Reviewerly/exHarmony huggingface dataset:
Genetic Programming for Screen Content Image Quality Assessment

The study of Screen Content Image Quality Assessment (SCI-QA) is a new and interesting topic due to its excellent potential for instruction and optimization in various processing systems, it has been attractive recently. In this paper, we proposed a full reference quality assessment method for screen content images named Genetic Programming based Screen Content Image Quality (GP-SCIQ). The proposed method operates via a symbolic regression technique using Genetic Programming (GP). Hence, for predicting subject scores of images in datasets we combined the objective scores of a set of Image Quality Metrics (IQM). Two largest publicly available image databases (namely SICAD and SCID) are used for training and testing the predictive models, according the k-fold-cross-validation strategy. The performance of the proposed approach is evaluated, and several experiments are carried using four performance indices (SRCC, PCC, KROCC and RMSE). The results achieve superior performance to state-of-the-art methods in predicting the perceptual quality of SCIs.
Automatic Sentiment Analysis Scalability Prediction for Information Extraction Using SentiStrength Algorithm

The social media platforms enable their users to provide feedback and voice complaints about the services and goods they have used. Sentiment analysis is a powerful tool that can help the software industry and company to better evaluate user needs and cater the software in a way to maximize the sales potential. One of the studying areas in natural language process (NLP) is sentiment analysis which is concerned for identifying the opinion or mood within a text. For extracting the information from the social big data, an automatic procedure is essential for decision-makers and marketers. For satisfying this requirement, an automatic sentiment analysis saleability prediction for information extraction using SentiStrength algorithm is presented in this paper. From consumers, data is collected through feedback forms on software product. Presented algorithm validity is proven through comparing the contrast rule-based sentiment analysis (CRbSA), general word counting, and extraction algorithms well-known sentiment information. Accuracy and processing time are two parameters used to analyze the performance of SentiStrength algorithm, and these values are 81.5% as accuracy and 15 ms as processing time. In a marketing system, this algorithm is employed for extracting the satisfaction of customers in particular, it serves as a warning mechanism for unfavorable remarks.
ANALYSIS OF SUITABILITY LAND FOR FLOOD-FREE SETTLEMENT IN BAOLAN DISTRICT TOLITOLI REGENCY

&lt;p&gt;&lt;span lang="EN-US"&gt;Baolan District, Tolitoli Regency, as a urban area is quite worrying, because it is often hit by floods. The settlement area is an area that is often affected, because the settlement area of Baolan District is in the vulnerable to very vulnerable class. This research was conducted in Baolan District from April to August 2021. The tools and materials used in this study were: a laptop, ArcGis 10.4 software, and secondary data (rainfall data, DEM data or Digital Elevation Model, soil type maps, and maps). RBI scale 1:50000). This type of research is quantitative descriptive with a spatial approach and using saturated sampling rules obtained from the entire population. The purpose of this study was to determine the distribution and level of suitability of land designated for flood-free settlements in Baolan District. The results of the analysis show that the suitability class that has the largest distribution area is the QS class (Quite Suitable) with an area of 31570.148 or 79.97%. Class S (Suitable) is the class with the smallest area of distribution, which is 538.892 or 1.37%, while the other class is class that is not suitable (N) with an area of 7368.287 or 18.66%. Class S (Suitable) indicates that the class is very wide good if it is developed for a useful area and inversely proportional to class N (Not Suitable). While QS (Quite suitable) is a class that is good enough to be developed into a useful area, but there are several things to consider. the condition of the area is quite steep and the topography is high, as a result it will cause natural hazards such as erosion and land slides. Another thing to consider is the area that covers the rain catchment area which should be predominantly functioned as a buffer zone.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;&lt;em&gt;&lt;/em&gt;
SpheriCol: A Driving Assistant for Power Wheelchairs Based on Spherical Vision

This paper presents a new vision-based navigation assistant for power wheelchairs, called SpheriCol. Inspired by the parking assist system in cars, an omnidirectional image captured by a twin-fisheye camera is overlaid with colored distance markers from range sensors mounted on the wheelchair, to generate an augmented view of the surrounding environment. Such an image is displayed in real time to the user on a screen. To evaluate the effectiveness of the proposed system, able-bodied subjects and older adults with motor impairments have been asked to drive a consumer-grade power wheelchair equipped with SpheriCol, via a standard joystick. Our clinical trials with different obstacle courses, consistently indicate that SpheriCol is effective in improving safety and comfort, and in supporting a driver’s decision during challenging but prevalent maneuvers, such as reversing out of an elevator, corridor centering, and turning on the spot.
Security of discrete-modulated continuous-variable quantum key distribution

Continuous variable quantum key distribution with discrete modulation has the potential to provide information-theoretic security using widely available optical elements and existing telecom infrastructure. While their implementation is significantly simpler than that for protocols based on Gaussian modulation, proving their finite-size security against coherent attacks poses a challenge. In this work we apply the entropy accumulation theorem, a tool that has previously been used in the setting of discrete variables, to prove finite-size security against coherent attacks for a discrete-modulated quantum key distribution protocol involving four coherent states and heterodyne detection. To do so, and contrary to previous approaches, we consider a protocol in which all the information is discretized. We first bound its asymptotic rate under a realistic photon number cutoff assumption. This bound is then upgraded into a finite-size security proof using entropy accumulation. Our analysis provides asymptotic rates in the range of $0.1-10^{-4}$ bits per round for distances up to hundred kilometres, while in the finite case and for realistic parameters, we get of the order of $10$ Gbits of secret key after $n=10^{12}$ rounds and distances of few tens of kilometres.
Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion

This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets. Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities. To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged. In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered. Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.
PyVBMC: Efficient Bayesian inference in Python

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020). VBMC is an approximate inference method designed for efficient parameter estimation and model assessment when model evaluations are mildly-to-very expensive (e.g., a second or more) and/or noisy. Specifically, VBMC computes: - a flexible (non-Gaussian) approximate posterior distribution of the model parameters, from which statistics and posterior samples can be easily extracted; - an approximation of the model evidence or marginal likelihood, a metric used for Bayesian model selection. PyVBMC can be applied to any computational or statistical model with up to roughly 10-15 continuous parameters, with the only requirement that the user can provide a Python function that computes the target log likelihood of the model, or an approximation thereof (e.g., an estimate of the likelihood obtained via simulation or Monte Carlo methods). PyVBMC is particularly effective when the model takes more than about a second per evaluation, with dramatic speed-ups of 1-2 orders of magnitude when compared to traditional approximate inference methods. Extensive benchmarks on both artificial test problems and a large number of real models from the computational sciences, particularly computational and cognitive neuroscience, show that VBMC generally - and often vastly - outperforms alternative methods for sample-efficient Bayesian inference, and is applicable to both exact and simulator-based models (Acerbi, 2018, 2019, 2020). PyVBMC brings this state-of-the-art inference algorithm to Python, along with an easy-to-use Pythonic interface for running the algorithm and manipulating and visualizing its results.
LERF: Language Embedded Radiance Fields

Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances. In this work we propose Language Embedded Radiance Fields (LERFs), a method for grounding language embeddings from off-the-shelf models like CLIP into NeRF, which enable these types of open-ended language queries in 3D. LERF learns a dense, multi-scale language field inside NeRF by volume rendering CLIP embeddings along training rays, supervising these embeddings across training views to provide multi-view consistency and smooth the underlying language field. After optimization, LERF can extract 3D relevancy maps for a broad range of language prompts interactively in real-time, which has potential use cases in robotics, understanding vision-language models, and interacting with 3D scenes. LERF enables pixel-aligned, zero-shot queries on the distilled 3D CLIP embeddings without relying on region proposals or masks, supporting long-tail open-vocabulary queries hierarchically across the volume. The project website can be found at https://lerf.io .
Model Based Explanations of Concept Drift

The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become inaccurate and need adjustment. While there do exist methods to detect concept drift or to adjust models in the presence of observed drift, the question of explaining drift, i.e., describing the potentially complex and high dimensional change of distribution in a human-understandable fashion, has hardly been considered so far. This problem is of importance since it enables an inspection of the most prominent characteristics of how and where drift manifests itself. Hence, it enables human understanding of the change and it increases acceptance of life-long learning models. In this paper, we present a novel technology characterizing concept drift in terms of the characteristic change of spatial features based on various explanation techniques. To do so, we propose a methodology to reduce the explanation of concept drift to an explanation of models that are trained in a suitable way extracting relevant information regarding the drift. This way a large variety of explanation schemes is available. Thus, a suitable method can be selected for the problem of drift explanation at hand. We outline the potential of this approach and demonstrate its usefulness in several examples.
Professional Training of the Future Teacher: Ethno-Cultural Component

The problem of preparing a future teacher in the system of higher education is considered one of the key in pedagogical science. The formation of competencies in the profession, values and personal qualities act as interacting factors of one phenomenon and influence the qualitative functioning of the pedagogical system. Modern pedagogy focuses on the importance of cultural components, and the cultural approach expands the content of teacher education, creating conditions for the development and self-development of future teachers in a cultural and educational environment. The purpose of the article is to reveal the ethno-cultural basis of the professional training of the future teacher at the university. In the context of the stated problem, the author focuses on strengthening the cultural orientation of training for pedagogical activity of the future teacher and substantiates the relevance of creating a cultural and educational space of the university, in which there is an active immersion of future teachers in culture, creating conditions for the assimilation of knowledge, norms and values, the translation of humanistic values in the whole variety of social relationships and relations. Analyzes the preparation of the future teacher from the perspective of ethno-cultural orientation, considered options to fill the content of teacher education of ethno-cultural components through the education of value attitudes to world culture in general and the diversity of national traditions in particular. In the conclusions the author justifies the fact that the professional training of future teachers from the perspective of ethno-cultural orientation contributes to the solution of problems of conservation of spiritual and moral values, the continuity of the cultural past and present; conservation, recreation and transmission of cultural achievements. The materials of the study can be useful for university teachers in designing the cultural and educational space of the university.
Simulation of Land Movement Detection System Using Accelerometer Sensors and Fiber Optic

Indonesia’s geographical conditions are one of the causes of land movement. This land movement can occur due to the movement of rock masses, soil, or debris material making up the slopes. The stability of a slope is influenced by several parameters such as material, soil strength, slope angle, climate, vegetation, and time. In Indonesia, land movement disasters are placed the third rank of natural disasters that occurred throughout 2021. Thus, the development of a land movement detection system is very important for monitoring land movement disasters. In this research, a land movement detection device was developed using the ADXL 335 accelerometer sensor and fiber optic. For data acquisition, Arduino Uno, LEDs, and photodetectors were used. Arduino Uno was used to convert analog signals to digital. In addition, LEDs were used as light sources, and photodetectors were used as a receiver. Changes in the output voltage due to macrobending loss are obtained when the curvature changes due to the pendulum system. The results of the study show that the average acceleration values on the x, y, and z axes of the accelerometer sensor are 0.118 g, 0.925 g, and -2.494 g. The maximum land displacement movement that can be represented by fiber optic is 4 cm. Further, the combination use of accelerometer sensors and fiber optic can show the magnitude of the force that causes displacement, the direction of land displacement, and the magnitude of the land displacement that occurs.
Distributed Deep Reinforcement Learning: An Overview

Deep reinforcement learning (DRL) is a very active research area. However, several technical and scientific issues require to be addressed, amongst which we can mention data inefficiency, exploration-exploitation trade-off, and multi-task learning. Therefore, distributed modifications of DRL were introduced; agents that could be run on many machines simultaneously. In this article, we provide a survey of the role of the distributed approaches in DRL. We overview the state of the field, by studying the key research works that have a significant impact on how we can use distributed methods in DRL. We choose to overview these papers, from the perspective of distributed learning, and not the aspect of innovations in reinforcement learning algorithms. Also, we evaluate these methods on different tasks and compare their performance with each other and with single actor and learner agents.
Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning

Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 200 images and achieve good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.
Quizbowl: The Case for Incremental Question Answering

Scholastic trivia competitions test knowledge and intelligence through mastery of question answering. Modern question answering benchmarks are one variant of the Turing test. Specifically, answering a set of questions as well as a human is a minimum bar towards demonstrating human-like intelligence. This paper makes the case that the format of one competition -- where participants can answer in the middle of hearing a question (incremental) -- better differentiates the skill between (human or machine) players. Additionally, merging a sequential decision-making sub-task with question answering (QA) provides a good setting for research in model calibration and opponent modeling. Thus, embedded in this task are three machine learning challenges: (1) factoid QA over thousands of Wikipedia-like answers, (2) calibration of the QA model's confidence scores, and (3) sequential decision-making that incorporates knowledge of the QA model, its calibration, and what the opponent may do. We make two contributions: (1) collecting and curating a large factoid QA dataset and an accompanying gameplay dataset, and (2) developing a model that addresses these three machine learning challenges. In addition to offline evaluation, we pitted our model against some of the most accomplished trivia players in the world in a series of exhibition matches spanning several years. Throughout this paper, we show that collaborations with the vibrant trivia community have contributed to the quality of our dataset, spawned new research directions, and doubled as an exciting way to engage the public with research in machine learning and natural language processing.
Systematic Literature Review and Bibliometric: Blockchain Technology in Archives

Disclosure of information makes it easy for the public to obtain information anytime and anywhere. New solutions for access control and monitoring of archives are urgently needed to keep things safe. Archival institutions as guarantors of the availability of information are expected to provide solutions and new ideas for developments in the implementation of national archives. Archiving in developed countries on blockchain technology is something new. The purpose of this research is to find out the advantages of blockchain technology in archiving and hope that readers or researchers benefit from Blockchain Technology research in archiving, which can be used for other research. Data collection techniques used the literature review method systematically and uses the VOSviewer application in this study and the findings of journal articles sourced from Scopus in 2017-2022.
Management of professional capacity development activities for elementary school teachers to meet the 2018 general education program

Professional capacity development for elementary school teachers to meet the 2018 General Education Program is a matter of both theoretical and practical importance. In order to effectively develop the professional capacity of elementary school teachers, it is necessary to strengthen the management of these activities. In this paper, the need for management of professional development activities for elementary school teachers is clarified, in which specific contents have been scientifically analyzed to meet the 2018 general education program. The need, orientation and content of professional capacity development for elementary school teachers to meet the 2018 General Education Program need to be mastered by management entities for effective management.
Towards the Triad of Digital Literacy, Data Literacy and AI Literacy in Teacher Education – A Discussion in Light of the Accessibility of Novel Generative AI

The training of future-relevant competencies – so called Future Skills or 21st Century Skills – is currently moving to the center of the educational discourse in light of increasing technological developments and accessibility of AI systems, e.g. ChatGPT. It is our view that bringing together, thinking about and promoting Digital-, Data- and AI literacy in teaching is a major concern of future education. We focus in particular on teacher education, since teachers have a dual role: on the one hand, pre-service teachers are also learners who must first develop the appropriate competencies themselves, and on the other hand, they are multipliers as teachers in schools who must impart the appropriate competencies. Particularly in the context of novel generative AI systems and large language models, it is important to take a holistic and critical-reflective look at them from a technological, socio-cultural and user-oriented perspective.