Reviewerly/exHarmony huggingface dataset:
Deep Learning Techniques for Automatic Modulation Classification: A Systematic Literature Review

Implementation of Automatic modulation classification originated in the military field several years ago. Threat analysis, surveillance, and welfare where of great concern, thus this urged the importance to study the application of automatic modulation classification for signal recognition. In this research survey a Systematic Literature Review is conducted to study the implementation of deep learning algorithms in modulation classification. Our survey conducts a study from four perspectives including: deep learning techniques/models, performance metrics used, deep learning models adopted, and types of modulation classification classified.
Infer-AVAE: An attribute inference model based on adversarial variational autoencoder

User attributes, such as gender and education, face severe incompleteness in social networks. Attribute inference aims to infer users’ missing attribute labels based on observed data to make this valuable data usable for downstream tasks like user profiling and personalized recommendation. Recently, variational autoencoder (VAE), an end-to-end deep generative model, has shown promising performance by handling the problem in a semi-supervised way. However, VAEs can easily suffer from over-fitting and over-smoothing when applied to attribute inference. Specifically, VAE implemented with multi-layer perceptron (MLP) can only reconstruct input data but fail to infer missing parts. While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from the neighborhood and generate indistinguishable user representations, known as over-smoothing. In this paper, we propose an attribute Inference model based on Adversarial VAE (Infer-AVAE) to cope with these issues. Specifically, to overcome over-smoothing, Infer-AVAE unifies MLP and GNNs in the encoder to learn positive and negative latent representations respectively. Meanwhile, an adversarial network is trained to distinguish the two representations, and GNNs are trained to aggregate less noise for more robust representations through adversarial training. Finally, to relieve over-fitting, mutual information constraint is introduced as a regularizer for the decoder to make better use of auxiliary information in representations and generate outputs not limited by observations. We evaluate our model on four real-world social network datasets, and experimental results demonstrate that our model averagely outperforms baselines by 7.0% in accuracy.
On Verification of Blur and Sharpness Metrics for No-reference Image Visual Quality Assessment

Natural images may contain regions with different levels of blur affecting image visual quality. No-reference image visual quality metrics should be able to effectively evaluate both blur and sharpness levels on a given image. In this paper, we propose a large image database BlurSet to verify this ability. BlurSet contains 5000 grayscale images of size 128×128 pixels with different levels of Gaussian blur and unsharp mask. For each image, a scalar value indicating the level of blur and the level of sharpness is provided. Several image quality assessment criteria are presented to evaluate how a given metric can estimate the level of blur/sharpness on BlurSet. An extensive comparative analysis of different no-reference metrics is carried out. Reachable levels of the quality criteria are evaluated using the proposed blur/sharpness convolutional neural network (BSCNN).
Systematic Scenario Creation for Serious Security-Awareness Games

While social engineering is still a recent threat, many organisations only address it by using traditional trainings, penetration tests, standardized security awareness campaigns or serious games. Existing research has shown that methods for raising employees’ awareness are more effective if adjusted to their target audience. For that purpose, we propose the creation of specific scenarios for serious games by considering specifics of the respective organisation. Based on the work of Faily and Flechais [11], who created personas utilizing grounded theory, we demonstrate how to develop a specific scenario for HATCH [4], a serious game on social engineering. Our method for adapting a scenario of a serious game on social engineering resulted in a realistic scenario and thus was effective. Since the method is also very time-consuming, we propose future work to investigate if the effort can be reduced.
Common Sense-Based Reasoning Using External Knowledge for Question Answering

Much research has been conducted recently on machines with human-level reasoning ability. Unlike domain knowledge, common sense is an extensive knowledge of the real world, and is included in human reasoning ability in many cases. However, it is difficult for machines to learn common sense because of the lack of well-written data. ComonsenseQA is a dataset in the form of multiple-choice questions and answers created for training and evaluating common sense in machines. RoBERTa, a pre-trained language model that performs well in natural language processing tasks, also performs well in ComonsenseQA. However, its performance is still inadequate compared to human performance. In this paper, we propose a model for predicting the correct answer by searching for information called evidence necessary to answer questions from external knowledge and by using the evidence as a context. The proposed model is comprised of three stages: An external knowledge finder to explore the required information from external knowledge; a triple-to-sentence converter, which converts triple-shaped in-formation such as (table, AtLocation, rug) into sentence form based on the configured evidence; and a reasoning module that predicts the correct answer. The performance of our proposed model showed a performance of 80.84% on the CommonsenseQA validation data and 76.14% on the test data. The latter is an improvement of 4.04% over the baseline model RoBERTa.
Training deep retrieval models with noisy datasets: Bag exponential loss

Although the CNNs are a very powerful tool for image retrieval, the need of training datasets properly adapted to the application at hand hinders the usefulness of such networks, specially since the datasets need to be free of noise to avoid spoiling the learning process. An ad hoc preprocessing of the dataset to mitigate the noise is a possible solution, but it is usually non-trivial and requires significant human intervention. In this paper, we pave the road for training CNNs for image retrieval with noisy datasets. In particular, we propose a novel Bag Exponential Loss function that, inspired by the Multiple Instance Learning framework, works with bags of matching images instead of single pairs, and allows a dynamical weighting of the relevance of each sample as the training progresses. The formulation of the proposed model is general enough and may serve to other purposes than dealing with noise if parameters are chosen appropriately. Extensive experimental results show the superior performance of the proposed loss with respect to the current state-of-the-art as well as its ability to cope with noisy training sets. Pytorch code available in https://github.com/tmcortes/BELoss
Understanding and Detecting Harmful Code

Code smells typically indicate poor design implementation and choices that may degrade software quality. Hence, they need to be carefully detected to avoid such poor design. In this context, some studies try to understand the impact of code smells on the software quality, while others propose rules or machine learning-based techniques to detect code smells. However, none of those studies or techniques focus on analyzing code snippets that are really harmful to software quality. This paper presents a study to understand and classify code harmfulness. We analyze harmfulness in terms of CLEAN, SMELLY, BUGGY, and HARMFUL code. By HARMFUL CODE, we define a SMELLY code element having one or more bugs reported. These bugs may have been fixed or not. Thus, the incidence of HARMFUL CODE may represent a increased risk of introducing new defects and/or design problems during its fixing. We perform our study with 22 smell types, 803 versions of 13 open-source projects, 40,340 bugs and 132,219 code smells. The results show that even though we have a high number of code smells, only 0.07% of those smells are harmful. The Abstract Function Call From Constructor is the smell type more related to HARMFUL CODE. To cross-validate our results, we also perform a survey with 60 developers. Most of them (98%) consider code smells harmful to the software, and 85% of those developers believe that code smells detection tools are important. But, those developers are not concerned about selecting tools that are able to detect HARMFUL CODE. We also evaluate machine learning techniques to classify code harmfulness: they reach the effectiveness of at least 97% to classify HARMFUL CODE. While the Random Forest is effective in classifying both SMELLY and HARMFUL CODE, the Gaussian Naive Bayes is the less effective technique. Our results also suggest that both software and developers' metrics are important to classify HARMFUL CODE.
AR-Chat: an AR-based instant messaging system

We describe a multi-user system enabling instant messaging in Augmented Reality. A user can get in contact with another one without requiring his/her identification number and can easily localize the person initiating the contact. It is also possible to exchange various types of personal information in a private manner. This innovative type of social interaction can significantly increase consumer interest for AR experiences.
Two-Level Hierarchical Planning in a Known Semi-Structured Environment

The application of motion planning for autonomous vehicles has been primarily focused either in highly structured or unstructured environments. However, many environments in the real-world share the characteristics of both and can be classified as semi-structured. The adaptation of the strategies from other environments to that of semi-structured, although possible, do not produce trajectories with the required characteristics, especially when the environment is dynamic. To that end, this paper introduced a two-level hierarchical planning strategy consisting of a discrete lane-network-based global planner and a Hybrid A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> local planner that: (i) generates a smooth, safe and kinematically feasible path in real-time; (ii) considers structural constraints of the environment from an a priori map. Furthermore, a valid lane-network-based sub-goaling strategy is proposed for providing a reference goal during the local planning process. Simulation and live tests have been conducted to evaluate the functionality of the strategy in several case studies. The implementation choices of: (i) using an open-source highly automated driving (HAD) map framework, Lanelet2, (ii) developing as plugins to an open-source navigation stack, Move Base Flex (MBF), allow the proposed strategy to be easily adopted on other autonomous platforms.
Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

The accuracy of object detection based on kitchen appliance scene images can suffer severely from external disturbances such as various levels of specular reflection, uneven lighting, and spurious lighting, as well as internal scene-related disturbances such as invalid edges and pattern information unrelated to the object of interest. The present study addresses these unique challenges by proposing an object detection method based on improved faster R-CNN algorithm. The improved method can identify object regions scattered in various areas of complex appliance scenes quickly and automatically. In this paper, we put forward a feature enhancement framework, named deeper region proposal network (D-RPN). In D-RPN, a feature enhancement module is designed to more effectively extract feature information of an object on kitchen appliance scene. Then, we reconstruct a U-shaped network structure using a series of feature enhancement modules. We have evaluated the proposed D-RPN on the dataset we created. It includes all kinds of kitchen appliance control panels captured in nature scene by image collector. In our experiments, the best-performing object detection method obtained a mean average precision mAP value of 89.84% in the testing dataset. The test results show that the proposed improved algorithm achieves higher detecting accuracy than state-of-the-art object detection methods. Finally, our proposed detection method can further be used in text recognition.
Motion Estimation for Spike Camera Data Sequence via Spike Interval Analysis

With the development of emerging computer vision applications, there is an increasing demand for capturing the scenes with high-speed motion. Recently, a novel retina-inspired spike camera has shown great potential for recording the dynamic scenes at high temporal resolution. Different from the conventional digital cameras that capture the visual scene by a single snapshot, the spike camera monitors the incoming light persistently, with each pixel producing a continuous stream of spikes. Recovering the motion process from the spike data sequence is an important problem to study for the spike camera, as it is the foundation of many other tasks, such as image reconstruction, object tracking and object detection. In this paper, we carefully analyze the characteristics of spike data and develop a motion estimation algorithm to recover the continuous high-speed motion process from the spike camera data sequence. Based on the assumption that the spike intervals passed by the same motion trajectories usually have the similar spike densities, we establish a data term constraint to model the temporal consistency of spike intervals. In addition, we integrate a local smoothness constraint with the proposed data term constraint to further improve the estimation accuracy. Experimental results demonstrate that our proposed algorithm can recover high-speed motion process from the captured spike data, and the recovered motion information is beneficial for i mage reconstruction.
AI in Photography: Scrutinizing Implementation of Super-Resolution Techniques in Photo-Editors

Judging the quality of a photograph from the perspective of a photographer we can ascertain resolution, symmetry, content, location, etc. as some of the factors that influence the proficiency of a photograph. The exponential growth in the allurement for photography impels us to discover ways to perfect an input image in terms of the aforesaid parameters. Where content and location are the immutable ones, attributes like symmetry and resolution can be worked upon. In this paper, I prioritized resolution as our cynosure and there can be multiple ways to refine it. Image super-resolution is progressively becoming a prerequisite in the fraternity of computer graphics, computer vision, and image processing. It's the process of obtaining high-resolution images from their low-resolution counterparts. In my work, image super-resolution techniques like Interpolation, SRCNN (Super-Resolution Convolutional Neural Network), SRResNet (Super Resolution Residual Network), and GANs (Generative Adversarial Networks: Super-Resolution GAN-SRGAN and Conditional GAN-CGAN) were studied experimentally for post-enhancement of images in photography as employed by photo-editors, establishing the most coherent approach for attaining optimized super-resolution in terms of quality.
Impact of digitalization on the pedagogical aspects of traditional education

Abstract Traditional education is focused on sources such as teachers, print media, and school. The only way a learner can reach the information source by enrolling with schools, libraries, and teachers. By the majority of people, information was not easily accessible. In case if you have access to the data, it might not be the latest. In the modern era, everyone wants access to the latest information, and the advancement in technology has made it possible. Digitalization has given us many learning opportunities.
Exploring the Impact of COVID-19 on Aviation Industry: A Text Mining Approach

Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020.Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period.We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative).Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic.The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry.Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic.
How to (Re)represent it?

Choosing an effective representation is fundamental to the ability of the representation's user to exploit it for the intended purpose. The major contribution of this paper is to provide a novel, flexible framework, rep2rep, that can be used by AI systems to recommend effective representations. What makes an effective representation is determined by whether it expresses the necessary information, supports the execution of tasks, and reflects the user's cognitive abilities. In general, there is no single `most effective' representation for every problem and every user, which makes it difficult to choose one from the plethora of possible representations. To address this, rep2rep includes: a domain-independent language for describing representations, algorithms that compute measures of informational suitability and overall cognitive cost, and uses these measures to recommend representations. We demonstrate the application of rep2rep in the probability domain. Importantly, our framework provides the foundations for personalised interaction with AI systems in the context of representation choice.
Towards a Holistic Approach to Medical IoT Forensics

Since Computer Forensics emerged at the beginning of the century, this new discipline has grown overwhelmingly due to rapid advancement in technology and enormous changes in society. From Computer Forensics to Digital Forensics and then to Internet of Things (IoT) Forensics, the study domain of the discipline has been expanded considerably. As Medical IoT (MIoT) technology is moving to the center stage of the current development, the need for exploring MIoT Forensics is obvious. To deal with new issues and challenges, a holistic approach to MIoT Forensics is proposed in this paper. Through the analysis on the difference between MIoT Forensics and others, the new approach presents what should be studied and developed in MIoT Forensics according to the reality we are facing. It is believed that the new approach will have a promising impact on the research and development in MIoT Forensics.
Target-Based Sentiment Analysis using a BERT Embedded Model

Opinion mining or sentiment analysis is used for understanding the opinion of the community on a specific product or a service. In this paper, we investigate the modeling power of contextualized representations from pre-trained language model BERT. There are many methods and techniques used to explore these features from unstructured comments. We developed a different approach to target extraction task in sentiment analysis using a deep learning model combining Gated Recurrent Unit (GRU) and Conditional Random Field (CRF). The task is typically modeled as a sequence labeling problem and solved using state-of-the-art labelers such as CRF. This model is trained on labeled data to extract and classify feature sets. Experiments have been conducted on two benchmark datasets and our framework achieves consistently superior results.
Modification and optimization of miller – rabin simplicity test algorithm implemented by parallel computation

Abstract The project of modification and optimization Miller–Rabin’s simplicity test algorithm implemented by parallel computation in the programming language C# is presented, which works faster on comparison with standard iterative algorithm by 50%, which makes it easier to create the keys for such well–known encryption algorithms as RSA, DSA, etc. These methods are similar on their functionality, but have a different estimation of structural complexity that has been shown various tests. Proceeding from it, modified by authors Miller–Rabin’s algorithm realized through parallel calculations is more reliably and faster, than the standard algorithm, that is experimentally confirmed in practice.
A focus on codemixing and codeswitching in Tamil speech to text

This paper attempts to develop an application that converts Tamil language speech to Tamil text, with a view to encourage usage and indirectly ensure linguistic preservation of a classical language. The application converts spoken Tamil to text without auto-correction, code-mixing or code-switching. Tamil is a syllabic language, similar to other Indian languages and some unique features such as instances of allophones, short & long vowels and lack of aspirated stops produces some challenges in developing a speech to text app. This project is a technology demonstration of a complete web application, which, when perfected, could be used to act as a teaching tool to encourage correct pronunciation of syllables and words for native and non-native Tamil speakers. A report by the Business Standard India e-publication in the year 2019 highlighted the decline in the usage of the Tamil language, and indeed separate reports in Singapore and Malaysia, which both have large numbers of Tamil speakers indicate that there have been concerns about the relevance and usage of the Tamil language as a spoken means of communication among the community. The research maintains that it is important to maintain the utilization of Tamil language via technology to help preservation of one of the oldest surviving languages in the world. The work further emphasizes on the indigenous design considerations for such applications which may be different to traditional software engineering approaches..
Hardware accelerator for training with integer backpropagation and probabilistic weight update

Advances in the architecture of inference accelerators and quantization techniques of neural networks allow effective on-device inference in embedded devices. Privacy issues for user data, as well as increasing needs of user-specific services, have led to a need for on-device training. The dot product operation required in backpropagation can be computed efficiently by multiplier–accumulators (MACs) in the inference accelerator if forward and backward propagation of the neural network have the same precision. This chapter introduces a quantization technique to enable computation by the digital neuron inference accelerator with the same precision as that using the forward path. Updating the 5-bit weights with gradients of higher precision is challenging. To address this issue, this chapter also introduces a probabilistic weight update. It also describes the hardware implementation of the probabilistic weight-update scheme. The proposed training technique achieves 98.15% recognition accuracy on the MNIST dataset.