Reviewerly/exHarmony huggingface dataset:
Advanced Patient Monitoring System with Diseases Prediction System using Machine Learning

IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.
An Anti-printing Scanning Watermarking Algorithm Based on Fusion Fonts

With the development of informatization and digitization in various industries, paper documents are still one of the most direct and effective means of information recording. Therefore, the protection and traceability of paper documents are particularly important. Due to the shortcomings of traditional anti-printing scanning watermarking algorithms in terms of capacity and stability, we combined the characteristics of character fonts to design an anti-printing scanning watermarking algorithm based on fusion fonts. The watermarking algorithm is divided into two stages. The first stage is to use style transfer and generative confrontation network technology to fuse multiple font features to generate new fusion fonts. The second stage is to use the generated fusion font to replace the original font in the file by encoding to realize the embedding of watermark information. In addition to resisting printing and scanning attacks, this watermarking algorithm also has a greater capacity than traditional anti-printing and scanning watermarks.
Does Bitcoin's Price Affect on the Economic Factors? Cross Country Evidence

In this paper, I aim to identify the relationship between the Bitcoin and the economic factors using the Fully Modified Least Square (FMOLS) estimator.In fact, the Key economic factors identified for the investigation are the exchange rate, the net trade, the consumer price index, inflation, and the interest rates.Therefore, to test the validity of the chosen technique, I conducted the unit root and the co-integration tests of Pedroni (1999Pedroni ( ,2004) ) based on monthly data over the period from January 2015 to December 2019.Actually, the obtained results indicated that, excepting the interest rate, the variables have a strong positive correlation with the Bitcoin, However, I did not find a positive correlation with the Bitcoin.Then, the long-run relationship between the Bitcoin price and the different variables considered in the FMOLS estimate is stronger than the short-run impact since inflation favors the use of the Bitcoin.
A Survey of Context-Aware Recommender Systems: From an Evaluation Perspective

In recent years, context-aware recommender systems (CARSs), which incorporate contextual information to achieve better recommendations, become a hot topic in the domain of recommender systems. Many context-aware recommendation methods have been proposed in the past decades. Some literatures provide survey of the research on CARSs. However, they mainly focus on context-aware recommendation methods and overlook the evaluation of them. The evaluation methods, evaluation properties and datasets of CARSs are different from those of traditional recommender systems where contexts are not considered. In this paper, we provide a review for evaluation of CARSs. We will introduce the basic concepts of CARSs, propose a new dataset partition method for each category of CARSs according to our classification of CARSs, summarize the evaluation method. Then we summarize the evaluation properties that CARSs pays attention to, which are different from the NCARSs. In addition, we also summarize the datasets specifically for CARSs, its applicable CARSs type and its evaluation dimensions and metrics. Based on our review, we draw some conclusions from evaluation perspective and point out future research directions.
Pemanfaatan E-Learning Sebagai Adaptasi Pembelajaran New Normal

This research was conducted to determine the use of e-learning as an adaptation of new normal learning. descriptive qualitative as data analysis with data collection techniques in the form of literature studies, documentation and questionnaires. From the test, through e-learning used properly because by utilizing e-learning it can provide convenience for students and lecturers. Students can easily study anytime anywhere without fear of missing out on the material because they are able to manage and store existing files. From the lecturer's point of view, they are not always the benchmark of learning resources so they have the opportunity to challenge themselves to learn, learning is achieved and lecturers become innovative in improving the presentation of material there and to students
Composite Refactoring: Representations, Characteristics and Effects on Software Projects

Context: code refactoring is a code transformation that aims to improve software quality. A composite refactoring (or, simply, composite) is defined by two or more interrelated refactorings. Developers often apply composite refactorings. Each composite needs to be somehow represented and has its own characteristics (e.g. code scope) as well as its effects on software quality. However, these basic elements of composites are rarely studied in a systematic manner. The lack of systematic knowledge also misguides the design of automated support tools for composites. Researchers have controversial views about basic elements of composite refactorings. Studies suggest composites remove code smells, and other studies mention composites introduce code smells, this is an example of a literature conflict. Objective: in this sense, our study aims analyzing the technical literature of composite refactoring in order to build a conceptual framework of the representation models, characteristics, and the effect of composite refactoring. Method: we then conducted a systematic mapping with 124 primary empirical studies about refactoring. Our systematic mapping summarizes the current knowledge on composites and also presents a conceptual framework of composites. A conceptual framework is a design artifact to characterize a phenomenon. Results: our conceptual framework presents seven representation models, nine characteristics, and thirteen effects of composites. We found out that studies used multidimensional representations, like graphs, to know what refactoring(s) may be suggested. On composite characteristics, studies mentioned developers often finish a composite in a month. Studies do not investigate why composites are applied in several weeks, like a month. We then discussed existing gaps about composites. Studies investigated in-depth the composite effect on internal software quality, e.g, code smells. The composite effect on external software quality is little explored. Conclusion: Our results can motivate future studies to investigate in-depth composite refactoring applications, and improve existing automated support tools for composite refactorings.
On Quasi-Cyclic Codes of Index 1½

Quasi-cyclic codes of index 1½ are considered in this paper.Our main aim is to show how we can obtain generator matrices of such codes.It should be emphasized that generator matrices are not uniquely determined.The method of obtaining generator matrices will be illustrated with examples.
The Impact of Technology on Student Achievement in Schools : The Effect of Classroom

This study emphasizes the use of learning by using classroom has a positive impact can be seen from student learning outcomes which are increasing every day through assignments and quizzes.Students' perceptions of subjects conducted online using the classroom application, namely students feel happy using classroom because it is easy and the teacher/educator is not burdensome by giving a lot of assignments, classroom is flexible, which is easy to access anywhere and anytime, constrained internet access from the absence of a data network or smartphone that is used to support all students for the implementation of e-learning learning.The research method used is the literature study method is a series of activities related to the methods of collecting library data, reading and taking notes, and managing research materials.
Imbalanced Data Generation and Fusion for In-Situ Monitoring of Laser Powder Bed Fusion

As one of the popular metal additive manufacturing (AM) techniques, laser powder bed fusion (LPBF) can produce near net shape products with complex geometries. Unfortunately, the LPBF technology still largely suffer from poor part consistency and process repeatability. This has driven significant research efforts combining process sensing and data-driven models to perform in-situ monitoring for ensuring part quality. However, the collected sensing datasets in actual applications may be imbalanced and making it challenging for in-situ monitoring. In this paper, we propose an imbalanced data generation and fusion approach to achieve in-situ quality monitoring with imbalanced datasets in the LPBF process. Three imbalanced datasets of layer-wise images, acoustic emission (AE) signals, and photodiode signals are first collected and created by the developed multi-sensor in-situ monitoring system. Then, a Generative Adversarial Network (GAN)-based data generation model is proposed to generate high-quality samples of the minority classes. Finally, a deep learning (DL)-based multi-sensor fusion method is established to fuse the three augmented balanced datasets. Results show that the generated high-quality samples can significantly improve the model performance, and the proposed DL-based multi-sensor fusion model outperforms the single-sensor-based quality classification models, providing a possible way for imbalanced quality monitoring in the LPBF process.
The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through

Language models are increasingly becoming popular in AI-powered scientific IR systems.This paper evaluates popular scientific language models in handling (i) short-query texts and (ii) textual neighbors.Our experiments showcase the inability to retrieve relevant documents for a short-query text even under the most relaxed conditions.Additionally, we leverage textual neighbors, generated by small perturbations to the original text, to demonstrate that not all perturbations lead to close neighbors in the embedding space.Further, an exhaustive categorization yields several classes of orthographically and semantically related, partially related and completely unrelated neighbors.Retrieval performance turns out to be more influenced by the surface form rather than the semantics of the text.
Investigation on the Influence of Actuators Excitation Position on Acoustic Performance of Bone Conduction

To study the effect of the actuator excitation position on the performance of bone conduction (BC) hearing aids, the BC sound transmission mechanism was studied. To facilitate this study, a coupled finite element (FE) model of the human head and the ear was built. First, a series of micro-CT images of a human head was used to establish an FE model of the human head, and the reliability of the model was verified by comparing the model-predicted mechanical impedance of the excitation position and the acceleration response of the promontory with the experimental data. Second, an FE model of the human ear, which consists of the ear canal, the middle ear, and the spiral cochlea incorporating the cochlear third windows, was also established and verified. Then, based on these two FE models, we constructed a coupled FE model of the human head and the ear by coupling the corresponding nodes and applied force excitation at different sites on the modelâ€™s head. Finally, through comparative analysis of the dynamic response characteristics of the basilar membrane (BM) in the coupled FE model, the effect of different excitation positions of the actuator on the hearing compensation performance of the BC hearing aid was studied. The results show that the best excitation position for the BC actuator is near the cochlea, especially close to the mastoid. In addition, excitating the human head close to the cochlea would improve the BC performance at low-mid frequencies.
Culturological paradigm of interuniversity interaction

At present, the interaction of universities and other institutional organizations is becoming relevant, it affects the development of the region positively both in economic, social and cultural terms. The article reveals the current trend in collaborations between universities and other organizations, as well as the cultural orientation of the interaction of regional universities. The main forms of relationships include competition and cooperation, and the authors prove the effectiveness of cooperation as the most productive form in which the maximum positive effect can be achieved for all participants in the educational process. The authors, analyzing the current situation, point to the contradictions and problems that regional universities face. In particular, the factors influencing the innovative development of the university are considered, which include academic mobility and internationalization, and the main subjects of these processes are teachers and students. The article presents the example of Ulyanovsk Pedagogical University in cooperation with other regional universities. Within the cooperation, the cultural potential inherent in the developed curricula and taught disciplines is realized, as well as the formation of the necessary competencies in accordance with the requirements of the current state educational standard. In addition, the programs implemented at the university for interaction with other universities in the field of culture and their products show students and the public the importance of the spiritual sphere and the possibility of its practical formation and transfer.
The Effective Use of Semiotics in Marketing Communication to Create Product Relevance Through Naming Strategy

Marketing communication effort employs various tools to garner brand identity and its relevance to its audience to increase sales.However, marketing communication tools such as advertising are often expensive, and other promotional tools are not necessarily beneficial in the market segment that is specification dependent where product from competing vendor offers the same function.As such brand identity is the sole differentiator between vendor in the category.One way to mitigate the issue is to use structural semiotics that puts attribution of the product right in the name of the product or brand itself, creating direct correlation at a glance.In this research, using phenomenological approach on a focus group study, it is found that semiotic use in naming strategy is very much preferred by the audience to easily identifies products that suits their needs.This in turn reduce cost of marketing communication effort but still create product relevance to the target audience.
HOLM: Hallucinating Objects with Language Models for Referring Expression Recognition in Partially-Observed Scenes

AI systems embodied in the physical world face a fundamental challenge of partial observability; operating with only a limited view and knowledge of the environment.This creates challenges when AI systems try to reason about language and its relationship with the environment: objects referred to through language (e.g.giving many instructions) are not immediately visible.Actions by the AI system may be required to bring these objects in view.A good benchmark to study this challenge is Dynamic Referring Expression Recognition (dRER) task where the goal is to find a target location by dynamically adjusting the field of view (FoV) in a partially observed 360 • scenes.In this paper, we introduce HOLM, Hallucinating Objects with Language Models, to address the challenge of partial observability.HOLM uses large pre-trained language models (LMs) to infer object hallucinations for the unobserved part of the environment.Our core intuition is that if a pair of objects coappear in an environment frequently, our usage of language should reflect this fact about the world.Based on this intuition, we prompt language models to extract knowledge about object affinities which gives us a proxy for spatial relationships of objects.Our experiments show that HOLM performs better than the state-of-the-art approaches on two datasets for dRER; allowing to study generalization for both indoor and outdoor settings.
Leveraging Wikipedia article evolution for promotional tone detection

Detecting biased language is useful for a variety of applications, such as identifying hyperpartisan news sources or flagging onesided rhetoric.In this work we introduce WikiEvolve, a dataset for document-level promotional tone detection in English.Unlike previously proposed datasets, it contains seven versions of the same article from Wikipedia, from different points in its revision history; one with promotional tone, and six without it.We adapt the gradient reversal layer framework to encode two article versions simultaneously, and thus leverage the training signal present in the multiple versions.In our experiments, our proposed adaptation of gradient reversal improves the accuracy of four different architectures on both in-domain and outof-domain evaluation.
A novel forgery detection technique for document images

Digital documents play an essential role in the day-to-day life of every once life. The documents are shared and exchanged in routine work. At the same time, protecting the document from fraud is a challenging task. The proposed method addresses document forgery detection. In this technique, first, we perform a document enhancement process by removing the noise like salt and pepper and Gaussian using various filtering techniques. Then the enhancement of the contrast by using Histogram Equalization and Contrast stretching. Finally, the deblurring is used to recover the document from degraded observations. In the second process, feature extraction is performed by extracting the color, grey, and entropy features, then the quality measures like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structure Similarity Index Matrix (SSIM) are also used as features. In addition, we use Optical Character Recognition (OCR) for fetching textual information. The proposed method performs both textual and image authentication. The proposed method achieved good performance for document forgery detection compared to previous techniques in terms of accuracy.
Guided K-best Selection for Semantic Parsing Annotation

Collecting data for conversational semantic parsing is a time-consuming and demanding process.In this paper we consider, given an incomplete dataset with only a small amount of data, how to build an AI-powered humanin-the-loop process to enable efficient data collection.A guided K-best selection process is proposed, which (i) generates a set of possible valid candidates; (ii) allows users to quickly traverse the set and filter incorrect parses; and (iii) asks users to select the correct parse, with minimal modification when necessary.We investigate how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy.In our user study, consisting of five annotators labeling 300 instances each, we find that combining keyword searching, where keywords can be used to query relevant candidates, and keyword suggestion, where representative keywords are automatically generated, enables fast and accurate annotation.
CUET-NLP@DravidianLangTech-ACL2022: Investigating Deep Learning Techniques to Detect Multimodal Troll Memes

With the substantial rise of internet usage, social media has become a powerful communication medium to convey information, opinions, and feelings on various issues.Recently, memes have become a popular way of sharing information on social media.Usually, memes is visuals with text incorporated into them and quickly disseminate hatred and offensive content.Detecting or classifying memes are challenging due to their region-specific interpretation and multimodal nature.This work presents a meme classification technique in Tamil developed by the CUET NLP team under the shared task (DravidianLangTech-ACL2022).Several computational models have been investigated to perform the classification task.This work also explored visual and textual features using VGG16, ResNet50, VGG19, CNN and CNN+LSTM models.Multimodal features are extracted by combining image (VGG16) and text (CNN, LSTM+CNN) characteristics.Results demonstrate that the textual strategy with CNN+LSTM achieved the highest weighted f 1 -score (0.52) and recall (0.57).Moreover, the CNN-Text+VGG16 outperformed the other models concerning the multimodal memes detection by achieving the highest f 1 -score of 0.49, but the LSTM+CNN model allowed the team to achieve 4 th place in the shared task.
Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding

Online alignment in machine translation refers to the task of aligning a target word to a source word when the target sequence has only been partially decoded.Good online alignments facilitate important applications such as lexically constrained translation where userdefined dictionaries are used to inject lexical constraints into the translation model.We propose a novel posterior alignment technique that is truly online in its execution and superior in terms of alignment error rates compared to existing methods.Our proposed inference technique jointly considers alignment and token probabilities in a principled manner and can be seamlessly integrated within existing constrained beam-search decoding algorithms.On five language pairs, including two distant language pairs, we achieve consistent drop in alignment error rates.When deployed on seven lexically constrained translation tasks, we achieve significant improvements in BLEU specifically around the constrained positions.
Research on Lightweight Face Recognition Algorithm

Face recognition has been successfully applied in security, financial verification, and other fields. Face detection experiments was implemented by the multi-task Convolutional Neural Network (MTCNN) and Retinaface under different conditions. According to the results of the two methods on the personal computer, Retinaface get better results in a complex environment conditions. Considering the practical application, Mobilenet0.25 was selected as the Retinaface backbone, Feature Pyramid Network (FPN) was modified to BI-FPN and data cleaning for Wider face. Experiments proved that the improved method of face detection speed up 42% only sacrifice 0.58% accuracy compared with resnet50 as the backbone, the recognition accuracy over 2% in Wider face compared with Mobilenet0.25 as the backbone, approximate to the state-of -the-art method while increased the speed of detection.