Reviewerly/exHarmony huggingface dataset:
Systems Thinking in an era of climate change: Does cognitive neuroscience hold the key to improving environmental decision making? A perspective on Climate-Smart Agriculture

Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the “best decision” possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and “better” environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.
Implementation and evaluation of EMAES – A hybrid encryption algorithm for sharing multimedia files with more security and speed

In this era of smartphones, a huge amount of multimedia files like audio, video, images, animation, and plain text are shared. And with this comes the threat of data being stolen and misused. Most people don’t think about the security of data before uploading it to any platform. Most apps used on smartphones upload our data to their server. Not only this, but other third-party apps can also read that data while it is being transmitted. One solution to this problem is encrypting the data before sharing it and decrypting it back at the other end so that even if it is intercepted in between the transmission, it would be impossible to decrypt it. In this paper, a newly designed hybrid encryption algorithm EMAES that includes the efficiency of MAES (Modified Advanced Encryption Standard) and security of ECC (Elliptic Curve Cryptography) was implemented in MATLAB as well as in android studio 4.0. using a mobile messaging application. Also, it was tested for different speeds and security parameters. Further, it was compared with standard algorithms like the RC4, RC6 and Blowfish as well as with other hybrid algorithms like RC4+ECC, RC6+ECC and Blowfish+ECC. The EMAES was found 30% more efficient in terms of encryption and decryption time. The security of EMAES also showed improvement when compared with other hybrid algorithms for parameters like SSIM (structural similarity index measure), SNR (Signal to Noise Ratio), PSNR(Peak Signal to Noise Ratio), MSE (Mean Squared Error) and RMSE (Root Mean Squared Error). And finally, no significant improvement was found in the CPU and RAM usage.
Decision trees, entropy, and the contrastive feature hierarchy

Dresher (2009) argues that language-particular hierarchies of features are the best way to identify contrastive features in a phonological inventory. While not universal, this ordering of features is also not fully unconstrained. But what limits the space of possible feature orders remains an open question. This paper demonstrates how the concept of entropy establishes a partial ordering of features that both allows for but also constrains language-particular variation. Specifically, a decision tree machine learning algorithm is employed to dynamically impose structure on the hypothesis space of possible feature orders.
Exploring CP-violation in $Y=0$ inert triplet with real singlet

In this article, we examine the Standard Model extended with a $Y=0$ Higgs triplet and a real singlet. We consider the Higgs triplet to be odd under the $Z_2$ symmetry, and hence the lightest stable particle from the inert triplet becomes the dark matter candidate, whereas the real singlet is considered to be even under the $Z_2$ symmetry. A dimension-5 effective term is introduced with the help of a real singlet, which breaks the CP symmetry and gives an additional source of CP-violation in the fermion sector. The phase transition proceeds in two-steps, with the symmetry breaking in the singlet direction occurring first and later leading to the usual electroweak symmetry breaking minima, while electroweak baryogenesis is associated with the second step. The parameters chosen for the electroweak phase transition are found to be consistent with the Planck scale stability and the perturbativity using two-loop $\beta$-functions. The DM mass bound for inert triplet, i.e., 1.2 TeV (below which it is under abundance), also comes out to be consistent with the strongly first-order phase transition, which was not possible solely with inert triplet. The upper bound on the triplet mass comes out to be $\leq 3.8$ TeV, which satisfies the strongly first-order phase transition. This particular benchmark point also satisfies the correct baryon asymmetry of the Universe $(6.13 \times 10^{-11})$, and the gravitational wave spectrum also lies within the detectable frequency range of LISA $(6.978 \times 10^{-4} - 1.690 \times 10^{-2} )$ Hz and BBO $(2.80\times 10^{-3}-1.096)$ Hz experiments.
Quantum Algorithmic Gate-Based Computing: Grover Quantum Search Algorithm Design in Quantum Software Engineering

The difference between classical and quantum algorithms (QA) is following: problem solved by QA is coded in the structure of the quantum operators. Input to QA in this case is always the same. Output of QA says which problem coded. In some sense, give a function to QA to analyze and QA returns its property as an answer without quantitative computing. QA studies qualitative properties of the functions. The core of any QA is a set of unitary quantum operators or quantum gates. In practical representation, quantum gate is a unitary matrix with particular structure. The size of this matrix grows exponentially with an increase in the number of inputs, which significantly limits the QA simulation on a classical computer with von Neumann architecture. Quantum search algorithm (QSA) - models apply for the solution of computer science problems as searching in unstructured data base, quantum cryptography, engineering tasks, control system design, robotics, smart controllers, etc. Grovers algorithm is explained in details along with implementations on a local computer simulator. The presented article describes a practical approach to modeling one of the most famous QA on classical computers, the Grover algorithm.
CyFence: Securing Cyber-physical Controllers Via Trusted Execution Environment

In the last decades, CPSs have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attacks. Since many CPS are used in safety-critical systems, such attacks entail high risks and potential safety harms. Although several defense strategies have been proposed, they rarely exploit the cyber-physical nature of the system. In this work, we exploit the nature of CPS by proposing CyFence, a novel architecture that improves the resilience of closed-loop control systems against cyber-attacks by adding a semantic check, used to confirm that the system is behaving as expected. To ensure the security of the semantic check code, we use the Trusted Execution Environment implemented by modern processors. We evaluate CyFence considering a real-world application, consisting of an active braking digital controller, demonstrating that it can mitigate different types of attacks with a negligible computation overhead.
Improving problem-solving skills with worksheets using the discovery learning model-assisted quick response code

Good teaching materials have an impact on the quality of mathematics learning and can help students become more engaged in their learning and develop their problem-solving skills. It is necessary to develop LKPD utilizing a discovery learning paradigm helped by QR codes in order to boost problem-solving abilities. This study uses a reliable and useful discovery learning paradigm with the use of a QR code to identify the characteristics of LKPD. The Plomp model, which has three steps total—the preparatory stage, the development stage, and the assessment—is used in this particular sort of study, which is called development research. For class VIII of junior high school, LKPD employs a discovery learning methodology with the use of a QR code. The tools employed are validation sheets, observation sheets, questionnaires, interviews, and final exams. The findings demonstrated the validity, applicability, and efficacy of the LKPD for class VIII junior high school students using the discovery learning approach. LKPD practicality was 87%, while LKPD validity was 89%. The average student score on tests measuring their ability to solve mathematical problems was 92%, which indicates the success of the program.
Shift in the ESL Classroom: Effectiveness of Mobile Assisted Language Learning (MALL) in English as a Second Language (ESL) Settings

The advancement of the mobile devices has created several benefits to the humankind, extending them in the field of education as well. Since these devices are generally used by the young generation, it becomes hard to be neglected using in the language learning process. The use of mobile applications for various purposes is commonly experienced in the Sri Lankan context as well. Although frequently used in written and oral discourses, phrasal verbs (PVs) are subject to be excluded if the correct use of them is challenging for the English as a Second Language (ESL) learners. Therefore, this research intends to inspect the efficacy of teaching PVs via one of the commonly used mobile applications, since it is prevalent in the society and is a novel method which is different from the traditional teaching done in the ESL classrooms. Thirty undergraduates were selected and divided into two groups of 15 each. Pre-test was given for both groups first and they were not given prior instructions in the subject. They had to use their prevailing knowledge on PVs to complete the test. For the post-tests, traditional classroom instructions were given to the controlled group within one hour and the experimental group was guided via a selected mobile application. Forty popular PVs were used here. The 1-way ANOVA found that the mean marks of the students of the post-test in the experimental group is significantly higher (p &lt; 0.05) than that of the students in the controlled group. A significant correlation (p&lt; 0.05) was found between the pre-test marks and the post-test marks irrespective of the two methods. Thus, 1-way ANCOVA was also carried out taking pre-test marks as a covariate and found that the adjusted mean marks of the students in the experimental group is significantly higher (p &lt; 0.05) than that of the students in the controlled group., Thus, it is recommended that the use of mobile assisted language learning (MALL) in the English Language Teaching (ELT) for ESL setting is an effective, innovative and a novel resource to use as a method of instruction.
Code-Mixing vs Code-Switching: a Study of Grammatical Perspective Through Code-Switching Varieties

This study highlights the grammatical boundaries of code-mixing and code-switching practices. The data were collected from several Indonesian scholars’ work on the topic of code-mixing and/or code-switching. The data were then analyzed using a descriptive interpretative approach. Many overlapping comprehensions and determinations of code-mixing and code-switching are found in Indonesian scholars’ work. This study concludes that intra-sentential code-switching has no certain differences from codemixing. Therefore, it is classified into code-mixing and code-switching, in which the grammatical boundaries of code-mixing are morpheme, word, and phrase, whereas the code-switching boundaries are clause, sentence, and utterance.&#x0D; Keywords: grammatical boundaries, code-mixing, code-switching, Indonesian works
Artificial Intelligence in Weapon Systems: The Overview

Abstract: Artificial intelligence (AI) has revolutionized modern weapon systems, offering increased speed, accuracy, and efficiency in military operations. However, the use of AI-based weapon systems raises significant ethical, legal, and operational challenges which needs to be fulfilled in development of AI based weapon system. This System have a vast history from guided missiles to autonomous weapon system, which makes its background more innovative. Also, these systems must comply with the principles and rules of IHL to ensure that they are used in a responsible and safe manner and do not cause unnecessary harm to civilians or civilian objects. The involvement of humans in the decision-making process is essential, there are limitations to this involvement that must be addressed through effective human-machine interfaces and other technological solutions It has a unended technological overview which consists of combination of machine learning algorithms, big data analytics, and advanced sensor technologies. Autonomous operation is a key feature of AI-based weapon systems, posing important questions regarding accountability and the risk of unintended consequences. The design of such systems must ensure they adhere to international laws and norms, as well as being reliable and safe. Machine learning algorithms and advanced sensor technologies, such as electro-optical and infrared sensors, are critical components of these systems, allowing them to analyse data and track targets precisely.
An Oversampling Mechanism for Multimajority Datasets using SMOTE and Darwinian Particle Swarm Optimisation

Data skewness continues to be one of the leading factors which adversely impacts the machine learning algorithms performance. An approach to reduce this negative effect of the data variance is to pre-process the former dataset with data level resampling strategies. Resampling strategies have been seen in two forms, oversampling and undersampling. An oversampling strategy is proposed in this article for tackling multiclass imbalanced datasets. This proposed approach optimises the state-of-the-art oversampling technique SMOTE with the Darwinian Particle Swarm Optimization technique. This proposed method DOSMOTE generates synthetic optimised samples for balancing the datasets. This strategy will be more effective on multimajority datasets. An experimental study is performed on peculiar multimajority datasets to measure the effectiveness of the proposed approach. As a result, the proposed method produces promising results when compared to the conventional oversampling strategies.
A new look in the representation of AES like S-box

Abstract— Substitution box plays an essential role in block ciphers as their main non-linear component in the round function, providing confusion. In this paper, it will be proven how the action of any substitution box on every fixed element can be represented through an affine transformation using an invertible matrix in a finite field. Furthermore, a particular way to represent the action of permutations on every element through a modular addition is given. An analysis of the AES substitution box is conducted based on the theoretical results obtained.
EMPOWERING CHILDREN THROUGH CIRCLE TIME: AN INTERVENTION TO PROMOTE LANGUAGE INTERACTION IN PRIMARY ESL CLASSROOM IN MALAYSIA

As Malaysian English curriculum changes in accordance to the needs and demands of education all across the world, so must the teaching of English as the second language (L2) in Malaysian schools. Every year there are many approaches and innovations introduced to the community of educators to cater to these changes and amongst many is the use of Circle Time. This paper investigates the use of Circle Time in supporting ESL pupils’ language interactions in classroom by looking at the types and frequencies of language interactions that occur during lessons. This is to determine if Circle Time is indeed beneficial in supporting second language interaction in the classroom. Classroom observations were conducted on two classes of Year Three which utilized Circle Time. Besides the frequency, the kind of interactions that occurred during Circle Time were also observed using an observation checklist. Lastly, two English teachers were interviewed to find out their perspectives on the implementation. Findings of this study showed that there were trends in the frequency of seven of the types of language interaction that occurred during Circle Time. Besides, the interviewed teachers identified two advantages of Circle Time on their pupils’ interactions and to sum up, Circle Time is a suitable intervention strategy to promote language interactions of English as a Second Language among Malaysian primary schools.
An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM

Deep learning (DL) algorithms have substantially increased research in recognizing day-to-day human activities All methods for recognizing human activities that are found through DL methods will only be useful if they work better in real-time applications. Activities of elderly people need to be monitored to detect any abnormalities in their health and to suggest healthy life style based on their day to day activities. Most of the existing approaches used videos, static photographs for recognizing the activities. Those methods make the individual to feel anxious that they are being monitored. To address this limitation we utilized the cognitive outcomes of DL algorithms and used sensor data as input to the proposed model which is collected from smart home dataset for recognizing elderly people activity, without any interference in their privacy. At early stages human activities the input for human activity recognition by DL models are done using single sensor data which are static and lack in recognizing dynamic and multi sensor data. We propose a DL architecture based on the blend of deep Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) in this research which replaces human intervention by automatically extracting features from multifunctional sensing devices to reliably recognize the activities. During the entire investigation process we utilized Tulum, a benchmark dataset that contains the logs of sensor data. We exhibit that our methodology outperforms by marking its accuracy as 98.76% and F1 score as 0.98.
PENGEMBANGAN MEDIA PEMBELAJARAN MENGGUNAKAN APLIKASI CANVA PADA PEMBELAJARAN TEMATIK TERPADU DI KELAS V SEKOLAH DASAR

This research is motivated by the limited use of attractive and easy-to-understand digital media that can stimulate students in learning. The purpose of this study was to produce learning animated videos using the Canva application for valid and practical integrated thematic learning for class V SD, as well as to determine the validity and practicality of the media being developed. This type of research is development research (R&amp;D). Using a 4D model consisting of several stages (Define, Design, Develop, Dissiminate). This research was only carried out until the development stage (Develop) due to limited time and available funds. The subjects of this study were 3 experts, 1 teacher and 27 students of class V SDN 12 Kampung Durian. The results of research on developing learning media using the Canva application obtained a validity level of 93.75% from the media aspect from the language aspect 95% and from the material aspect 88%. Based on the results of the teacher's response and also the results of the responses of students in research schools in schools it can be seen that learning using learning media the percentage of teacher responses at SDN 12 Kampung durian is 94% while the percentage of student responses is 96.6%. Thus it was concluded that the learning media using the Canva application in integrated thematic learning for class V elementary school had been declared valid and practically used in the learning process. Based on the results of the teacher's response and also the results of the responses of students in research schools in schools it can be seen that learning using learning media the percentage of teacher responses at SDN 12 Kampung durian is 94% while the percentage of student responses is 96.6%. Thus it was concluded that the learning media using the Canva application in integrated thematic learning for class V elementary school had been declared valid and practically used in the learning process. Based on the results of the teacher's response and also the results of the responses of students in research schools in schools it can be seen that learning using learning media the percentage of teacher responses at SDN 12 Kampung durian is 94% while the percentage of student responses is 96.6%. Thus it was concluded that the learning media using the Canva application in integrated thematic learning for class V elementary school had been declared valid and practically used in the learning process
Systematic review of deep learning models in ultrasound tongue imaging for the detection of speech disorders

&lt;p&gt; The purpose of this study is to review the latest developments and challenges in the application of deep learning algorithms for diagnosing Speech Sound Disorders &lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
A Wafer Pre-Alignment Algorithm Based on Weighted Fourier Series Fitting of Circles and Least Squares Fitting of Circles

The wafer pre-aligner is a crucial component in the lithography process to correct the wafer center and notch orientation. To improve the precision and the efficiency of pre-alignment, a new method to calibrate the center and the orientation of a wafer based on the weighted Fourier series fitting of circles (WFC) method and the least squares fitting of circles (LSC) method, respectively, is proposed. The WFC method effectively suppressed the influence of the outliers and had high stability compared with the LSC method when fitted to the center of the circle. While the weight matrix degenerated to the identity matrix, the WFC method degenerated into the Fourier series fitting of circles (FC) method. The fitting efficiency of the FC method is 28% higher than that of the LSC method, and the fitting accuracy of the center of the FC method is the same as that of the LSC method. In addition, the WFC method and the FC method perform better than the LSC method in radius fitting. The pre-alignment simulation results showed that the absolute position accuracy of the wafer was ±2 µm, the absolute direction accuracy was 0.01°, and the total calculation time was less than 3.3 s in our platform.