Reviewerly/exHarmony huggingface dataset:
Improving Optimization Prowess of Ant Colony Algorithm Using Bat Inspired Algorithm

Metaheuristic algorithms such as Ant Colony Optimization (ACO) algorithm and Bat Optimization Algorithm (BOA) have been widely employed in solving different optimization problems in several fields. ACO is modelled based on the social behaviour of ants that look for appropriate answers to a given optimization issue by recasting it as the case of locating the least expensive path on a weighted graph. A set of parameters linked to graph components (either nodes or edges) whose values are changed by the ants during runtime constitute the pheromone model, which biases the stochastic solution generation process. However, the effectiveness of ACO declines as the quantity of packets rises, making them ineffective for reducing traffic congestion. As more packets are transmitted, their strength decreases, causing packet congestion, rendering them useless for reducing packet traffic congestion. On the contrary, BOA which was modelled after the behavior of bats has also been employed in fixing network routing issues by listening to every sound in a space and taking note of what is going on around it. In order to further improve ACO algorithm and decrease packet traffic congestion, packet loss, and the time it takes a packet to reach its destination in a network system, this study employs the strength of BOA. Results obtained revealed the prowess of BOA in improving the performance of ACO for network packet routing.
SmartCall: A Real-time, Sign Language Medical Emergency Communicator

Communication is essential for individuals to convey feelings and emotions. Persons with speech impairment, on the other hand, find it challenging to share their thoughts, especially during medical emergencies. In this study, we propose a low-cost embedded device that allows individuals with a speech impairment to communicate during medical emergencies. A 1D-convolution neural network (CNN) model extracting features from an onboard inertial measurement unit (IMU) for the classification of selected American sign language (ASL) medical emergencies word. The model was trained offline before deployment to a resource-constrained embedded device for real-time ASL word classification. A pilot test on two volunteers resulted in an offline accuracy of 91.2% and an average online accuracy of 92 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for the 8-bit optimized model. The results demonstrate the feasibility to aid individuals with a speech impairment to communicate during medical emergencies. Furthermore, an extended application of the proposed design is for the intuitive learning of sign languages using artificial intelligence.
Mitigating Social Engineering Attack: A Focus on the Weak Human Link

Social engineering is a security concern that can be mitigated most efficiently from the most neglected aspect in the security ecosystem which is humans. Technological advancement focused at devices cannot prevent psychological human manipulation. This paper sort to determine the security practices and disposition of humans in a situation of vulnerability to social engineering attacks. Interview was used for data collection. 70 persons were interviewed using structured questions. The result showed that being a former victim of social engineering activity is not enough to prevent being another victim which is a consequence of security practices by the human. It is recommended that security practices against social engineering should be institutionalised in everyday human living.
Enhanced Optical Double Phase Image Encryption Using Random Gaussian Noise

The advent of digitalization makes image security inevitable as a result of that, an asymmetric cryptography algorithm which is based on the improvement of double random phase encoding is proposed. This algorithm makes use of Gaussian random noise to boost the security of the existing method and was implemented on the MATLAB 2020a software application. The results obtained from the simulations show a slight decrease in the peak-to-signal ratio of 0.3304 and an increase in the mean square error when comparing the existing method with the enhanced method. An image encrypted using Enhanced Double Random Phase Encoding (EDRPE) has strong protection against intruders due to the introduction of the random Gaussian noise as one of the components of the masks in the encryption process. However, a slight noisy effect is produced on the recovered image which is not obvious to the receiver. The simulation result validates the algorithm's potential against security attacks but does not eliminate the presence of noise.
Ring Learning With Error-Based Encryption Scheme for the Privacy of Electronic Health Records Management

The electronic health (e-health) systems support a range of electronic devices, wireless links, transmission and storage of data. E-health systems allows communication through a gateway (or central point) in the cloud. Health professionals and teams utilize e-health systems to perform virtual consultations to patients, remote treatment or diagnosis. The success story of e-health systems is often met with problems including: insecure channels of communication, eavesdropping of messages across channels by adversary, profound insider attacks on private information on servers, and healthcare services disruptions. Cryptography or encryption algorithms have been considered as capable of overcoming the privacy and security problems of electronic medical records management. However, certain issues persist with cryptographic-based schemes such as slow processing speed, weak security mechanisms, high computational overheads, and weak public-private keys. In this paper, a lattice-based cryptography, Ring Learning With Error (RLWE) encryption is used to propose a privacy scheme for EMR in cloud environment. The choice of RLWE is due to its provable hardness among conventional lattice problem. The outcomes revealed that, the proposed encryption scheme outperformed comparable asymmetric schemes in terms of elapsed time (0.04sec) against ECDSA (1.11sec), ECC (16.62sec), and RSA (37.95sec). Again, the public key size was better for RLWE (32-bits) only after ECDSA (10-bits), against ECC (97-bits), and RSA (191-bits). Similarly, the private key size for ECC (9-bits) was only better than RLWE(10-bits), against ECDSA (58-bits), and RSA (687-bits) respectively. The proposed encryption scheme is time and memory-efficient; and holds promise for EMRs privacy.
Japanese Neural Incremental Text-to-Speech Synthesis Framework With an Accent Phrase Input

Work in the development of neural incremental text-to-speech (iTTS), which is attracting increasing attention, has recently pursued low-latency processing by generating speech on the fly before reading complete sentences.Most current state-of-the-art iTTS systems use a prefix-to-prefix neural iTTS framework with look-ahead of 1-2 unit segments (i.e., phonemes or words).However, since the Japanese language is based on accent phrase units that are longer than words, using a prefix-to-prefix neural iTTS with a look-ahead approach increases latency.Here, we propose an alternative to the end-to-end neural iTTS architecture that does not apply look-ahead input when synthesizing speech chunks.We further propose a method to use information from the previous time step by connecting the synthesized vector and the model's internal state to the current time step.We experimentally investigated the latency of various iTTS systems with different modeling and synthesis chunks.The experimental results show that, for Japanese, the proposed iTTS is able to synthesize better speech quality, with a similar latency range, than the conventional baseline prefix-to-prefix neural iTTS with word units.Moreover, we found that our proposed approach improved the prosodic naturalness among synthesized units in the Japanese language.Subjective evaluations also revealed that the proposed approach with an incremental unit of two accent phrases achieved the best scores in Japanese iTTS systems.
An Algorithm Based on Hodgkin-Huxley Model and Latin Square for Image Encryption

Image transmission is happening more frequently in this era of technologically sophisticated digital information.Additionally, more individuals are becoming aware of its importance.In order to secure images, many academics are participating in research, which is advantageous for guaranteeing data security.In order to strengthen the security of images during transmission, we have investigated new encryption algorithms to guarantee this.First, a current representing the Lorenz chaotic system is introduced into the neuron model.The neuron model generates sequences after receiving the current signal.The next move is made as the current shifts based on whether the sequence that results is chaotic or not.If so, the subsequent operation is carried out; otherwise, the current is altered until chaotic sequences are produced.Second, a global scrambling with de-duplication technique is used to scramble the image using the resulting chaotic sequence.To complete the dislocation effect, the Latin square is used to dislocate the image after the initial dislocation.Fourth, the image that has been scrambled is subjected to two rounds of additive mode diffusion.They are diffusion in the forward additive mode and diffusion in the inverse additive mode.Lastly, to improve the diffusion effect, the image is diffused in the finite domain.Eventually, the encrypted image is obtained.After evaluation tests and comparison with related literature, it can be found that the algorithm of this study has certain advantages.Also, the resistance to attack is good.It can protect the security of the image.
Modelling Second-Order Uncertainty in State Machines

Modelling the behaviour of state-based systems can be challenging, especially when the modeller is not entirely certain about its intended interactions with the user or the environment. Currently, it is possible to associate a stated level of uncertainty with a given event by attaching probabilities to transitions (producing ‘Probabilistic State Machines’). This captures the ‘First-order uncertainty’ - the (un-)certainty that a given event will occur. However, this does not permit the modeller to capture their own uncertainty (or lack thereof) about that stated probability - also known as ‘Second-order uncertainty’. In this article we introduce a generalisation of probabilistic finite state machines that makes it possible to incorporate this important additional dimension of uncertainty. For this we adopt a formalism for reasoning about uncertainty called Subjective Logic. We present an algorithm to create these enhanced state machines automatically from a conventional state machine and a set of observed sequences. We show how this approach can be used for reverse-engineering predictive state machines from traces.
Verification of Fuzzy Decision Trees

In recent years, there have been major strides in the safety verification of machine learning models such as neural networks and tree ensembles. However, fuzzy decision trees (FDT), also called soft or differentiable decision trees, are yet unstudied in the context of verification. They present unique verification challenges resulting from multiplications of input values; in the simplest case with a piecewise-linear splitting function, an FDT is piecewise-polynomial with degree up to the depth of the tree. We propose an abstraction-refinement algorithm for verification of properties of FDTs. We show that the problem is NP-Complete, like many other machine learning verification problems, and that our algorithm is complete in a finite precision setting. We benchmark on a selection of public data sets against an off-the-shelf SMT solver and a baseline variation of our algorithm that uses a refinement strategy from similar methods for neural network verification, finding the proposed method to be the fastest. Code for our algorithm along with our experiments and demos are available on GitHub at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/autonlab/fdt_verification</uri> .
A Low Latency and Compact GCD Design Using an Intelligent Seed-Selection Scheme of LL-PRNG

Stochastic computing has shown great promise for a variety of applications, including image processing circuits, due to its design simplicity, and low power consumption. This work proposes a hardware efficient and low-latency implementation of the greatest common divisor (GCD) circuit. It uses a low latency parallel random number generator (LL-PRNG) architecture with an intelligently chosen seed value that eliminates the use of correlators and decorrelators in the circuit, reducing the hardware overhead to a great extent. The proposed approach is compared with the conventional LFSR-based design showing reasonable accuracy while the latency in computation greatly reduced. The circuit is evaluated for blind image deconvolution employing GCD and has shown encouraging results.
Improving Semantic Segmentation Under Hazy Weather for Autonomous Vehicles Using Explainable Artificial Intelligence and Adaptive Dehazing Approach

Haze-level discriminators are highly desirable for autonomous vehicles to handle segmentation tasks successfully in hazy and foggy outdoor environments. Neural networks trained to detect clear images show more false positives and unrecognize the pixel patterns for the class categories when they encounter hazy images. Here, we propose a novel dehazing scheme called Adaptive Dehazing (AD) which can separate the unacceptable hazy images and apply the dehazing technique only to those images before passing it to the neural network for segmentation. Various thresholds have been defined to classify hazy images into four hazy categories: heavy, moderate, slight and clear. Also, a hazy image generator is implemented to create synthetic hazy images that replicate the actual hazy road scene conditions for testing the proposed algorithm. In addition, we use the Explainable Artificial Intelligence (XAI) method to understand the feature selected by different layers in the network before and after applying the AD and their contribution to improving the Intersection over Union (IoU) and pixel accuracy (PA) of the segmented categories. Extensive experiments demonstrate quantitatively as well as qualitatively the performance improvement in the segmentation task. Our AD scheme significantly outperforms the previous state-of-the-art dehazing methods.
Predicting Compliance: Leveraging Chat Data for Supervised Classification in Experimental Research

Many experimental research designs offer participants the opportunity to chat with each other. While experimental research has traditionally treated text as process data, this paper proposes a novel approach to interpret and use chat data in a structured supervised classification task. Specifically, we provide a framework for supervised classification where chat text serves as input and a related numeric variable serves as the target to be predicted. We show that the combination of chat data and decision data is an ideal precondition for supervised classification, but that experimental data present unique challenges, such as being small or heavily skewed. To overcome these challenges, we propose a methodological framework that systematically investigates various classification setups to maximize predictive performance in small datasets from experimental behavioral research. We demonstrate the framework's utility in predicting honest decisions in a tax evasion game. To ensure the wider accessibility and usability of our proposed framework, we have created an open-access GitHub repository with clear instructions on how to use the codebase and available data. Additionally, the repository includes slides with theoretical background and quizzes, making it a valuable educational resource for researchers and students alike. Our results show that our best model performs better than random chance, despite the challenges presented by the small, skewed dataset. We also demonstrate that dictionary-based approaches can be used to gain insight into the data even in the absence of raw text. Overall, this paper provides a valuable toolbox for the research community to build upon and encourages further exploration of chat data in supervised classification tasks in experimental research.
The Exploration of Contemporary Innovations as a ‘Bridge’ to Narrow the Generation Gap and Awaken the Vitality for Sichuan Opera in China

As the highly developed society's every feature, such as lifestyle, habits, aesthetics, and so on, changes from generation to generation, fewer and fewer young people spend time learning, knowing, or attending Sichuan opera shows.In this essay, the author will explore how to narrow the generation gap and awaken the vitality for Sichuan opera in China from two different aspects: social media and creative and cultural products.Both methods have advantages but also shortcomings.At the end of the essay, the author will set out the future expectation about Sichuan opera, like the Fans' community that will be build soon.
The Embodiment and Thinking of “Identity” in Shooting Records

Youth subculture is a kind of time-varying cultural expression form.On the one hand, youth subculture is closely linked with the development of the times, promoting cultural innovation to a certain extent, enriching cultural forms, and making certain contributions to the development of the entertainment industry.On the other hand, from a negative perspective, the youth subculture may excessively pursue attention in the process of creating visual images, which leads to the weakening of the subject's discrimination ability.Moreover, if people indulge in watching the visual images of youth subculture, it is easy to have serious psychological losses.In addition, some unhealthy visual images in youth subculture may affect the construction of positive social culture.This paper takes a live music stage photography by the researcher as an example to explore the specific process of the image construction of rock fans by using the method of text analysis.Reflecting on the negative influence of the visual image of youth subculture in urban space and giving full play to the positive influence of the visual image of youth subculture in urban space is the guarantee to promote the healthy and sound development of the visual image of youth subculture in urban space.The results of this study show that people should improve their visual literacy, the subcultural youth should adhere to the creative attitude of being born for beauty, and should increase their interaction with urban space.At the same time, media reports should be inclusive and warm, and should clarify the social responsibility and status of subcultural youth in the new era.
Analysis of “Crazy Thursday” from the Perspective of Meme Theory

With the advantages of low cost and high efficiency, Internet marketing has become the first choice of marketing methods for countless businesses.Online communities and social media promote the public to participate in the imitation, replication, innovation and dissemination of information.Such information dissemination can be explained by meme theory.This paper attempts to explain the communication characteristics of KFC's "Crazy Thursday" marketing campaign from the perspective of meme theory, explain the communication mechanism of "Crazy Thursday" marketing campaign with the communication mode of meme theory, analyze the psychology of the communication audience at each stage, and provide a reference for such Internet marketing campaign.
Roles of Vowels: Existing in Our Daily Life

The vowel is a natural part of a language that carries information in grammar and lexicon.The vowel in every language is a must for learners.If we notice it carefully, we will recognize that there are so many details of vowels in our daily life that we might have concluded them as common phenomena that are hard to explain.In this essay, a brief analysis and discussion will be done to disclose these miserable phenomena made by vowels in our daily lives.Relevant areas include brand naming, learning ability, dialect, and the disabled.The whole research background is based on China and Chinese society.
Rendering Morphosyntactic Features of Legal Spanish Judgments Using Neural and Statistical Machine Translation

Addressing machine translation (MT) in the legal context, this chapter compares Spanish-to-English neural MT (NMT) and statistical MT (SMT) output from the same MT provider during these different paradigm periods. The chapter focuses on translation renditions of various morphosyntactic features originating from a morpho-syntactically complex text of judgment summaries issued by the Supreme Court of Spain. One the one hand, the chapter evaluates NMT and SMT translation solutions of frequent morphosyntactic features revealed by means of corpus analysis software in the areas of verb and subject order and active and passive voice. On the other, a set of complex sentences is analysed to reveal how NMT and SMT translations of these and other morphosyntactic features might fare under more dispersed or broader contextual conditions. NMT appears to provide more adequate solutions in the case of most of the individual morphosyntactic features analysed, as well as a tendency to be more consistently reliable from a grammatical equivalence perspective. SMT, however, may provide more peculiar or contextually desirable solutions in certain cases, but these solutions cannot be relied upon as much as the syntactically thorough or grammatically equivalent approaches provided by NMT on a more consistent basis.
Application of Mixed Teaching in Military Academy Curriculum

In this paper, the mixed teaching is applied to the teaching of military academy curriculum. We firstly discussed the necessity and principle of carrying out the mixed teaching in the military academy curriculum, and then analyzed how to design and implement the mixed teaching. The research shows that carrying out mixed teaching in military academies is a key step to explore the training mode of excellent students to adapt to the army. It needs to be close to the needs of the army, and give full play to the role of students as the main body, teachers as the leader, and schools as a strategic unit.
Research on the Reform of Ethnic Music Education in the Context of Curriculum Civics

With the rapid development of China's economy, technology and other aspects in the context of curriculum thinking, society's demand for talents has also shown a diversified trend. In such a context, folk music, which is of great significance to the cultivation of students' overall comprehensive quality and innovative ability, has a unique local advantage over other disciplines. At the same time, however, folk music in the traditional sense exists only as an art form in people's minds, and there are still problems in folk music education, such as superficial teaching content, rigid educational models, lack of teachers and insufficient heritage, that need to be addressed. Based on the above background, this paper discusses how the three parties, namely universities, teachers and students, can improve their comprehensive quality through effective means and combine the spirit of innovation to better build and create the cause of folk music education. By combining the actual situation at home and abroad and based on the background of curriculum thinking and politics in the new era, this paper hopes to provide some reference for the current path of ethnic music education reform in China and to promote the comprehensive development of ethnic music in colleges and universities.