Reviewerly/exHarmony huggingface dataset:
A New Approach of Cryptography for Data Encryption and Decryption

Nowadays in the modern digital world, everything is rapidly going to be fully dependent on internet communication. Effective use of the internet makes our life easier. The information that we share on the internet has great security risks and challenges in the present day. Cryptography is the solution to secure data from different security risks. To enhance the security of communication systems better cryptosystems technology is obvious in the area of cryptography. Our research focuses on data encryption and decryption technique for a better cryptosystem; where we have proposed a new approach that ensures better performance in comparison to the state-of-the-art solutions. In this work, after generating a unique key using random characters the plain text is encrypted into ciphertext. To do this encryption, a divide and circular left and right shift approach is followed, and conversely, the reverse is maintained for decryptions as well. According to the experimental results, our proposed algorithm provides better results using the chi-square test while comparing with different cryptography algorithms.
Design and implementation of an intelligent tutoring system in the view of learner autonomy

Purpose In intelligent tutoring systems (ITS), learners were often granted limited authority and are forced to obey the decision of the system which might not satisfy their needs. Failure to grant learners sufficient autonomy could yield unexpected effects that hinder learning, including undermining learners’ motivation, priming learners’ aversion to the algorithm. On the contrary, granting learners overwhelming autonomy could also be harmful as the absence of learning support would also have a negative impact on learning. As such, this study aims to design and implement an intelligent tutoring system that offers learners proper autonomy. Design/methodology/approach The main learning activity in the system is doing exercises, and by finishing exercises learners could earn virtual coins. Based on item response theory, exercises are administered to learners with proper difficulty. Based on a recommended difficulty parameter predicted by the system, learners could manually modify the difficulty of the exercises, they could earn more credits by finishing more challenging exercises. Meanwhile, a pedagogical agent is embedded. Learners could customize the agent’s personality jointly with the system to create the learning context they prefer. Findings A intelligent tutoring system with proper learner autonomy (LA) is designed and implemented. Originality/value Few previous researches have noticed the potentially important role that LA plays in ITS. Learning might be facilitated using such a design.
A New Edge and Pixel-Based Image Quality Assessment Metric for Colour and Depth Images

Measuring the quality of digital image is a complicated and importance task in image processing. This task is possible using Image Quality Assessment (IQA) metrics. Among them Pixel and edge-based IQA metrics are so crucial in dealing with a digital image. So, combination of edge and pixel features could handle not all but, almost all aspects of an image. Most recently using edge-based image quality metrics are popular, due to weakness of traditional image quality assessment metrics such as Peak Signal-to Noise Ratio. Also, majority of IQA metrics are belonged to color images, but recently new metrics for depth images are emerged. This paper proposes a new Full-Reference image quality assessment metric for color and depth images, which works based on edge and pixel features. Proposed method is a combination of improved Edge Based IQA and Peak Signal-to Noise Ratio methods. Proposed method is called Edge and Pixel-based Image Quality Assessment Metric (EPIQA). The system is validated using famous and benchmark performance metrics or quality measures such as Spearman Rank-Order Correlation Coefficient (SROCC), along with comparison with other similar methods on well-known related databases. Color databases have proper and diverse number of noises, but there is no proper depth noisy database, which it is decided to make one. Proposed method returned promising and satisfactory results in different tests.
Digitally enabled hemovigilance allows real time response to transfusion reactions

Transfusion carries a risk of transfusion reaction that is often underdiagnosed due to reliance on passive reporting. The study investigated the utility of digital methods to identify potential transfusion reactions, thus allowing real-time intervention for affected patients.The hemovigilance unit monitored 3856 patients receiving 43,515 transfusions under the hemovigilance program. Retrospective comparison data included 298,498 transfusions. Transfusion medicine physicians designed and validated algorithms in the electronic health record that analyze discrete data, such as vital sign changes, to assign a risk score during each transfusion. Dedicated hemovigilance nurses remotely monitor all patients and perform real-time chart reviews prioritized by risk score. When a reaction is suspected, a hemovigilance trained licensed clinician responds to manage the patient and ensure data collection. Board-certified transfusion medicine physicians reviewed data and classified transfusion reactions under various categories according to the Centers for Disease Control hemovigilance definitions.Transfusion medicine physicians diagnosed 564 transfusion reactions (1.3% of transfusions)-a 524% increase compared to the previous passive reporting. The rapid response provider reached the bedside on average at 12.4 min demonstrating logistic feasibility. While febrile reactions were most diagnosed, recognition of transfusion-associated circulatory overload demonstrated the greatest relative increase. Auditing and education programs further enhanced transfusion reaction awareness.The model of digitally-enabled expert real-time review of clinical data that prompts rapid response improved recognition of transfusion reactions. This approach could be applied to other patient deterioration events such as early identification of sepsis.
Time Domain Adversarial Voice Conversion for ADD 2022

In this paper, we describe our speech generation system for the first Audio Deep Synthesis Detection Challenge (ADD 2022). Firstly, we build an any-to-many voice conversion (VC) system to convert source speech with arbitrary language content into target speaker’s fake speech. Then the converted speech generated from VC is post-processed in time-domain to improve the deception ability. The experimental results show that our system has adversarial ability against anti-spoofing detectors with a little compromise in audio quality and speaker similarity. This system ranks top in Track 3.1 in the ADD 2022, showing that our method could also gain good generalization ability against different detectors.
Improving Data Security with Blockchain and Internet of Things in the Gourmet Cocoa Bean Fermentation Process

Brazil was one of the largest cocoa producers in the world, mainly supported by the South of Bahia region. After the 1980s, the witch-broom disease demolished plantations, and farmers were forced into bankruptcy. The worldwide search for gourmet cocoa has rekindled interest in this production, whose fermentation process is a key step in obtaining fine cocoa, thanks to the fact that many processing properties and sensory attributes are developed in this phase. This article presents a blockchain-IoT-based system for the control and monitoring of these events, aiming to catalog in smart contracts valuable information for improvement of the cocoa fermentation process, and future research. Blockchain is used as a distributed database that implements an application-level security layer. A proof of concept was modeled and the performance of the emulated system was evaluated in the OMNet simulator, where a technique based on the SNMP protocol was applied to reduce the amount of data exchanged and resources served/consumed in this representation. Then, a physical platform was developed and preliminary experiments were performed on a real farm, as a way to verify the improvement of the cocoa fermentation process when using a technological approach.
Tangiball: Foot-Enabled Embodied Tangible Interaction with a Ball in Virtual Reality

Interaction with tangible user interfaces (TUIs) in virtual reality (VR) is known to offer several benefits in terms of user experience. Incorporating identical-formed tangible objects for foot-enabled embodied interaction in VR is not a well-researched area. To address this gap, in this study, we explored foot-enabled embodied interaction in VR through a room-scale tangible soccer game (Tangiball). Users interacted with a physical ball with their feet in real time by seeing its virtual counterpart inside a VR head mounted display (HMD). Tangiball included a custom-built transparent physical ball, inside which motion trackers were secured using custom 3D-printed attachments. A between-subjects user study was performed with 40 participants, in which Tangiball was compared with the control condition of foot-enabled embodied interaction with a purely virtual ball. The results revealed that tangible interaction improved user performance and presence significantly, while no difference in terms of motion sickness was detected between the tangible and virtual versions. This paper discusses the development and evaluation of Tangiball along with implications of the user study results.
Co-evolutionary probabilistic structured grammatical evolution

This work proposes an extension to Structured Grammatical Evolution (SGE) called Co-evolutionary Probabilistic Structured Grammatical Evolution (Co-PSGE). In Co-PSGE each individual in the population is composed by a grammar and a genotype, which is a list of dynamic lists, each corresponding to a non-terminal of the grammar containing real numbers that correspond to the probability of choosing a derivation rule. Each individual uses its own grammar to map the genotype into a program. During the evolutionary process, both the grammar and the genotype are subject to variation operators. The performance of the proposed approach is compared to 3 different methods, namely, Grammatical Evolution (GE), Probabilistic Grammatical Evolution (PGE), and SGE on four different benchmark problems. The results show the effectiveness of the approach since Co-PSGE is able to outperform all the methods with statistically significant differences in the majority of the problems.
Rendering Natural Bokeh Effects Based on Depth Estimation to Improve the Aesthetic Ability of Machine Vision

Machine vision is the key to realizing computer-vision tasks such as human–computer interaction and autonomous driving. However, human perception of an image’s beauty is innate. If a machine can increase aesthetic awareness, it will greatly improve the comfort of human perception in human–computer interaction. The bokeh effect is one of the most important ways to improve the artistic beauty of photographic images and the image aesthetic quality. Bokeh rendering of an image can highlight the main object of the image and blur unnecessary or unattractive background details. The existing methods usually have unrealistic rendering effects with obvious artifacts around the foreground boundary. Therefore, we propose a natural bokeh-rendering method based on depth estimation that satisfies the following characteristics: objects in the focal plane are clear and out-of-focus objects are blurred; and the further away from the focal plane, the more blurred the objects are. Our method consists of three modules: depth estimation, background subdivision, and bokeh rendering. The background-subdivision module can select different focal planes to obtain different blur radii, making the bokeh-rendering effect more diverse, so that it does not oversegment objects. The bokeh-rendering module adjusts the degree of bokeh by adjusting the blur-radius factor. In the experimental section, we analyze the model results and present the visualization results.
Deep instance envelope network-based imbalance learning algorithm with multilayer fuzzy C-means clustering and minimum interlayer discrepancy

Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides, the existing methods are likely to generate noisy instances. To solve these problems, this paper proposes a deep instance envelope network-based imbalanced learning algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This algorithm can guarantee high quality balanced instances using a deep instance envelope network in the absence of prior knowledge. First, the MlFCM is designed for the original minority class instances to obtain deep instances and increase the diversity of instances. Then, the MIDMD is proposed to avoid the generation of noisy instances and maintain the consistency of the interlayers of instances. Next, the multilayer FCM and minimum interlayer discrepancy mechanism are combined to construct a deep instance envelope network – the MlFC&IDMD. Finally, an imbalance learning algorithm is proposed based on the MlFC&IDMD. In the experimental section, thirty-three popular public datasets are used for verification, and over ten representative algorithms are used for comparison. The experimental results show that the proposed approach significantly outperforms other popular methods.
Adaptively Weighted k-Tuple Metric Network for Kinship Verification

Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted k -tuple metric network (AW k -TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on k -tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AW k -TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1.
Remote geochemical alteration proxy for mineral exploration: A case study from Lawa gold mines, India

Geochemical alteration indexes viz. carbonate chlorite pyrite index, ishikawa alteration index, hashiguchi alteration index, chemical alteration index and silicification index are conventionally used to study the degree and type of alteration. Thin section microscopic and X-ray Fluorescence analysis got similar roles for alteration study. Due to sparse and expensive sampling, the regional depiction of alteration patterns and their quantitative analysis are usually unavailable for exploration geologists. Here, we provided an alternative remote proxy-based approach to map the degree and type of alteration on a large scale, using visible-near infrared and shortwave infrared bundles of WorldView-3 imagery. The chosen study area is sparsely vegetated Lawa gold deposit in the North Singhbhum Mobile Belt, India. Partial least squares regression (PLSR) modelling of geochemical alteration indexes with reflectance spectra was used to understand the spatial variation in the degree of alterations. The remotely derived alteration indexes can be linked with field and microscopic studies of Cu-Au mineralization. We found dominance of alteration processes in the following order: ferruginisation, silicification, sericitization and chloritization. Demarcated intensely hydrothermally altered zones are present near the exposed mineralized outcrop. In addition, we identified new alteration zones with ore phases as a plausible mineralized zone.
A Scheme for Quickly Simulating Extraterrestrial Solar Radiation over Complex Terrain on a Large Spatial-Temporal Span—A Case Study over the Entirety of China

Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena. Since the influence of ESR variation on actual rugged terrain is a diverse, complex, and dynamic process, simulating ESR over a large spatial-temporal span, especially with a high-resolution digital elevation model (DEM), is a significant challenge. In this paper, we developed a new scheme for simulating ESR over the entirety of China using a DEM with a resolution of 30 m. To fully consider regional terrain status, the feature variables used were elevation, slope, and aspects of the located grid and the surrounding four grids to reveal the topography. In addition, latitude was used as a feature variable to consider the geographical location, and the month number was used to consider the duration. On the basis of different geographical locations, the training dataset was established from 20,000 grids. With the feature variable composition and training dataset, a backpropagation artificial neural network (BP ANN) was found to have the best performance compared with the other three machine learning methods in simulating ESR for a DEM. In terms of the proposed scheme and BP ANN, we drew an ESR map of China with a resolution of 30 m. The determination coefficient of the simulation result achieved 0.99 and the root-mean-square error was less than 50 MJ/m2 in all sample areas, confirming its remarkable accuracy. In terms of efficiency, the time consumption of ESR simulated using the proposed scheme shrinks over 150 times in all sample areas compared to that simulated via the theoretical model. Simultaneously, the developed scheme was also used to simulate an ESR for a DEM with a resolution of 90 m to verify the universality and robustness of the developed scheme. In addition, we used the proposed scheme to derive the direct solar radiation and global solar radiation, thereby further proving the reliability and applicability of our study. Overall, our work convincingly proved that the proposed scheme is a potential and effective approach for quickly simulating ESR with high accuracy. This study provides the basis for different solar radiation inversions of long time series and large spatial scales, offering additional insights for simulating ESR on a large spatial-temporal span.
Cybersecurity Skills among European High-School Students: A New Approach in the Design of Sustainable Educational Development in Cybersecurity

Cybersecurity has increasingly become a headline feature in news media in recent years, generally prompted by spectacular security breaches in various information systems. The importance of cybersecurity awareness for the sustainable development of society is now recognized widely, but the problem of how to build an educational ecosystem which will include the most relevant target audiences that need to develop cybersecurity skills, is not yet solved. This paper elaborates the state of cybersecurity skills and knowledge in European high-school students by collecting data from the students, their teachers and parents by means of surveys and interviews in nine European countries. The analysis of the information collected has revealed the required topics from the area of cybersecurity that need to be introduced in high school educational programs and the most suitable delivery methods for educational content, such as videos and serious games. A selection of thirteen serious games related to cybersecurity was evaluated and then presented to a class of high-school students. The study of the collected data has shown that cybersecurity education at high school level requires innovative and interactive approaches that build the required skills for a more effective sustainable education and social development.
Point spread function estimation for blind image deblurring problems based on framelet transform

One of the most important issues in image processing is the approximation of the image that has been lost due to the blurring process. These types of matters are divided into non-blind and blind problems. The second type of problem is more complex in terms of calculations than the first problem due to the unknown of original image and point spread function estimation. In the present paper, an algorithm based on coarse-to-fine iterative by $$l_{0}-\alpha l_{1}$$ regularization and framelet transform is introduced to approximate the spread function estimation. Framelet transfer improves the restored kernel due to the decomposition of the kernel to different frequencies. Also, in the proposed model, a fraction gradient operator is used instead of the ordinary gradient operator. The proposed method is investigated on different kinds of images such as text, face and natural. The output of the proposed method reflects the effectiveness of the proposed algorithm in restoring images from blind problems.
A Testbed for Exploring Multi-Level Precueing in Augmented Reality

Precueing information about upcoming subtasks prior to performing them has the potential to make an entire task faster and easier to accomplish than cueing only the current subtask. Most AR and VR research on precueing has addressed path-following tasks requiring simple actions at a series of locations, such as pushing a button or just visiting that location. We present a testbed for exploring multi-level precueing in a richer task that requires the user to move their hand between specified locations, transporting an object between some of them, and rotating it to a designated orientation.
Typological Analysis of Articles in World Languages

A number of second and third language acquisition studies have been conducted on the acquisition of articles in order to examine the learnabilities of the feature from various L1 backgrounds, such as Chinese, Korean, Japanese, Spanish, Russian, Turkish among many others.Numerous studies have revealed ongoing difficulties of acquiring (in)definite articles by L2 and L3 language learners even at the most advanced stages of acquisition.Considering the issue of learnabilities of articles, it would be interesting and meaningful to investigate how (in)definiteness is realized in natural languages and figure out whether using articles to denote (in)definiteness is a