Reviewerly/exHarmony huggingface dataset:
Using Case Based Reasoning, in the Student Graduation Prediction System (CBR)

The performance of an educational institution is considered to have a negative impact due to the high percentage of students who do not graduate on time. Therefore, the number of students must be calculated and anticipated, so this paper needs to propose a model for predicting student graduation rates and predicting the time of graduation for each student. The K-Nearest Neighbor (KNN) method and algorithm were developed using Case-Based Reasoning (CBR). The research was conducted with training data taken from data from students who had graduated, and as a trial, the sixth-semester student data was used. The research attributes required to consist of the grade 1 to semester 5 of the achievement index (IPS) and the students' age. Model testing is carried out with four scenarios where the distribution of training data and testing data is also needed as support. The experimental results show that the stability of the data accuracy value on CBS using the KNN algorithm is found in the 90% training data scenario and 10% test data, namely 90.24% with the closest neighbors K = 5, K = 7, and K = 9. It can be concluded that the model. The resulting prediction system can predict whether students will graduate on time or not. This research will be helpful if implemented in higher education performance to increase student achievement to graduate on time.
Permutation Tests for Metaheuristic Algorithms

Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the underlying data that must be true before it can be used. Permutation tests (P-Tests) are statistical tests with minimal number of assumptions. These tests are simple, intuitive and nonparametric. In this paper, we argue researchers in the field of metaheuristics to adopt P-Tests to compare their algorithms. We define two statistic tests and then present an algorithm that uses them to compute the p-value. The proposed process is used to compare 5 metaheuristic algorithms on 10 benchmark functions. The resulting p-values are compared with the p-values of two widely used statistical tests. The results show that the proposed P-test is generally consistent with the classical tests, but more conservative in few cases.
Blockchain-Based Self-Auditing Scheme with Batch Verification for Decentralized Storage

Data owners outsource their data to remote storage providers without keeping local replicas to save their precious storage resources. However, the ownership and management of data are separated after outsourcing. How to ensure the integrity and recoverability of outsourced data becomes a significant problem. Provable Data Possession (PDP) and Proofs of Retrievability (POR) are two cryptographic protocols that enable users to verify the integrity of outsourced data. Nevertheless, the state-of-the-art PDP and POR schemes either need users to perform the complicated audit tasks by themselves or delegate these tasks to a Third-Party Auditor (TPA). Moreover, these schemes are constructed on a centralized storage framework which vulnerably suffers single-point-of-failure. In this paper, we propose a blockchain-based decentralized self-auditing scheme with batch verification. Firstly, data owners outsource their data to decentralized storage nodes, which can achieve self-auditing based on blockchain without TPA. Secondly, our scheme uses Pedersen-based polynomial commitment to significantly reduce the number of authenticators. Furthermore, we propose a batch verification algorithm, which can verify multiple proofs from different storage nodes to improve the verification efficiency. Finally, we analyze the security of our scheme and implement a gas-efficient system prototype using the smart contracts of the Ethereum Reposten test network. The results demonstrate that the scheme is practical.
Compression of Noisy Images Taking into Account Visual Quality: A Comprehensive Study

This chapter deals with lossy compression of images that have been corrupted by additive noise. The chapter's key contribution is that the analysis is done from the perspective of compressed image visual quality. Several coders are explored for which the compression ratio is regulated in various ways. Lossless coding usually does not produce sufficient compression ratios for many practical applications. Visual quality metrics that are the most adequate for the considered application (WSNR, MS-SSIM, PSNR-HVS-M and PSNR-HVS) are used. The objective is to analyze is optimal operation point (OOP) possible according to visual quality metrics. It is demonstrated that, under certain conditions, visual quality of compressed images can be slightly better than quality of original noisy images due to image filtering through lossy compression, i.e., OOP might exist. The “optimal” parameters of coders for which this positive effect can be observed depend upon standard deviation of the noise. We propose an algorithm to determine coder parameters in OOP. This enables the development of automated techniques for compressing noisy images at the vicinity of the optimal operation point, i.e. when visual quality improves or declines insufficiently. Another advantage is that compression ratio for this case is quite high. The results of a series of grayscale test images with various noise variations are shown.
Didactic Design as the Basis for the Professional Spaces of Pedagogical Education

Problem statement reflects on the actual issues of creating the optimus for culturally oriented polylinguistic security of the current professional educational environment. Moreover, the focus is on its didactic design within the frameworks of pedagogical design determined as the most problematic aspect of the national education due to its permanent transformations. The goal of the article is to present a didactic design as a valuable component of the professional culture of the teacher of the future that requires reasonable conditions for effective interaction of the participants of the educational process. The research context is based on the leading principles of environmental and multifunctional approaches in pedagogical science and practice. Moreover, the pedagogical design focused on the ideal track of personal development in the aspect of changing the format of communication-oriented learning, functions as the leading methodological message of this micro-research. The results of the study are reflected in the presentation of a multi-level model for the formation of a portfolio of educational practices for students and adults of specialists. The portfolio positioned as a factor and condition for a positive impact on the online (e-learning; on-line) education market in a polyphonic sounding and multilingual content of the information and communication space, subject to language security. In conclusion, the conditions of genuine communication of participants in the creation of pedagogical design of continuing education are clarified in terms of maintaining psychological balance and in the aspect of critical understanding of the content-based technology of culturally oriented practices of professional formation and personal growth within the framework of the national project “Teachers of the Future”.
Blockchain and Federated Learning-enabled Distributed Secure and Privacy-preserving Computing Architecture for IoT Network

With the adoption of the 5G network, the exponen-tial increase in the volume of data generated by the Internet of Things (IoT) devices, pushes the system to learn the model locally to support real-time applications. However, it also raises concerns about the security and privacy of local nodes and users. In addition, the approach such as collaborative learning where local nodes participate in the learning process of global model also raise critical concern regarding the cyber resilience of the network architecture. To address these issues, in this article, we identify the research gaps and pro-pose a blockchain and federated learning-enabled distributed secure and privacy-preserving computing architecture for IoT network. The proposed model introduces the lightweight authentication and model training algorithms to build secure and robust system. The proposed model also addresses the reward and penalty issues of the collaborative learning with local nodes and propose a reward system scheme. We con-duct the experimental analysis of the proposed model based on various parametric metrics to assess the effectiveness of the model. The experimental result shows that the proposed model is effective and capable of providing a cyber-resilience system.
Effects of emotions on head motion predictability in 360° videos

While 360° videos watched in a VR headset are gaining in popularity, it is necessary to lower the required bandwidth to stream these immersive videos and obtain a satisfying quality of experience. Doing so requires predicting the user's head motion in advance, which has been tackled by a number of recent prediction methods considering the video content and the user's past motion. However, human motion is a complex process that can depend on many more parameters, including the type of attentional phase the user is currently in, and their emotions, which can be difficult to capture. This is the first article to investigate the effects of user emotions on the predictability of head motion, in connection with video-centric parameters. We formulate and verify hypotheses, and construct a structural equation model of emotion, motion and predictability. We show that the prediction error is higher for higher valence ratings, and that this relationship is mediated by head speed. We also show that the prediction error is lower for higher arousal, but that spatial information moderates the effect of arousal on predictability. This work opens the path to better capture important factors in human motion, to help improve the training process of head motion predictors.
Automatic Evaluation of Speaker Similarity

We introduce a new automatic evaluation method for speaker similarity assessment, that is consistent with human perceptual scores. Modern neural text-to-speech models require a vast amount of clean training data, which is why many solutions switch from single speaker models to solutions trained on examples from many different speakers. Multi-speaker models bring new possibilities, such as a faster creation of new voices, but also a new problem - speaker leakage, where the speaker identity of a synthesized example might not match those of the target speaker. Currently, the only way to discover this issue is through costly perceptual evaluations. In this work, we propose an automatic method for assessment of speaker similarity. For that purpose, we extend the recent work on speaker verification systems and evaluate how different metrics and speaker embeddings models reflect Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) scores. Our experiments show that we can train a model to predict speaker similarity MUSHRA scores from speaker embeddings with 0.96 accuracy and significant correlation up to 0.78 Pearson score at the utterance level.
Augmentation of AI-based Process for Blood Doping Discovery in Sports using Random Forest Ensembles with LightGBM

Metadata is an exploration of the given data. It organizes the data by grouping the collected information on a particular structure for easy understanding. Metadata reduces the computational burden on data mining algorithms by keeping an organized record. Data that are related to healthcare application requires serious attention on privacy concern and therefore such information are encrypted in most cases. Processing of encrypted data is difficult, and it may lead to estimate the prediction with a faulty output. Hence, a blockchain based data securing system is proposed in the work for securing the data that are transmitted from a ubiquitous computing device. The paper also incorporates the proposed work with a whale optimization algorithm for reducing the execution time required on the blockchain based data storage and retrieval process.
Su-MICL: Severity-Guided Multiple Instance Curriculum Learning for Histopathology Image Interpretable Classification

Histopathology image classification plays a critical role in clinical diagnosis. However, due to the absence of clinical interpretability, most existing image-level classifiers remain impractical. To acquire the essential interpretability, lesion-level diagnosis is also provided, relying on detailed lesion-level annotations. Although the multiple-instance learning (MIL)-based approach can identify lesions by only utilizing image-level annotations, it requires overly strict prior information and has limited accuracy in lesion-level tasks. Here, we present a novel severity-guided multiple instance curriculum learning (Su-MICL) strategy to avoid tedious labeling. The proposed Su-MICL is under a MIL framework with a neglected prior: disease severity to define the learning difficulty of training images. Based on the difficulty degree, a curriculum is developed to train a model utilizing images from easy to hard. The experimental results for two histopathology image datasets demonstrate that Su-MICL achieves comparable performance to the state-of-the-art weakly supervised methods for image-level classification, and its performance for identifying lesions is closest to the supervised learning method. Without tedious lesion labeling, the Su-MICL approach can provide an interpretable diagnosis, as well as an effective insight to aid histopathology image diagnosis.
Apps and downs: Cross-Country Variations in the Success of Contact-Tracing Technology

With the COVID-19 pandemic, we have witnessed a plethora of policies deployed by the governments to curb the spread of the virus and simultaneously sustain the fledgling economies. One of the tactics deployed by the governments related to slowing down the spread of the virus by tracing the contacts of individuals who were in proximity with those people that have been tested positive for COVID-19. Countries showed uneven success. The variety in responses was not only related to the extent to which the countries were impacted by the pandemic, but also by the way in which the governments decided to intervene in the management of this crisis. Hence, in this article, we analyze the variation in contact tracing technologies across countries. We propose that the type and quality of government intervention drive the differences in the rates of diffusion and adoption of contact tracing apps across countries. We explain this as an outcome of the complex signaling problems and institutional drivers related to the nature of governments’ intervention. We test our hypotheses on a unique, self-assembled dataset of contact tracing apps and their governments’ characteristics across a large number of countries. The signaling mechanisms deployed by the governments seem to have varying effects on the success of contact tracing technologies, with trust and privacy being the major drivers in adoption by citizens of such technologies. We find that the success of tracking strategies is inversely related to the quality of institutions – state capitalist countries fared better than less interventionist, more liberal economies.
Analysis of Development of Artificial Intelligence in the Game Industry

A game, sometimes known as a video game, is a form of modern technology-based game. Gaming is currently one of the major industries on the planet. Because gaming aficionados from all over the world number in the hundreds of millions, if not billions, this sector is very substantial. The game may be played on a PC, an Android device, or a game machine both offline and online. Artificial intelligence is also used in video games. Artificial Intelligence (AI) is artificial intelligence implemented on a computer system that allows players to compete against the computer in a game similar to that of other players. Artificial intelligence, often known as machine intelligence, is a mimic of human intellect that has been trained to think like humans. Artificial intelligence is a technology that uses data as knowledge in order for the intelligence created to improve and learn from prior failures. When employed by humans, artificial intelligence can be activated by human orders or by itself based on AI experience. The goal of this research was to figure out how artificial intelligence was progressing in the gaming business.
Ultra-Low-Bitrate Speech Coding with Pretrained Transformers

Speech coding facilitates the transmission of speech over low-bandwidth networks with minimal distortion. Neural-network based speech codecs have recently demonstrated significant improvements in quality over traditional approaches. While this new generation of codecs is capable of synthesizing high-fidelity speech, their use of recurrent or convolutional layers often restricts their effective receptive fields, which prevents them from compressing speech efficiently. We propose to further reduce the bitrate of neural speech codecs through the use of pretrained Transformers, capable of exploiting long-range dependencies in the input signal due to their inductive bias. As such, we use a pretrained Transformer in tandem with a convolutional encoder, which is trained end-to-end with a quantizer and a generative adversarial net decoder. Our numerical experiments show that supplementing the convolutional encoder of a neural speech codec with Transformer speech embeddings yields a speech codec with a bitrate of $600\,\mathrm{bps}$ that outperforms the original neural speech codec in synthesized speech quality when trained at the same bitrate. Subjective human evaluations suggest that the quality of the resulting codec is comparable or better than that of conventional codecs operating at three to four times the rate.
The Feel of the Guitar in Popular Music Performance

Popular-music guitarists frequently reference the physicality of the fretboard when discussing songwriting and performance. Brittany Howard, for example, advocates that the feel of her guitar and physical gestures — chord shapes, licks, and riffs — are more critical to her craft than theoretical knowledge regarding pitch and harmony. However, in-depth gestural perspectives on musical organization are underrepresented in popular-music scholarship. This video article responds by investigating the relationship between fretboard gestures and popular music’s features in two contexts. First is a close analysis of “Short and Sweet” that focuses on how Howard coordinates chord voicings and larger left-hand shifts with important rhetorical moments reflected in the song’s lyrics. A following motion-capture study then surveys how similar gestural trends might generalize to performances by other guitarists. Data from performances by 14 local practicing guitarists demonstrate that the musicians typically prefer to articulate moments of formal transition with the largest physical gestures. Such findings suggest a degree of gestural intentionality and strategy amongst guitarists and clarify the contexts in which a guitarist might choose to maintain or abandon fretboard affordances.
Masked Symmetric Key Encrypted Verification Codes for Secure Authentication in Smart Grid Networks

Although many smart grid authentication protocols have been presented in literature, majority of them remain susceptible to numerous attacks. In addition, some of these protocols are based on computationally intensive cryptographic primitives, which render them inefficient. To address some of these challenges, a protocol based on masked symmetric key encrypted verification codes is presented in this paper. The security analysis carried out shows that this protocol provides message confidentiality, unlinkability, and traceability for malicious network entities. It is also resilient against side-channel and forgery attacks. In terms of efficiency, limited numbers of elliptic curve multiplication and one way-hashing operations are executed during mutual authentication, message signing and verification. As such, this protocol exhibits the least computation and communication complexities in comparison with its peers.
Aerial Manipulation Using Contact with the Environment by Thrust Vectorable Multilinked Aerial Robot

In recent years, an increasing number of research works have been focusing on the manipulation by aerial robots. Previous works using aerial robots with robotic arms have two problems: underactuation and external disturbances. We propose the fully-actuated control method and motion strategy using contact with the environment to solve these problems, along with the mechanical approach required. First, each propeller's 1 degree-of-freedom (DoF) thrust vectoring units are applied to enable fully-actuated flight control. In order to obtain the desired thrust and vectoring angle inputs for aerial manipulation satisfying hardware limits, we developed a fully-actuated control method using non-linear optimization. Second, we propose a manipulation motion strategy that treats the multilink robot body as a fixed manipulator by making contact with the environment. The contact mechanism attached to the link end is developed to maintain contact and resist external disturbances. In a real machine experiment, the robot successfully opened the door while in contact with the wall, demonstrating the feasibility of the proposed methods.
How Do Individuals Understand Multiple Conceptual Modeling Scripts?

Because most real-world domains intended to be supported by an information system are complex, practitioners often use multiple different types of conceptual modeling scripts to understand them. We performed two experiments to examine how two theoretical factors of multiple scripts— combined ontological completeness and ontological overlap—influence how users develop an understanding of a real-world domain from multiple scripts. Results of the first experiment show that to some degree, ontological overlap improves participants’ understanding of a domain, more so than combined ontological completeness. In the second experiment, we tracked the eye movement data of participants to understand how ontological overlap between scripts impacts users’ information search and cognitive integration processes. We found that some occurrence of semantically similar constructs between scripts helps individuals to identify and relate constructs presented in different scripts. Users, therefore, can identify and focus on script areas that are relevant to their problem tasks. However, a high level of ontological overlap decreases the attention paid by participants to relevant task-specific areas because they spend more time searching for relevant information. Together, our findings both refine and extend existing conceptual modeling theory. We clarify the dialectics between the full and parsimonious real-world representations offered through multiple scripts and the individual’s understanding of the domain that is represented by those scripts.