Reviewerly/exHarmony huggingface dataset:
The Matter of Cybersecurity Expert Workforce Scarcity in the Czech Republic and Its Alleviation Through the Proposed Qualifications Framework

This paper is focused on challenges connected with the persisting imbalance between the supply and demand of the cybersecurity expert workforce. We analyse the current situation in the Czech Republic, finding that although the shortage of experts affects the private and public sectors both, the public sector is constrained by a massive financial undervaluation of cybersecurity experts and other legal and systematic deficiencies and obstacles and therefore has a much lower chance of attracting talents in this field. The inability of public institutions to find relevant workforce causes, among other things, problems with formulating public procurements, assessing offers and communicating their requirements to the supply-side of the labour market. One of the solutions to this crisis might be in the systematic support of education programmes. However, the cybersecurity field is so dynamic and fragmented that the alignment of the education programme with the market needs presents a significant challenge. There, a unified qualifications framework could serve as a basis for finding common ground. We focus on the benefits of creating such a framework, especially the benefits that a united taxonomy can bring to the cybersecurity labour market by bolstering cybersecurity higher education. Finally, we summarise the key features of the cyber-qualifications framework that is being developed under our current project and highlight its potential use for labour market optimisation and efficient development of new cybersecurity study programs and further education.
Learning Transferable Features for Diagnosis of Breast Cancer from Histopathological Images

Nowadays, there is no argument that deep learning algorithms provide impressive results in many applications of medical image analysis. However, data scarcity problem and its consequences are challenges in implementation of deep learning for the digital histopathology domain. Deep transfer learning is one of the possible solutions for these challenges. The method of off-the-shelf features extraction from pre-trained convolutional neural networks (CNNs) is one of the common deep transfer learning approaches. The architecture of deep CNNs has a significant role in the choice of the optimal learning transferable features to adopt for classifying the cancerous histopathological image. In this study, we have investigated three pre-trained CNNs on ImageNet dataset; ResNet-50, DenseNet-201 and ShuffleNet models for classifying the Breast Cancer Histopathology (BACH) Challenge 2018 dataset. The extracted deep features from these three models were utilised to train two machine learning classifiers; namely, the K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) to classify the breast cancer grades. Four grades of breast cancer were presented in the BACH challenge dataset; these grades namely normal tissue, benign tumour, in-situ carcinoma and invasive carcinoma. The performance of the target classifiers was evaluated. Our experimental results showed that the extracted off-the-shelf features from DenseNet-201 model provide the best predictive accuracy using both SVM and KNN classifiers. They yielded the image-wise classification accuracy of 93.75% and 88.75% for SVM and KNN classifiers, respectively. These results indicate the high robustness of our proposed framework.
Computing Minimal Unique Substrings for a Sliding Window

Abstract A substring u of a string T is called a minimal unique substring ( MUS ) of T if u occurs exactly once in T and any proper substring of u occurs at least twice in T . In this paper, we study the problem of computing MUSs for a sliding window over a given string T . We first show how the set of MUSs can change when the window slides over T . We then present an $$O(n\log \sigma ')$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>log</mml:mo> <mml:msup> <mml:mi>σ</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -time and O ( d )-space algorithm to compute MUSs for a sliding window of size d over the input string T of length n , where $$\sigma '\le d$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>σ</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mo>≤</mml:mo> <mml:mi>d</mml:mi> </mml:mrow> </mml:math> is the maximum number of distinct characters in every window.
SwiftFace

Computer vision is a field of artificial intelligence that trains computers to interpret the visual world in a way similar to that of humans. Due to the rapid advancements in technology and the increasing availability of sufficiently large training datasets, the topics within computer vision have experienced a steep growth in the last decade. Among them, one of the most promising fields is face detection. Being used daily in a wide variety of fields; from mobile apps and augmented reality for entertainment purposes, to social studies and security cameras; designing high-performance models for face detection is crucial. On top of that, with the aforementioned growth in face detection technologies, precision and accuracy are no longer the only relevant factors: for real-time face detection, speed of detection is essential. SwiftFace is a novel deep learning model created solely to be a fast face detection model. By focusing only on detecting faces, SwiftFace performs 30% faster than current state-of-the-art face detection models.
A Symmetric Key Dynamic Gates Encryption Algorithm Based on Quantum Computing

Quantum cryptography utilizes the quantum properties of photons to construct an unbreakable crypto-algorithm in real-time. In this paper, based on quantum and classical computation principles, a new quantum encryption algorithm using the symmetric key is presented. It is a block-cipher algorithm which means that each message contains blocks in which 4 bits for each. Firstly, keyed rotations and permutation of the message bits are used. Secondly, the encrypted message is converted to qubits then several dynamic quantum gates are applied to them key dependently to make the ciphertext state reach the state of non-orthogonality. In consequence of the principles of quantum mechanics, the eaves cannot distinguish the non-orthogonal states of the encrypted message. The proposed algorithm is proofed to increase the message space, the various algorithm analysis proofed that our proposed algorithm has an improved strength against various attacks, including the brute force attack.
Unattached irregular scene text rectification with refined objective

Recently, text recognition tasks have reached a high level with deep learning-based methods. The techniques are widely applied in different fields, and nowadays most researchers aim to build an effective approach to deal with irregular text in scene images. In this work, we propose a GAN-based framework to rectify scene text with rotation, curving, or other distortions. Unlike previous rectification modules that rely on the recognition networks, our model can be utilized either as an independent model or an extra component. Therefore, annotations of the text content are not required to train the model. And we utilize a refined training objective with the proposed sample loss, which is able to effectively control pixels in the output images that are supposed to be sampled from input ones. Experiments on public benchmarks demonstrate the effectiveness of our method. The code will be publicly available on github soon.
Eye Blink-Based Liveness Detection Using Odd Kernel Matrix in Convolutional Neural Networks

Spoofing attacks on biometric systems are one of the major disorders to use for secure applications. In the case of face recognition, it is not easy for the computer to detect whether the person is live or not. Thus, liveness detection came into existence. In traditional methods of liveness detection, hard computing techniques have been used, which are highly complex in nature. Other existing methods tried to eliminate this problem using machine learning techniques but failed to eliminate over-fitting issues. Recent growth in convolutional neural networks has paved the way for accurate results in detection and recognition tasks. In this work, a new approach to detect face liveness based on eye-blinking status using deep learning has been implemented. Eye blinking is an action that can be identified by the sequence of human images, which involves human eyes with close and open states. The proposed Odd Kernel Matrix Convolution Neural Network (OKM-CNN) method uses a modified kernel matrix to accommodate the need of finding the eye-blinking status. Furthermore, comparisons are done using various activation functions. The experimental result shows 96% of accuracy for the proposed OKM-CNN liveness detection model.
Enhancing Accuracy of Semantic Relatedness Measurement by Word Single-Meaning Embeddings

We propose a lightweight algorithm of learning word single-meaning embeddings (WSME), by exploring WordNet synsets and Doc2vec document embeddings, to enhance the accuracy of semantic relatedness measurement. In our model, each polyseme is decomposed into a series of monosemous words with diverse WordNet synset tags which represent different word meanings, and there is a one-to-one correspondence between a word meaning and a vector. Our algorithm proceeds in 3 steps. First, the word sense disambiguation of each polyseme in different contexts is achieved by computing the maximum relatedness between the context of this polyseme and all its candidate meaning definitions in WordNet. Second, each tagged word is lemmatized according to its synset tag to alleviate the word sparsity problem caused by polysemes decomposition. Third, the word single-meaning embeddings are learned from the meaning-tagged corpus, and the semantic relatedness between words can be more accurately measured based on such embeddings. Our experimental results show that our algorithm achieves better performance on the semantic relatedness measurement compared with existing techniques.
A performance comparison of machine learning classifiers for Covid-19 Arabic Quarantine tweets sentiment analysis

The COVID-19 pandemic has spread across the world and has become an international public health emergency. The outbreak of COVID-19 has focused attention on the use of quarantine and social distancing as the primary defense strategies against community infection. Arabic language, as one of the most spoken languages in the world, and the fastest-growing language on the Internet motivates us to provide reliable automated tools that can perform sentiment analysis to reveal users' opinions. This paper was proposed to utilize machine learning (ML) methods for Arabic Sentiment Analysis to understand the positive and negative opinions related to quarantine and social distancing during the outbreak of COVID-19. We provided a model of different essential and ensembles ML classifiers and compared their effectiveness in classifying the collected imbalanced dataset. Moreover, the application of a variety of SMOTe (Synthetic Minority Over-sampling Technique) for our imbalanced dataset was evaluated. The results demonstrated that SMOTe Edited Nearest Neighbor (SMOTEENN) outperformed other SMOTe techniques. Moreover, the results showed that ensemble classifiers are more robust with imbalanced datasets than single classifiers. On the other hand, the overall average of F1 score of single classifiers is more robust than ensemble classifiers when applying SMOTEENN.
RFC-HyPGCN: A Runtime Sparse Feature Compress Accelerator for Skeleton-Based GCNs Action Recognition Model with Hybrid Pruning

Skeleton-based Graph Convolutional Networks (GCNs) models for action recognition have achieved excellent prediction accuracy in the field. However, limited by large model and computation complexity, GCNs for action recognition like 2s-AGCN have insufficient power-efficiency and throughput on GPU. Thus, the demand of model reduction and hardware acceleration for low-power GCNs action recognition application becomes continuously higher.To address challenges above, this paper proposes a runtime sparse feature compress accelerator with hybrid pruning method: RFC-HyPGCN. First, this method skips both graph and spatial convolution workloads by reorganizing the multiplication order. Following spatial convolutions channel-pruning dataflow, a coarse-grained pruning method on temporal filters is designed, together with sampling-like fine-grained pruning on time dimension. Later, we come up with an architecture where all convolutional layers are mapped on chip to pursue high throughput. To further reduce storage resource utilization, online sparse feature compress format is put forward. Features are divided and encoded into several banks according to presented format, then bank storage is split into depth-variable mini-banks. Furthermore, this work applies quantization, input-skipping and intra-PE dynamic data scheduling to accelerate the model. In experiments, proposed pruning method is conducted on 2s-AGCN, acquiring 3.0x-8.4x model compression ratio and 73.20% graph-skipping efficiency with balancing weight pruning. Implemented on Xilinx XCKU-115 FPGA, the proposed architecture has the peak performance of 1142 GOP/s and achieves up to 9.19x and 3.91x speedup over high-end GPU NVIDIA 2080Ti and NVIDIA V100, respectively. Compared with latest accelerator for action recognition GCNs models, our design reaches 22.9x speedup and 28.93% improvement on DSP efficiency.
ML-Based Smart Shopping System with Recipe Recommendation

Huge supermarket chains like Big Bazaar, Reliance Fresh, D-Mart and so on have been the center of attraction for a major part of the population for their household monthly groceries. This is mainly due to their offers and exclusive prices on products. On an average, a person spends a lot of time standing in queues at the billing counter, scanning each item. There has to be a system in place to avoid such huge frustrating queues and to enhance the shopping experience of customers. This research proposes an online shopping and billing system implemented through an Android application by which the user can scan the QR-code/Bar-code present on each item and add it to the virtual E-cart and pay online in the application via various payment options like UPI, G-Pay, Pay-TM and so on. Also, customers have a hard time figuring out what food items to cook throughout the day. To solve this issue, the application will also have a Recipe Recommendation System which will give the user numerous recipe options to select from, based on the food items in the cart. This will be done with the help of Natural Language Processing algorithms. There will be an Admin Portal to keep track of the items in the mart which are currently being purchased and a statement of all the payments by the customers. Finally, with the implementation of this system, the customer will save her/his precious time and shopping will become much easier.
Adversarial Unsupervised Video Summarization Augmented With Dictionary Loss

Automated unsupervised video summarization by key-frame extraction consists in identifying representative video frames, best abridging a complete input sequence, and temporally ordering them to form a video summary, without relying on manually constructed ground-truth key-frame sets. State-of-the-art unsupervised deep neural approaches consider the desired summary to be a subset of the original sequence, composed of video frames that are sufficient to visually reconstruct the entire input. They typically employ a pre-trained CNN for extracting a vector representation per RGB video frame and a baseline LSTM adversarial learning framework for identifying key-frames. In this paper, to better guide the network towards properly selecting video frames that can faithfully reconstruct the original video, we augment the baseline framework with an additional LSTM autoencoder, which learns in parallel a fixed-length representation of the entire original input sequence. This is exploited during training, where a novel loss term inspired by dictionary learning is added to the network optimization objectives, further biasing key-frame selection towards video frames which are collectively able to recreate the original video. Empirical evaluation on two common public relevant datasets indicates highly favourable results.
Action Quality Assessment With Ignoring Scene Context

We propose an action quality assessment (AQA) method that can specifically assess target action quality with ignoring scene context, which is a feature unrelated to the target action. Existing AQA methods have tried to extract spatiotemporal features related to the target action by applying 3D convolution to the video. However, since their models are not explicitly designed to extract the features of the target action, they mis-extract scene context and thus cannot assess the target action quality correctly. To overcome this problem, we impose two losses to an existing AQA model: scene adversarial loss and our newly proposed human-masked regression loss. The scene adversarial loss encourages the model to ignore scene context by adversarial training. Our human-masked regression loss does so by making the correlation between score outputs by an AQA model and human referees undefinable when the target action is not visible. These two losses lead the model to specifically assess the target action quality with ignoring scene context. We evaluated our method on a diving dataset commonly used for AQA and found that it outperformed current state-of-the-art methods. This result shows that our method is effective in ignoring scene context while assessing the target action quality.
Subaperture stitching computation time optimization using a system of linear equations

Measurement of large or aspheric optical surface shapes as a single aperture using interferometry is problematic for many reasons. A typical problem is the numerical aperture limitation of the interferometer transmission element and the surface slope deviation of aspheres. This deviation typically causes vignetting and spatial aliasing on the camera. A solution is subaperture measurement and subsequent subaperture stitching. A stitching algorithm, in principle, uses overlaps between subapertures to eliminate aberrations of each subaperture to obtain a full aperture for further analysis. This process is computation time demanding and requires optimization in order to obtain a result in a reasonable time to reduce, in turn, the overall manufacturing time. In this paper, a novel, to the best of our knowledge, and fast stitching method based on a system of linear equations is proposed and mathematically described. The developed method was compared with other algorithms, and theoretical computation complexity was calculated and compared. The method was tested practically, with real data measured on spherical surfaces using QED ASI (QED Technologies aspheric stitching interferometer) and an experimental interferometer, and the results are presented. Stitching quality was quantified for results and compared to other algorithms.
Answer me this: Constructing Disambiguation Queries for Explanation Generation in Robotics

Our architecture seeks to enable robots collaborating with humans to describe their decisions and evolution of beliefs. To achieve the desired transparency in integrated robot systems that support knowledge-based reasoning and data-driven learning, we build on a baseline system that supports non-monotonic logical reasoning with incomplete commonsense domain knowledge, data-driven learning from a limited set of examples, and inductive learning of previously unknown axioms governing domain dynamics. In the context of a simulated robot providing on-demand, relational descriptions as explanations of its decisions and beliefs, we introduce an interactive system that automatically traces beliefs, and addresses ambiguity in the human queries by constructing and posing suitable disambiguation queries. We present results of evaluation in scene understanding and planning tasks to demonstrate our architecture’s abilities.
Application of Computer Technology in the Visualized Landscape Design of Modern Urban Garden Environment

Abstract The landscape design of the garden environment is closely developed with the combination of aesthetics, environmental science, and planning technology. To solve the dynamic landscape characteristics, this paper relies on computer technology to carry out the visualized landscape design of modern urban gardens, realizes 3D solid modeling and 3D real-time virtual presentation of design scenes, lays a theoretical foundation for garden landscape planning, and discusses in detail the simulation in professional modules process. Finally, an engineering example is used to verify the effectiveness of the system.
Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.
Robust Light Field Synthesis From Stereo Images With Left-Right Geometric Consistency

We propose a lightweight yet effective deep learning pipeline for light field synthesis from a single stereo image pair. Our pipeline consists of a convolutional network (CNN) that enforces a left-right consistency constraint on the light fields synthesized from left and right stereo views, a stage that merges light fields synthesized from left and right stereo views with a novel alpha blending technique, and a final refinement network using a unique 3D convolution operation. Our experiments quantitatively and qualitatively confirm the effectiveness and robustness of the proposed model, which performs favorably against state-of-the-art algorithms for light field synthesis from extremely sparse (only one, two, or four) views while using much fewer parameters.
Counterfactual Explanations in Explainable AI: A Tutorial

Deep learning has shown powerful performances in many fields, however its black-box nature hinders its further applications. In response, explainable artificial intelligence emerges, aiming to explain the predictions and behaviors of deep learning models. Among many explanation methods, counterfactual explanation has been identified as one of the best methods due to its resemblance to human cognitive process: to deliver an explanation by constructing a contrastive situation so that human may interpret the underlying mechanism by cognitively demonstrating the difference.