Reviewerly/exHarmony huggingface dataset:
Green City Landscape Design and Planning Based on GIS and Analytic Hierarchy Process Model

The spiritual connotation of contemporary cities has gradually become rich and colorful, and the concept of a green city has also been accepted by the public. People living in cities have higher and higher requirements for landscape, but traditional landscape design cannot achieve efficient and accurate planning. Therefore, the old design method, which cannot meet the complex and changeable urban needs and cannot keep up with the development and changes of the times, is doomed to be eliminated and replaced. This study introduces a GIS system and uses an AHP method to assist landscape design. Through rational resource utilization of topography, vegetation growth, population density, and other conditions, the city can be green and environmentally friendly, reduce energy consumption, and meet people's concept of healthy life. The experimental results show that: (1) through the GIS system, a multidecision model is constructed to choose a reasonable location for the garden. Based on the opinions and scores of all parties, the B place with a score as high as 8.97 was selected. (2) Using comprehensive landscape sensitivity to divide four levels of landscape land-use value and looking for the best landscape vision, it is divided into five scenic functional areas and 12 scenic spots. SPEI and NDVI were used to select cultivated plants and landscapes. (3) Route multifactor comprehensive model is designed to plan garden route. The coincidence degree between the model and the actual road condition is about 87.62%. Citizen satisfaction is between 7 and 9 points, and the comprehensive evaluation is high. (4) The evaluation system of landscape architecture, in which both B-level index and C-level index are higher than 7 points is constructed, which is in line with the expectations of the public and experts and convenient for feedback correction. The method based on this study is feasible and can be further optimized.
Big Data System for Dragon Boat Rowing Action Training Based on Multidimensional Stereo Vision

With the rapid advancement of artificial intelligence technology and the widespread use of sensing technology in education, human-computer interaction teaching has gradually developed in sports and education. Traditionally, teachers explain and demonstrate the fundamentals of movements first, then organize exercises, and students gradually consolidate technical movements through repetition. This process requires teachers to repeatedly explain, such that students can develop movement concepts, and to assist students in correcting their movements through practice. Eventually, students can master the dragon boat’s paddling movements. Teachers frequently struggle to observe all of their students’ movements and are therefore unable to correct them in a timely and effective manner. To address the aforementioned issues, this paper proposes a big data system for multidimensional stereo vision training in dragon boat paddling action. The stock price action recognition of dragon boat paddles and the movement of students’ dragon boat paddles are realized through the multidimensional fusion of attention mechanism and spatiotemporal graph convolution. Make judgments to more effectively guide and train students’ paddling movements.
The impact of Social Media on Employee Productivity at the Workplace

Social media has become a familiar reality in many institutions and organizations, as it is considered one of the most important means of personal and organizational communication. Despite this, there are many challenges that organizations face when using social media among employees, for example: disrupting their productivity. This study aimed to identify the types of social media that are used by employees during their work and to know the extent of their impact on their productivity. A systematic literature review approach was followed. By reviewing and analyzing 37 articles and studies, the study concluded that social media, which can be categorized into social network, messages, email, forums, blogs, Wikis and many more, has become a common communication method for employees to exchange knowledge and collaborate among colleagues in the organization. By presenting and analyzing the previous literature related to the subject of the study, most of the primary studies demonstrated that the use of social media is beneficial to organizational and personal productivity. However, some studies have shown negative effects of social media use in the workplace. Most studies are not able to explain the actual causality of the employment factor that actually affects employee productivity in the workplace because they present their findings based on cross-sectional data. Therefore, this review can contribute to enriching the literature and serve as a basis for future studies concerned with determining the causality of the use of social media on employee productivity. Accordingly, this review proposed the intervention model for Internet addiction as a tool through which the causality of the use factor that contributes to the adverse effects of the organization's productivity and intervention can be addressed.
Video Captioning Using Neural Networks

Abstract: Researchers in the fields of computer vision and natural language processing have been concentrating their efforts in recent years on automatically developing natural language descriptions for videos. Although video comprehension has a variety of applications, such as video retrieval and indexing, video captioning is a difficult topic to master due to the complex and diverse nature of video content. Understanding the relationship between video content and natural language sentences, on the other hand, is still a work in progress, and several approaches for improved video analysis are being developed. Because of their superior performance and high-speed computing capabilities, deep learning approaches have shifted their focus to video processing. This research aims at the end-to-end structure of a deep learning based encoder-decoder network for creating natural language descriptions for video sequences. The use of a CNN-RNN model paired with beam search to generate captions for the MSVD dataset is explored in this study. We have compared the results with beam search and greedy search approach. The generated captions from this model is generally grammatically incorrect. Our paper focuses on improving those grammatical errors using encoder-decoder model. Grammatical errors include spelling mistakes, incorrect use of articles, prepositions, pronouns, nouns, etc or even poor sentence construction. Using beam search for k=3, the captions generated by our algorithm get a BLEU score of 0.72. After passing the generated captions through a grammar error correction mechanism, the results improve to a BLEU score of 0.76. The results increased by 5.55% after grammar correction. The blue score reduces as the value of k decreases, but the time it takes to generate captions decreases as well. Index Terms: Video captioning, end-to-end structure, MSVD dataset, encoder-decoder model, beam search, grammar correction
CONTRIBUTION OF SUPER RESOLUTION TO 3D RECONSTRUCTION FROM PAIRS OF SATELLITE IMAGES

Abstract. The photogrammetric 3D stereo reconstruction from pairs of strereo images is rising interest in the past few years in space field downstream. Nowadays, it is conceivable that a large production of DSMs from satellite images can become the primary source of 3D information on a global scale. However, in urban areas, DSMs produced with current technology suffer from poor quality. Indeed, even using very high resolution (VHR) images, there is too little information to generate disparity maps that reproduce very well defined shaped objects such as buildings.To address this issue, one solution may be to artificially increase image resolution beyond the sensor limits. Super resolution (SR) algorithms are designed to recover high frequencies, introducing significant information in a scene characterized by strong and frequent discontinuities such as a city. State-of-the-art methods relying on Deep Learning have shown remarkable results in this sense. The aim of this work is therefore to assess the contribution of single image SR Deep Learning techniques to the stereo matching and DSMs generation in an urban context, highlighting potential advantages and limitations that can show up when introducing such a technology in a multi-view stereo pipeline. The proposed contributions are: a methodology for super resolution of VHR data that takes into account realistic simulation of a satellite product; a testbed for the evaluation of the impact of super resolution on 3D photogrammetric reconstruction; a local analysis of the consequences of deep learning SR of VHR images on stereo matching.
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience; while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision.
How Abusive User Behavior and Emotion Expression via Light Affects the Perception of Conversational Agents

The negative consequences of abusive user behavior towards (female) conversational agents (CAs) are widely discussed.This online experiment (N = 195) examined the impact of observing such behavior and related emotional expressions by CAs using light to foster observers' empathy towards CAs.We therefore used four different emotion expressions (neutral, joy, anger, sorrow) and two contrary user reactions to a misunderstanding (forgiving, abusive).Participants observed an interaction between Alexa and a user and evaluated the emotional state and perception of Alexa.Results indicate that abusive behavior cause a more negative perception of CAs and emotion expression via light can evoke empathetic reactions.This was stronger for negative emotions and when a congruent interaction context was present.Evoking empathy in the user via emotion expression is a potential solution to inhibit abusive behavior without using submissive verbal expressions that could be problematic in terms of gender stereotypes.
Cybercrime under the New Iraqi Draft Cybercrime Law

Notwithstanding the big role of contemporary technologies, they are not without negatives, containing the emergence of different and modern forms of offences. In addition, determining the legal nature of these offences cannot be completely based on the general rules in criminal legislation, due to the inability of the current penal texts or their failure to apply to modern forms of these offences. In addition, the need for special procedural provisions is occasionally different from the provisions that can be used in traditional offences, particularly those related to criminal evidence. The absence of a special act dealing with electronic offences leads to the widening of the judge’s authority to interpret penal texts and this violates the principle of legality, which obligatory the intervention of the Iraqi criminal legislator to legislate a special act to address these offences. Providing individuals with legislative protection from misuse of these devices protects the interests of individuals, and ensures a balance between the individual's interest in using these technologies and their interest in protecting their lives, properties and private lives from misusing them. However, this act, when it was first to read in the Iraqi parliament in 2011, faced many criticisms and objections that necessitated amendments to its draft before re-discussion and approval, so we will try in our research to diagnose the most important weaknesses in it and propose its correction to meet the requirements of justice.
Towards a Progression-Aware Autonomous Dialogue Agent

Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios spanning a diverse set of tasks, from general chit-chat to focused goal-oriented discourse.While these agents excel at generating high-quality responses that are relevant to prior context, they suffer from a lack of awareness of the overall direction in which the conversation is headed, and the likelihood of task success inherent therein.Thus, we propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes, and use this signal to inform planning for subsequent responses.Our framework is composed of three key elements: (1) the notion of a "global" dialogue state (GDS) space, (2) a task-specific progression function (PF) computed in terms of a conversation's trajectory through this space, and (3) a planning mechanism based on dialogue rollouts by which an agent may use progression signals to select its next response.
A Study to Examine the Relationship between English Language Proficiency and Academic Achievement of Students in Higher Education Institutions

English language proficiency is an important indicator for better academic achievement of students, where the English is second language. The current study aims to identify the relationship between English language proficiency and the academic performance of students enrolled in higher education institutions. The data was collected from three hundred respondents enrolled in undergraduate programs in two higher education institutions. The data was collected through an English proficiency test developed by the British council. The data was analyzed through descriptive statistics and correlation analysis, the results indicate that there is a significant positive relationship between language proficiency and academic performance. Thus led to the recommendation to arrange more trainings/ courses for language proficiency of students at the undergraduate level.
Applying Artificial Intelligence to create risk stratification visualization for underserved patients to improve population health in a community health setting

Abstract Current healthcare visualizations utilize unstructured Electronic Health Record (EHR) data that lack user requirement analysis for intuitive visualizations. Since clinicians are the end-users, it is crucial to consider end users’ input to improve clinical workflow efficiency. In this paper, we developed a user-centered design through cognitive task analysis (CTA) to visualize unstructured EHR data using artificial intelligence (AI) and natural language processing (NLP). The research team conducted CTA with 8 clinicians. The interviews were transcribed, and a content analysis was performed. Themes were coded, and user requirements helped us to understand the clinical workflow. The CTA resulted in 5 different themes: 1) Gathering patient information, 2) Filtering and searching for necessary information, 3) Subjective, objective assessment, plan of the patient, 4) Visualization of unstructured EHR data, and 5) Progression of trends and comparisons in patients. The intuitive visualization dashboard utilizing unstructured EHR population data was successfully developed. Design elements included an interactive dashboard with a snapshot and basic information for all patients, filter and search keywords, and visualizations of patient trends and lab results through graphs. Finally, a system usability scale (SUS) survey was completed to assess the usability of the dashboard by 20 participants. The completed 20 SUS surveys resulted in an average score of 80.9, which concluded that the platform had a high usability. The health analytics dashboard demonstrated unstructured data containing diverse information that can support identifying underserved populations, enhance workflow efficiency and create intuitive design interface.
On Equivalence of Anomaly Detection Algorithms

In most domains, anomaly detection is typically cast as an unsupervised learning problem because of the infeasibility of labeling large datasets. In this setup, the evaluation and comparison of different anomaly detection algorithms is difficult. Although some work has been published in this field, they fail to account that different algorithms can detect different kinds of anomalies. More precisely, the literature on this topic has focused on defining criteria to determine which algorithm is better, while ignoring the fact that such criteria are meaningful only if the algorithms being compared are detecting the same kind of anomalies. Therefore, in this article, we propose an equivalence criterion for anomaly detection algorithms that measures to what degree two anomaly detection algorithms detect the same kind of anomalies. First, we lay out a set of desirable properties that such an equivalence criterion should have and why; second, we propose Gaussian Equivalence Criterion (GEC) as equivalence criterion and show mathematically that it has the desirable properties previously mentioned. Finally, we empirically validate these properties using a simulated and a real-world dataset. For the real-world dataset, we show how GEC can provide insight about the anomaly detection algorithms as well as the dataset.
CAPTCHA Robustness- AI Approach Using to Web Security

Human verification is important to avoid spamming over the internet. Internet has grown tremendously in the last decade. Introduction to preference personalization make the users experience and attract more people. This has deemed it necessary to verify that some of the actions are carried out by a human rather than a computer program to generation of invalid traffic and prevent fraudulence. Methods like Captcha and ReCaptcha have extensively used to overcome this challenge but with the advancements in machine leaning and artificial intelligence, these techniques have started to become obsolete. So, to address this issue we are proposing a phase based human verification process using a combination of neural network and machine learning.
A mixture of Noise Image Denoising using Sevenlets Wavelet Techniques

The noise has always been present in digital images when coding, image acquisition, transmission and processing steps has often corrupted by noise. Noise is challenging to confiscate from the digital images without the noise model’s prior information preserving edges. That is why the assessment of noise models is essential in the revise of image denoising techniques. A novel approach to improve the performance of an image’s quality and visual perception must be noise-free. The essential features like edge details should be retained as much as possible due to the increased traffic caused by multimedia information and digitized form of representation of images. This research articulates a brief general fundamental proposal of the noise model. The input image has debased with different noise probability density of Gaussian (G), Speckle (S), Salt and Pepper (SP) noise and a mixture of noise (G + S + SP). The Wavelet technique’s methodology using Sevenlets wavelet are Haar, Daubechies, Coiflets, Symlets, Discrete Meyer, Biorthogonal and Reverse Biorthogonal input Lena standard image has decomposed using Discrete Wavelet Transform (DWT). The decomposition process, as accomplished by discriminating the input image with lower and higher image coefficients. Filtering techniques are employed to deplete the noise present in an image. Hence the quantitative investigation of noise model at hard and soft thresholding is analyzed, improving image quality by increasing PSNR and decreasing MSE to have better performance.&#x0D; HIGHLIGHTS&#x0D; &#x0D; The images may be corrupted randomly in the transmission line; it is significant to retrieve exactly without loss of data; it is a challenging task. The images are examined with a different mixture of noises with corrupting at various percentage levels. In transmission channels the data will be corrupted, the primary aim is to retrieve actual data.&#x0D; For the testing purpose Gaussian (G), Speckle (S), and Salt &amp; Pepper (SP) noise added to the Lena image have been corrupted and denoised. Denoising using methodology Discrete Wavelet Transform (DWT), to obtain better clarity image quality furthermore noise is filtered using Weiner and Median Filters.&#x0D; The mixture of noises of 0.1, 1, and 10 % density noise (G+S) (Gaussian (G) pulse Speckle (S)), G+S+SP (Gaussian (G) plus Speckle (S) pulse Salt &amp; Pepper (SP)) added to the Lena image and denoised, have obtained improved denoised images comparing to previous research results.&#x0D;
Developing a Realistic Learning Approach on Mobile-based Apps: An Ethnomathematics Tedhak Siten

Realistic mathematics learning by utilizing culture as well as integrating technology can be used as an ideal starting point for learning. This study was intended to develop a mobile learning-based mathematics device with a realistic learning approach in the context of the Tedhak Siten ceremony to improve students' critical thinking skills. This type of research was development research that refers to the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The study used the mobile learning application, the pretest, and post-test questions, in accordance with the expert validation questionnaire, the media expert validation questionnaire (3 experts), and the student response questionnaire from 32 students. The results revealed that the validity aspect obtained a good category from the media and material expert validator. It obtained a good category from the media expert validators and material experts. It turned out that the media expert's score was 58 and the material expert's 192. Both scores were in a Good category, thus the application was declared valid to be used. Furthermore, the practical aspect of students as users obtained a total score of 2055 and was included in the good category. The effectiveness aspect obtained a significant increase in pretest to posttest scores. It was obtained that the increase in the results of the pretest to the results of the posttest was 24.46. Since there is an increase in testing, it can be concluded that the mobile learning developed is effectively used for learning mathematics. Based on the results, mobile learning has met all the product quality criteria, namely valid, practical, and effective. Therefore, mobile learning with a realistic approach to mathematics using the Tedhak Siten ceremony can improve students' critical thinking skills and can be used for learning.