Reviewerly/exHarmony huggingface dataset:
An N-gram Based Deep Learning Method for Network Traffic Classification

Various attacks have become the main threat in the Internet world. Traffic classification is the first step in network exception detection or network-based intrusion detection systems, and plays an important role in the field of network security. With the development of Internet technology, the source and complexity of network attacks are getting higher and higher, making it difficult for traditional anomaly detection systems to effectively analyze and identify malicious traffic. In recent years, the method of deep learning has been widely used in the field of traffic recognition, and the characteristics of traffic data can be automatically identified. Because of the size limit of the input data of the neural network, the flow data needs to be trimmed to feed into the network for learning, so the neural network cannot learn the characteristics of the traffic data well. In this paper, we propose an N-gram-based data processing method to convert the raw traffic data into N-gram features to represent more information. Then our method uses a detector based on convolutional neural network (CNN) to classify and detect data. Our experiments show that the detection accuracy of using N-gram feature data is better than the method using raw traffic. This method can more effectively detect malicious traffic data.
Memorable Password Generation with AES in ECB Mode

This chapter presents ALP: an adjustable lexicon-generated password mechanism. ALP is a password encryption web application. In ALP, a password is generated randomly from an imported dictionary of words. For example, with an entire dictionary, it will pick out random strings that add up to a 128-bit hex key and return the key to the user. The password will be “easy to remember” since it will be made of a chosen pool of words. It will then take in the user’s message to be encrypted and output the encrypted message according to the key. The password generator, if need be, can be switched to a completely random mode which will make it output a string of random numbers and letters if the user does not choose to use the easy-to-remember password generator function. The application will also be able to decrypt the AES-128 encrypted message and transform it back into normal text. This experiment suggests that the proposed model is a block cipher that can successfully generate a memorable random password.
Adversarial Response Generation Against Topic Relevance

Abstract In recent years, generative adversarial networks have performed well in the field of dialogue generation to improve the information diversity of dialogue responses. Often overlooked, however, is that the query and response are not relevant on the topic. In order to improve the topic relevance of chat conversation, the paper proposed a topic-relevance adversarial response generation model, TR-ARG, which is composed of generator G, discriminator D and topic classifier T. The experiment was evaluated on OpenSubtitles, an open dialog dataset, and compared with the current baseline models SEQ2SEQ and GAN-AEL. The results show that our model can effectively improve the topic relevance of generated responses.
Neural Network Study Quantum Synchronization and Quantum Correlation Under Non-zero Temperature

As an important part of quantum mechanics, quantum measurement can not only enable us to get the information of the quantum system, but also play an indispensable role in the control of the quantum system. The quantum synchronization phenomenon and the quantum correlation between the detector and the quantum system are very useful in quantum measurement process. However, there are still few studies on the nature of the quantum synchronization process and the realization of the classification of quantum synchronization. In this work, a two-qubit open system model which consists of a qubit system in a cavity and probing tool (another qubit) is proposed, the properties of quantum correlation are explored under non-zero temperature, and the characterizes of quantum synchronization also has been illustrated in different situation. It is shown the effect of different temperature for the quantum synchronization and the quantum correlation. Additionally, it is shown in the article that the process of synchronization under different parameter. Meanwhile, the artificial neural networks method is used to further study the classification of the quantum synchronization at non-zero temperature and it is shown an effective consequence. The results pave a way for subsequent research on the quantum synchronization and quantum correlation.
Growth and harvest induce essential dynamics in neural networks

Training neural networks with faster gradient methods brings them to the edge of stability, proximity to which improves their generalization capability. However, it is not clear how to stably approach the edge. We propose a new activation function to model inner processes inside neurons with single-species population dynamics. The function induces essential dynamics in neural networks with a growth and harvest rate to improve their generalization capability.
Design and Implementation of a Virtual Reality Insects Learning System with Its User Experience Evaluation

This work proposed a Virtual Reality Insects Learning System for elementary school students. Since the application of Virtual Reality has been gradually involved into education recently, the investigation of the effect and its usefulness could be an interesting topic for both content provider and teachers. The proposed work includes fifteen different kinds of insects with individual explanation; students can interact with the system to learn to know each insect. The proposed system is further used to evaluate students' learning experience and perceptions. The experimental results show that students perceived positive motivation and attitudes accompany with less cognitive load in Virtual Reality learning. The results of this work also demonstrate that the Virtual Reality related content provide a brand new learning experience which is helpful for students' study.
Applied Geology and Geoinformatics for Ground Water Exploration, Protection and Management

Groundwater[aut]Papatheodorou, Konstantinos protection leading to a sustainable managementManagement of the resource is greatly supported by the use of groundwaterGroundwater monitoringMonitoring systems. The respective conceptual models representing the groundwaterGroundwater cycle as well as the quality of data input to those systems, defines their efficiency. The chapter is based on previous successful case studies regarding groundwaterGroundwater explorationGroundwater exploration for resource allocation, vulnerability and risk assessment, managementManagement and protection using groundwaterGroundwater information systems. Key outputs show that it is feasible to: (i) identify and accurately map water flow paths and plan efficient exploration while at the same time, identifying and delineating recharge areas for protection; (ii) successfully assess vulnerability and risk using existing models and plan protection from surface pollutionPollution/contaminationContamination; (iii) use geo-informatics science and technologies to acquire data and information at the required level in order develop reliable conceptual groundwaterGroundwater models, to support groundwaterGroundwater protection and managementManagement from the aquifer to consumption and reuse; (iv) protect groundwaterGroundwater throughout its cycle by making informed decisions based on developing groundwaterGroundwater monitoringMonitoring systems for early warning and managementManagement.
Predicting Inclusive Futures: Wearables, Automation, and Design Speculation

For the past 15 years wearable technology, Artificial Intelligence (A.I.), and the Internet of Things (IoT) have been gaining in popularity adoption in our everyday lives and in industrial design courses. In years past, there was an excitement around the seemingly limitless potential of these advances in technology to change lives and to usher humanity into the future. As many things ahead of their time, there have been a few bumps in the road, and it has taken use several decades to see where these innovations have landed in the marketplace. A.I. and machine learning are understood for their success in targeted advertising and route optimization as well as their inherent biases and lack of inclusion. IoT has paved the way for digital realities of paperless ticketing and smart toasters. Wearable technology, once so poised to push society into the world science reality, has not moved far beyond the wristwatch or earbuds, and seems to be advancing fastest in the gaming and health industries. In our everyday lives “wearables” has become synonymous with fitness trackers, health monitors, and VR headsets. Why has the creative promise of wearable technology seemed to slow down? In this paper, the authors will discuss university community-based and speculative project case studies that place wearable technology in critical contexts that center new and diverse voices, societal and embodied complexities, and raise ethical critiques on the role wearables might play in shaping a more inclusive society.
Visual images of well-being in consumer culture: analysis and interpretation of the images

This article analyzes the impact of the visual images of well-being upon interpretation of the concept of well-being in consumer culture. The object of this research is the paintings by postmodern artists, constructivists. and hyperrealists &amp;ndash; A. Warhol, R. Rauschenberg, J. Jones, L. Lawler, T. Matin, as well as the posters of advertising constructors of A. Rodchenko. Examination is conducted on the artworks, since the time horizon of paintings is not limited to the &amp;ldquo;here and now&amp;rdquo;. The author discusses the images from semiotic and hermeneutical perspectives, which allow revealing the content of meanings expressed in the visual form. Artistic images become the object of research in the field of visual semiotics. The scientific novelty consists in the comprehensive socio-philosophical research of the process of creation, proliferation, and inverting meanings in the visual discourse on well-being. The article employs philosophical, rather than art history discourse. As a result, the author establishes correlation between visual images, social culture, and social conventions. The conclusion is made that the visual images of well-being emphasize the social component of well-being, dictate the logic of public consumption, and manipulate collective consciousness. Visual images become the markers of social well-being, translators of the myths on consumer culture, delicately manipulating the constructed Self.
Detection Types of Steganographic Attacks Based on Semi-Fragile Watermarks Using Machine Learning

This article discusses how to detected types of steganographic attacks on digital images using semi-fragile watermarks using machine learning techniques. This article discusses different methods of introducing watermarks: into the spatial domain and the transformation domain. The attacks in this case are filters that can be applied to images: blurring, adding noise, sharpness, contrast, color balance. Methods have been identified that allow you to determine with higher accuracy the specific type of attack on the embedded digital watermark in the image. The peculiarity of this method is that the original image does not need to be used to determine the attack (blind method [1]).
Open Access Scholarly Communications Series: How to Improve Research Visibility and Impact

Paper presented at a webinar on How to Improve Research Visibility and Impact”scheduled on July 8, 2021, 9am- 11am, University of Pretoria.
Input High-Dimensional Expansion Convolution: Convolution Optimization for Spatially Varying Convolution

In this paper, in order to improve the execution speed of complex image processing functions in convolutional neural networks, we propose an optimization algorithm for convolution. This algorithm is aimed at optimizing the special convolution calculation of complex image processing functions in image processing, in which the weights of the kernel change with the position of the convolution kernel My algorithm mainly expands the image and variable convolution kernel to higher dimensions to reduce the number of cycles through vectorization operations, and optimizes the method of image expansion to higher dimensions. The experimental results show that my algorithm fully utilizes the parallel computing power of the CPU, which is more than 20 times faster than the direct method.
Statistical Field Theory of the Transmission of Nerve Impulses

BackgroundStochastic processes leading voltage-gated ion channel dynamics on the nerve cell membrane are a sufficient condition to describe membrane conductance through statistical mechanics of disordered and complex systems.ResultsVoltage-gated ion channels in the nerve cell membrane are described by the Ising model. Stochastic circuit elements called “Ising Neural Machines” are introduced. Action potentials are described as quasi-particles of a statistical field theory for the Ising system.ConclusionsThe particle description of action potentials is a new point of view and a powerful tool to describe the generation and propagation of nerve impulses, especially when classical electrophysiological models break down. The particle description of action potentials allows us to develop a new generation of devices to study neurodegenerative and demyelinating diseases as Multiple Sclerosis and Alzheimer’s disease, even integrated by connectomes. It is also suitable for the study of complex networks, quantum computing, artificial intelligence, machine and deep learning, cryptography, ultra-fast lines for entanglement experiments and many other applications of medical, physical and engineering interest.
Towards the Use of Eye Gaze Tracking Technology: Human Computer Interaction (HCI) Research

With a growing number of digital devices around us, and the increase in the amount of time we spend for interacting with such devices, we are strongly interested in finding new interaction methods which will ease the use of digital devices or increase interaction efficiency. Eye tracking seems to be a promising technology to achieve this goal. This study follows two different approaches on how to utilize eye tracking for computer input. The first approach researches eye gaze as pointing device in combination with a touch sensor for multimodal input and presents a method using a touch sensitive mouse. The second approach examines people's ability to perform gestures with the eyes for computer input and the separation of gaze gestures from natural eye movements. The findings from this study uncover the prospects of developing a usability tool for recording of interaction and gaze activity. All approaches present results based on user studies conducted with prototypes developed for the purpose. .The methods and approach used in this study are mostly experimental which is uncommon in computer science but appropriate in the field of Human-Computer Interaction (HCI). An iterative concept helps to get systematically closer to the development of new interaction methods based on gaze. Starting with a problem to solve the first step is to create ideas how to solve the problem. The resulting task is to realize the idea by creating a prototype.
From punch card to compiler

Based on personal experience, the author traces the path from acquaintance with the basics of programming in high school to the creation of a compiler for the Ada language as a university thesis. The article describes the role in this played by the newly introduced structures of teaching programming for schoolchildren, created for the first time in the USSR, focused not only on out-of-school education, but projecting the introduction of teaching programming to the mass school.
Loss of Smartphones, and its Implications to Contact Tracing

A Contact Tracing system collects, stores, and processes data to identify the contacts and the cases for an infection. All currently deployed contact tracing solutions are built with smartphones. The Bluetooth LE (BLE) present in smartphone is used in exchanging proximity data. There are solutions where this is complemented by tracking the subjectâ€™s location using cellular or GPS technologies or both. Every year close to 70 million smartphones are lost globally, and most of them are lost for good. This is a large number and this paper investigates its implication to global contact tracing solution.
Motion-specific browsing method by mapping to a circle for personal video Observation with Head-Mounted Displays

Understanding the temporal changes of movement and posture in two-dimensional (2D) videos is not easy, leading users to repeat observations to understand them. However, the video interface relying on timeline representations, which depend on one’s memory, makes it hard more than necessary for a user to observe movements because they lack original motion information. Therefore, we propose a Motion Seek Circle as a user interface for Head-Mounted Displays. They visualize the motion in a video on a circle in three-dimensional space, representing temporal and spatial changes with trails and humanoid models. This automatically generated circle combined with a 2D video provides a visual overview of the movement. Our user test and study show proof of concept in a personal training context. Results indicate the proposed method is easily learnable for untrained people. By creating a visual overview of movements, we intend to solve traditional video-learning difficulties.
Using subspaces of weight matrix for evaluating generative adversarial networks with Fréchet distance

Summary Fréchet inception distance (FID) has gained a better reputation as an evaluation metric for generative adversarial networks (GANs). However, it is subjected to fluctuation, namely, the same GAN model, when trained at different times can have different FID scores, due to the randomness of the weight matrices in the networks, stochastic gradient descent, and the embedded distribution (activation outputs at a hidden layer). In calculating the FIDs, embedded distribution plays the key role and it is not a trivial question from where obtaining it since it contributes to the fluctuation also. In this article, I showed that embedded distribution can be obtained from three different subspaces of the weight matrix, namely, from the row space, the null space, and the column space, and I analyzed the effect of the each space to Fréchet distances (FDs). Since the different spaces show different behaviors, choosing a subspace is not an insignificant decision. Instead of directly using the embedded distribution obtained from hidden layer's activations to calculate the FD, I proposed to use projection of embedded distribution onto the null space of the weight matrix among the three subspaces to avoid the fluctuations. My simulation results conducted at MNIST, CIFAR10, and CelebA datasets, show that, by projecting the embedded distributions onto the null spaces, possible parasitic effects coming from the randomness are being eliminated and reduces the number of needed simulations in MNIST dataset, in CIFAR10, and in CelebA dataset.
What Did Our Model Just Learn? Hard Lessons in Applying Deep Learning to Human Factors Data

Deep learning is revolutionizing all areas of data science, including human factors research. Much of human factors data, however, have fundamental idiosyncrasies that make applying deep learning challenging. Further, the complexity of deep learning can make finding errors challenging and deducing what was actually learned by the model near impossible. This paper provides two case-studies in which our research group faced and overcame such challenges. It examines the root causes of each issue and discusses how they may lead to common challenges. We describe how we discovered problems and describe how we rectified them. It is our hope, that by sharing our experiences with likely common challenges, we can help other researchers in avoiding similar pitfalls.
Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment

Using multiple monitors is commonly thought to improve productivity, but this is hard to check experimentally. We use a survey, taken by 101 practitioners of which 80% have coded professionally for at least 2 years, to assess subjective perspectives based on experience. To improve validity, we compare situations in which developers naturally use different setups-the difference between working at home or at the office, and how things changed when developers were forced to work from home due to the Covid-19 pandemic. The results indicate that using multiple monitors is indeed perceived as beneficial and desirable. 19% of the respondents reported adding a monitor to their home setup in response to the Covid-19 situation. At the same time, the single most influential factor cited as affecting productivity was not the physical setup but interactions with co-workers-both reduced productivity due to lack of connections available at work, and improved productivity due to reduced interruptions from coworkers. A central implication of our work is that empirical research on software development should be conducted in settings similar to those actually used by practitioners, and in particular using workstations configured with multiple monitors.
Energy Efficient and Multi-Parameters Based VM Selection for Cloud Data Centers

The cloud data centers across the world are major source of power consumption. The energy efficiency can be achieved with VM (virtual machine) consolidation approach in which a VM running on an overloaded host is selected for migration to another normal loaded host. In this paper, we have proposed an energy efficient virtual machine selection algorithm by considering multi features of the host system. To evaluate the performance of the proposed work we have used cloudsim 3.0.3 simulator. The outcome of experimental results indicates that the proposed scheme have the good proficiency in reducing the power consumption at appropriate level of quality of service. The proposed scheme also outperforms many existing schemes.
Pandemics throughout history and geography – an interactive environment to educate the new generation of students

The new coronavirus pandemic (COVID-19) has induced a radical change in the way people are educated. Distance or online learning has become the norm and it is important to provide new resources to ensure that continuing education takes place during this difficult time. The main objective is, therefore, to provide a service that not only supports school teaching but that can also increasingly motivate students while learning. In recent years, an increasing emphasis has been placed on geo-education in middle and high school curricula, transforming the way certain subjects are now taught to provide students with a thorough understanding of the dynamics and interconnections in the world, which are based also on relevant geographic areas information. This paper proposes the integration of advanced geospatial technology into traditional interactive learning tools as a way to describe experiences that help students understand phenomena and improve their skills, focusing on pandemic diseases, as they have happened in the history of humankind. The system is the result of a usability engineering process aimed at providing users with an effective learning experience, by iteratively analyzing their expectations and needs concerning georeferenced content. The resulting learning environment was tested in the context of a middle school program. A usability study was conducted to analyze the impact in terms of perceived quality, engagement, and learning performance of students.