Reviewerly/exHarmony huggingface dataset:
Deformation Monitoring Method Based on Adaptive Segmentation and Correlation Matching

Deformation measurement is an important method in the monitoring of structural engineering safety. Most of the traditional measuring methods require humans to conduct surveys and field tests, which is complicated to operate, and the uncertainty of measurement accuracy is difficult to overcome. To tackle with this problem, this paper designs an automatic monitoring system for deformation and displacement based on adaptive segmentation and correlation matching method. By fixed-point monitoring technology, real-time images of bridges/buildings are obtained, and the coordinates of targets are dynamically obtained and converted into deformation data to achieve high-precision non-contact real-time measurement, based on computer vision and target positioning techniques. According to our experiments, the deformation and displacement monitoring system achieves the purpose of all-weather monitoring of structure health.
Heterogeneous distribution of water in wet mantle plume beneath Changbaishan, NE China: constraints from machine learning

The genesis of the Cenozoic intraplate basalts in Changbaishan area is considered to be related to a low-velocity zone in the underlying mantle.Previous studies suggested that this low-velocity zone corresponds to a wet mantle plume which originated from mantle transition zone.However, the origin and distribution of water in the wet mantle plume still remain unclear.In this study, the new approach of machine learning is used to provide a perspective on this issue.The major and trace element compositions of island arc basalts (IABs) and ocean island basalts (OIBs) are collated from GEOROC as training data.Based on the support vector machine (SVM) model, a hyperplane in high-dimensional space for binary classification is established.The signed distance of each sample to the hyperplane (i.e. the greater the distance is, the more similar the sample is to each class), is taken as the output value of the model.Positive values represent a similarity to IABs, and vice versa.Then we apply the trained SVM model to basalts in Changbaishan area.The predicted values of samples are close to IABs, indicating that the water in the mantle source of basalts in Changbaishan area is probably from the subducted Pacific slab.More importantly, the predicted values of samples display a negative relationship with their distances to the center of Changbaishan.That is to say, the water content in the mantle source of basalts is decreasing from the center of Changbaishan area to the edge.This finding further confirms the existence of the wet mantle plume beneath Changbaishan and indicates the geographical characteristics of water distribution in the wet mantle plume.
Computing with Matrices and Vectors

This chapter is about numerical linear algebra, that is, matrix computations. Numerical computations with matrices (and vectors) are central to a great many algorithms, so there has been a great deal of work on this topic. We can only scratch the surface here, but you should feel free to use this as the starting point for finding the methods and analysis most appropriate for your application(s).
Implementasi Myoware Sensor Otot untuk Alat Bantu Terapi Penderita Drop hand dan Pasca Stroke

The manufacture of therapeutic aids for patients with drop hand and post-stroke controlled by a microcontroller has been carried out. The objectives of this study are (a) applying muscle sensor myoware to make therapeutic aids for patients with hand drop and post-stroke controlled by a microcontroller and (b) measuring the performance of the device. The research method starts from making tools, and measuring tool performance. The results of measuring the performance of the tool are shown by the myoware muscle sensor readings, when the muscle contracts the voltage is 4.8V and the ADC is 984. The sensor accuracy level is MAX30100 for 99% oxygen readings based on the sensor measurement results compared to a medical pulse oximeter. The measurement results are displayed on the OLED LCD. Overall the tool can function well. Suggestions for further research, can be added data communication based on Internet of Things and machine learning for training motion methods.
Bengali paper classification using ensemble machine learning algorithms

Text classification is one of the most challenging problems in natural language processing (NLP). Language models are at the heart of NLP. The ability to represent texts as numbers has given rise to many NLP tasks, for example, text categorisation, translation, and summarisation. Unfortunately, NLP for Bengali texts has not reached the state-of-art level of other Languages like English yet, mostly due to the scarcity of resources and the complexities seen in Bengali grammar. Therefore, not much work has been done in this field. In this paper, we have studied one of the word embedding methods, Word2vec, based on continuous bag of words (CBOW) with several ensemble machine learning algorithms, e.g., Adaptive Boosting Classifiers, Light Gradient Boosting Machine, XGboost, and random forest classifiers (RFC). The model is trained on a large corpus of Bengali newspapers of a considerable size that has 99283949 words and 8284804 sentences in 392772 documents. In our experiment, Word2vec CBOW model with XGboost algorithm performed much better than other models and achieved 92.24% accuracy.
Conclusions and Recommendations

The Wajid members showed various facies, texture, sorting, and depositional environments, including fluvial braided stream and shallow marine environments. Sandstone is quartz arenite and subarkose composition, mainly of transitional to continental tectonic setting. Porosity is mostly intergranular, ranges from 12 to 40%, and reaches a minimum of 1%. The poor to moderate relation or correlation between porosity and permeability and other textural parameters may be attributed to facies changes, depositional environments, stratigraphic architecture, and diagenesis. Porosity values follow Gaussian statistical models; however, permeability values for the same four profiles revealed positively skewed statistical distributions. Variogram models of porosity and log-transformed permeability in horizontal and vertical directions generally follow the simple spherical model with no detected trend. However, most models reflect slight cyclicity patterns due to either lateral and vertical depositional and diagenetic variations at the outcrop scale. Almost all models show geometrical anisotropies between horizontal and vertical directions. Exceptions are variogram models of the Lower Dibsiyah outcrop and the vertical permeability model of the Khusayyayn outcrop, which show zonal anisotropy due to well-identified layering in the vertical direction. The geological (statistical and geostatistical) modeling models developed in this research are expected to add value. They significantly contribute to a better understanding Wajid aquifer by modeling its hydraulic parameters in its outcrops and determining the controls on aquifer quality and architecture.
Puzzle-Mopsi: A location-puzzle game

&lt;abstract&gt; &lt;p&gt;This paper presents a new class of games: location puzzle games. It combines puzzle games with the use of the geographical location. The game class is closely related to location-based games except that no physical movement in the real world is needed as in most mobile location-based games. For example, we present a game called Puzzle-Mopsi, which asks users to match a given set of images with the locations shown on the map. In addition to local knowledge, the game requires logical skills as the number of possible matches grows exponentially with the number of images. Small-scale experiments show that the players found the game interesting and that the difficulty increases with the number of targets and decreases with the player's familiarity with the area.&lt;/p&gt; &lt;/abstract&gt;
Children – Gamification – Climate Literacy

The author shares his ideas and experience of approbation of educational model “child – play – climate literacy (competence)” in 6 – 7 year old children in the process of management and guidance of games for the formation of smart growth; proposes the design of pedagogical situations on topics that form climate competence in 6 – 7 year old children; shares conclusions and generalizations of the project.
Secure Sampling with Sublinear Communication

Random sampling from specified distributions is an important tool with wide applications for analysis of large-scale data. In this paper we study how to randomly sample when the distribution is partitioned among two parties’ private inputs. Of course, a trivial solution is to have one party send a (possibly encrypted) description of its weights to the other party who can then sample over the entire distribution (possibly using homomorphic encryption). However, this approach requires communication that is linear in the input size which is prohibitively expensive in many settings. In this paper, we investigate secure 2-party sampling with sublinear communication for many standard distributions. We develop protocols for $$L_1$$ , and $$L_2$$ sampling. Additionally, we investigate the feasibility of sublinear product sampling, showing impossibility for the general problem and showing a protocol for a restricted case of the problem. We additionally show how such product sampling can be used to instantiate a sublinear communication 2-party exponential mechanism for differentially-private data release.
Simulation-Based Algorithms

In this chapter, we consider the general problem of reinforcement learning in dynamic environments. Up to now, we have only examined a solution method for bandit problems, which are only a special case.
Inverted N-Type Lightweight Network Based on Back Projection for ImageSuper-Resolution Reconstruction

Aiming at the problem that the super-resolution reconstruction algorithms based on deep learning increase the complexity of the network model and cause a large number of parameters by increasing the depth of neural network to improve the super-resolution reconstruction performance, an inverted N-type lightweight network based on back projection for image super-resolution reconstruction is proposed. First, the initial convolutional block is used to extract the shallow features of low-resolution images. Secondly, the deep features of low-resolution images are extracted through two rounds of gradual model compression based on the inverted N-type network of back projection and two rounds of gradual model restoration of the inverted N-type network. Then, the extracted shallow features and deep features are combined by global residual learning and scaled up to the desired output reconstructed image size by using the upsampling module. Finally, the reconstruction module is used to reconstruct the super-resolution image. The results of experiments on the Set5, Set14, BSDS100 and Urban100 testing sets show that the proposed algorithm has a lighter network structure than other algorithms, and the reconstructed super-resolution images not only have higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), but also have better visual effects.
Face Recognition Based on Inverted Residual Network in Complex Environment of Mine

The recognition of personnel entering and leaving the mine is an important link to ensure safe production. As an effective identity recognition technology, face recognition has been widely and deeply studied while facing the problem that the recognition rate is not high in the complex and harsh environment of mine such as facial expressions, pose variation, and low-resolution of face images. For effectively improving the face recognition rate of miners in uneven illumination environment, a face recognition method based on inverted residual network is proposed. In this method, through the optimization of the activation function, the amount of calculation can be greatly reduced while keeping the almost equivalent performance. And by the fusion of inverted residual network, the problem of partial feature information loss in face image recognition model training is effectively solved, which greatly improves the accuracy of recognition. The experimental results show that the accuracy of the inverted residual face recognition model is 81.4%, which is 5.7% higher than the residual network algorithm with additional 4.3% of time overhead, and 9.9% higher than the MTCNN model with only the 1/13 recognition time of MTCNN.
PENGEMBANGAN MODUL ELEKTRONIK RANCANGAN PENELITIAN PADA S1 PENDIDIKAN TEKNIK BANGUNAN FT UNJ

The purpose of this research is to develop teaching material regarding research design in the Electronic Module-based Research Methodology course which consists of problems, theoretical basis, hypothesis submission, research methods and to find out the value of the effectiveness of electronic module teaching materials through limited distribution. This study uses a research and development research method (R&amp;D) with the Thiagarajan 4D model. The results of the validation by material experts were 0,94 in the very high category, while the results of the validation by the media experts were 0,89 in the very high category. The limited trial results showed an increase in the average value of the pre test by 62,45 and post test by 78,53. Therefore, it can be concluded that the electronic module teaching materials for this electronic module can increase understanding in terms of material and are declared fit for use in learning.
Construction of a 3D Model of Kidney Using Visual SLAM and Deep Learning with Robotic Ultrasound

Ultrasonography is less invasive and safer than MRI or CT. However, image acquisition is dependent on the skill of the probe operator, and it is difficult for an untrained examiner to understand the three-dimensional structure of the organ. For the above reason, we use a robot to acquire ultrasound images and estimate the position of the probe using Visual SLAM. The obtained images are segmented and combined with position information to construct a three-dimensional model of the organ. In this study, the right kidney of the phantom was used as the target, and the results of position estimation and segmentation accuracy were high. The model of the right kidney was constructed based on these results.
FPGA Implementation of an Image Scalar Method for Multimedia Applications

Scaling images is a method used in a variety of image processing applications. This paper presents a method for an adaptive edge improvement approach for 2-D image scaling applications. Adaptive scaling techniques are used on a portion of the pixels in a picture. The proposed method for scaling images is made up of an edge detector, a spatial filter with low complexity, and bilinear interpolation. Interpolated pictures become smooth and lose edge information due to bilinear interpolation, which determines pixel intensity by averaging the four closest pixels. The objective of the filter is to highlight the blurriness and distortion of the bilinear interpolation generated edges. According to the study, edges are maintained effectively and interpolation distortions are reduced. Comparing the PSNR values discovered by various approaches demonstrates the ad vantages of this strategy.
Analisis Psikologi Sastra Dalam Novel Sin: God Hates The Sin, Not The Sinner Karya Faradita

Novel is a work of fiction that tells the life of a character. Therefore, one of the important elements in the novel is the character. This study aims to describe the psychology of the main and additional characters in the SIN novel "God hates the sin, not the sinner" by Faradita. Literary psychology is a text analysis by considering the relevance and role of psychological studies. The psychological theory used is the theory of Behaviorism described by Skinner. This research is a type of qualitative research with descriptive method. The data source is in the form of dialogue and narration in the sin novel "god hates the sin, not the sinner". The research data are in the form of dialogues and narratives containing literary psychology. Data was collected using the read and note method. The novel's story begins with the meeting of Metta and Raga at an entertainment venue. Raga who saves Metta from his ex-lover's evil plan. Because of this meeting, Metta and Raga establish a relationship and there is a conflict between the two of them so that their relationship cannot be maintained. Based on data analysis, the research results obtained are the psychology of the main character, namely; 1) stimulus from the school environment, 2) family environment, and 3) friendship environment and 1) arrogant response, 2) not understanding other people's circumstances, and 3) difficulty regulating feelings. The two additional psychology characters are; 1) a stimulus from the family environment and 2) a friendly environment and 1) a response to feeling like a puppet of his father and 2) a sense of disapproval.
Syntax

Syntax, in a broad sense, concerns what programs look like without considering their meaning. This is closely related to the grammars of natural languages: These mandate or describe how to correctly construct texts in a language, but do not describe what these texts mean.
Nilai Kepedulian Tokoh dalam Novel Mimpi Anak Papua, Kajian Sosiologi Sastra

The value of caring is aimed at providing solutions for other people and communities in need. This study aims to describe Nilia's concern for characters in the novel Mimpi Anak Papua by Jackie Ambadar and Hendra G Lukito, a study of the sociology of literature. This research is a descriptive study using the sociology of literature approach. The subject of this research is the novel Mimpi Anak Papua by Jackie Ambadar and Hendra G Lukito. This research is focused on issues related to the value of caring figures with the sociology of literature study. The results of this study indicate that the value of caring for characters in the novel Mimpi Anak Papua has four types, namely the first, socio-cultural concern in human relationships has a variant of preserving local culture, the second, self-care in human relations has a variant of helping others sincerely, which the third concern for others in human relations has a variant of accepting people who have different races and cultures with grace, helping each other where friends who need help and sharing experiences to heal friends who are sick, and the fourth is a concern for fellow humans in giving attention and affection has a variant of parental affection for children, child affection for parents and affection between friends. Based on the results of research in the novel Mimpi Anak Papua, several conclusions can be drawn that the value of caring has three types, the first is the value of socio-cultural care in human relations which has a variant in the form of preserving local culture, the second is the value of caring for oneself in human relations has a variant of helping others by ikhlas, the third value of caring for others in human relationships has a variant in the form of accepting people who have different races and cultures with grace, helping each other where friends who need help, sharing experiences to heal friends who are sick and the four values ​​of caring for fellow humans in giving attention and affection have variants of parental affection for children, cases of children's affection for parents and affection between friends.
CODING WITH PYTHON AT 6TH GRADE. CREATE ANIMATED OBJECTS. KEYBOARD OBJECT MOVEMENT CONTROL

The paper presents an example lesson plan for teaching the school subject Computer Modelling and ICT at 6th grade. The focus is on coding with Python. Ways of creating animated objects are presented. The control of the movement of an object from the computer keyboard is programmed. Keywords: computer modelling, animation, keyboard control, coding, Python.
Thinking and Feeling Cognitive Security?

Cognitive computing is a deliberate transdisciplinary approach to computing whose goal is to mimic human thought, feeling, and decision-making capacity using self-aware computation. The term “self-aware” as related to consciousness, however, has some features that are poorly understood and this serves to constrain the rapid emergence of purely cognitive technology. Cognitive security draws upon many principles underlying cognitive computing, while extending the traditional notions of cybersecurity to include the perplexing problem of misinformation disinformation. Cognitive security has the potential to reinforce edge device user authentication. It lends itself to various network architectures and It applies to the Internet of Things. At the enterprise scale, however, cognitive security serves best to greatly augment, not replace, human decision-making concerning effective cybersecurity response profiles.
From Defeasible Logic to Counterfactual Reasoning

Counterfactual reasoning has been the subject of extensive study in philosophy, logics, and AI. The connection between counterfactual reasoning and theory revision is well-known since Ramsey’s intuition, according to which “to find out whether the counterfactual ‘if A were true, then B would be true’ is satisfied in a state S, change the state S minimally to include A, and test whether B is satisfied in the resulting state". In this paper we study how to model this idea in Defeasible Logic for devising logics for counterfactual reasoning and suitable selection function models.
A Model for Biometric Selection in Public Services Sector

The need to authenticate people using their biometric attributes and tighten information security in organisations significantly increased over the years and public services are no exception. Selecting suitable, robust, relevant and beneficial multimodal biometric attributes in public services environment for person authentication and access control is essential. The major challenge is deploying the wrong multimodal biometric technology in the organisation, which results in failed system deployment. Artificial intelligence (AI) has the potential to significantly drive the adoption and deployment of multimodal biometric authentication in public services. The study recommends a multimodal biometrics selection model for authentication to prevent fraudulent and invalid documents for identification. This study focuses on the human factor elements of public awareness, acceptance, perception and usability relevant to multimodal biometric deployment success. The formalised model proposed in the study could be of value to public services that need to deploy multimodal biometric authentication technologies, thereby minimising future failed deployments.