Reviewerly/exHarmony huggingface dataset:
Attitudes Towards Digital Educational Technologies, Academic Motivation and Academic Achievements Among Russian University Students

The purpose of present research is to reveal and compare the relationship of attitudes towards the digital educational technologies (DET) with academic motivation and academic achievements in Russian university students. The sample includes 173 (61% - female) first- and second-year university students of different fields of study (Natural Sciences, Medicine, and Psychology). To determine the motivation of students’ educational activity, the “Academic motivation scales” questionnaire by T.O. Gordeeva et al. was used. To diagnose students’ attitudes towards DET, the authors’ questionnaire was developed. GPA was used to assess the academic achievements of last academic year. The descriptive statistics methods, coefficients Cronbach’ α and McDonald’s ω, the Spearman’s correlation analysis, and factor analysis (Varimax) were used for statistical analysis. Statistical processing was carried out in the R software environment for statistical computing and graphics, version 3.6.1., psych package version 1.9.6. Findings of our research showed that university students with more pronounced intrinsic academic motivation are more involved in the digital space in general and more involved in the use of DET, while more amotivated students, on the contrary, less involved in the digital space and in the use of DET. At the same time, higher performing students tend to be more involved in the digital space in general. However, there is a specificity of these relations in students from different field of study. The obtained data must be taken into account when DET are implemented in the educational programs for students of different fields of study, based on their psychological characteristics.
Multiple strategies grey wolf optimizer for constrained portfolio optimization

Grey Wolf Optimizer (GWO) is competitive to other population-based algorithms. However, considering that the conventional GWO has inadequate global search capacity, a GWO variant based on multiple strategies, i.e., adaptive Evolutionary Population Dynamics (EPD) strategy, differential perturbation strategy, and greedy selection strategy, named as ADGGWO, is proposed in this paper. Firstly, the adaptive EPD strategy is adopted to enhance the search capacity by updating the position of the worst wolves according to the best ones. Secondly, the exploration capacity is extended by the use of differential perturbation strategy. Thirdly, the greedy selection improves the exploitation capacity, contributing to the balance between exploration and exploitation capacity. ADGGWO has been examined on a suite from CEC2014 and compared with the traditional GWO as well as its three latest variants. The significance of the results is evaluated by two non-parametric tests, Friedman test and Wilcoxon test. Furthermore, constrained portfolio optimization is applied in this paper to investigate the performance of ADGGWO on real-world problems. The experimental results demonstrate that the proposed algorithm, which integrates multiple strategies, outperforms the traditional GWO and other GWO variants in terms of both accuracy and convergence. It can be proved that ADGGWO is not only effective for function optimization but also for practical problems.
Towards safe collaborative interaction empowered by face recognition

This paper presents the implementation of a face recognition node for a Sawyer collaborative robot using Robot Operating System (ROS), and its preliminary experimental validation. The node acquires images through the head camera of a Sawyer cobot and elaborates it using OpenCV libraries, applying the Viola-Jones algorithm to detect faces. The aim is to use face recognition functionalities to make the robot start its operations only when it correctly recognizes an operator in its workspace, thus increasing the safety of the human-robot interaction. The developed ROS node recognizes multiple faces at the same time and publishes the information required on a dedicated topic. The validation performed analyzed the success rate of the procedure at different orientations of the user's face.
Variational Sequential Modeling, Learning and Understanding

Normalizing flow comprises of a series of invertible transformations. With careful design in transformations, it can generate images or speeches with fast sampling speed. Inference can also be efficient in maximum likelihood manner. In addition to generating scenes or human faces, it can be used to transform probability distributions. On the other hand, learning latent structures of sentences in a global manner is always challenging. Variational autoencoder (VAE) is haunted by the issue of posterior collapse, where the latent space is poorly learned. To improve inference and generation of VAE in learning sequence data, we propose the amortized flow posterior variational recurrent autoencoder (AFP-VRAE). Variational recurrent autoencoder (VRAE) has RNN based encoder and decoder and learns global representations of sentences. To learn latent space that well preserves the semantic information of data, we use the normalizing flow to generate flexible variational distributions. Furthermore, we adopt the amortized regularization to encode similar embeddings to neighboring latent representations, and we use the skip connections to reinforce the representations to predict every output directly. The benefits can be shown in the experiments as we evaluate the models for language modeling, sentiment analysis and document summarization. AFP-VRAE reports good results on variational modeling for sequence data.
Color Endoscopic Image Enhancement Technology Based on Nonlinear Unsharp Mask and CLAHE

Since the existing endoscopic image enhancement algorithms cannot take into account the problems of detail enhancement, brightness preservation and color preservation, this paper proposes an endoscopic image enhancement based on an improved unsharp mask and CLAHE algorithm. First, perform local brightness adjustment and saturation correction on the input image, and secondly, it is also the key point to perform contrast enhancement, which is divided into low-frequency background enhancement and high-frequency detail enhancement. CLAHE is used to balance the background intensity of low frequency components, and then the high-frequency parts are nonlinearly sharpened. The improved nonlinear transfer function proposed is used, and the high-frequency texture part is emphasized in combination with canny edge detection. Finally, the objective and subjective quality assessment of the image is carried out. The results show that the proposed nonlinear method enriches the texture details such as blood vessels. The peak signal-to-noise ratio and Tenengrad gradient are better than those of the compared algorithm. And the algorithms in this paper are all enhanced in the brightness channel, which has the function of color preservation, so as to achieve the natural enhancement effect as much as possible.
Comparative Analysis of Smart Voice Assistants

Lately, voice-activated interfaces are becoming more popular, such as Amazon Alexa, Google Assistant, and Microsoft Cortana voice recognition applications. This paper represents the outcome of gauging these three intelligent voice assistant applications. Their answers are based on user questions and how the user perceives these three intelligent voice assistant applications. As per the survey conducted on these three smart voice assistant applications, users feel that Alexa and Google Assistant applications are superior to Microsoft Cortana. Alexa and Google Assistant applications do not have any remarkable differences. The development and implementation of Artificial Intelligence objects ensure that voice-overs between humans and machines are realistic, and there are not many dependencies on human interactions. The new Voice Protocol Assistants system can be implemented in other different environments of applications, including education assistance, medical assistance, robotics and vehicles, an assistant system for physically challenged persons, home automation, and security access control. This paper compares the three most adopted voice assistants and identifies its strength and weakness compared with their peers.
Defending against Return-Oriented Programming attacks based on return instruction using static analysis and binary patch techniques

Return-Oriented Programming (ROP) is one of the most common software vulnerability exploitation techniques. Although many solutions have been proposed to defend against ROP attacks, they still have various drawbacks, such as requiring source code, increasing run-time overhead of the binary, and making the binary unstable. In this paper, we propose a method: using static analysis and binary patch techniques to defend against ROP attacks based on return instruction. It eliminates the parameter registers by adding extra instructions before the ret instruction, so that ROP attacks cannot chain short snippets of existing code to perform arbitrary command execution on the target binary. Evaluations on real software show that it can make the binary have the ability to defend against ROP attacks based on return instruction. In addition, evaluations on SPEC INT2006 benchmark show that it can eliminate 95.40% of the effective gadgets and only introduce 2% run-time overhead and 10.75% increase in size.
Segmentation of renal tumors in CT images by 3D U-Net preserving rotational symmetry in axial slices

We present a method to improve accuracy of renal tumor segmentation in CT images by considering the structure of kidneys. We apply deep learning based on the following protocol. First, kidney regions are extracted from the CT images. Second, adjacent eight slices along axial direction are picked up as a patch. Third, the center of gravity of the kidney region in the patch is aligned to the center of the patch in sagittal and coronal direction. Fourth, we apply data augmentation with scaling and rotation around the center, which preserves the basic slice structure of kidneys. Finally, these patches are fed to a 3D U-Net for training. Compared with the conventional 3D U-Net, the proposed method improves the DICE score from 0.507 to 0.604.
A Resource-Efficient Online Target Detection System With Autonomous Drone-Assisted IoT

Mobile onboard target detection system with autonomous drone-assisted Internet of Things, due to its inherent agility and coverage-effective deployment, is beneficial in city management, ecosystem monitoring, etc. However, most advanced detection methods become inefficient or even malfunction, since the computation resources for online detection in such a high-altitude dynamic environment are far beyond the capability of the drone. To this end, we design and implement ODTDS—an online drone-based target detection system that performs online data processing and autonomous navigation simultaneously with restricted resources. Specifically, to prolong detection durations with limited energy providing for continuous processing and flying, we develop an adaptive motion planner for autonomous and energy-efficient navigation. Meanwhile, to perform online target detection from complex environments, we propose a hybrid method integrating feature pyramid feedback with the speed up robust features, to enhance the background information to achieve high accuracy on the restricted computation platform of the drone. Based on these two designs, our system can address resource-constrained challenges to accomplish detection missions autonomously. Finally, we implement an ODTDS prototype and evaluate it through outdoor high-altitude experiments. The results of various performance evaluations can demonstrate the effectiveness of our system.
Query Change as a Contextual Markov Model for Simulating User Search Behaviour

Search engine users issue queries to formulate their information need and gain useful insights. However, it is challenging for search engines to understand different users’ search type intents and return appropriate results. Simulating user search behaviour allows information retrieval systems (IR) to parameterise the a-priori distribution of search types using different back-end configurations and user interface variants to improve the retrieval functionality. In this paper, we propose a formal Markov approach in which we utilise the context discovery process to model user-type specific behaviour by capturing the user’s query change in a search session. Contextual Markov models have been used in the past to improve the prediction of user intentions, we investigate here their efficiency in simulating user-type specific interactions. Additionally, we provide an empirical and classification-based evaluation that can be used in simulation assessment. Overall, we report that the proposed approach reliably simulates user-type specific behaviour on a real-world academic search engine log dataset.
Path Planning Algorithm for Dynamic Obstacles Based on Support Vector Machines Towards Autonomous Navigation

Autonomous car navigation algorithm is proposed to achieve a safe and smooth path to the target destination by integrating dynamic obstacles avoidance and path tracking algorithms. Multi obstacle avoidance algorithm integrates both support vector machines and Gaussian process regression. When obstacles are detected, Gaussian regression predicts the position of the obstacles for the forthcoming time steps, while the SVM classifier finds the optimal separating hyperplane, which guarantees maximum margin to obstacles. The generated path to target goal is smooth, and safe while travels all the way through numerous static and dynamic obstacles. Path tracking control algorithm is designed to ensure minimum tracking error, this algorithm is based on a nonlinear model predictive controller considering vehicle dynamics. The simulation results reveal the effectiveness of proposed algorithms in different complicated scenarios.
Thai Fingerspelling Recognition Using Hand Landmark Clustering

As of June 2021, there are around 393,000 registered hearing-impaired persons in Thailand. Sign language is the only communication tool for them but, besides the deaf, only very small groups of people have learned how to use it. The deaf can learn things and the sign language interpreters may come to translate the voices for them, but it is tough for them to explain things to normal people that do not know anything about sign language. To overcome this obstacle, several groups of researchers are developing systems for sign language recognition. Although sign languages are different from region to region, their basis, fingerspelling, is somehow similar. Gestures of fingerspelling are simple, but they can be similar to others with different meanings. In this paper, we propose a two-level approach for Thai fingerspelling recognition. We focus on 25 basic gestures that can be combined to spell all Thai alphabets. The contribution of our paper is three folds. First, Hand Landmark, recently introduced in 2020, has been used as the data representation. Thus, instead of using a whole figure of size, says 640x480 pixels, our data representation is very compact as merely 21 points per hand. Second, two-level classification has been introduced for handling similar fingerspelling gestures by using key point clustering to grouping similar hand postures. Third, different from other studies, completely unseen data has been used in the experiments. Most studies just split the data to both training set and testing set. In our study, we firstly split people into two groups: one for training and one for testing. Only the gestures of the people in the former group are used during the training process. The latter group will be used during the testing process. Then, no leak of information from the latter group to the model. We evaluated our model by using precision, recall, accuracy, and F-measure. The results show that our method is performing well on the traditional train/test splitting scheme while it is still robust on the completely unseen data.
Remove adversarial perturbations with linear and nonlinear image filters

In recent years, deep neural networks (DNN) have improved expressive performance in many artificial intelligence (AI) fields. However, they can easily induce incorrect behavior due to adversarial examples. Therefore, defense against adversarial examples have started to proliferate, but the shortcomings of the existing defense methods are the following: they are practical only for specific attacks, e.g., Optimization-based; the training of network structures to detect and identify adversarial examples requires enormous computational resources; the processing of adversarial examples introduces other side effects. In this paper, we apply mirror flip to the adversarial examples processed by image filtering algorithms (both linear and nonlinear), to reduce the adversarial perturbations' impact and address the drawbacks mentioned above. Experimental results on ImageNet show that we can substantially improve the effectiveness of resisting adversarial attacks at the expense of partial image sharpness. For example, the accuracy improved from 33.54% to 82.03% for Project Gradient Descent (PGD) L∞ Attack with ε= 1/256 (the accuracy of the training set that we tested is 84.71%). Moreover, our approach can be generalized to most advanced attack algorithms in different adversarial perturbation magnitudes, with better performance in accuracy improvement and image quality evaluation when compared to other input transformation-based defense methods.
Quality in Use Evaluation of a GraphQL Implementation

The software development trend uses service-oriented software architecture (SOA), which provides efficiency, agility, and ease of growth. The architectural design most commonly used in SOA application development is REST (Representational State Transfer); however, some data management problems have been identified in its Application Programming Interface called API-REST. Several technological options have emerged to appease these problems, such as SPARQL, Cypher, Gremlin, and the most popular GraphQL. GraphQL was developed by Facebook in 2012 and released in 2015 to the community as an open-source project, used by companies such as GitHub, Airbnb, Amazon, Apollo, IBM, and Facebook. The goal of this research is to demonstrate whether GraphQL implementations work. Therefore, we based the research design on Design Science Research (DSR) to evaluate the quality-in-use of a GraphQL implementation that automated the systematic mapping studies (SMS) process for technology researchers at Universidad Técnica del Norte - Ecuador. We used the ISO/IEC 25000 series of standards to evaluate the quality in use; the results showed that the implementation met 84.11% of the established quality model's expected value. The detailed evaluation by quality characteristics was: Effectiveness 96.62%, Efficiency 78.90%, and Satisfaction 70.26%.
Neural Network Verification in Control

Learning-based methods could provide solutions to many of the long-standing challenges in control. However, the neural networks (NNs) commonly used in modern learning approaches present substantial challenges for analyzing the resulting control systems’ safety properties. Fortunately, a new body of literature could provide tractable methods for analysis and verification of these high dimensional, highly nonlinear representations. This tutorial first introduces and unifies recent techniques (many of which originated in the computer vision and machine learning communities) for verifying robustness properties of NNs. The techniques are then extended to provide formal guarantees of neural feedback loops (e.g., closed-loop system with NN control policy). The provided tools are shown to enable closed-loop reachability analysis and robust deep reinforcement learning.Software– https://github.com/mit-acl/nn_robustness_analysis
A Deep Pyramid Attention Network for Single Image Super-resolution

The pyramid attention (PA) network is a new structure developed for digital image processing. This network was designed to extract long-range features at different locations and scales. Recently, PA architecture has been introduced in single image super-Resolution (SISR) to improve the model's ability to benefit from data's self-similarity. However, the effects of location and number of PA on extracting the self-similarity are not explored. In this paper, a Deep Pyramid Attention Network (DPANet) is proposed for SISR based on exploring the PA block. This is performed by studying the effect of varying the number of PA blocks and their locations on the model performance. Moreover, the effect of the residual scale on the PA's performance is studied. Evaluated based on five benchmark datasets, we concluded that using five PA blocks without down-scale residual interchanging with Resblocks in the network achieves significantly better results compared to the state-of-the-art methods. In addition, our model achieves superior visual quality and accuracy.
A patient-centric doctor referral model based on hyperledger chaincode

In a variety of areas such as the financial sector, the energy sector, the Internet of Things, and most importantly, healthcare, blockchain is evolving and advancing to serve as a secure and reliable platform to facilitate effective and secure data sharing. Electronic Health Data Management privacy issue has been raised when cloud-based data is shared between stakeholders or when records are outsourced to the cloud, resulting in sensitive or private information being disclosed by unauthorized individuals. As a result of the doctor referral management process, stakeholder communications are conducted via emails, faxes, and phone calls, which may result in errors, mistakes, and misunderstandings. This paper proposes the following solutions to the identified problems: To facilitate better sharing of healthcare records between clinicians and health care facilities through an efficient referral mechanism based on advanced smart contracts named as Patient Centric Healthcare Data Management Access Control via Smart contract (PCHDMAC-SC). Doctor referrals are made through the List referral system, which is patient-centric and restricted to doctors and other authorized providers. The results demonstrate that unauthorized access cannot be traced to healthcare records as all healthcare records are encrypted as part of the working model, which keeps personal information secure, protects personal information, makes data more scalable, and allows medical records to be shared and accessed more securely. Because of this, the system of doctor referrals results in scalability, a higher level of trust, and flexibility for all stakeholders involved.
The Prototype Development of Thai Language Understanding based on Mental Image Directed Semantic Theory

Nowadays, Artificial Intelligence (AI) has been embedded in various tools such as smart phones, even in those we might use unconsciously every day. However, AI technologies for automatic understanding human languages, namely, Natural Language Understanding (NLU) has been still far from its goal because of very difficult problems of semantics. This paper describes a prototype NLU system for Thai language based on Mental Image Directed Semantic Theory in order to help Thai language beginners more comprehensively. For the first step, Thai sentences of basic structures intended for amateur users were chosen for the system to process. In the system, input sentences are fragmented to detect constituent words and their grammatical relationships, and then transduced to semantic representations in a formal language named Language for Mental-image Description. The semantic interpretations are evaluated through reasoning processes such as disambiguation and entailment. Finally, the system returns animations as its understanding results of the inputs, in order to help language learners understand the text contents in a more intuitive way.