Reviewerly/exHarmony huggingface dataset:
Continuum-Body-Pose Estimation From Partial Sensor Information Using Recurrent Neural Networks

Soft continuum arms have significant potential for use in various applications due to their extremely high degrees of freedom. For example, these soft arms can be used for grasping and manipulating fragile materials in the deep sea or carrying a human to rescue in unstructured environments. However, in these situations, the environment is often dark and visual cues are not always usable. Therefore, these arms must estimate their pose from proprioceptive sensors to control their behavior and execute their tasks in dark places. Estimating the pose in a dynamic situation is still challenging because of the arms' high dimensionality and the complex structural changes in the body shape. Therefore, this study demonstrates a novel method for estimating the pose of proprioceptive bending sensors using recurrent neural networks (RNNs). In particular, an RNN framework known as deep reservoir computing was used for this purpose. Results from experiments using an octopus-inspired soft robotic arm clearly indicate that the proposed method significantly outperforms existing methods using long short-term memory models or linear models. We expect that our proposed method will enable behavioral control of these arms in dark places such as the deep sea, space, and inside the human body in future applications.
A Symmetric Extensible Protocol for Quantum Secret Sharing

This paper introduces the Symmetric Extensible Quantum Secret Sharing protocol, a novel quantum protocol for secret sharing. At its heart, it is an entanglement-based protocol that relies on the use of maximally entangled GHZ tuples, evenly distributed among the players, endowing the spymaster with the ability to securely share a secret message with the agents. Its security stems from the fact that it is highly improbable for a malicious eavesdropper or a rogue double agent to disrupt its successful execution. It is characterized by symmetry, since all agents are treated indiscriminately, utilizing identical quantum circuits. Furthermore, it can be seamlessly extended to an arbitrary number of agents. Finally, after the completion of the quantum part of the protocol, the spymaster will have to publicly transmit some information, in order to allow the agents to unlock the secret message. This part of the protocol can be considered as an additional advantage, due to the fact that it gives the spymaster the privilege of deciding if, or when, it is the right time for the agents to unlock the secret message, after the completion of the quantum part of the protocol.
Invalid point removal method based on error energy function in fringe projection profilometry

Invalid points, such as shadow and background, in the captured fringe patterns of fringe projection profilometry (FPP) are often inevitable due to the limited field of view measurements of three-dimensional (3D) imaging equipment. To ensure the quality of 3D reconstruction data, these invalid points must be identified and removed. FPP captures co-frequency-based fringe pattern sequences and approximately distributes this data along an ideal cosine curve. We propose an invalid points removal method based on an error energy function. By analyzing the relationship between the pixel values of a series of captured co-frequency patterns and an ideal cosine curve, we quantize the error energy by using a Gaussian weighted Euclidean distance. An improved Gaussian filtering method based on modulation intensity is used to significantly increase the error energy difference between the valid points of the target and the invalid points of the shadow/background area. Finally, the points whose error energy is greater than a certain error threshold are removed as invalid points. Experimental results indicate that this method can efficiently remove invalid points in fringe patterns and performs better than existing traditional methods.
Hybrid Approach for Improving the Performance of Data Reliability in Cloud Storage Management

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research
Quantum Key Distribution: Modeling and Simulation through BB84 Protocol Using Python3

Autonomous "Things" is becoming the future trend as the role, and responsibility of IoT keep diversifying. Its applicability and deployment need to re-stand technological advancement. The versatile security interaction between IoTs in human-to-machine and machine-to-machine must also endure mathematical and computational cryptographic attack intricacies. Quantum cryptography uses the laws of quantum mechanics to generate a secure key by manipulating light properties for secure end-to-end communication. We present a proof-of-principle via a communication architecture model and implementation to simulate these laws of nature. The model relies on the BB84 quantum key distribution (QKD) protocol with two scenarios, without and with the presence of an eavesdropper via the interception-resend attack model from a theoretical, methodological, and practical perspective. The proposed simulation initiates communication over a quantum channel for polarized photon transmission after a pre-agreed configuration over a Classic Channel with parameters. Simulation implementation results confirm that the presence of an eavesdropper is detectable during key generation due to Heisenberg's uncertainty and no-cloning principles. An eavesdropper has a 0.5 probability of guessing transmission qubit and 0.25 for the polarization state. During simulation re-iterations, a base-mismatch process discarded about 50 percent of the total initial key bits with an Error threshold of 0.11 percent.
Real-Time Health Monitoring Using Wearable Devices

The increasing acceptance of wearable devices in recent years leads to the fact that a varied range of physiological and functional data are captured continuously by these devices, which can be used for real-time health monitoring by using these wearable devices.
An Examination of Bias of Facial Analysis based BMI Prediction Models

Obesity is one of the most important public health problems that the world is facing today. A recent trend is in the development of intervention tools that predict BMI using facial images for weight monitoring and management to combat obesity. Most of these studies used BMI annotated facial image datasets that mainly consisted of Caucasian subjects. Research on bias evaluation of face-based gender-, age-classification, and face recognition systems suggest that these technologies perform poorly for women, dark-skinned people, and older adults. The bias of facial analysis-based BMI prediction tools has not been studied until now. This paper evaluates the bias of facial-analysis-based BMI prediction models across Caucasian and African-American Males and Females. Experimental investigations on the gender, race, and BMI balanced version of the modified MORPH-II dataset suggested that the error rate in BMI prediction was least for Black Males and highest for White Females. Further, the psychology-related facial features correlated with weight suggested that as the BMI increases, the changes in the facial region are more prominent for Black Males and the least for White Females. This is the reason for the least error rate of the facial analysis-based BMI prediction tool for Black Males and highest for White Females.
Evaluating e-learning systems success in the new normal

The goal of this study is to develop and verify a model for successful e-Learning based on the experiences of students in the "new normal". From Jordanian universities, 550 students who have taken any e-Learning course were randomly selected. Data were collected via a survey questionnaire, and Structural Equation Modeling (SEM) was employed to test the proposed study model. The findings indicate that contactless learning and high-quality e-learning systems have a beneficial impact on student satisfaction. In addition, e-Learning cognitive involvement was found to solidify e-Learning satisfaction. Furthermore, the results show a positive and significant impact of e-Learning cognitive involvement and e-Learning satisfaction on e-Learning achievement. Also, e-Learning system quality positively affects e-Learning cognitive involvement, besides a direct impact of contactless learning quality on e-Learning cognitive involvement.
ALLNet: Acute Lymphoblastic Leukemia Detection Using Lightweight Convolutional Networks

Methods for detecting Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) based on the analysis of blood images are being increasingly researched in the context of Computer Aided Diagnosis (CAD) systems, which help the pathologist in performing the diagnosis. Within CAD systems, approaches using Deep Learning (DL) and Convolutional Neural Networks (CNN) currently exhibit the highest accuracy in detecting the presence of lymphoblasts, which indicate the possible presence of ALL. Recently, approaches based on histopathological transfer learning have been proposed to increase the accuracy of ALL detection in the presence of databases with a small number of samples, by pretraining the CNN on histopathological data instead of using general-purpose datasets such as ImageNet. However, all the approaches in the literature consider CNN architectures with an extremely high number of learnable parameters, which easily tend to overfit. To compensate for these drawbacks, in this paper we propose ALLNet, the first approach in the literature for ALL detection using a lightweight architecture based on fixed binary kernels that replicate the Local Binary Patterns and that uses only ≈ 1.6% of the learnable parameters of a traditional CNN. We evaluated our approach on a public ALL database, achieving better results with respect to the state of the art in terms of classification accuracy.
Semantic segmentation method of underwater images based on encoder-decoder architecture

With the exploration and development of marine resources, deep learning is more and more widely used in underwater image processing. However, the quality of the original underwater images is so low that traditional semantic segmentation methods obtain poor segmentation results, such as blurred target edges, insufficient segmentation accuracy, and poor regional boundary segmentation effects. To solve these problems, this paper proposes a semantic segmentation method for underwater images. Firstly, the image enhancement based on multi-spatial transformation is performed to improve the quality of the original images, which is not common in other advanced semantic segmentation methods. Then, the densely connected hybrid atrous convolution effectively expands the receptive field and slows down the speed of resolution reduction. Next, the cascaded atrous convolutional spatial pyramid pooling module integrates boundary features of different scales to enrich target details. Finally, the context information aggregation decoder fuses the features of the shallow network and the deep network to extract rich contextual information, which greatly reduces information loss. The proposed method was evaluated on RUIE, HabCam UID, and UIEBD. Compared with the state-of-the-art semantic segmentation algorithms, the proposed method has advantages in segmentation integrity, location accuracy, boundary clarity, and detail in subjective perception. On the objective data, the proposed method achieves the highest MIOU of 68.3 and OA of 79.4, and it has a low resource consumption. Besides, the ablation experiment also verifies the effectiveness of our method.
Long-term availability of data associated with articles in PLOS ONE

The adoption of journal policies requiring authors to include a Data Availability Statement has helped to increase the availability of research data associated with research articles. However, having a Data Availability Statement is not a guarantee that readers will be able to locate the data; even if provided with an identifier like a uniform resource locator (URL) or a digital object identifier (DOI), the data may become unavailable due to link rot and content drift. To explore the long-term availability of resources including data, code, and other digital research objects associated with papers, this study extracted 8,503 URLs and DOIs from a corpus of nearly 50,000 Data Availability Statements from papers published in PLOS ONE between 2014 and 2016. These URLs and DOIs were used to attempt to retrieve the data through both automated and manual means. Overall, 80% of the resources could be retrieved automatically, compared to much lower retrieval rates of 10-40% found in previous papers that relied on contacting authors to locate data. Because a URL or DOI might be valid but still not point to the resource, a subset of 350 URLs and 350 DOIs were manually tested, with 78% and 98% of resources, respectively, successfully retrieved. Having a DOI and being shared in a repository were both positively associated with availability. Although resources associated with older papers were slightly less likely to be available, this difference was not statistically significant, suggesting that URLs and DOIs may be an effective means for accessing data over time. These findings point to the value of including URLs and DOIs in Data Availability Statements to ensure access to data on a long-term basis.
XII International Conference on Mathematics, Science and Technology Education: conference report

This article represents the proceedings of the XII International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2020) held at the Kryvyi Rih State Pedagogical University, Ukraine, 15–17 October 2020. Background information and the organizational structure of the meeting, a summary of the papers, and acknowledgements of the contributions of the many people who made the conference a success are presented.
New Dominant Color Descriptor Features Based on Weighting of More Informative Pixels using Suitable Masks for Content-Based Image Retrieval

Content-based image retrieval (CBIR) is a process of retrieving images based on their content in a dataset automatically. CBIR is a common solution to search images similar to a desired image among all images in dataset. To do this, many methods have been developed to extract images features. Here, a new Dominant Color Descriptor (DCD) method is proposed to improve CBIR accuracy. In the first step, Canny edges of images are extracted. In the next step, edges are widened by employing morphological operations. Finally, pixels that are not at the edges are weighted less than the pixels which are located at edges. Indeed, pixels in regions with low color variations are less weighted and more informative pixels are more weighted in providing DCD features. To show the effectiveness of the proposed method, experiments are performed on three datasets Corel-1k, Corel-10k and Caltech256. Results demonstrate that the proposed method outperforms competitive methods.
Data quality analytics, business ethics, and cyber risk management on operational performance and fintech sustainability

This study conducted a test to see the influence of data quality analytics, business ethics, and cyber risk management on operational performance and its implication on corporate sustainability of Fintech P2P Lending companies registered and licensed in Indonesian Financial Services Authority (OJK). This study used descriptive analysis and statistical method Structural Equation Modeling (SEM)-Lisrel. The data was collected by using questionnaires given to 104 managers from 91 Fintech P2P Lending companies registered and licensed at OJK until the end of December 2021. The results show that data quality analytics and cyber risk management had a positive and significant influence on operational performance. The results also show that analytical data quality, business ethics and cyber risk management had a positive and significant influence on operational performance. The findings of this study added to the limitations of the research literature on the elaboration of variables that determine performance and business sustainability in Fintech P2P lending.
Securing Physical Layer of 5G Wireless Network System over GFDM Using Linear Precoding Algorithm for Massive MIMO and Hyperchaotic QRDecomposition

This paper introduces a novel voice encryption wireless system, which is based on characteristics of massive Multiple Input Multiple Output (MIMO) wireless channel.By exploiting the Minimum Mean Square Error (MMSE) precoding technique, channel fading values are permutated and substituted using various chaotic generators.The voice samples are mixed with channel values and a chaotic sequence before being transmitted.The proposed model was applied on a 5G network that included Generalized Frequency Division Multiplexing (GFDM), Parallel Spatial Modulation (PSM), and massive MIMO.Two chaotic sequences are generated, the first one used for the substitution stage by QR decomposition for the Henon map and then combining the outcome with Bernoulli and logistic maps to Q and R matrices, respectively.New chaotic signals are added to the real and imaginary parts of the MMSE precoding matrix.The second key is generated by the Tent map to perform the permutation stage for the overall precoding matrix.Several security metrics tests were used to verify effectiveness the of the proposed method resistance against attacks .The obtained tests results are: Signal to Noise Ratio (SNR=-23.1223),Root Mean Square (RMS=0.88565),Crest Factor (CF=10.6717),correlation coefficient (r_xy=0.000471),Linear Predictive Code Distance (dLPC=1.2758),Spectral Segment SNR (SSSNR=-27.6149),Cepstral Distance (dCD=9.4159),number of samples change rate (NSCR=33.334%),unified average changing intensity (UACI=99.9998%),keyspace=2500, key sensitivity, and speed=0.0329.The simulation results show that the proposed scheme has an excellent level of security and resistance to various forms of attacks, which outperforms many recent similar voice encryption methods.
Motives to Learn English as a Foreign Language: An Analysis from Vroom’s Theory

In Malaysia, English is a foreign language to many , and perhaps a second language for some.Many have seen the importance of English in Malaysia, more so during the pandemic.Information online is mainly written in English.In addition to that, online interactions has increased during online classes and language learners find themselves motivated to be better in their English.Getting language learners to stay motivated in foreign language classes is a challenge, now measures need to be taken to encourage language learners to remain motivated to learn foreign languages online.This study uses Vroom's (1964) classic motivation theory to explore the motives behind learners learning English in a public university in Malaysia.Vroom's (1964) expectancy theory presents three components and they are expectancy, instrumentality and valence.35 students responded to a survey that explored how learners' motivation is influenced by expectancy, instrumentality, valence.The instrument used is a survey adapted from Nguyen (2019).Apart from the demographic profile in Section A., there are 2 other sections.Section B has 7 items on expectancy, section C has 7 items on instrumentality and section D has 6 items on valence.Findings revealed that learners learn English for instrumental reasons.Their drive to learn English is what they expect to use their language proficiency for.Findings has interesting implications in the teaching and learning of English a s foreign language.
Pattern Recognition and Segmentation of Administrative Boundaries Using a One-Dimensional Convolutional Neural Network and Grid Shape Context Descriptor

Recognizing morphological patterns in lines and segmenting them into homogeneous segments is critical for line generalization and other applications. Due to the excessive dependence on handcrafted features in existing methods and their insufficient consideration of contextual information, we propose a novel pattern recognition and segmentation method for lines, based on deep learning and shape context descriptors. In this method, a line is divided into a series of consecutive linear units of equal length, termed lixels. A grid shape context descriptor (GSCD) was designed to extract the contextual features for each lixel. A one-dimensional convolutional neural network (1D-U-Net) was constructed to classify the pattern type of each lixel, and adjacent lixels with the same pattern types were fused to obtain segmentation results. The proposed method was applied to administrative boundaries, which were segmented into components with three different patterns. The experiments showed that the lixel classification accuracy of the 1D-U-Net reached 90.42%. The consistency ratio was 92.41%, when compared with the manual segmentation results, which was higher than either of the two existing machine learning-based segmentation methods.