Reviewerly/exHarmony huggingface dataset:
Fast Authentication from Aggregate Signatures with Improved Security

An attempt to derive signer-efficient digital signatures from aggregate signatures was made in a signature scheme referred to as Structure-free Compact Rapid Authentication (SCRA) (IEEE TIFS 2017). In this paper, we first mount a practical universal forgery attack against the NTRU instantiation of SCRA by observing only 8161 signatures. Second, we propose a new signature scheme ( $$\texttt {FAAS}$$ ), which transforms any single-signer aggregate signature scheme into a signer-efficient scheme. We show two efficient instantiations of $$\texttt {FAAS}$$ , namely, $$\texttt {FAAS}\hbox {-}{} \texttt {NTRU}$$ and $$\texttt {FAAS}\hbox {-}{} \texttt {RSA}$$ , both of which achieve high computational efficiency. Our experiments confirmed that $$\texttt {FAAS}$$ schemes achieve up to 100 $$\times $$ faster signature generation compared to their underlying schemes. Moreover, $$\texttt {FAAS}$$ schemes eliminate some of the costly operations such as Gaussian sampling, rejection sampling, and exponentiation at the signature generation that are shown to be susceptible to side-channel attacks. This enables $$\texttt {FAAS}$$ schemes to enhance the security and efficiency of their underlying schemes. Finally, we prove that $$\texttt {FAAS}$$ schemes are secure (in random oracle model), and open-source both our attack and $$\texttt {FAAS}$$ implementations for public testing purposes.
Automatic Text Generation in Macedonian Using Recurrent Neural Networks

Neural text generation is the process of a training neural network to generate a human understandable text (poem, story, article). Recurrent Neural Networks and Long-Short Term Memory are powerful sequence models that are suitable for this kind of task. In this paper, we have developed two types of language models, one generating news articles and the other generating poems in Macedonian language. We developed and tested several different model architectures, among which we also tried transfer-learning model, since text generation requires a lot of processing time. As evaluation metric we used ROUGE-N metric (Recall-Oriented Understudy for Gisting Evaluation), where the generated text was tested against a reference text written by an expert. The results showed that even though the generate text had flaws, it was human understandable, and it was consistent throughout the sentences. To the best of our knowledge this is a first attempt in automatic text generation (poems and articles) in Macedonian language using Deep Learning.
Using machine learning models to classify stroke risk level based on national screening data

With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke risk factors and conducts high-risk population interventions for people aged over 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, atrial fibrillation, smoking, lack of exercise, apparently overweight or obese and family history of stroke. People with more than two risk factors or with a history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk level for people with "unknown" values in the fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in the statistical results at the national level. In this paper, firstly, we construct the training set and test set and process the imbalanced training set based on oversampling and undersampling method. Then, we develop logistic regression model, decision tree model, neural network model and random forest model for stroke risk classification, and evaluate these models based on the recall and precision. The results show that the model based on random forest achieves best performance considering recall and precision. The models constructed in this paper can improve the screening efficiency and avoid unnecessary rescreening and intervention expenditures.
Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks

Manual segmentation of rodent brain lesions from magnetic resonance images (MRIs) is an arduous, time-consuming and subjective task that is highly important in pre-clinical research. Several automatic methods have been developed for different human brain MRI segmentation, but little research has targeted automatic rodent lesion segmentation. The existing tools for performing automatic lesion segmentation in rodents are constrained by strict assumptions about the data. Deep learning has been successfully used for medical image segmentation. However, there has not been any deep learning approach specifically designed for tackling rodent brain lesion segmentation. In this work, we propose a novel Fully Convolutional Network (FCN), RatLesNet, for the aforementioned task. Our dataset consists of 131 T2-weighted rat brain scans from 4 different studies in which ischemic stroke was induced by transient middle cerebral artery occlusion. We compare our method with two other 3D FCNs originally developed for anatomical segmentation (VoxResNet and 3D-U-Net) with 5-fold cross-validation on a single study and a generalization test, where the training was done on a single study and testing on three remaining studies. The labels generated by our method were quantitatively and qualitatively better than the predictions of the compared methods. The average Dice coefficient achieved in the 5-fold cross-validation experiment with the proposed approach was 0.88, between 3.7% and 38% higher than the compared architectures. The presented architecture also outperformed the other FCNs at generalizing on different studies, achieving the average Dice coefficient of 0.79.
Synthesising Images and Labels Between MR Sequence Types with CycleGAN

Real-time (RT) sequences for cardiac magnetic resonance imaging (CMR) have recently been proposed as alternatives to standard cine CMR sequences for subjects unable to hold the breath or suffering from arrhythmia. RT image acquisitions during free breathing produce comparatively poor quality images, a trade-off necessary to achieve the high temporal resolution needed for RT imaging and hence are less suitable in the clinical assessment of cardiac function. We demonstrate the application of a CycleGAN architecture to train autoencoder networks for synthesising cine-like images from RT images and vice versa. Applying this conversion to real-time data produces clearer images with sharper distinctions between myocardial and surrounding tissues, giving clinicians a more precise means of visually inspecting subjects. Furthermore, applying the transformation to segmented cine data to produce pseudo-real-time images allows this label information to be transferred to the real-time image domain. We demonstrate the feasibility of this approach by training a U-net based architecture using these pseudo-real-time images which can effectively segment actual real-time images.
Filling the Gaps in Atrous Convolution: Semantic Segmentation With a Better Context

The main challenge for scene parsing arises when complex scenes with highly diverse objects are encountered. The objects not only differ in scale and appearance but also in semantics. Previous works focus on encoding the multi-scale contextual information (via pooling or atrous convolutions) generally on top of compact high-level features (i.e., at a single stage). In this work, we argue that a rich set of cues exist at multiple stages of the network, encapsulating low, mid and high-level scene details. Therefore, an optimal scene parsing model must aggregate multi-scale context at all three levels of the feature hierarchy; a capability that lacks in state-of-the-art scene parsing models. To address this limitation, we introduce a novel architecture with three new blocks that systematically aggregate low, mid and high tier features. The heart of our approach is a high-level feature aggregation module that augments sparsely connected atrous convolution with dense local and layer-wise connections to avoid gridding artifacts. Besides, we employ a novel feature pyramid augmentation and semantic refinement unit to generate low- and mid-level features that are mixed with high-level features at the decoder. We extensively evaluate our proposed approach on the large-scale Cityscapes and ADE2K benchmarks. Our approach surpasses many latest models on both datasets, achieving mean intersection-over-union (mIoU) scores of 80.5% and 44.0% on Cityscapes and ADE20K, respectively.
Estimating the new Initial Value of Trial Division Algorithm for Balanced Modulus to Decrease Computation Loops

The aim of this paper is to propose the new initial value of Trial Division Algorithm (TDA) which is suitable for balanced modulus. The traditional initial value is ordinary ⌊√n⌋ where n is represented as modulus but the proposed value is always smaller than one. Moreover, it is still larger than a smaller prime factor. That means some integers between the new initial value and ⌊√n⌋ are deleted from the computation. Therefore, the computation time is also decreased. In fact, the key to implement process is the way to choose both of initial values of Fermat's Factorization Algorithm (FFA) in order to apply with the proposed method. The reason is that half of the addition between both of them is always less than the higher prime factor. Hence, the first trial divisor from dividing n by the result is greater than the smaller factor. Therefore, it can be assigned as the new initial value for the implementation. The experimental results show that most of n can be factored with decreased loops whenever the new initial value is estimated. However, loops are stable for some values of n when the higher initial value of FFA is not changed.
Scalebridge: Design and Evaluation of Adaptive Difficulty Proportional Reasoning Game for Children

We present the design and evaluation of Scalebridge, an educational game that teaches children the mathematical skill of proportional reasoning. We also propose an adaptive level difficulty model that uses low-cost brain-computer interface device to modulate difficulty of the game based on the player's focus. We show that the game achieves the educational learning outcome of developing proportional reasoning skill both in the adaptive and in the non-adaptive version of game.
Ordia: A Web Application for Wikidata Lexemes

Since 2018, Wikidata has had the ability to describe lexemes, and the associated SPARQL endpoint Wikidata Query Service can query this information and visualize the results. Ordia is a Web application that displays the multilingual lexeme data of Wikidata based on embedding of the responses from the Wikidata Query Service via templated SPARQL queries. Ordia has also a SPARQL-based approach for online matching of the words of a text with Wikidata lexemes and the ability to use a knowledge graph embedding as part of a SPARQL query. Ordia is available from https://tools.wmflabs.org/ordia/ .
Knowledge Extraction and Integration for Information Gathering in Penetration Testing

Assets identification is an important aspect of penetration test on which security practitioner develop their defense mechanism. In addition, assets identification is an essential piece of information for penetration testers to find a weakness in the targeted organization. Information gathering is the process of extracting knowledge to recognize the organizations' assets available on the internet. There are many open source tools available for information gathering. However, penetration tester needs to put manual effort (during several hours to multiple days) to extract useful knowledge from the output of one tool and integrate that knowledge in another tool. Penetration tester can increase speed and accuracy of the overall information gathering process by automating the knowledge extraction and integration. This paper review and identify open source subdomain enumeration and service scanning tools and present an approach to integrate and automate identified tools. The result reveals that there is a significant improvement of the information gathering process by using our approach due to the reduction of manual tasks.
Audio Compression Algorithm using Discrete Cosine Transform (DCT) and Lempel-Ziv-Welch (LZW) Encoding Method

This paper proposes a new approach to Audio Compression that incorporates lossless text compression algorithm. The purpose of Audio Compression is to reduce the amount of data required to represent the digital audio by removing redundant data. In the present work Discrete Cosine Transform and lossless text compression method (Lempel-Ziv-Welch method) based Audio Compression algorithm has been proposed which also includes audio normalization, scalar quantization and encoding. The LZW dictionary obtained after compression is used for transmission and storage. The performance of the proposed algorithm is analyzed using various audio specimens of distinct size with distinct audio signal parameters. The compression performance is assessed using Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR). The audio specimen outcomes indicate the propitious performance of proposed algorithm. The compression ratio can be enhanced by changing various parameters of the system.
A Twitter Based Opinion Mining to Perform Analysis Geographically

Twitter is a free and most popular social networking service that allows registered and authorised user to transfer their opinion and reviews with other people and this type of transfer is called short messages which is also known as tweets. In this research, Sentiment analysis on Twitter dataset will be performed to predict the current affairs on Twitter data to make survey and prediction about the current affairs and also about the user's behaviour and opinion on the twitter. A current affair includes all tweets which are in the form of people opinion on Twitter like product reviews of customers, political tweets, movie ratings etc. All streaming as well as offline data on the tweets are analysed in this research. In this paper, we will describe about the different feature extraction methods with a new proposed work, its results and then the analysis of that results.
KS(conf): A Light-Weight Test if a Multiclass Classifier Operates Outside of Its Specifications

We study the problem of automatically detecting if a given multi-class classifier operates outside of its specifications (out-of-specs), i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier's predictions cannot be guaranteed if it operates out-of-specs. Previously proposed methods for out-of-specs detection make decisions on the level of single inputs. This, however, is insufficient to achieve low false positive rate and high false negative rates at the same time. In this work, we describe a new procedure named KS(conf), based on statistical reasoning. Its main component is a classical Kolmogorov-Smirnov test that is applied to the set of predicted confidence values for batches of samples. Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change.
Facial Landmark Activity Features for Depression Screening

Depression is the most common mood disorder in the world which is also the leading cause of suicide. Depression might be diagnosed using varying modalities, as it affects many aspects of the patient, not only the patient will appear melancholic, but the patient's sleep quality and life quality might also be disrupted. In this study, we aim to extract facial landmark features unique to depression patients and build a machine learning model to classify the depression severity. The facial landmark activities considered in this study include: speed statistics of the landmarks, the standard deviation of eye pupils, and statistical features of mouth area. We found several facial landmark activity features with significant differences between healthy volunteers and depressed patients. We also successfully built machine learning models for automatic depression severity prediction.
A Complete Bengali Stop Word Detection Mechanism

Stop word lists of different languages have been developed already around the world. The Bengali language is a grammatically enriched language. It has a large vocabulary containing a massive number of stop words. There is no proper set of stop words for the Bengali language. A proper set of stop word list is highly required in modern information retrieval systems. In this paper, a corpus-based methodology for detection and extraction of Bengali stop word is proposed. We have used machine learning library NLTK from python environment. As there is no introduction and classification available for Bengali stop word, this paper discusses those issues of Bengali Stop word. There is no work available for Bengali stop word removal. This motivates us to develop Bengali stop word elimination algorithm for the Bengali language. Our proposed approach is the first attempt for this task in the Bengali language. In our experiment, we have achieved 100% precision but we have an accuracy of 70 - 75%.
Establishing Trusted Channel for Data Exchange between Source and Receiver by Modified One-time Password Method

The one-time password method is one of the most common and effective methods of authentication of the source and receiver of information. The main threat to the trusted communication channel of exchange information is a potential attacker who randomly generates and transmits messages to the receiver, that may be mistakenly perceived as an authenticated source. This article discusses an authentication method based on a modified one-time password method, in which the decision on the legality of the received message is made not only on the basis of the content of its verification sequence but also on the basis of the history of the received verification messages. The parameters of the algorithm for generating authenticated data are analyzed, the influence of these parameters on the probability of authentication errors in the communication channel is investigated.
Interval Type-2 fuzzy logic for dynamic parameter adjustment in the imperialist competitive algorithm

In this paper we propose the use of interval type-2 fuzzy systems to dynamically adjust the parameters of the imperialist competitive algorithm (ICA). A type-1 fuzzy system was taken as a basis, with the input variable as the decades and beta as the output variable, then after that we extended to an interval type-2 fuzzy system, with some variants base on triangular, Gaussian and trapezoidal membership functions. The imperialist competitive algorithm takes as its basis the concept of imperialism, where the strongest countries absorb the weakest and make then their colonies. To measure the performance of the proposed approach 6 benchmark functions are used and finally, a comparison was made between the variants and the results obtained with the type-1 fuzzy system to observe their behavior applied to benchmark mathematical functions.
Internet Memes: A Novel Approach to Distinguish Humans and Bots for Authentication

More than half the web traffic is believed to be made of bots. Over the last few years, bots have grown increasingly sophisticated to carry out repetitive jobs, gain control over systems, run automated scripts and can also play games. Recently, bots have been capable of authenticating themselves as human beings. They have been known to conquer text based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), image based CAPTCHA as well as several other authentication bypassing mechanisms. Further, other techniques like neural networks and machine learning have been instrumental in imparting enough training to bots to behave as humans, which has made it very difficult to distinguish bot behavior from human for the purpose of authentication. In this article, we present the concept of the Internet Memes which may successfully tell a bot and human apart. Considering the ever dynamic nature of Internet Memes, this may be one of the strongest techniques to distinguish between a human and a bot based on conscience and interpretation.
Stratospheric QKD: feasibility analysis and free-space optics system concept

Quantum key distribution (QKD) is one of the most mature quantum technologies and can provide quantum-safe security in future communication networks. Since QKD in fiber is limited to a range of few hundred kilometers, one approach to bridge continental scale distances may be the use of high altitude pseudo satellites (HAPS) as mobile trusted nodes in the stratosphere. In parallel, free-space laser communication for high rate data transmission has been a subject of research and development for several decades and its commercialization is progressing rapidly. Important synergies exist between classical free-space communication and QKD systems since the quantum states are often implemented using the same degrees of freedom such as polarization or field amplitude and phase. These synergies can be used to benefit from the progress in classical free-space laser communication in QKD applications. In this paper, the use case of QKD in a stratospheric environment is described wherein HAPS may serve as relay station of secret keys and encrypted data. The mission scenario and HAPS capabilities are analyzed to derive special requirements on the stratospheric laser terminal, the link geometry and the ground segment with respect to a feasibility demonstration. To obtain a flexible and compatible system, discrete variable and continuous variable QKD protocols are considered to be implemented side by side in the HAPS payload. Depending on the system parameters, it can be beneficial to use the one or the other kind of protocol. Thus, a direct comparison of both in one and the same system is of scientific interest. Each of the protocols has particular requirements on coupling efficiency and implementation. Link budget calculations are performed to analyze possible distances, key rates and data transmission rates for the different schemes. In case of the QKD system, the mean coupling efficiency is of main interest, i.e. signal fluctuations arising from atmospheric turbulence must be taken into account in the security proof, but the buffered key generation relaxes real-time requirements. This is different to classical communications, where the corresponding fading loss must be assessed. A system architecture is presented that comprises the optical aircraft terminal, the optical ground terminal and the most important subsystems that enable implementation of the considered QKD protocols. The aircraft terminal is interfaced with the dedicated quantum transmitter module (Alice) and the ground station with the dedicated quantum receiver module (Bob). The optical interfaces are SMF couplings which put high requirements on the receiving optics, in particular the need for wave-front correction with adaptive optics. The findings of the system study are reviewed and necessary next steps pointed out.