Reviewerly/exHarmony huggingface dataset:
An advanced potential field method proposed for mobile robot path planning

In this paper, both the local minimum and the goal non-reachable with obstacles nearby (GNRON) problems have been solved on the artificial potential field (APF) for mobile robot path planning in a bounded environment. The GNRON problem occurs when the goal’s position is inside the circle of influence of the obstacle. This phenomenon gives rise to a larger repulsive force with regard to the attractive force. Therefore, a stronger attractive function is proposed to ensure that the robot reaches the target successfully. The local minimum problem occurs when the robot, the goal and the centre of the obstacle are aligned. This situation engenders a total potential force equal to zero before reaching the goal position. To overcome this problem, a novel of repulsive potential function is proposed by activating a virtual escaping force when a local minimum is detected. This force behaves as a rotational force allowing the robot to escape from the deadlock positions and turn smoothly away from obstacles in the direction of the target. The combination of the new attractive force and the novel repulsive force could solve the local minimum and the GNRON problems. Finally, some methodic simulations, based on a comparative study with other methods from the literature, are carried out to validate and demonstrate the effectiveness of the advanced potential field method to handle the local minimum and GNRON problems.
Feature analysis of encrypted malicious traffic

In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and firewalls typically will not have access to encryption keys, and therefore direct detection of malicious encrypted data is unlikely to succeed. However, previous work has shown that traffic analysis can provide indications of malicious intent, even in cases where the underlying data remains encrypted. In this paper, we apply three machine learning techniques to the problem of distinguishing malicious encrypted HTTP traffic from benign encrypted traffic and obtain results comparable to previous work. We then consider the problem of feature analysis in some detail. Previous work has often relied on human expertise to determine the most useful and informative features in this problem domain. We demonstrate that such feature-related information can be obtained directly from machine learning models themselves. We argue that such a machine learning based approach to feature analysis is preferable, as it is more reliable, and we can, for example, uncover relatively unintuitive interactions between features.
Visual to Text: Survey of Image and Video Captioning

Visual data such as images and videos are easily accessible nowadays, and they play critical roles in many real-world applications like surveillance. This raises a series of technological demands for automatic visual understanding and content summarization, which has guided the research community to move towards a better achievement of such capabilities. Meanwhile, it presents the big challenge of semantic understanding of video content and automatically translating them into human language. When developing such automatic translation systems, one critical issue is how to bridge the gap between low level features and high level semantic information. Furthermore, as a large amount of videos are captured under unconstrained conditions by nonprofessional users, this issue becomes even more serious. Therefore, brand new sets of technologies are required to address these difficulties and narrow the semantic gap effectively. These thoughts drive us to survey the complete state-of-the-art techniques in the visual to text topic. Existing methods, popular datasets, technical difficulties, and promising future directions are discussed systematically. In particular, we classify existing methods by their mechanism to link visual information (including both images and videos) and text descriptions, and emphasize the latest advances on deep learning based approaches. The quantitative evaluations of representative approaches on benchmark dataset are also presented and discussed. Finally, we provide with the promising research directions on this topic.
Deep Learning-Based Sentiment Analysis for Roman Urdu Text

Sentiment Analysis has significant attention due to its versatile approach to analysis user’s sentiments on various social networks, forums, e-marketing sites and blogs. Sentiments related data on the web has great importance and impact on customer’s, readers and business firms.Reccurent Neural Network has been widely applied to perform Natural Language Processing tasks because it is designed for modeling the sequential data efficiently. In this paper we used Deep Neural Long-short time memory model (LSTM).It has extraordinary capability to Capture long-range information and solve gradient attenuation problem, as well as represent future contextual information, semantics of word sequence magnificently. This paper is the foundation of adapting Deep learning methods to perform Roman Urdu Sentiment Analysis. Our experimental results shows the significant accuracy of our model and surpassed accuracy of baseline Machine learning methods.
Comparative assessment of CNN architectures for classification of breast FNAC images

Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. FNAC is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, the advent of digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a comparison of various deep convolutional neural network (CNN) based fine-tuned transfer learned classification approach for the diagnosis of the cell samples. The proposed approach has been tested using VGG16, VGG19, ResNet-50 and GoogLeNet-V3 (aka Inception V3) architectures of CNN on an image dataset of 212 images (99 benign and 113 malignant), later augmented and cleansed to 2120 images (990 benign and 1130 malignant), where the network was trained using images of 80% cell samples and tested on the rest. This paper presents a comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.
Improved salp swarm algorithm based on weight factor and adaptive mutation

Salp Swarm Algorithm (SSA) is a novel swarm intelligent algorithm with good performance. However, like other swarm-based algorithms, it has insufficiencies of low convergence precision and slow convergence speed when dealing with high-dimensional complex optimisation problems. In response to this concerning issue, in this paper, we propose an improved SSA named as WASSA. First of all, dynamic weight factor is added to the update formula of population position, aiming to balance global exploration and local exploitation. In addition, in order to avoid premature convergence and evolution stagnation, an adaptive mutation strategy is introduced during the evolution process. Disturbance to the global extremum promotes the population to jump out of local extremum and continue to search for an optimal solution. The experiments conducted on a set of 28 benchmark functions show that the improved algorithm presented in this paper displays obvious superiority in convergence performance, robustness as well as the ability to escape local optimum when compared with SSA.
Production perspectives of heavy metal record producers

Abstract The study of the recorded artefact from a musicological perspective continues to unfold through contemporary research. Whilst an understanding of the scientific elements of recorded sound is well documented, the exploration of the production and the artistic nature of this endeavour is still developing. This article explores phenomenological aspects of producing heavy metal music from the perspective of seven renowned producers working within the genre. Through a series of interviews and subsequent in-depth analysis, particular sonic qualities are identified as key within the production of this work: impact; energy; precision; and extremity. A conceptual framework is then put forward for understanding the production methodology of recorded heavy metal music, and how developing technology has influenced the production of the genre.
A Fast and Efficient Text Steganalysis Method

With the rapid development of natural language processing technology in the past few years, the steganography by text synthesis has been greatly developed. These methods can analyze the statistical feature distribution of a large number of training samples, and then generate steganographic texts that conform to such statistical distribution. For these steganography methods, previous steganalysis methods show unsatisfactory detection performance, which remains an unsolved problem and poses a great threat to the security of cyberspace. In this letter, we proposed a fast and efficient text steganalysis method to solve this problem. We first analyzed the correlations between words in these generated steganographic texts. Then, we map each word to a semantic space and used a hidden layer to extract the correlations between these words. Finally, based on the extracted correlation features, we used the softmax classifier to classify the input text. Experimental results show that the proposed model can achieve a high detection accuracy, which shows a state-of-the-art performance.
Anthropomorphic virtual assistants and the reactions of Internet users: An experiment on the assistant’s voice

Virtual assistants are increasingly common on commercial websites. In view of the benefits they offer to businesses for improving navigation and interaction with the consumers, researchers and practitioners agree on the value of providing them with anthropomorphic characteristics. This study focuses on the effect of the voice of the virtual assistant. Although there are some studies of human–computer interaction in this field, there is no work that addresses the topic from a marketing perspective and compares the effect of a human voice versus a synthetic voice. Our findings show that consumers who interact with a virtual assistant with a human voice have a stronger impression of social presence than those interacting with a virtual assistant with a synthetic voice. The human voice also builds trust in the virtual assistant and generates stronger behavioural intentions.
Single-Image De-Raining With Feature-Supervised Generative Adversarial Network

De-raining, which aims at rain-steak removal from images, is a practical task in computer vision. However, it is difficult due to its ill-posed nature. In this letter, we propose a deep neural network architecture, feature-supervised generative adversarial network (FS-GAN) for single-image rain removal. Its main idea is to train a generative adversarial network (GAN) for which the supervision from ground truth is imposed on different layers of the generator network. We design a feature-supervised generator, a discriminator, an optimization target, as well as the detailed structure of FS-GAN. Experiments show that the proposed FS-GAN achieves better performance than state-of-the-art de-raining methods on both synthetic and real-world images in terms of quantitative and visual quality.
Energy-Constrained Multi-UAV Coverage Path Planning for an Aerial Imagery Mission Using Column Generation

This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.
CDNet: Single Image De-Hazing Using Unpaired Adversarial Training

Outdoor scene images generally undergo visibility degradation in presence of aerosol particles such as haze, fog and smoke. The reason behind this is, aerosol particles scatter the light rays reflected from the object surface and thus results in attenuation of light intensity. Effect of haze is inversely proportional to the transmission coefficient of the scene point. Thus, estimation of accurate transmission map (TrMap) is a key step to reconstruct the haze-free scene. Previous methods used various assumptions/priors to estimate the scene TrMap. Also, available end-to-end dehazing approaches make use of supervised training to anticipate the TrMap on synthetically generated paired hazy images. Despite the success of previous approaches, they fail in real-world extreme vague conditions due to unavailability of the real-world hazy image pairs for training the network. Thus, in this paper, Cycle-consistent generative adversarial network for single image De-hazing named as CDNet is proposed which is trained in an unpaired manner on real-world hazy image dataset. Generator network of CDNet comprises of encoder-decoder architecture which aims to estimate the object level TrMap followed by optical model to recover the haze-free scene. We conduct experiments on four datasets namely: D-HAZY [1], Imagenet [5], SOTS [20] and real-world images. Structural similarity index, peak signal to noise ratio and CIEDE2000 metric are used to evaluate the performance of the proposed CDNet. Experiments on benchmark datasets show that the proposed CDNet outperforms the existing state-of-the-art methods for single image haze removal.
Cooperative Sensing and Wearable Computing for Sequential Hand Gesture Recognition

Hand gestures recognition (HGR) has been considered as one of the crucial research fields of human-computer interaction (HCI). Computer vision is a very active research field in the HGR, traditional vision-based methods, which used camera and ultrasonic/optical sensor to collect the videos or images of the hand gestures shown by participants, have some limitations, such as fixed in-lab location, complex lighting conditions, and cluttered backgrounds. In order to provide new approaches, we described the development of a novel hand gesture recognition system that combined wearable armband and smart glove made by customizable pressure sensor arrays to detect sequential hand gestures. A deep learning technique long short-term memory (LSTM) algorithm had been computed to build an effective model to classify hand gestures by training and testing the collected inertial measurement unit (IMU), electromyographic (EMG), and finger and palm's pressure data. Furthermore, we built a relatively large database of ten sequential hand gestures consisted by five dynamic gestures and five air gestures collected from ten participants. Our experimental results showed an outstanding classification performance of the proposed LSTM algorithm. These findings have promising implications for sequential hand gesture recognition and the HCI research status.
A novel firefly algorithm based on gender difference and its convergence

Firefly algorithm is a population-based algorithm, because the search process is simple and easy to implement, it has received extensive attention from scholars. However, the algorithm has low accuracy and is easy to fall into local optimum. Therefore, an improved firefly algorithm based on gender difference (GDFA) is proposed to enhance the performance of the algorithm in this paper. In the basic firefly algorithm, fireflies have no gender distinction, which is not consistent with reality. According to this point, fireflies are divided into two subgroups based on gender. In addition, different update equations are designed for two subgroups on the basis of their moving ways. Male fireflies are guided by two randomly selected female fireflies to focus on global search and female fireflies move to the best male firefly to implement local search, which aim to balance the exploration and exploitation of the algorithm. To further improve the search accuracy, the chaos search is conducted near the best position of the population. Several simulation experiments and constrained optimization problems are carried out to verify the effectiveness of GDFA, and the time complexity of GDFA is analyzed. Finally, the convergence analysis of proposed algorithm is given based on Markov chain theory.
Traffic Sign Detection and Recognition using a CNN Ensemble

In today's world, almost everything we do has been simplified by automated tasks. In an attempt to focus on the road while driving, drivers often miss out on signs on the side of the road, which could be dangerous for them and for the people around them. This problem can be avoided if there was an efficient way to notify the driver without having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays an important role here by detecting and recognizing a sign, thus notifying the driver of any upcoming signs. This not only ensures road safety, but also allows the driver to be at little more ease while driving on tricky or new roads. Another commonly faced problem is not being able to understand the meaning of the sign. With the help of this Advanced Driver Assistance Systems (ADAS) application, drivers will no longer face the problem of understanding what the sign says. In this paper, we propose a method for Traffic Sign Detection and Recognition using image processing for the detection of a sign and an ensemble of Convolutional Neural Networks (CNN) for the recognition of the sign. CNNs have a high recognition rate, thus making it desirable to use for implementing various computer vision tasks. TensorFlow is used for the implementation of the CNN. We have achieved higher than 99% recognition accuracies for circular signs on the Belgium and German data sets.
A novel binary artificial bee colony algorithm

This paper presents a novel artificial bee colony algorithm for binary optimization in general. Our proposal, named NBABC, features a mechanism which limits the number of dimensions that can be changed in the employed and onlookers bees' phase. We compare the NBABC to other five binary variants of the continuous ABC, including the state-of-the-art versions for binary optimization, and other four well-known methods. We employ different types of tasks to assess the performance of all the algorithms including the OneMax problem, five variations of the 0/1 Knapsack problems and Feature Selection using eight public datasets. The experiments show that the new proposal obtained competitive results, and in some cases outperformed not only the binary-based ABCs but also the other binary swarm-based and evolutionary-based optimizers.
PlaneVR

Virtual reality (VR) headsets allow wearers to escape their physical surroundings, immersing themselves in a virtual world. Although escape may not be realistic or acceptable in many everyday situations, air travel is one context where early adoption of VR could be very attractive. While travelling, passengers are seated in restricted spaces for long durations, reliant on limited seat-back displays or mobile devices. This paper explores the social acceptability and usability of VR for in-flight entertainment. In an initial survey, we captured respondents' attitudes towards the social acceptability of VR headsets during air travel. Based on the survey results, we developed a VR in-flight entertainment prototype and evaluated this in a focus group study. Our results discuss methods for improving the acceptability of VR in-flight, including using mixed reality to help users transition between virtual and physical environments and supporting interruption from other co-located people.
Simple High-Level Code for Cryptographic Arithmetic - With Proofs, Without Compromises

We introduce a new approach for implementing cryptographic arithmetic in short high-level code with machine-checked proofs of functional correctness. We further demonstrate that simple partial evaluation is sufficient to transform into the fastest-known C code, breaking the decades-old pattern that the only fast implementations are those whose instruction-level steps were written out by hand. These techniques were used to build an elliptic-curve library that achieves competitive performance for 80 prime fields and multiple CPU architectures, showing that implementation and proof effort scales with the number and complexity of conceptually different algorithms, not their use cases. As one outcome, we present the first verified high-performance implementation of P-256, the most widely used elliptic curve. implementations from our library were included in BoringSSL to replace existing specialized code, for inclusion in several large deployments for Chrome, Android, and CloudFlare.
Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure

In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.