Reviewerly/exHarmony huggingface dataset:
Visualising Rave Music in Virtual Reality: Symbolic and interactive approaches

The virtual reality (VR) experience Cyberdream VR was presented at various events in 2019, including the Event Two exhibition of computer art held at the Royal College of Art.This short demo for Oculus Gear VR provided a c.5 minute sonic journey, in which the user moves through a series of symbolic environments based on the futuristic techno-utopian or dystopian imagery of 1990s rave flyers.These environments accompanied an original soundtrack of rave music and vaporwave, allowing users to enjoy the music whilst feeling as though they are inside synaesthetic virtual spaces related to the symbolic imagery of rave culture.This paper will discuss the subsequent development of this project, which is now being adapted for the Oculus Quest VR headset.Rajmil Fischman's concept of 'music in the holodeck', suggests a possible new paradigm for performing with sound in VR, which is informative for the latest phase of Cyberdream's development.Drawing on ideas such as this, at a macro level of compositional structure, Cyberdream seeks ways to provide a continuous experience analogous to a DJ or VJ set, through blending of music tracks between scenes.Meanwhile, at a micro level of compositional structure, the user can trigger audio-visual 'sound toys' with the Oculus Quest Touch controllers.These are conceived as a means through which the user can intuitively 'paint with sound' in 3D space, improvising with the music, whilst also generating synaesthetic imagery.The design of the audiovisual 'sound toys' allows both the macro and micro elements to rhythmically interlock, so that sound can be generated in synchronisation with the music, while the corresponding visual imagery merges with the spatial environments.This paper will discuss on-going work on this project, advancing the discourse regarding the visualisation of hardcore rave music in virtual reality.
Cyber-security research by ISPs: A NetFlow and DNS Anonymization Policy

Internet Service Providers (ISPs) have an economic and operational interest in detecting malicious network activity relating to their subscribers.However, it is unclear what kind of traffic data an ISP has available for cyber-security research, and under which legal conditions it can be used.This paper gives an overview of the challenges posed by legislation and of the data sources available to a European ISP.DNS and NetFlow logs are identified as relevant data sources and the state of the art in anonymization and fingerprinting techniques is discussed.Based on legislation, data availability and privacy considerations, a practically applicable anonymization policy is presented.
Achieving "Good Enough" Software Security

Today's software development projects need to consider security as one of the qualities the software should possess. However, overspending on security will imply that the software will become more expensive and often also delayed. This paper discusses the role of objectivity in assessing and researching the goal of good enough security. Different understandings of objectivity are introduced, and the paper explores how these can guide the way forward in improving judgements on what level of security is good enough. The paper recommends adopting and improving upon methods that include different perspectives, support the building of interactive expertise, and support confirmability by keeping documentation of the basis on which judgements were made.
Cross-Project Software Fault Prediction Using Data Leveraging Technique to Improve Software Quality

Software fault prediction is a process to detect bugs in software projects. Fault prediction in software engineering has attracted much attention from the last decade. The early prognostication of faults in software minimize the cost and effort of errors that come at later stages. Different machine learning techniques have been utilized for fault prediction, that is proven to be utilizable. Despite, the significance of fault prediction most of the companies do not consider fault prediction in practice and do not build useful models due to lack of data or lack of enough data to strengthen the power of fault predictors. However, models trained and tested on less amount of data are difficult to generalize, because they do not consider project size, project differences, and features selection. To overcome these issues, we proposed an instance-based transfer learning through data leveraging using logistic linear regression as a base proposed statistical methodology. In our study, we considered three software projects within the same domain. Finally, we performed a comparative analysis of three different experiments for building models (targeted project). The experimental results of the proposed approach show promising improvements in (SFP).
On the Role of Question Summarization and Information Source Restriction in Consumer Health Question Answering.

Despite the recent developments in commercial Question Answering (QA) systems, medical QA remains a challenging task. In this paper, we study the factors behind the complexity of consumer health questions and potential improvement tracks. In particular, we study the impact of information source quality and question conciseness through three experiments. First, an evaluation of a QA method based on a Question-Answer collection created from trusted NIH resources, which outperformed the best results of the medical LiveQA challenge with an average score of 0.711. Then, an evaluation of the same approach using paraphrases and summaries of the test questions, which achieved an average score of 1.125. Our results provide an empirical evidence supporting the key role of summarization and reliable information sources in building efficient CHQA systems. The latter finding on restricting information sources is particularly intriguing as it contradicts the popular tendency ofrelying on big data for medical QA.
Forecasting 5G Network Multimedia Traffic Characteristics

The task of forecasting multimedia data traffic with an important amount of pulsations and the long-term dependence is considered. The results of forecasting multimedia data traffic characteristics were obtained. The use of the spline-extrapolation method has several advantages over other known methods, namely, ease of practical implementation, high accuracy of forecasting, the ability to accurately extrapolate peak "bursts" of traffic, which is especially important when solving problems in real time. The results will allow the raising the efficiency of network equipment use, as well as the provision of the required of buffer devices and the avoidance of network congestion. In addition it will balance the network object download without exceeding the regulatory QoS characteristics values.
High Throughput Spatial Convolution Filters on FPGAs

Digital signal processing (DSP) on field-programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30-60 FPS, while maintaining functional flexibility.
Trains, Games, and Complexity: 0/1/2-Player Motion Planning Through Input/Output Gadgets

We analyze the computational complexity of motion planning through local “input/output” gadgets with separate entrances and exits, and a subset of allowed traversals from entrances to exits, each of which changes the state of the gadget and thereby the allowed traversals. We study such gadgets in the zero-, one-, and two-player settings, in particular extending past motion-planning-through-gadgets work [3, 4] to zero-player games for the first time, by considering “branchless” connections between gadgets that route every gadget’s exit to a unique gadget’s entrance. Our complexity results include containment in L, NL, P, NP, and PSPACE; as well as hardness for NL, P, NP, and PSPACE. We apply these results to show PSPACE-completeness for certain mechanics in Factorio, [the Sequence], and a restricted version of Trainyard, improving the result of [1]. This work strengthens prior results on switching graphs, ARRIVAL [5], and reachability switching games [6].
Automated Damage Detection in Operational Vehicles Using Mask R-CNN

One of the important aspects of day-to-day life is transportation and there is a huge surge in the number of accidents every year due to the negligence of the vehicle conditions. Human errors during the inspection are another major concern. Hence, to improve the quality of testing of vehicles, we have come up with a solution that could detect anomalies in the vehicle due to negligence. This paper describes a solution to vehicle damage detection by the use of convolutional neural networks (CNN) to detect and highlight faults such as bumps, dents, and scratches to the vehicle's outer body. Primarily, Mask R-CNN, an avant-garde algorithm for object detection, localization and instance segmentation of natural images to solve the above problem in a quick and efficient manner.
An Implementation of KFDD based Reversible Logic using IBM-Q Experience

Reversible logic is a circuit which has the ability to reduce the power dissipation. This type of logic circuit is used for the process of running the system both forward and backward.One of the main advantages of using any reversible logic is to get back the original data and to reduce the garbage output. Logic synthesis based on the Kronecker Functional Decision Diagram (KFDD) are generated for different reversible cascade methods both complemented and uncomplemented edge by using different reversible gates such as the CNOT (Controlled NOT), NOT, Toffoli gates, Peres gates, etc., and implemented in a quantum processor. In order to reduce the qubits which in turn reduces the quantum cost, it is implemented using reversible logic synthesis in KFDD method.The proposed method shows the implementation of these reversible circuits in an IBM quantum computer to find the probabilities, as it gives less quantum cost and also consumes less time when compared to other decision diagram. Reversible logic finds a wide range of applications in quantum computing and also in areas which deals with the low power design.This paper presents the implementation of KFDD using quantum circuits in IBM Quantum (IBM-Q) processor using different reversible logics and the output is discussed.
Heat Transfer and Temperature Characteristics of a Working Digital Camera

Digital cameras represented by industrial cameras are widely used as image acquisition sensors in the field of image-based mechanics measurement, and their thermal effect inevitably induces thermal-induced errors of the mechanics measurement. To deeply understand the errors, the research for digital camera's thermal effect is necessary. This study systematically investigated the heat transfer processes and temperature characteristics of a working digital camera. Concretely, based on the temperature distribution of a typical working digital camera, the heat transfer of the working digital camera was investigated, and a model describing the temperature variation and distribution was presented and verified experimentally. With this model, the thermal equilibrium time and thermal equilibrium temperature of the camera system were calculated. Then, the influences of thermal parameters of digital camera and environmental temperature on the temperature characteristics of working digital camera were simulated and experimentally investigated. The theory analysis and experimental results demonstrate that the presented model can accurately describe the temperature characteristics and further calculate the thermal equilibrium state of working digital camera, all of which contribute to guiding mechanics measurement and thermal design based on such camera sensors.
Modular granular neural network optimization using the firefly algorithm applied to time series prediction

In this chapter the combination of modular neural network, granular computing, and a firefly algorithm is presented to perform time series prediction. The Mackey–Glass time series is used to prove the effectiveness of the proposed method. The main contributions of the proposed method are the division of the data points used for the training phase of the modular granular neural network, creating granules of information for each submodule and optimizing the modular granular neural network architecture by a firefly algorithm. Four tests are performed using different numbers of data points for the learning phase and also with and without optimization to prove if there are advantages using the firefly algorithm for neural network optimization.
On computing the degree of a Chebyshev Polynomial from its value

Algorithms for interpolating a polynomial f from its evaluation points whose running time depends on the sparsity t of the polynomial when it is represented as a linear combination of t Chebyshev Polynomials of the First Kind with non-zero scalar coefficients are given by Lakshman and Saunders (1995), Kaltofen and Lee (2003) and Arnold and Kaltofen (2015). The term degrees are computed from values of Chebyshev Polynomials of those degrees. We give an algorithm that computes those degrees in the manner of the Pohlig and Hellman algorithm (1978) for computing discrete logarithms modulo a prime number p when the factorization of p−1 (or p+1) has small prime factors, that is, when p−1 (or p+1) is smooth. Our algorithm can determine the Chebyshev degrees modulo such primes in bit complexity log⁡(p)O(1) times the squareroot of the largest prime factor of p−1 (or p+1).
News Headlines Categorization Scheme for Unlabelled Data

Text categorization without training data is a difficult task and requires enough amount of hand labelled data to apply supervised methods, while manual labelling is a tedious job. In this paper a news categorization scheme is proposed to filter out and categorize news headlines related to Pakistan Stock Exchange (PSX) using negligible manual effort. By using domain knowledge, category names are selected manually then these category names are used as seed keyword to filter out news headlines. Natural Language Processing (NLP) based technique is used to extract context of seed keyword from initially filtered news headlines. These context terms are added in keyword list for string matching that further refines news filtration. Each news headline in a filtered news group is labelled and assigned a seed keyword term as a category label. Finally, a supervised classification technique is used to ensure the segregation of news categories as well as validates the performance of multiclass classification. Prepared dataset will be published in near future for potential uses explored by research community.
The cognitive and speech genes are jointly shaped by both positive and relaxed selection in the human lineage

The emergence of a coordinated network of cognitive and speech genes in the human lineage performing overlapping functions is a great evolutionary puzzle. Prior studies on the speech gene FOXP2 are inconclusive on the nature of selection operating on this gene in the human lineage. Here, I show that the evolution of FOXP2 is accelerated in the human lineage due to relaxation of purifying selection (relaxed selection). Five potential genes associated with human-specific intelligence and speech genes have evolved under the impact of positive selection and three genes including FOXP2 have undergone relaxation of purifying selection in the human lineage. Overall, three evolutionary processes namely positive selection, relaxation of purifying selection and neutral evolution have contributed for the genomic evolution of extraordinary cognitive ability and speech in the hominin lineage. The cognitive and speech genes subjected to natural selection in the human lineage have demonstrated a coevolutionary trend.
An Educational Experience to Raise Awareness About Space Debris

Space debris represents a threat to space missions and operational satellites.Failing to control its growth might lead to the inability to use near-Earth space.However, this issue is still largely unknown to most people.In this paper, we present an educational experience in virtual reality created to raise awareness about the problem of space debris.The application exploits the entity-component-system (ECS) programming pattern to manage around 20000 orbiting objects with a high frame rate to convey a fluid experience.We preliminarily validated our application, in terms of usability as well as quality of user experience, during several events involving both a broad audience (e.g., citizens of all ages, from teenagers to elders) and an experienced audience (e.g., engineering students enrolled in the aerospace degree).The results of the evaluation were extremely positive, showing once again that virtual reality can be an effective means to engage people in captivating and interactive activities, making them experience what can only be imagined -the thousands pieces of space debris surrounding our planet.
An integrated autoencoder-based filter for sparse big data

We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilises auxiliary information to mitigate data sparsity. The proposed model achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. We implement experiments on a GPS trajectory dataset, and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.
Software Security Specifications and Design

Huge numbers of worldwide-deployed software suffer from poor quality and possess vulnerabilities with serious impact. Meanwhile, people are using such software to save and manage their valuable information including their monetary data. This has increased the hackers' appetite to attack software. Henceforth, researchers and practitioners are convinced that software security is not an added value or a gold-plating need. Consequently, security requirements specification and implementation become vital during the software development process. Unfortunately, researchers and practitioners are doing so in a rush. This has made them mix concepts and practices up in a way that can terribly make the problem of delivering software overdue more chronic which will result in a security and technical debt. This research represents a corrective study that sheds light on what has been achieved in analyzing and designing secure software and what are the problems committed and how to handle them.
Internet-Based Communications: A Threat or Strength to National Security?

This study examined how internet-based communication channels impact on national security in Nigeria. The study was conducted in Enugu State, South East Nigeria. Three research questions guided the study and three null hypotheses were tested at .05 level of significance. A total of 385 internet users were selected using a combination of purposive and snow ball sampling techniques. In the analysis of data for the study, correlation analysis and multiple regression were used. The result of the analysis showed that internet-based media of communication like the Facebook, Gmail, YouTube, Yahoo mail, and Twitter significantly correlate national security. It was also found that there are specific ways through which negative use of the internet could impact negatively on national security. Such predictors include using internet-based media to recruit agents of national security challenges, to finance such efforts, to radicalize such agents, to train such agents, and to spread incitement that may be inimical to national security. It was also found that internet-based media could be useful for creating awareness on national security threats, raising alarm on national security issues, and making people alert on national security issues. Base on the result of this study, the researcher recommends, among others, that the traditional media like radio, TV, newspapers, and magazines should form a synergy with security agencies on how to create awareness to Nigerians regarding how security alertness for the overall benefit of national security.
Image Restoration Using Fixed-Point-Like Methods for New TVL1 Variational Problems

In this paper, we first propose two TVL1 variational problems for restoring images degraded by blurring and impulse noise, and then we propose two fixed-point-like methods, using proximal operators, for solving the new proposed TVL1 problems. Numerical experiments for several test images blurred by Gaussian kernel and corrupted by salt-and-pepper impulse noise are provided to demonstrate the efficiency and reliability of the proposed fixed-point-like methods. Numerical results show that two fixed-point-like methods for solving the new TVL1 variational problems perform very well in both PSNR (Peak signal-to-noise ratio) values and CPU time as compared with the fixed-point-like methods for solving two existing TVL1 variational problems.
Swarm Intelligence Algorithm and its Application:A Critical Review

In an attempt to solve problems with multi-objectives, science has come to realize that natural processes will go a long way to achieve such goal. Swarm Intelligence (SI) is a population based process that is widely used for solving various complex real world problems. Literatures in this area have not address the implementation of SI algorithm in a simplified way, and also comparing the strength and weaknesses of SI methods is of major importance to its application. To fill these gap three categories of literatures were reviewed along different SI algorithms. This paper seek to outline guidelines for the implementation of some SI algorithms for feature selection to support researchers in their findings by touching in details the characteristics and principles of these SI techniques. Although SI is a promising research area, it is still largely empirical therefore theoretical judgment may sometimes lead to a misleading result.
Image Style Transfer based on Generative Adversarial Network

The traditional style transfer based on GAN model is limited by paired images. CycleGAN solves this problem effectively, but its structure is complex and training time-consuming. This paper proposed a style transfer model based on generative adversarial network, which abandons the redundant structure of two GAN models trained by CycleGAN, and trains only one generator and one discriminator without pairing image samples. And the semantic content between the input image and the generated image is constrained by the VGG network feature map. In order to accelerate the convergence of the model, a pretraining stage is introduced. The experimental results show that the proposed model is effective than CycleGAN, and outperforms state-of-the-art methods.
Transmission Line Image Object Detection Method Considering Fine-Grained Contexts

It takes a huge amount of works to take pictures of transmission line towers and check electrical fittings manually. In spite of the introduction of deep learning technology to transmission line inspection, it is not well utilized that fine-grained contexts on components in state-of-the-art research. On the basis of region-based fully convolutional network (R-FCN), a novel object detection method is proposed considering fine-grained contexts among electrical fittings. Deformable convolution layers and squeeze-and-excitation (SE) blocks are adopted in the detection method. A comparison experiment is conducted on a transmission line aerial inspection dataset. The proposed method shows better accuracy than R-FCN.