Reviewerly/exHarmony huggingface dataset:
Comparative Analysis Between Standards Oriented to Web Services: SOAP, REST and GRAPHQL

The use of web services has increased and has become the most widely used implementation today. The most crucial part of a web development project is the choice of the right tools for application development, this decision significantly influences the requirements to implement solutions for these services. The comparative analysis between SOAP, REST and GraphQL web services aims to assess the effectiveness of data transfer capabilities. The methods used were a systematic mapping to define the metrics to use for comparison such as response time and performance. A test environment was implemented, starting with the development of a web application using each of the technologies to be evaluated and in different programming languages. Then, the performance of web services was tested with the defined metrics and tools. We found, from experimental tests, that GraphQL has a faster response time and better performance than web services based on SOAP and REST. The result of the comparison can help developers choose the optimal and appropriate technology based on the performance of web services and other metrics that influence an essential aspect such as software quality.
Model of Cocoaer as alternative learning to prevent the potential misconceptions of high school students

The Cocoaer learning model was developed to prevent the emergence of Misconception Potential (FMD) of high school students (SMA). This study aims to develop a valid, practical cocoaer learning model to prevent the emergence of high school students’ PMK. Development of a cocoaer learning model based on needs analysis. R and D development research (Research and Development) refers to the Borg & Gall’s (1997) model, namely the development of models carried out through repetitive activities from starting to design to limited test models. The data was analyzed descriptively. The results of the study show that: (1) the cocoaer model and the supporting model are classified as valid and reliable, (2) the syntax implementation of the model and the observations of student activities in accordance with the design of the cocoaer model.
Radial polarizing phase-shifting interferometry with applications to single-shot n interferogram measurements and potential usage for white light interferogram analysis

In this research, we present an interferometric system to analyze transparent samples using interferograms generated by a phase-shifting radial shear grating interferometer for two cases: the first obtaining <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>n</mml:mi> </mml:math> simultaneous phase-shifting interferograms using a coherent light source and the second one using sequential phase steps with a white light source. For the first case, the simultaneous interferograms are generated using two optical systems: the first one generates the polarized pattern while the second one consists of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>4</mml:mn> </mml:mrow> <mml:mi>f</mml:mi> </mml:math> system creating replicas of the output interferograms. By using a 2D sinusoidal phase grating, we have the advantage of obtaining up to nine replicated interferograms, all of them with comparable intensities and having amplitudes modulated by the 2D sinusoidal phase grating diffraction orders as zero-order Bessel’s functions. To obtain the optical phase map, several phase shifts are generated by placing a polarizing filter covering each replicated interferogram. We highlight the advantage of using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>n</mml:mi> </mml:math> simultaneous interferograms by comparing resulting optical phases processed by a conventional four-step algorithm against those obtained by an implemented <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>N</mml:mi> </mml:mrow> <mml:mo>+</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> method, reducing errors with noisy interferograms. Results for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>7</mml:mn> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>9</mml:mn> </mml:mrow> </mml:math> cases are presented. In addition, we have tested the setup with white light interference techniques by employing the polarizer radial shearing interferometer; for this case, the optical phase is calculated with the four-step and the three-step algorithms. Results of testing the developed system to examine static and dynamic phase objects are also included.
Removing Rain Streaks by a Linear Model

Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems. In this paper, we present an efficient method to remove rain streaks. First, the location map of rain pixels needs to be known as precisely as possible, to which we implement a relatively accurate detection of rain streaks by utilizing two characteristics of rain streaks.The key component of our method is to represent the intensity of each detected rain pixel using a linear model: p = αs + β, where p is the observed intensity of a rain pixel and s represents the intensity of the background (i.e., before rain-affected). To solve α and $\beta $ for each detected rain pixel, we concentrate on a window centered around it and form an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm cost function by considering all detected rain pixels within the window, where the corresponding rain-removed intensity of each detected rain pixel is estimated by some neighboring non-rain pixels. By minimizing this cost function, we determine α and β so as to construct the final rain-removed pixel intensity. Compared with several state-of-the-art works, our proposed method can remove rain streaks from a single color image much more efficiently - it offers not only a better visual quality but also a speed-up of several times to one degree of magnitude compared to other conventional deraining methods.
Small-Scale Deep Network for DCT-Based Images Classification

The need to acquire high performance deep neural network models is a research trend in recent years. Many examples have shown that achieving high validation accuracies require a very large number of parameters in most cases and therefore, the space used to store these models becomes very large. This may be a disadvantage on small storage size and low performance CPU edge devices during image processing that are embedded with neural networks for object recognition tasks. In this paper, we investigate the effect of input images which are partially compressed using the Discrete Cosine Transform (DCT) algorithm on two different Convolutional Neural Network (CNN) performances, known as CNN-C (large model) and CNN-RC3 (small model). DCT is used to reduce some data redundancies but also the risk of losing valuable features for the network to learn efficiently. However, the results show that both CNN architectures with DCT features perform as well as with raw image data, concluding that a properly designed CNN model can still achieve high performance on further compressed images regardless of its information reductions.
4D image construction from free-breathing MRI slice acquisitions of the thorax based on a concept of flux

Retrospective 4D image construction from continuously acquired 2D slices is a necessary step to achieve high-quality 4D images. Self-gating methods, which extract breathing signals only from image information without any external gating technology, have much potential, such as in pediatric patients with thoracic insufficiency syndrome (TIS) who suffer from extreme malformations of the chest wall, diaphragm, and spine, leading to breathing that is very complex with lots of abnormal respiration cycles, including very deep or shallow cycles. Existing methods do not work well in this clinical scenario and most are not fully automatic, requiring some manual interactive operations. In this paper, we propose a fully automatic 4D dMRI construction method based on the concept of flux to address the 4D image construction from 2D slices of subjects with complex respiration. Firstly, we extract the breathing signal for each location based on the flux of the optical flow vector field of the body region from the image series. Then, we give a full analysis for all cycles and extract several normal ones and map them to one cosine respiration model for each location. After that, we re-sample one normal cycle from the respiration model for each location independently. All of these resampled normal cycles form the final constructed 4D image. Qualitative and quantitative evaluations on 25 subjects show that the proposed method can handle datasets from subjects with more complex respiration and achieves good self consistency results while maintaining time and space continuity.
How to Pre-train Your Model? Comparison of Different Pre-training Models for Biomedical Question Answering

Using deep learning models on small scale datasets would result in overfitting. To overcome this problem, the process of pre-training a model and fine-tuning it to the small scale dataset has been used extensively in domains such as image processing. Similarly for question answering, pre-training and fine-tuning can be done in several ways. Commonly reading comprehension models are used for pre-training, but we show that other types of pre-training can work better. We compare two pre-training models based on reading comprehension and open domain question answering models and determine the performance when fine-tuned and tested over BIOASQ question answering dataset. We find open domain question answering model to be a better fit for this task rather than reading comprehension model.
Mind the Middle Layer: The HADES Design Strategy Revisited

The HADES design strategy combines the classical SPN construction with the Partial SPN (PSPN) construction, in which at every encryption round, the non-linear layer is applied to only a part of the state. In a HADES design, a middle layer that consists of PSPN rounds is surrounded by outer layers of SPN rounds. The security arguments of HADES with respect to statistical attacks use only the SPN rounds, disregarding the PSPN rounds. This allows the designers to not pose any restriction on the MDS matrix used as the linear mixing operation. In this paper we show that the choice of the MDS matrix significantly affects the security level provided by HADES designs. If the MDS is chosen properly, then the security level of the scheme against differential and linear attacks is significantly higher than claimed by the designers. On the other hand, weaker choices of the MDS allow for extremely large invariant subspaces that pass the entire middle layer without activating any non-linear operation (a.k.a. S-box). We showcase our results on the Starkad and Poseidon instantiations of HADES. For Poseidon, we significantly improve the lower bounds on the number of active S-boxes with respect to both differential and linear cryptanalysis provided by the designers – for example, from 28 to 60 active S-boxes for the $$t=6$$ variant. For Starkad, we show that for any variant with t (i.e., the number of S-boxes in each round) divisible by 4, the cipher admits a huge invariant subspace that passes any number of PSPN rounds without activating any S-box (e.g., a subspace of size $$2^{1134}$$ for the $$t=24$$ variant). Furthermore, for various choices of the parameters, this invariant subspace can be used to mount a preimage attack on the hash function that breakes its security claims. On the other hand, we show that the problem can be fixed easily by replacing t with any value that is not divisible by four. Following our paper, the designers of Starkad and Poseidon amended their design, by adding requirements which ensure that the MDS matrix is chosen properly.
On a class of constacyclic codes of length 4ps over 𝔽pm + u𝔽pm

Let [Formula: see text] be a prime such that [Formula: see text]. For any unit [Formula: see text] of [Formula: see text], we determine the algebraic structures of [Formula: see text]-constacyclic codes of length [Formula: see text] over the finite commutative chain ring [Formula: see text], [Formula: see text]. If the unit [Formula: see text] is a square, each [Formula: see text]-constacyclic code of length [Formula: see text] is expressed as a direct sum of an -[Formula: see text]-constacyclic code and an [Formula: see text]-constacyclic code of length [Formula: see text] If the unit [Formula: see text] is not a square, then [Formula: see text] can be decomposed into a product of two irreducible coprime quadratic polynomials which are [Formula: see text] and [Formula: see text], where [Formula: see text] and [Formula: see text]. By showing that the quotient rings [Formula: see text] and [Formula: see text] are local, non-chain rings, we can compute the number of codewords in each of [Formula: see text]-constacyclic codes. Moreover, the duals of such codes are also given.
Quantum Indistinguishability for Public Key Encryption

In this work we study the quantum security of public key encryption schemes (PKE). Boneh and Zhandry (CRYPTO’13) initiated this research area for PKE and symmetric key encryption (SKE), albeit restricted to a classical indistinguishability phase. Gagliardoni et al. (CRYPTO’16) advanced the study of quantum security by giving, for SKE, the first definition with a quantum indistinguishability phase. For PKE, on the other hand, no notion of quantum security with a quantum indistinguishability phase exists. Our main result is a novel quantum security notion ( for PKE with a quantum indistinguishability phase, which closes the aforementioned gap. We show a distinguishing attack against code-based schemes and against LWE-based schemes with certain parameters. We also show that the canonical hybrid PKE-SKE encryption construction is -secure, even if the underlying PKE scheme by itself is not. Finally, we classify quantum-resistant PKE schemes based on the applicability of our security notion. Our core idea follows the approach of Gagliardoni et al. by using so-called type-2 operators for encrypting the challenge message. At first glance, type-2 operators appear unnatural for PKE, as the canonical way of building them requires both the secret and the public key. However, we identify a class of PKE schemes - which we call recoverable - and show that for this class type-2 operators require merely the public key. Moreover, recoverable schemes allow to realise type-2 operators even if they suffer from decryption failures, which in general thwarts the reversibility mandated by type-2 operators. Our work reveals that many real-world quantum-resistant PKE schemes, including most NIST PQC candidates and the canonical hybrid construction, are indeed recoverable.
Evaluation framework for automatic privacy auditing tools for hospital data breach detections: A case study

We aim to 1) design an evaluation framework to examine the accuracy of automatic privacy auditing tools, 2) apply the evaluation method at a hospital to validate the performance of an auditing tool that uses a machine learning algorithm to automate user access auditing, and 3) recommend further improvements in auditing for the hospital.Using the black box method of user acceptance testing, we have designed an evaluation framework consisting of appropriate and inappropriate behaviour scenarios to examine the privacy auditing tools. The scenarios were designed from clinical and non-clinical hospital staff perspective, taking expert opinions from the privacy officers and considering examples from the Information and Privacy Commission (IPC) and were tested using Mackenzie Richmond Hill Hospital's data.The case study using this evaluation framework found that on average 98.09 % of total accesses of the hospital were identified as appropriate and the tool was unable to explain the remaining 1.91 % of accesses. In addition, a statistically significant (P < 0.05) increasing trend on categorizing appropriate accesses by the tool have been observed. Furthermore, an analysis of unexplained accesses revealed the contributing factors and found issues related to hospital workflows and data quality (information was missing about staff roles and departments).Given that adoption of these machine learning tools is increasing in hospitals, this research provides an evaluation framework and an empirical evidence on the effectiveness of automated privacy auditing and detecting anomalies for dynamic hospital workflows.
Evaluation of stability of swipe gesture authentication across usage scenarios of mobile device

Abstract Background User interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures , which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device. Methods The evaluations were performed using state-of-the-art touch-based classification algorithms—support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes—to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method. Results Performance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement—caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope , standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes. Conclusions The performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios.
CIES: Cloud-based Intelligent Evaluation Service for video homework using CNN-LSTM network

Abstract Video (used as a form of examination or homework) as an efficient approach for examining students’ abilities is drawing increasing attention in the education field. How to assess video assignments effectively and accurately has become a significant topic in academia. This work proposes a method based on a multi-channel CNN-LSTM hybrid architecture to extract and classify image features such as students’ actions and expressions, as well as audio features such as speech rates and pauses in the video assignments, and then conducts a two-category assessment of “qualified” or “unqualified”. Additionally, build this system in a cloud computing environment as a Cloud-based Intelligent Evaluation Service application could provide universal service to meet the needs of multiple teaching units. The proposed method is shown to be feasible and effective through experiments.
Deep LSTM-Based Sequence Learning Approaches for Action and Activity Recognition

In this chapter, the authors aim to study only vision RGB-based action and activity recognition methods in which long short-term memory (LSTM) has been deployed for sequence learning. They explore sequence learning concepts integrated with LSTM for action and activity recognition. The authors describe the concept of sequence learning for action and activity recognition using LSTM and its variants such as multi-layer or deep LSTM and bidirectional LSTM networks. They explain the working of traditional recurrent neural networks (RNNs) and their limitations, along with why LSTM is better than RNN. RNNs are a type of artificial neural network that has turned out to be more popular in recent years because of its power to learn time series data. The RNN is a special network that consists of recurrent connections, unlike traditional feedforward neural networks. The vanishing gradient problem can be solved by utilizing a special type of RNN known as LSTM.
A Practical Approach to Stakeholder-driven Determination of Security Requirements based on the GDPR and Common Criteria

We propose and apply a requirements engineering approach that focuses on security and privacy properties and takes into account various stakeholder interests. The proposed methodology facilitates the integration of security and privacy by design into the requirements engineering process. Thus, specific, detailed security and privacy requirements can be implemented from the very beginning of a software project. The method is applied to an exemplary application scenario in the logistics industry. The approach includes the application of threat and risk rating methodologies, a technique to derive technical requirements from legal texts, as well as a matching process to avoid duplication and accumulate all essential requirements.