Reviewerly/exHarmony huggingface dataset:
MENUMBUHKAN KARAKTER GEMAR MEMBACA MELALUI STRATEGI SAMISATE DAN SABUSOTO

Abstract The distance between teachers and students during distance learning during the current Covid-19 pandemic makes it increasingly difficult to cultivate character. Teachers must have tips and tricks to insert efforts to cultivate student character on the sidelines of learning. This study aims to comprehensively describe the cultivation of a character who likes to read during distance/online learning that the author uses the SAMISATE (One Week One Theme) and SABUSOTO (One Month One Character) method. This study uses a descriptive qualitative research design with data collection techniques from 124 students, covering students' responses to reading assignments and their learning outcomes in Indonesian subjects for two semesters. The results showed that the SAMISATE and SABUSOTO methods had an impact on increasing students' active participation in reading activities as evidenced by the increasing number of students who submitted reports on their reading results to the teacher, reaching 95% of the total number of students. In addition, it also has an impact on increasing Indonesian language learning outcomes in semester tests which reach an average of 90 compared to the previous semester's score of 83. This is because the increase in reading habits has an impact on increasing students' literacy competence which is closely related to the ability to understand exam question. The research findings indicate that the SAMISATE and SABUSOTO methods can be used to instill the character of reading fondness in students. Abstrak Berjaraknya antara guru dan siswa ketika pembelajaran jarak jauh di masa pandemi Covid-19 sekarang ini membuat semakin sulitnya penanaman karakter. Guru harus memiliki tips dan trik untuk menyisipkan upaya penanaman karakter siswa di sela-sela pembelajaran. Penelitian ini bertujuan untuk mendeskripsikan secara komprehensif mengenai penanaman karakter gemar membaca saat pembelajaran jarak jauhdaring yang penulis lakukan dengan menggunakan strategi SAMISATE (Satu Minggu Satu Tema) dan SABUSOTO (Satu Bulan Satu Orang Tokoh). Penelitian ini menggunakan desain penelitian kualitatif deskriptif dengan teknik pengumpulan data dari 124 siswa, meliputi respon siswa terhadap tugas membaca dan hasil belajar mereka dalam mata pelajaran Bahasa Indonesia selama satu semester. Hasil penelitian menunjukkan bahwa strategi ini berdampak pada meningkatnya partisipasi aktif siswa dalam kegiatan membaca. Selain itu juga berdampak pada peningkatan hasil belajar Bahasa Indonesia pada ulangan harian dan ulangan semester. Kompetensi siswa dalam menulis pun meningkat. Temuan penelitian mengindikasikan strategi SAMISATE dan SABUSOTO dapat digunakan untuk menanamkan karakter gemar membaca pada siswa.
Bio-Touchpass: Efficient Password Mechanism to Overcome Spyware Attacks

Abstract: This is a work exaggerated historical authentication structures supported the personal identification number (PIN) and one time password (OTP) through the fusion of biometric statistics as a second stage of person authentication. In this project, users can draw every digit of the parole on the touch screen device instead of usual method. A whole exploration of this bio metric gadgets is to drop a relative discriminative energy of every written notation and consequently the hardness once growing the size of the parole and consequently the variety of approached samples. The advanced eBioDigit data, that includes on line written notation from zero to nine, has been non heritable manipulation of the finger as enter on an android device. The data is employed within the experiments in accordance’s in the course of this work and it is on the market at the side of benchmark. Eventually, we have a tendency to discuss precise important points for the education of our innovation strategy on the present PIN and OTP systems, reaching outcomes with equal error rates (EERs) ca. 4.0% once the assaulter is aware of the parole. These outcomes inspire the practice of our innovation strategy in contract to historical PIN and OTP systems anywhere the assault would have a hundred percent success amount underneath steady faux situation. Keyword: Mobile User Authentication; Password; Biometrics; Handwriting; PIN; OTP; Touchscreen; Touch Interaction
Implementation of Predicting Student Performance Using Linear Regression Algorithm

Abstract: This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school-related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two data sets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details). Keywords: Decision Tree Algorithm, Naïve Bayes Algorithm, Support Vector Machine
Harvest Forecasting of Crop Yield Using Machine Learning

Abstract: As an agriculturally dependent country, India's economic standing is totally and partially reliant on it. Agricultural yield is influenced by Causes include organic, economic, and seasonal factors. Calculation of this country's agricultural output is a major challenge. Taking into account the current population situation Recently, Nowadays, the people that grow these and similar items include Due to the suddenness of the production, it is extremely unstable. environmental factors such as weather and a lack of groundwater resources. The major goal is to collect data that can be used to make decisions. stored and analyzed for crop yield predictions For Machine learning techniques for agricultural yield prediction implemented. This aids farmers in selecting the best crops. appropriate crop In addition, this study tries to provide an enhancement in the world of agriculture, by improving crop production prediction accuracy. A statistical model is constructed using machine learning techniques and sufficient optimizations to produce accurate and precise decisions. The results of this research will assist farmers in selecting the best crops to cultivate based on characteristics such as season and available land, with the least amount of risk. Keywords: logistic regression, naïve bayes, random forest
Dynamics of Land Use and Land Cover Change in Jammu and Kashmir

Abstract: Agriculture is the main occupation for the people of Jammu &amp; Kashmir. About 65 per cent of the people are directly or indirectly dependent on agriculture and allied activities for their livelihood. Agriculture and its allied activities are the predominant sectors of the economy of Jammu &amp; Kashmir. The cropping land use in the state also underwent drastic changes which led to a decrease in area under food crops and an increase in area under cash crops/plantation agriculture. The study of this shifting land use was necessary for the sustainable agriculture of the state. Knowledge of cropping land use helps in maximization of productivity and conservation of land. The study tried to assess the cropping pattern of land use and land change dynamics, the land use and land cover change among different land-use classes and to study the extent of productive and unproductive land-use patterns in Jammu &amp; Kashmir. The study has revealed a major shift of land from the desirable to undesirable land-use classes. The area under the three major food crops (Paddy, Maize and wheat) grown in the state has decreased at the state level though with regional variations, it has increased in some districts as well. But the area under paddy recorded a decline in all the districts and the area under orchards has registered highly positive growth in all the districts of Jammu &amp; Kashmir. Keywords: Agriculture, Land Use, Land Cover Change, Cropping Pattern, Jammu &amp; Kashmir
StyleTalker: One-shot Style-based Audio-driven Talking Head Video Generation

We propose StyleTalker, a novel audio-driven talking head generation model that can synthesize a video of a talking person from a single reference image with accurately audio-synced lip shapes, realistic head poses, and eye blinks. Specifically, by leveraging a pretrained image generator and an image encoder, we estimate the latent codes of the talking head video that faithfully reflects the given audio. This is made possible with several newly devised components: 1) A contrastive lip-sync discriminator for accurate lip synchronization, 2) A conditional sequential variational autoencoder that learns the latent motion space disentangled from the lip movements, such that we can independently manipulate the motions and lip movements while preserving the identity. 3) An auto-regressive prior augmented with normalizing flow to learn a complex audio-to-motion multi-modal latent space. Equipped with these components, StyleTalker can generate talking head videos not only in a motion-controllable way when another motion source video is given but also in a completely audio-driven manner by inferring realistic motions from the input audio. Through extensive experiments and user studies, we show that our model is able to synthesize talking head videos with impressive perceptual quality which are accurately lip-synced with the input audios, largely outperforming state-of-the-art baselines.
What deep reinforcement learning tells us about human motor learning and vice-versa

Machine learning and specifically reinforcement learning (RL) has been extremely successful in helping us to understand neural decision making processes. However, RL's role in understanding other neural processes especially motor learning is much less well explored. To explore this connection, we investigated how recent deep RL methods correspond to the dominant motor learning framework in neuroscience, error-based learning. Error-based learning can be probed using a mirror reversal adaptation paradigm, where it produces distinctive qualitative predictions that are observed in humans. We therefore tested three major families of modern deep RL algorithm on a mirror reversal perturbation. Surprisingly, all of the algorithms failed to mimic human behaviour and indeed displayed qualitatively different behaviour from that predicted by error-based learning. To fill this gap, we introduce a novel deep RL algorithm: model-based deterministic policy gradients (MB-DPG). MB-DPG draws inspiration from error-based learning by explicitly relying on the observed outcome of actions. We show MB-DPG captures (human) error-based learning under mirror-reversal and rotational perturbation. Next, we demonstrate error-based learning in the form of MB-DPG learns faster than canonical model-free algorithms on complex arm-based reaching tasks, while being more robust to (forward) model misspecification than model-based RL. These findings highlight the gap between current deep RL methods and human motor adaptation and offer a route to closing this gap, facilitating future beneficial interaction between between the two fields.
Deep Structural Causal Shape Models

Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association. In medical imaging, for example, we may want to study the causal effect of genetic, environmental, or lifestyle factors on the normal and pathological variation of anatomical phenotypes. However, while anatomical shape models of 3D surface meshes, extracted from automated image segmentation, can be reliably constructed, there is a lack of computational tooling to enable causal reasoning about morphological variations. To tackle this problem, we propose deep structural causal shape models (CSMs), which utilise high-quality mesh generation techniques, from geometric deep learning, within the expressive framework of deep structural causal models. CSMs enable subject-specific prognoses through counterfactual mesh generation ("How would this patient's brain structure change if they were ten years older?"), which is in contrast to most current works on purely population-level statistical shape modelling. We demonstrate the capabilities of CSMs at all levels of Pearl's causal hierarchy through a number of qualitative and quantitative experiments leveraging a large dataset of 3D brain structures.
The DialPort tools

The DialPort project http://dialport.org/, funded by the National Science Foundation (NSF), covers a group of tools and services that aim at fulfilling the needs of the dialog research community. Over the course of six years, several offerings have been created, including the DialPort Portal and DialCrowd. This paper describes these contributions, which will be demoed at SIGDIAL, including implementation, prior studies, corresponding discoveries, and the locations at which the tools will remain freely available to the community going forward.
Exploitation of the Conditionally Linear Structure in Visual-Inertial Estimation

In this work, estimators for platform pose and landmark maps for visual-inertial data are analysed. It is shown that the full, non-linear, visual-inertial problem has a conditionally linear substructure in the 2D case which can be exploited for efficient solutions, e.g., Block Coordinate Descent (BCD). It is also shown that the measurement noise from the non-linear model becomes parameter dependent resulting in biased estimates if that fact is ignored. However, the bias can be accounted for using the Iteratively Reweighted Least Squares (IRLS) method. In the 3D case the conditionally linear substructure is not separable. However, it can be shown that the Jacobian of the non-linear substructure can be calculated recursively resulting in an efficient solution. A simulated 2D visual-inertial example is used to illustrate the theoretical results.
Fast optimize-and-sample method for differentiable Galerkin approximations of multi-layered Gaussian process priors

Multi-layered Gaussian process (field) priors are non-Gaussian priors, which offer a capability to handle Bayesian inference on both smooth and discontinuous functions. Previ-ously, performing Bayesian inference using these priors required the construction of a Markov chain Monte Carlo sampler. To converge to the stationary distribution, this sampling technique is computationally inefficient and hence the utility of the approach has only been demonstrated for small canonical test problems. Furthermore, in numerous Bayesian inference applications, such as Bayesian inverse problems, the uncertainty quantification of the hyper-prior layers is of less interest, since the main concern is to quantify the randomness of the process/field of interest. In this article, we propose an alternative approach, where we optimize the hyper-prior layers, while inference is performed only for the lowest layer. Specifically, we use the Galerkin approximation with automatic differentiation to accelerate optimization. We validate the proposed approach against several existing non-stationary Gaussian process methods and demonstrate that it can significantly decrease the execution time while maintaining comparable accuracy. We also apply the method to an X-ray tomography inverse problem. Due to its improved performance and robustness, this new approach opens up the possibility for applying the multi-layer Gaussian field priors to more complex problems.
Multimodal feature fusion for concreteness estimation

In recent years the idea of fusing diverse type of information has often been employed to solve various Deep Learning tasks. Whether these regard an NLP problem or a Machine Vision one, the concept of using more inputs of the same type has been the basis of many studies. Considering NLP problems, attempts of different word embeddings have already been tried, managing to make improvements to the most common benchmarks. Here we want to explore the combination not only of different types of input together, but also different data modalities. This is done by fusing two popular word embeddings together, mainly ELMo and BERT, with other inputs that embed a visual description of the analysed text. Doing so, different modalities -textual and visual- are both employed to solve a textual problem, a concreteness task. Multimodal feature fusion is here explored through several techniques: input redundancy, concatenation, average, dimensionality reduction and augmentation. By combining these techniques it is possible to generate different vector representations: the goal is to understand which feature fusion techniques allow to obtain more accurate embeddings.
Pemetaan Kompetensi Siswa Untuk Mengikuti Seleksi Lomba Kejuruan Multimedia Menggunakan Metode K-Means

The process of mapping competent students who will take part in the competition is the selection of students who have the best scores in the vocational field, which is carried out by the academic side in order to include students who are competent in the vocational field to be included in the vocational competition. So far, the school still has difficulties in selecting competent and less competent students due to the limitations of the presentation and processing of the data carried out. To facilitate the mapping of competent students in the vocational field, this activity is computerized into software. Time accuracy and effectiveness are important factors so that the mapping process of competent students can run well and as it should. In this research, an application is made that can cluster competent students so that they can take part in Multimedia vocational competitions appropriately and accurately using the K-Means Algorithm. It applies variables such as Academic Values and Attitude Values.
Penerapan Metode Profile Matching untuk Penempatan Karyawan Berbasis Kompetensi di Bidang IT

Placement of employees is one of the complex problems in the world of work. The discrepancy between work placement and the ability of employees who are placed in areas of work that are not in accordance with their expertise will certainly have an impact on work comfort which also affects work performance. For that we need a decision support system that is able to be a solution in dealing with problems faced by human resource managers in the difficulty of selecting candidates to occupy vacant positions. By applying the profile matching method in the employee job placement system, it can help the role of human resource managers in finding candidates who have a great chance to fill positions according to the required criteria. A feasibility test has been carried out on the developed system with a percentage value of 93.9%, which means the system is in the "Very Eligible" category for use.
Perancangan Sistem Informasi Pembayaran Online Menggunakan PHP dan Paypal

The conventional payment transaction system, which has begun to be abandoned due to the rapid development of technology, requires changes in the payment system. Technological developments help users of these technologies in making transactions easier.in this study, the payment transaction process is still conventional or manual which causes problems that occur, such as the length of time in the transaction, prone to errors, data management is not so good. The application of online payment transactions can help overcome these problems, such as saving time, good data management and fewer errors that will occur. For the description of the system in terms of system development using SDLC method and the model used is Waterfall, where the waterfall process starts from system analysis to system maintenance. For system design using the UML (usecase &amp; Activity Diagram) process, whit the use of UML, which describes the payment transaction process. And the online payment transaction model using the payment facility with PayPal.
Penerapan Metode Naïve Bayes Untuk Rekomendasi Pemilihan Asisten Laboratorium Komputer Di Perguruan Tinggi

Binaniaga Indonesia University is one of the universities in the city of Bogor, just like other university in the learning process, computer laboratory assistants are often found whose job is to assist in providing appropriate directions to students who have difficulty during learning. Based on the results of interviews that have been conducted, there are indications that students who have been selected to become computer, laboratory assistants in the previous period, namely their performance is less than optimal, seen in daily life when assisting practicum courses in the computer laboratory, namely not providing the direction needed by practitioners who experience obstacles or difficulties, and also not optimal in terms of time while working in the computer laboratory. in this study, it can provide recommendations for selection of computer laboratory assistants to minimize errors in choosing computer laboratory assistants by applying the Naive Bayes method. The variables use is based on academic fields such as attendance, GPA (Gradual Achievement Index), the value of programming fundamentals courses, the value of programming language 1 or web systems and technology courses, and semester. The percentage of accuracy test results obtained by using the confussion matrix is the accuracy of 95.31%.
Penerapan Algoritma K-Means Untuk Pemetaan Potensi Calon Mahasiswa Baru

The process of mapping the potential of prospective new students is a grouping of prospective students based on various criteria which will be grouped based on their potential, both low and high, in order to assist the PR/promotion bureau in providing reference data and information in order to determine what promotion strategy will be carried out in the next period. This research can provide a mapping of potential new students using the K-Means Clustering Algorithm, namely by analyzing the initial data group, transforming the initial data and performing grouping calculations, after that the results of the grouping calculations can be re-analyzed to see where the school came from, the location of the school and the location of the school. the origin of information for each member in each group. In it applied the variables, namely the origin of the school, the location of the origin of the school and the origin of the information. This is done to map the potential of prospective new students, in order to assist the public relations/promotion bureau in providing reference data and information in order to determine what promotion strategy will be carried out in the next period. A cluster validity test has been carried out using the Silhoutte Coefficiency of the K-Means algorithm which is applied with a value of 0.6113 which means that the cluster created is included in the "Medium Structure" category, it is based on the Sillhoutte Category table according to Kauffman and Roussseeuw.