as well as test/analysis correlation at critical load-holds are presented. Finally, advantages and limitations of the full-field measurement technique as applied to flight hardware acceptance tests are discussed.
Spatial Descriptions on a Functional-Geometric Spectrum: the Location of Objects

Experimental research on spatial descriptions shows that their semantics are dependent on several modalities, among others (i) a geometric representation of space (“where”, geometric knowledge) and (ii) dynamic kinematic routines between objects that are related (“what”, functional knowledge). In this paper we examine whether geometric and functional bias of spatial relations is also reflected in large corpora of images and their corresponding descriptions. In particular, we examine whether the variation in object locations in the usage of a relation is a predictor of that relation’s functional or geometric bias. Previous experimental psycho-linguistic work has examined the bias of some spatial relations, however our corpus-based computational analysis allows us to examine the bias of spatial relations and verbs beyond those that have been tested experimentally. Our findings have also implications for building computational image descriptions systems as we demonstrate what kind of representational knowledge is required to model spatial relations contained in them.
Research on Edge Intelligence-based Security Analysis Method for Power Operation System

At present, the on-site safety problems of substations and critical power equipment are mainly through inspection methods. Still, manual inspection is difficult, time-consuming, and uninterrupted inspection is not possible. The current safety management is mainly guaranteed by rules and regulations and standardized operating procedures. In the on-site environment, it is very dependent on manual execution and confirmation, and the requirements for safety supervision and operating personnel are relatively high. However, the reliability, the continuity of control and patrol cannot be fully guaranteed, and it is easy to cause security vulnerabilities and cause security accidents due to personnel slackness. In response to this shortcoming, this paper uses edge computing and image processing techniques to discover security risks in time and designs a deep convolution attention mechanism network to perform image processing. Then the network is cropped and compressed so that it can be processed at the edge, and the results are aggregated to the cloud for unified management. A comprehensive security assessment module is designed in the cloud to conduct an overall risk assessment of the results reported by all edges, and give an alarm prompt. The experimental results in the real environment show the effectiveness of this method.
Towards a hermeneutic definition of software

Abstract The paper intends to establish a comprehensive definition of software from a postphenomenological and hermeneutic point of view. It intends to show the contribution of continental philosophy to the study of new technologies. In section “Introduction: why do we need a comprehensive definition of software?,” I underline the need for a philosophical analysis that can highlight the multifaceted and paradoxical nature of software. In section “Engineering in written form: the five criteria,” starting from some remarks on the history of programming languages, I define a list of minimal requirements (five criteria) that something needs to meet to be qualified as software. All these requirements share two essential features: the written form and the effectiveness, that is, the need to be executed by a physical machine. In section “Software as text: a hermeneutic model,” I focus on software as form of writing. I develop this idea by using Ricoeur’s hermeneutic model. I claim that software is a type of text. In section “The grammatology of microprocessor,” I focus on the second aforementioned feature: the effectiveness of software. I claim that this effectiveness is based on the analogy between electric circuitries and Boolean logic. Software is a writing and re-writing process that implies an interpretation on two levels, epistemological and ontological.
Automatic Understanding of Code Mixed Social Media Text: A State of the Art

Social media content is often addressed as noisy or informal text due to the existence of zigzag conversational patterns. People do not always use Unicode rather they mix multiple languages. Hence, the processing of code mixed data postures computational challenges ahead. Since decades, social media content and its analysis have gained momentum worldwide. In parallel, the pace of research on Indian languages is also commendable. In India, the users of social media hail from different religions, regions, subdivisions and culture. The major concern of the paper is to throw light on the works done in Indian languages with code mixed social media as a concern. The journey of the research in the respective field has various milestones between basic tasks of natural language processing and deep learning. This paper focusses on the works done on Indian languages with respect to language identification, normalization and POS tagging. Efforts have been done to discuss the tools, techniques and the corpora used by researchers in different Indian languages. In the digital age, we have an abundancy of tools and APIs available for extracting code mixed text. Still, there is paucity of public data available for analysis. The need of the hour seems to be protruding toward deep learning and extending the public availability of code mixed corpora.
Counterfactual-based minority oversampling for imbalanced classification

A key challenge of oversampling in imbalanced classification is that the generation of new minority samples often neglects the usage of majority classes, resulting in most new minority sampling spreading the whole minority space. In view of this, we present a new oversampling framework based on the counterfactual theory. Our framework introduces a counterfactual objective by leveraging the rich inherent information of majority classes and explicitly perturbing majority samples to generate new samples in the territory of minority space. It can be analytically shown that the new minority samples satisfy the minimum inversion. Therefore, most of them are located near the decision boundary. The empirical evaluation of the six benchmark datasets shows that our approach clearly outperforms the state-of-the-art methods.
A Improved Histogram Probabilistic Multiple Hypothesis Tracking Algorithm for Maneuvering Weak Target

A improved histogram probabilistic multiple hypothesis tracking (H-PMHT) algorithm based on the extended set-membership filtering (ESMF) is proposed for maneuvering weak targets detection and tracking. As an effective track-before-detect (TBD) method, H-PMHT algorithm is applied to synthesizing target measurements from multi-frame radar observation data directly, so as to avoid the problem of information loss in traditional threshold detection. Since the prior knowledge of maneuvering weak targets in real situations is always unknown but bounded, the ESMF algorithm is applied to obtain the estimated state from the synthesized measurements. Simulation results show that the ESMF based H-PMHT algorithm is capable of detecting and tracking dim targets, and has a better performance in tracking accuracy compared with the Kalman filter based H-PMHT algorithm.
A New Method of Specific Emitter Identification

Specific Emitter Identification (SEI) techniques have been widely used as a tool of spectrum management operation to enhance the security by matching the received signal with the specific emitter. However, there are few methods focusing on the unintentional modulation (UIM) of signals. In this letter, we propose a novel method which extracts the UIM part in the frequency spectrum and instantaneous phase from the original signals as UIM radio frequency fingerprinting, and then use the non-Gaussian measuring tools to extract the dimension-reduced secondary features. The real-world signals are used to evaluate the performance of the proposed method and the Support Vector Machine (SVM) classifier and the Gaussian Mixture Model (GMM) classifier are applied to make the classification. Experiment results verified that the proposed method has remarkable performance and less computational complexity, which is relatively practical.