Reviewerly/exHarmony huggingface dataset:
Social Demographic Factors Associated with Drug Abuse Among Undergraduates in Ekiti State

This study examined social demographic factors associated with drug abuse among undergraduates in Ekiti State. Specifically, the study examined the level of knowledge on drug abuse expressed by university undergraduate students; level of drug abuse among university undergraduate students; and common drugs known as expressed by university undergraduate students. The research design adopted for this study was a descriptive survey. The population of this study consisted of all the students of Ekiti State University, Ado-Ekiti, while the sample was 200 undergraduate students drawn from seven faculties. The respondents were randomly selected from seven faculties. A self-designed constructed questionnaire tagged “Drug Abuse Questionnaire (DAQ)” was used to collect relevant data for the study. The instrument consisted of five sections namely: Sections A, B, C, D, and E. The validity methods used were face and content validity. The items in the Questionnaire were presented to experts in the fields of Tests and Measurement, Social Studies Education and Guidance &amp; Counselling. The reliability of the instrument was estimated through test re-test method. A co-efficient value of 0.88 was obtained which was considered statistically high to make the instrument reliable. The data collected from the questionnaire were analyzed using descriptive and inferential statistics. The findings of the study revealed that majority of the respondents have a high knowledge of drug abuse while only few engage in drug abuse. In addition, peer influence was related to drug abuse among undergraduates but family structure was not related to drug abuse. It was further revealed that male students have higher prevalence of drug abuse than female students despite having similar knowledge of drug abuse. It was recommended among others that discipline should be put in-place by university management to curb drug abuse among undergraduates.
The Use of Diary Writing to Improve The Writing Skills of Eight Grade At MTs S Madinatussalam

This study focuses on problem solving in class and obtains research results that writing diaries can improve students' writing skills. This research was conducted in two cycles and is an action research. The subjects in this study were students of class VIII Mts S Madinatussalam, with a total of 30 students. The instruments used in collecting data are tests and field observations. After implementing diary writing in two types of texts, namely descriptive and recount texts, the researcher then analyzed the collected data, and found that the students' grades gradually increased, especially when writing recount texts. In the first cycle the researcher found two weaknesses; namely the slowness of students submitting their diaries which made researchers late in providing feedback, and the limited vocabulary of students so that they tend to use simple sentences and the same vocabulary. Responding to these weaknesses, researchers used a reward strategy. In addition, the researcher also carried out activities to write works in pairs in the second cycle so that students learned from their friends.
Causality in Machine Learning: Innovating Model Generalization through Inference of Causal Relationships from Observational Data

Extracting causal mechanisms from observational data represents a paradigm shift for machine learning, unlocking more robust generalization capabilities. This quantitative study investigates techniques to infer directed causal graphs from diverse datasets using constraint-based, score-based, and neural structure learning algorithms. Results demonstrate score-based methods for recovering meaningful causal relationships from complex, high-dimensional data across domains including healthcare, finance, and computer vision. The inferred causal graphs exhibit explanatory power and invariance, containing domain-general insights unavailable from statistical correlations alone. Integrating discovered causal relationships shows significant potential to enhance model generalization and accuracy by facilitating accurate extrapolation, increased robustness to distribution shifts, transfer learning, and interpretability. However, performance remains contingent on domain knowledge and dataset biases. Further innovation in causal discovery and rigorous evaluation of generalization improvements is imperative. Overall, equipping machine learning with causal reasoning abilities allows more reliable, adaptable, and trustworthy systems. This research crystallizes the imperative and concrete path toward assimilating causal inference into machine learning. Limitations exist, necessitating ethical and responsible integration. Nonetheless, by elucidating initial integrating techniques, this pioneering study illuminates promising frontiers at the intersection of causality and machine learning toward more powerful intelligible systems.
Human Creative Contribution to AI-Based Output – One Just Can(’t) Get Enough

Abstract The article attempts to clarify the issue of the copyrightability of AI-based output by streamlining the assessment of human creative contribution to the creation process assisted by generative AI systems. It starts with briefly outlining the state of the art of modern generative AI systems contributing to a better understanding of AI. Then the article presents a five-part test to distinguish between sufficient and insufficient human creative participation and contemplates the following authorship scenarios: sole authorship of AI designers or users; their joint authorship; and non-authorship. The article proceeds with applying the test to output produced using Midjourney, a generative AI tool. It concludes that in many cases of using AI to create output human creative participation remains sufficient for copyright protection eligibility. However, there are also developments and circumstances that drive the increase in ‘authorless’ AI-based output. Moreover, depending on the specific circumstances, using even the same AI system may lead to different test results.
Multimodal foundation model (GPT-4) for chest X-ray impression generation: Performance, perception, and evaluation. (Preprint)

<sec> <title>UNSTRUCTURED</title> Exploring the generative capabilities of the multimodal GPT-4, our study uncovered significant differences between radiological assessments and automatic evaluation metrics for chest x-ray impression generation and revealed radiological bias. </sec>
PENGARUH MEDIA STORIGAMI TERHADAP KETERAMPILAN MENYIMAK CERITA FIKSI SISWA KELAS IV DI SD NEGERI 79 PALEMBANG

This study aims to determine the effect of using story-based media on the listening skills of fourth grade students at SDN 79 Palembang. This type of research uses a quantitative approach with a quasi-experimental method and a posttest control group design research design. The population of this study were fourth grade students at SDN 79 Palembang. Samples were taken using a purposive sampling technique using two classes as the experimental group and the control group. The data collection technique in this study is using a test instrument. The data analysis technique was processed by testing the parametric independent samples t-test or t-test with the help of SPSS 25 software. Data analysis showed the sig. (2-tailed) 0.003 &lt;0.05 which means that there is a difference in the average posttest scores in the control class and the experimental class. Based on the results of the t-test analysis, it shows t-count = 3,150 &gt; t-table = 2,000 which means Ho is rejected. So it can be concluded that there is an effect of the use of storytelling media on the skills of listening to fictional stories of fourth grade students at SDN 79 Palembang
Servant Leadership and Authentic Leadership on Hotel Front Liner Service Recovery Performance

This study inspects the effect of organizational characteristics on a service firm's capacity to restore unsatisfied customers' satisfaction over service recovery.That is to say, it studies the likely effect of frontline employees' service recovery performance on the organization's overall performance.To see the impact on the job outcome, the management's commitment to service quality will be measured.Two main theories will be tested together with Psychological Capital and Appraisal to support future results.The theories involved are Servant Leadership (SL) and Authentic Leadership (AL) theory.Since Servant and Authentic leadership have several characteristic comparisons, both are meritorious and lead to positive leadership styles.Servant and authentic are both believed in empowering the employees they serve since the leadership styles possess a high standard of values, and displays compassion and passion for integrity to their followers.It is anticipated that the results would demonstrate that organizational commitment through SL and AL has a significant positive impact on the service recovery performance of frontline workers, as do job satisfaction and recognizing service quality.
Hotel Service Recovery: Management Commitment Through Authentic Leadership Theory

Regrettably, in the last two decades, the problem of poor service recovery was not handled adequately by service firms, consequently, customers are not satisfied with the company's effort toward service recovery, and complainant satisfaction is decreasing.As service recovery performance is referred to, it is the degree of personal assessment of the service that an employee delivers to the customers.Therefore, it is important for this study to link together the authentic leadership theory to be tested with psychological capital to confirm the viability of the job outcome itself; the service recovery performance.A quota sampling will be applied to the population of hotel front liners in the 5 regions in Malaysia (Northern, Central, East Coast, Southern and East Malaysia).Data will be analyzed with the use of the SEM (Smart PLS 3).Expected results would provide to towards stakeholders in the hospitality and tourism industry in Malaysia.Especially after the COVID-19 pandemic, on how the front liner in this business responded to declining customers in pursuing holidays and travelling.
Word classes

Word classes IntroductionIn assigning lexemes to word classes in Nese, I consider their grammatical properties, in particular the distributional properties of the lexemes and the range of morphosyntactic functions they have.This follows Schachter's (1985, p. 6) argument that 'parts-of-speech classes must be distinguished from one another on the basis of a cluster of properties, none of which by itself can be claimed to be a necessary and sufficient condition for assignment to a particular class'.Nese has two open word classes: nouns ( §3.2) and verbs ( §3.3).The latter includes a subclass of stative intransitive verbs, given that there is no distinct adjective class in the language.These two open word classes are distinguished on the basis of several grammatical properties.Other forms belong to the following closed word classes: pronouns ( §3.4), prepositions ( §3.5), adverbs ( §3.6), quantifiers ( §3.7), conjunctions and subordinators ( §3.8), relational classifiers ( §3.9), directionals ( §3.10), demonstratives ( §3.11), negative particles ( §3.12), verbal particles ( §3.13), greetings and leavetakings ( §3.14) and interjections ( §3.15).Although greetings and leave takings are discussed together in §3.14, they do not form a word class.Some lexemes are members of more than one word class and this issue of flexibility of class membership is discussed in §3.16.
Content Validation and Reliability Study of Workplace Solid Waste Segregation Behaviour

One of the causes of environmental pollution is a waste generation, and the fundamental solution for this is waste minimisation through waste segregation.Many studies have been done for the behaviour modification for the waste separation at source, but a lack of study targets the educational institution staff as the respondent.Plus, at the organisational level, less study on the factors influencing the workers to practice green behaviour individually and the tools or instruments readily tested do not cover the organisation and individual perspective.Thus, this research aims to evaluate the validity and reliability of the items in the questionnaire to assess green human resource management, pro-environmental concern and solid waste segregation behavioural intention.Nine experts were involved in the validation study, and 37 university staff responded to the reliability study pre-testing.The content validation was analysed using the content validation index, which is the item content validation index (I-CVI), scale content validation index (S-CVI) and experts agreement (Fleiss Kappa Statistic) (k).The reliability study used the value of Cronbach's alpha (Cronbach's α) as the guideline for the item's reliability.The I-CVI for all the items ranges from 0.78 to 1.00, and the average of each domain is between 0.89 to 1.00 for its relevancy and clarity.The items also show the perfect expert agreement when each domain's average (k) values were between 0.88 to 1.00.The Cronbach's α of all items ranged from 0.87 to 0.97.Thus, all the items generated are validated and reliable for testing.
Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation using a UNet-PWP Deep Learning Model on CT Scan Images

Motivation: It is essential for the diagnosis and treatment of renal cancers to segment kidney tumours precisely and effectively. In medical image segmentation tasks, deep learning models have demonstrated promising results, and the UNet model is widely employed in this field. However, optimising the UNet model for kidney tumour segmentation further can improve its efficacy and deployment feasibility. Related Works: Previous works have explored various techniques to improve the efficiency of deep learning models in medical image segmentation. Image partitioning methods divides the input image into smaller regions, enabling parallel processing and reducing memory requirements. Pruning techniques eliminates redundant or insignificant weights, neurons and connections, resulting in a more compact and efficient model architecture. However, the UNet model architecture and its complexity in the context of kidney tumor segmentation using UNet remains unexplored. Methodology: The proposed methodology consists of adaptive partitioning and weight pruning. Adaptive partitioning divides the UNet model into smaller sub-models, facilitating parallel processing and accelerating inference without compromising segmentation accuracy. Weight pruning techniques remove redundant or less significant weights from the UNet-P model, reducing complexity and improving inference time by eliminating unnecessary computations. The adaptive partitioning and weight pruning processes are seamlessly integrated within the UNet-P architecture, resulting in an optimized model for kidney tumor segmentation. Results: We performed experiments utilising a dataset of KiT 23 CT scan images containing images of kidney malignancies. Compared to the standard UNet model, the optimised UNet-PWP model with adaptive partitioning and weight pruning obtained significant efficiency gains. The adaptive partitioning permitted parallel computation of sub-models, which accelerated inference times. Weight pruning decreased the UNet-PWP model&amp;rsquo;s complexity without compromising segmentation accuracy, thereby enhancing efficiency. The results of our experiments demonstrated the efficacy of the proposed method with 98% accuracy, demonstrating its potential for deployment in health sector.
Emergent Information Processing: Observations, Experiments, and Future Directions

Scientists are gradually becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines ranging from cosmology, physics, across chemistry, biochemistry, and ending in biology. This leads us to the main motivation and simultaneously to the central thesis of this review: “Can we design artificial, massively-parallel, self-organized, emergent, error-resilient, computational environments?” A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. This all together is opening the crucial question whether there is existing a deeper, beyond the Turing machine theoretical description of massively-parallel computing. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python software GoL-N24. The perspective, future directions including applications in robotics and biology of this research are discussed in the light of known information.
Semantic Similarity Loss for Neural Source CodeSummarization

Abstract This paper presents an improved loss function for neural source code summariza-tion. Code summarization is the task of writing natural language descriptions ofsource code. Neural code summarization refers to automated techniques for gen-erating these descriptions using neural networks. Almost all current approachesinvolve neural networks as either standalone models or as part of a pretrainedlarge language models e.g., GPT, Codex, LLaMA. Yet almost all also use acategorical cross-entropy (CCE) loss function for network optimization. Twoproblems with CCE are that 1) it computes loss over each word prediction one-at-a-time, rather than evaluating a whole sentence, and 2) it requires a perfectprediction, leaving no room for partial credit for synonyms. We propose and eval-uate a loss function to alleviate this problem. In essence, we propose to use asemantic similarity metric to calculate loss over the whole output sentence pre-diction per training batch, rather than just loss for each word. We also proposeto combine our loss with traditional CCE for each word, which streamlines thetraining process compared to baselines. We evaluate our approach over severalbaselines and report an improvement in the vast majority of conditions.
New Design of Encryption with Covertext and Reordering

Documents for some entities are confidential and important, so security is required. Encryption with Covertext and Reordering (ECR) is a text-based document security model. ECR uses a random key to generate the ciphertext. The ECR’s random key is selected using the human-generated method. This research aims to increase the level of document security based on the ECR mechanism. This paper proposes a new method by using a random key in a permutated table. The random key is generated automatically by a function. The entropy is used as a measurement of the security level of the encrypted documents. The experimental shows that the permutated table inside the ECR provides better entropy values. It implies a better security level. The use of permutated tables also makes it easier to use ECR to secure documents.
Data Mining Modeling Feasibility Patterns of Graduates Ability With Stakeholder Needs Using Apriori Algorithm

This The speed of information, the accuracy of data, the ease of information services, and accountability are very important reasons for the implementation of the system. Semarang University (USM) is a private university in Semarang that has the most 2 students in Central Java. Based on the 2019 USM tracer data showing horizontal alignment, namely how close the relationship between the field of study and alumni work is, it appears that there is still a discrepancy in the ability of graduates with stakeholders. The Apriori algorithm is the best-known algorithm for finding high-frequency patterns Rules that state associations between attributes are often called affinity analysis or market basket analysis. The use of the Apriori Algorithm in data mining calculations using data from the Semarang University tracer that the limit of the minimum support is 50% and the minimum confidence is 100% so that it forms 4 rules. From the four rules produced that modeling using the Apriori Algorithm can produce several rule formations so that it can provide an evaluation to the University for compiling steps, this can be seen because the resulting rules are different because each graduate relationship with the desired desires and different styles.&#x0D;
Sound analysis and extraction of data from Smart Contract

Abstract With the addition of multiple blockchain platforms in the ecosystem, the Dapp owners need to migrate their smart contracts from one platform to another to remain competitive , cost-effective, and secure. A smart contract is a piece of code that contain logic and data. To migrate a smart contract, whether it’s on the same blockchain platform or a different one, we need both its source code that represents the logic and data which indicate the state of the contract. The source code can be easily set up, but to complete the migration, we have to extract the current state of the contract. In this paper, we have developed an advanced state extraction technique that uses static analysis to analyze the smart contract’s call graph and events, and extracts the entire storage state from the storage trie, along with the proper associations across function calls, enabling users to visualize, manage, and transform the state as desired for migration. The soundness of the extracted state was confirmed using the method of abstract interpretation. Further, the migration adapter is designed that transform extracted state into slot-value pair and migrated it to the target blockchain. Our new approach has allowed us to analyze 14% more smart contracts with the extraction of 15% more data from 67,993 contracts, and migrate some of them to the Polygon test-net.
Spontaneous segregation of visual information between parallel streams of a multi-stream convolutional neural network

Abstract Visual information is processed in hierarchically organized parallel pathways in the primate brain. In lower cortical areas, color information and shape information are processed in a parallel manner, while in higher cortical areas, various types of visual information, such as color, face, animate/inanimate, are processed in a parallel manner. In the present study, the possibility of spontaneous segregation of visual information in parallel streams was examined by constructing a convolutional neural network with parallel architecture in all of the convolutional layers. The results revealed that color information was segregated from shape information in most model instances. Deletion of the color-related stream decreased recognition accuracy in the inanimate category, whereas deletion of the shape-related stream decreased recognition accuracy in the animate category. The results suggest that properties of filters and functions of a stream are spontaneously segregated in parallel streams of neural networks.
RefCap: Image Captioning with Referent Objects Attributes

Abstract In recent years, significant progress has been made in visual-linguistic multi-modality research, leading to advancements in visual comprehension and its applications in computer vision tasks. One fundamental task in visual-linguistic understanding is image captioning, which involves generating human-understandable textual descriptions given an input image. This paper introduces an end-to-end referring expression image captioning model that incorporates supervision of interesting objects. Our model utilizes user-specified object keywords as a prefix to generate specific captions that are relevant to the target object. The model consists of three modules including: i) visual grounding, ii) referring object selection, and iii) image captioning modules. To evaluate its performance, we conducted experiments on the RefCOCO and COCO captioning datasets. The experimental results demonstrate that our proposed method effectively generates meaningful captions aligned with users' specific interests.