Reviewerly/exHarmony huggingface dataset:
Flow-based Service Type Identification using Deep Learning

Automatic identification of the service type used by network flows (e.g., HTTP and MySQL) is an essential part of many cloud management and monitoring tasks for quality of service, security monitoring, resource allocation, etc. Several studies have adapted deep learning models for accurate service type identification of network traffic. These models vary in how the message flow data is used and what datasets are considered. There are no published guidelines on selecting the best approach for automating the service identification process. In this paper, we opt to fill such a technical gap and provide a detailed study of the trade-offs of different deep-learning based approaches for service type identification of network traffic. Towards this end, we generate flow-based datasets for a wide range of service types that are commonly deployed in the cloud. We consider two different deep learning models that have shown promising results in this context, and show their performance for both payload- and header-based datasets, considering fundamental parameters such as dynamic service port configuration, flow direction and the packet order in the flow stream.
Probability density evolution filter

Based on probability density evolution method (PDEM) and Bayes’ law, a new filter strategy is proposed, in which the prior probability of system state of interest is predicted by solving the generalized density evolution equation (GDEE), the posterior probability of system state is then updated in terms of Bayesian formula. Furthermore, a Chebyshev polynomial-based collocation method is developed to obtain numerical solutions of the prior probability. Three illustrative examples are finally presented to validate the probability density evolution filter (PDEF). Overall, PDEF evolves the prior probability through a differential equation, and exhibits accuracy close to PF without any resampling algorithm.
Studies on the influence of speckle density on the accuracy of digital image correlation method based on numerical simulation

Abstract Digital image correlation method is a high-precision non-contact modern optical mechanics method whose measurement accuracy is directly affected by the quality of the speckle pattern used. In this paper, a numerical simulation experiment is designed to measure and analyze the accuracy of DIC method using speckle patterns which are generated with different speckle densities. The results show that the measurement error of DIC decreases first and then increases with the increase of speckle density, and the error is minimum when the duty ratio is about 60% to 70%. The result show that in practical application the duty ratio of speckle on the specimen surface should be appropriately high, preferably between 60% and 70%. This study is helpful for speckle selection and related research in practical application.
A Cascaded Structure for Generalized Graph Filters

One of the main challenges of graph filters is the stability of their design. While classical graph filters allow for a stable design using optimal polynomial approximation theory, generalized graph filters tend to suffer from the ill-conditioning of the involved system matrix. This issue, accentuated for increasing graph filter orders, naturally leads to very large (small) filter coefficients or error saturation, casting a shadow on the benefits of these richer graph filter structures. In addition to this, data-driven design/learning of graph filters with large filter orders, even in the case of classical graph filters, suffers from the eigenvalue spread of the input data covariance matrix and mode coupling, leading to convergence-related issues as the ones observed when identifying time-domain filters with large orders. To alleviate these conditioning and convergence problems, and to reduce the overall design complexity, in this work, we propose a cascaded implementation of generalized graph filters and an efficient algorithm for designing the graph filter coefficients in both model- and data-driven settings. Further, we establish the connections of this implementation with so-called graph convolutional neural networks and demonstrate the performance of the proposed structure in different network applications. By the proposed approach, further error reduction and better design stability are achieved.
Variational Bayesian Learning

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Intelligent Assistive Sensors and Smart Systems for the Control and Analysis of Driver Reaction Times

Intelligent assistive sensors and smart systems for powered wheelchairs are presented and used to analyze ways to improve mobility and enhance the quality of life of powered wheelchair users. Time-delays are introduced to investigate their effects. Systems interpret joystick signals and sensor signals and then mix them together. Ultrasonics are used to identify hazards and the intelligent systems suggest safer directions and speeds. Wheelchair drivers were observed while they completed tasks. Time-delays were introduced, wheelchair users undertook tests with and without intelligent systems, as delays were extended then more failures were recorded. Systems were practically tested. Results from drivers completing a series of timed routes are presented Results are compared and contrasted with recent systems and systems presented in this paper outperformed recent systems. Also the level of support provided by the systems should be adjusted depending on conditions and environments. Clinical trials will be conducted at Chailey Heritage Foundation as part of broader research conducted by the authors.
A Smartphone User Verification Method Based on Finger-Writing of a Simple Symbol

Writer verification based on finger-writing of a simple symbol on a touch screen is proposed herein. The users write a simple and well-known symbol, for example, a circle, triangle, or square. In addition, the users write the symbol using their finger instead of a pen on a tablet. This allows more convenience with the use of the proposed method. However, it was observed that the original approach and obtained verification performance were not reliable. In this work, we create a new database using thirty participants. By examining individual features extracted from the database, the risk of misjudgment is found. In order to solve this problem, a coordinate transformation method is introduced. Moreover, normalization is examined for fusing individual features by comparing three normalization methods. The proposed method with appropriate coordinate transformation and normalization achieves an equal error rate of 10.6% even when all participants write only a simple circle.
Using dual-process theory and cues of identifiability to explain supportive exchanges on social question and answer sites

On social question and answer sites (Q&As), users engage each other in a community built on seeking and providing informational support. On these sites, question askers select and assess advice provided by other users. Using the dual-process theory of supportive communication and advice response theory, we examined how message features and user characteristics influence question askers’ selection and evaluation of the quality of provided answers. Through a content analysis of Yahoo! Answers questions and answers (N = 450), we found that message features signaling social relationships were associated with a question asker’s selection and rating of answers. We discuss theoretical implications of these findings.
A Security Requirement Engineering Case Study: Challenges and Lessons Learned

This paper is a case study of applying a Security Requirements Engineering approach, named CompASRE, on a graduate school grade management system. The goal is to illustrate CompASRE’s use, evaluate it and propose improvements. Security requirements are elicited from goals and risks. One of the main challenges to applying CompASRE was the separation between thinking purely about security goals and thinking prematurely about threats when eliciting requirements from goals. It was also learnt that the risk assessment phase in this case was time and effort consuming without much added value, contributing with only 8.3% of elicited requirements. Conformity to security standards was particularly challenging and not always possible. Another challenge was the blurred line between the operation of refining goals and the operation of eliciting singular undividable requirements. Therefore, suggested improvements to CompASRE are to replace the risk assessment phase with a less complicated step consisting of threat modeling; limit thinking about threats and discussing them when identifying goals and eliciting requirements from goals; and finally unite refining goals and eliciting requirements in the same phase.
An Interactive and Immersive Remote Education Platform based on Commodity Devices

Virtual reality (VR) holds a great potential to provide interactive and immersive learning experiences for students in remote education by using existing mobile devices, which is extremely meaningful during the current pandemic. In such a VR application, satisfactory user experience requires: 1) high-resolution panoramic image rendering; 2) high frame rate; 3) synchronization among users. This requires that either mobile devices perform fast image rendering or today's wireless network can support multi-Gbps traffic with extremely low delay, neither of which is the case in current practice. In this demo, we develop a platform for interactive and immersive remote education based on commodity devices, where a server performs rendering to ensure that the rendered images have high-resolution (2560×1440 pixels) and are displayed at a high frame rate (60 frames per second) on the client-side. We further leverage motion prediction to overcome the diverse round-trip time (RTT) between a server and users and ensure synchronization among users (average 9.2 ms frame latency difference among users), which improves at least 60% and 20% compared to the existing local-rendering and server-rendering methods, respectively.
A Characterisation of Open Bisimilarity using an Intuitionistic Modal Logic

Open bisimilarity is defined for open process terms in which free variables may appear. The insight is, in order to characterise open bisimilarity, we move to the setting of intuitionistic modal logics. The intuitionistic modal logic introduced, called $\mathcal{OM}$, is such that modalities are closed under substitutions, which induces a property known as intuitionistic hereditary. Intuitionistic hereditary reflects in logic the lazy instantiation of free variables performed when checking open bisimilarity. The soundness proof for open bisimilarity with respect to our intuitionistic modal logic is mechanised in Abella. The constructive content of the completeness proof provides an algorithm for generating distinguishing formulae, which we have implemented. We draw attention to the fact that there is a spectrum of bisimilarity congruences that can be characterised by intuitionistic modal logics.
Research on the Psychological Distribution Delay of Artificial Neural Network Based on the Analysis of Differential Equation by Inequality Expansion and Contraction Method

Abstract This article first introduces neural networks and their characteristics. Based on a comparison of the structure and function of biological neurons and artificial neurons, it focuses on the structure, classification, activation rules, and learning rules of neural network models. Based on the existing literature, this article adds a distributed time lag term of the neural network system. In the actual problem, history has a very important influence on the current change situation, and it is not only at a specific time in the past. It has an impact on the current state change rate. Therefore, based on the existing literature that only has discrete time lags, this paper adds distributed time lags. Such neural network systems can better reflect real-world problems. In this paper, we use three different inequality scaling methods to study the existence, uniqueness, and global asymptotic stability of a class of neural network systems with mixed delays and uncertain parameters. First, using the principle of homeomorphism, a new upper-norm norm is introduced for the correlation matrix of the neural network, and enough conditions for the existence of unique equilibrium points in several neural network systems are given. Under these conditions, the appropriate Lyapunov is used. Krasovskii functional, we prove that the equilibrium point of the neural network system is globally robust and stable. Numerical experiments show that the stability conditions of the neural network system we obtained are feasible, and the conservativeness of the stability conditions of the neural network system is reduced. Finally, some applications and problems of neural network models in psychology are briefly discussed.
A Hybrid Solution Approach for Large-scale Batch Scheduling with Quantum Computing

In this work, we demonstrate the applicability and efficiency of hybrid quantum computing (QC) based techniques for solving large-scale batch scheduling problems. We propose a novel solution approach that integrates classical and quantum techniques to overcome their individual shortcomings. The hybrid solution framework allows us to take advantage of the complementary strengths of the two methods. We demonstrate the use of this hybrid approach in developing efficient solution algorithm for an intractable batch scheduling problem formulated as mixed-integer fractional program. We then propose a novel QC-based parametric decomposition algorithm for solving large-scale batch scheduling problems. The performance and efficiency of the developed hybrid models and methods are validated by performing several computational experiments and comparing the obtained computational results with those of the state-of-the-art classical off-the-shelf optimization solvers..
Re-weighting features selection based on wrapper filter and genetic algorithms for figurative images recognition

This paper describes a feature selection technique for logo recognition to deal with a large number of images in the trademark registration system. The proposed approach is based on genetic algorithms with features re-weighting process. Diverse types of features are combined to perform an evolutionary process applied on four logotypes. Comparative results show that our method can support invariance in partial image deformations and outperforms other existing techniques that use one feature alone.
SemanticPhish: A Semantic-based Scanning System for Early Detection of Phishing Attacks

In the fight against phishing attacks, time is of the essence. Each individual attack is usually short-lived, but many people are still victimized during that short timeframe. To curb the problem, one way is to detect the attack shortly after the site is deployed, before victims have a chance to access it. Monitoring every new URL on the internet clearly is not a practical option, but monitoring sites that have a good chance of hosting an attack can be done. One of the ways to spot such a site is to monitor domain names. It is known that a growing number of phishing attacks are hosted by the attacker [1], [2], using their own domain names. Therefore, domain names might help spotting likely attacks. In this paper, we look at the following questions: can we currently tell apart domain names used in phishing attacks from other domains? If so, can we train a system to automatically detect these domains? And can such a system find attacks before they are being reported by victims? We show that the semantic of the words used by many phishing domains is different from the semantic of the words used by benign domain names, and that we can train a classifier to reliably flag these domains. We propose a system, SemanticPhish, which efficiently monitors these domains and is able to detect many phishing attacks without requiring the attack to be reported first. SemanticPhish can find attacks several days before Google’s “safe browsing” starts flagging them.
Tunisian High School Students and Digital Media Literacy: A Quantitative Study

Digital media literacy has become an intrinsic component in shaping high school students’ knowledge acquisition and critical thoughts. Over the last two decades, internet and computers have been the implemented tools to reach such goals and promote the students’ learning. This article looks for the impact of Information and Communication Technology (ICT) on Tunisian secondary school students by detecting their technical skills as well as their critical understanding. This quantitative study relies on a self-reporting approach and targets 150 Tunisian secondary students. It proves the necessary consideration of technological and social variables in helping sort out major digital handicaps related to secondary students and displays the interconnectedness between the different dimensions of digital media literacy. It also displays that Tunisian high school students cannot be referred to as digital-media literate people. The study contributes to the field of digital media literacy as it offers a solid empirical background to build on and indicates the necessity of integrating digital media literacy into the school-based initiatives.
Minimizing Off-Chip Memory Access for CNN Accelerators

Convolution neural network (CNN) accelerators are commonly used to boost the CNN application's performance. The energy efficiency of the CNN accelerators is of paramount importance for battery-operated devices like smartphones. A substantial fraction of their energy consumption is due to off-chip memory accesses. These accelerators connect to the off-chip memory by a wide bus to improve the throughput. However, accessing the data from an unaligned address or size that is not a multiple of bus width leads to low bus width utilization and wastage of energy. Memory accesses can be reduced considerably by partitioning the data in a way that increases the number of aligned accesses and optimally utilizes bus width. We propose an approach that factors in the architectural parameters to evaluate the memory access. Our tool determines optimal partitioning and data reuse scheme for convolution and fully connected layers to minimize the off-chip memory accesses for these accelerators. Compared to state-of-the-art, our approach reduces off-chip memory accesses of AlexNet, VGG16, and ResNet:50 by 9%, 16%, and 28% on 64 bits data bus and by 16%, 29%, and 46% on 128 bits data bus, respectively.
An improved exact sampling algorithm for the standard normal distribution

In 2016, Karney proposed an exact sampling algorithm for the standard normal distribution. In this paper, we study the computational complexity of this algorithm under the random deviate model. Specifically, Karney’s algorithm requires the access to an infinite sequence of independently and uniformly random deviates over the range (0, 1). We give a theoretical estimate of the expected number of uniform deviates used by this algorithm until it completes, and present an improved algorithm with lower uniform deviate consumption. The experimental results also shows that our improved algorithm has better performance than Karney’s algorithm.
Assessment of the Impact of the Hybrid Software Development Approach

Abstract The paper focuses on enhancing the current or modern software development practices in Information Technology (IT) organizations. The research paper aims to address the gap between various hybrid software development approaches and existing practices that contribute to software development practices. The methodology adopted here is to obtain data from software professionals and understand how the current practices in the software development industry have contributed to the productivity of the employees and the IT organization, which has been done with the help of a survey questionnaire and by analyzing existing research articles and papers on the software development practices. This research is a conceptual model focused on a modern hybrid approach for software development, rather than focusing on a single software development approach, which can help IT organization increase productivity and deliver business value beyond customer expectations. The cost to build and time to market a software product or a software service can reduce by integrating the required software development practices. Today, staying relevant in the competitive market can be made easier by choosing the best available software development practices, frameworks, and models to fit an organization’s value and knowledge system. The conceptual model aims to reduce the gap between various hybrid software development approaches and existing practices contributing to software development. The paper concludes by discussing the benefits of the hybrid software development approach and urge IT organizations to adopt it as per their software development requirements and customer requirements.
Effect of dual aircraft formation spacing on target position tracking on measurement conversion by monopulse radar

Aimed at the current problem of insufficient analysis of the influence of the diversified formation of dual aircraft on monopulse radar position tracking, the dual aircraft echo equivalent two point source jamming model is built. We designed the dual aircraft formation penetration movement scenario and three different formation spacings of 150, 250, and 350 m and extracted the dynamic radar cross section sequences of dual aircraft under different formation spacings. We established the dual aircraft formation equivalent scenario and calculated the dynamic signal-to-noise ratio and distance measurement accuracy of the radar received echo signals with different formation spacings. We fully considered the randomness characteristics of the phase difference of the actual dual aircraft echo and built the random angle error model of dual aircraft echo equivalent jamming. The distribution and random numerical characteristics of the random angular error are simulated and analyzed, and we combined the radar measurement conversion target position tracking algorithm to analyze the influence of different formation spacings on the monopulse radar tracking target position and position tracking accuracy. The simulation results show that a formation spacing of 350 m produces a greater randomness of angular error for dual targets, resulting in a greater dispersion of radar measurement traces, and the final average position mean square error is 132.8 m, which improves the position error by 15.6% compared with 150 m spacing.
Research on Knowledge Graph Construction for Intelligent Operation and Maintenance of Electrical Transformers

Abstract In the process of transformer equipment operation and maintenance, power companies encounter some problems, including of unstructured text data difficult to use, full caliber data difficult to deeply integrate and shallow equipment knowledge application. Based on artificial intelligence technology such as semantic web, knowledge map, natural language processing, etc., this paper studies the key technologies of equipment intelligent service, and proposes a knowledge intelligent technology framework for transformer. This framework includes three components: unstructured text intelligent recognition and extraction, device centered device knowledge representation and storage, and device knowledge intelligent application.