Reviewerly/exHarmony huggingface dataset:
Improvement Design of Functional Modules of College English Network Teaching System Based on Decision Tree Algorithm

English courses are a compulsory course for non-English core courses. Studying English at university is an important part of studying at a non-English university. The purpose of a university education in English is to enhance the comprehensive language skills of students in English. In this way, they can communicate effectively in English during future work, study, and social interactions, while strengthening their ability to meet the needs of social development and international exchange. The functional improvement design of the college English network teaching system will help the system to provide technical support and services for college English teaching more effectively. It can more effectively cultivate students’ language communicative ability and autonomous learning ability. This article first improves the practical units of the education system and then adds the decision tree algorithm. It uses improved decision-making algorithms to evaluate student performance with an improved learning system. The number of leaf nodes of the improved decision tree algorithm on the three datasets is 19, 14, and 15, respectively, and the number of summary points is 27, 23, and 7, respectively.
UNCOVER: Development of an efficient steganalysis framework for uncovering hidden data in digital media

This paper presents the general goals of Horizon 2020 project UNCOVER, whose overall purpose is to close the gap between academic work and operational needs in the fields of data-hiding. While digital data-hiding is a relatively new area of research, our motivation in this project has been rooted in the growing gap between the academic community and the operational needs of a ”real-life” scenario of object inspection in order to UNCOVER the presence of data secretly hidden.
Low-light Image Enhancement Algorithm Based on Exposure Prediction and Hybrid Feature Weighted Fusion Strategy

Abstract First, based on a given low-light image, moderately exposed and overexposed images are generated through the Exposure Image Prediction Model (EIPM).Then all three generate feature maps by a Feature Extraction Module (FEM),and then the feature maps of them are denoised. Subsequently, the feature map of the low-light image is fused with the feature maps of the two exposure rates images respectively. Finally, an adaptive weighted fusion method is used to obtain ultimate enhanced image. Experiments show that by comparing with the current SOTA algorithms,the proposed ELLIE algorithm, both in the detail feature retention and in color recovery have been significantly improved, and the subjective and objective evaluation indicators also have obvious advantages.
The paradox of the virtual iñárritu's Carne y Arena between innovative spect-actor and traditional fruition

This article analyses the relationships between text and participant in the mixed-reality film/installation Carne y Arena by the Mexican director Alejandro Inarritu (2017), which I experienced in 2018 at Prada Foundation in Milan, Italy. The film is divided into three sections: the first theatrical, the second based on virtual reality (VR) and the third concerning TV. Theoretically, the article draws on Boal's spect-actor and on theories analyzing the position of the audience between the opposite poles of passivity and activity; and on conceptualizations of how the digital media have increased participants' activity. Starting from all of this, the article applies rhetorical narratology analysis to the three parts of Inarritu's film to find out what kind of narrative experience the film/installation offers the participant. The results show that the second part of the installation mostly surprises and excite the participant. In contrast, the materiality of the theatrical part and the informational value of the TV part talk to the participant more completely and concretely. In the end, Boal's spect-actor is more present when the participant deals with old and traditional forms of communication than when they are only apparently immersed in the VR experience. Thus, to bridge its gap, VR needs help from old languages such as theatre and TV. This is the paradox of the virtual mentioned in the title.
Integrating 3D Objects in Multimodal Video Annotation

This paper presents and discusses the introduction of 3D functionalities for an existing web-based multimodal video annotation tool. Over the past years, we have developed a multimodal web video annotation tool that now combines 3D models and 360º content with more traditional annotation types (e.g., text, drawings, images), offering users the possibility of adding extra information in their annotation work. We show how 3D models augment the annotation work and add advantages like viewing or exploring objects in detail and from different angles. The paper reports detailed feedback from a pilot study in form of a workshop with traditional dance experts to whom these new features were presented. We conclude with an outlook of future iterations of the video annotator based on the experts’ feedback.
Blockchain Technology—A Brief Survey

Today’s world is all about innovations. At present, any human cannot lead a normal lifestyle without the application of software technologies in their day-to-day activities. In such case, security has become a great threat to maintain sensitive records of people. Blockchain technology provides a solution in this case. A blockchain is indeed a public ledger that is open to all but without any control by a central authority. It is a technology that enables individuals and businesses to work together with trust and accountability. Cryptographic currencies like Bitcoin are one of the best-known blockchain applications. Blockchain technology is considered as the driving factor behind the next fundamental IT revolution. Several blockchain technology implementations are commonly accessible today, each with its particular strength for a particular application domain. This paper gives a brief introduction about various applications of blockchain technology in various domains.
Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study

Machine unlearning is the task of updating machine learning (ML) models after a subset of the training data they were trained on is deleted. Methods for the task are desired to combine effectiveness and efficiency (i.e., they should effectively “unlearn” deleted data, but in a way that does not require excessive computational effort (e.g., a full retraining) for a small amount of deletions). Such a combination is typically achieved by tolerating some amount of approximation in the unlearning. In addition, laws and regulations in the spirit of “the right to be forgotten” have given rise to requirements for certifiability (i.e., the ability to demonstrate that the deleted data has indeed been unlearned by the ML model). In this paper, we present an experimental study of the three state-of-the-art approximate unlearning methods for logistic regression and demonstrate the trade-offs between efficiency, effectiveness and certifiability offered by each method. In implementing this study, we extend some of the existing works and describe a common unlearning pipeline to compare and evaluate the unlearning methods on six real-world datasets and a variety of settings. We provide insights into the effect of the quantity and distribution of the deleted data on ML models and the performance of each unlearning method in different settings. We also propose a practical online strategy to determine when the accumulated error from approximate unlearning is large enough to warrant a full retraining of the ML model.
Systematic analysis of digital tools to provide STEM and science education

Abstract Recently, attention is paid to conceptual aspects of STEM or particular usage of specific tools during STEM. This paper is devoted to systemize all digital tools used during STEM classes. It is proposed to systemize all equipment into instrumental, computer simulation, and modern ones (VR, AR, and AI and 3d-modelling). It is proved the dominant role of computer simulation and modern instruments during providing STEM classes due to their simplicity and absence of necessity of expensive equipment usage. Using any of the described tools do not guaranty providing STEM, but they can be used as an element of the STEM didactic approach.
Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems

The performance evaluation of state estimators for nonlinear regular systems, in which the current measurement only depends on the current state directly, has been widely studied using the Bayesian Cramér-Rao lower bound (BCRLB). However, in practice, the measurements of many nonlinear systems are two-adjacent-states dependent (TASD) directly, i.e., the current measurement depends on the current state as well as the most recent previous state directly. In this paper, we first develop the recursive BCRLBs for the prediction and smoothing of nonlinear systems with TASD measurements. A comparison between the recursive BCRLBs for TASD systems and nonlinear regular systems is provided. Then, the recursive BCRLBs for the prediction and smoothing of two special types of TASD systems, in which the original measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the proposed recursive BCRLBs for the prediction and smoothing of TASD systems.
Improvement of College English Teaching Model under the Background of Information Technology

In order to improve the effect of college English teaching reform, this paper combines information technology to improve the college English teaching mode. According to the digital processing requirements of English teaching resources, in order to make the loop establishment circuit time of automatic gain control independent of the input signal, this paper uses a logarithmic amplifier to detect it. Moreover, this paper combines the SC algorithm and the SC algorithm based on the time-domain PN sequence to propose a new frame synchronization algorithm. The algorithm has precise frame synchronization points and reduces the usage of hardware resources. Finally, this paper constructs a college English teaching system based on information technology. The experimental research results show that the college English teaching system based on information technology proposed in this paper has a good teaching effect.
Character Segmentation from Offline Handwritten Gujarati Script Documents

Modern innovations make great impacts on the human lifestyle and their way of working. It boosts the efficiency and productivity of people by reducing efforts, which help to handle several tasks at a time. Nowadays, all the government offices, banks, businesses, and education systems are influenced by paperless technology. It improves documentation and the secure sharing of information by saving space and resources. Paperless technology works on Optical Character Recognition (OCR) to convert all physical documents into machine-based documents. OCR consists of mainly two steps: segmentation, and recognition. The success rate of character recognition depends on the segmentation of required regions of interest. This paper introduced an algorithm that utilized projection profile, bounding box, and connected component labeling techniques for the development of Gujarati handwritten dataset and segmentation of handwritten text from Gujarati documents into line, word, and characters.
Movement Trajectory Control of an Intelligent Mobile Robot Controlled by Machine Vision

Abstract Intelligent mobile robots can take up various repetitive labor tasks instead of humans, but with the increase in labor requirements, the requirements for the autonomous working ability of intelligent mobile robots have also increased. This research used machine vision to identify obstacles in the working environment; then, a plane model was constructed for the working environment of the intelligent mobile robot with the help of machine vision. The moving path was planned by the ant colony algorithm. The robot adopted the trajectory tracking control law to track the planned path to realize the autonomous movement of the intelligent mobile robot. Finally, experiments were carried out, and the trajectory control of the robot moving on the path planned by the ant colony algorithm was compared with that planned by the genetic algorithm. The results showed that the ant colony algorithm converged faster and planned a shorter path after stability; when the coordinates of the starting and end points of the planned path were (0.0 m, 2.5 m) and (5.0 m, 0.0 m), respectively, the turning point coordinates of the paths planned by the genetic and ant colony algorithms were (2.5 m, 2.0 m) and (3.5 m, 0.0 m), respectively; the robot had shorter movement trajectory, smaller deviation, and less movement time on the path planned by the ant colony algorithm than the genetic algorithm.
A content-aware tool for converting videos to narrower aspect ratios

The ability to make videos for different aspect ratios (known as video retargeting) contributes to optimal viewing experience on different video platforms. In this paper, we present an idiom-based tool for retargeting videos, from the most common 16:9 aspect ratio into narrower aspect ratios. In contrast to earlier retargeting approaches, which distort the video and are completely automated, our tool enables cropping and panning with user input and oversight. Users can select and order idioms from six cinematic idioms to control video retargeting, and the tool applies selected idioms in order and generates the retargeting results. We performed a pilot study for the feasibility of the tool, and conducted quantitative analysis to inform further work on crafting intelligent cropping and panning tools. In addition, we interviewed an experience video editor on how retargeting is done manually and the quality of the output of the tool.
Experience in the development and implementation of a system of visualized teaching cases in Physics using a digital computer measuring system Einstein

Abstract Time has come to form a new education system. This is confirmed by the UNESCO initiative on the future of education for 2050 and thereafter and provides an opportunity to understand how education can shape the future of humanity and the planet. Today, the development of ICT, artificial intelligence, digital platforms and information growth are agents of change in the educational process. The amount of information in the world doubles every day; information-oriented society (a community connected to each other and the world through information and technology) is being formed. Open information systems, global educational networks, interacting digital resources, developing digital educational and information resources provide an opportunity to improve the quality of education. In view of this, special information educational resources are developed in accordance with the trends of science, regional context, individual characteristics and needs of students. In recent years, STEM education has been actively developing both in the world and in Ukraine. This contributes to students’ involvement into STEM disciplines, and young people’s choosing engineering professions. This article substantiates the feasibility of developing a system of visualized teaching cases in Physics. A classification of educational cases is proposed. The paper describes the experience of development and introduction of visualized cases using a combination of Google-services and digital computer measuring system Einstein in the process of teaching Physics as a STEM-discipline. The method of organization of classes with the help of digital computer measuring complex Einstein in the context of situational teaching of Physics is suggested. The article also presents the analysis of the results of implementation of the system of visualized Physics teaching cases into educational process on the basis of Physics and Educational Technologies Laboratory of Kherson State University.
CPU Burst Time using Machine Learning Algorithm’s

The amount of time required by a process for executing on the CPU is called CPU-Burst Time, on which implementation of some CPU-scheduling algorithms such as Shortest Job First (SJF) and Shortest-Remaining Time First (SRTF) are relied on. There are many vivid methods for prognosticating the CPU-Burst length, one among them is Machine Learning based approach to calculate the burst- time of the processes in the ready queue. This approach involves the selection of most appropriate attributes of the process by making use of selection techniques, and based on these attributes, the CPU-Burst for the process is calculated in the grid. The ML algorithms used for this are Linear Regression, K-Nearest Neighbors (KNN), and Decision Tree (DT). This approach results in higher efficiency as it obtained linear relationship between the process and CPU Burst-Time. When tested and evaluated the workload dataset. The efficiency of ML approach is high because KNN provides better results in terms of CC and RAE, and for the factors like time, space and burst-estimation, attributes selection techniques are used.
Efficient Federated Learning Using Layer-Wise Regulation and Momentum Aggregation

Federated Learning (FL) enables multiple parties to train a global model collaboratively without sharing local data. However, a key challenge of FL is data distribution heterogeneity across participants, which causes model drift in local training and significantly reduces the model performance. To address this challenge, we analyze the inconsistency differences between different model layers of local models and further propose Layer-wise Distance Regularization (LWDR) and Layer-wise Momentum Aggregation (LWMA). The proposed LWDR and LWMA optimize the local training and model aggregation processes, respectively, to improve the convergence performance of FL on data in the nonindependent and identically distributed (Non-IID) scenarios. Our experiments on well-known datasets show that our algorithm significantly outperforms the state-of-the-art FL algorithms in convergence speed, accuracy, and stability in different Non-IID scenarios.
Smart interpretable model (SIM) enabling subject matter experts in rule generation

Current Artificial Intelligence (AI) technologies are widely regarded as black boxes, whose internal structures are not inherently transparent, even though they provide powerful prediction capabilities. Having a transparent model that enables users to understand its inner workings allows them to appreciate the learning and inference process, leading to trust and higher confidence in the model. While methods that help with interpretability have been created, most of them require the user to have a certain level of AI knowledge and do not allow a user to fine-tune them based on prior knowledge. In this paper, we present a smart interpretable model (SIM) framework that requires little to no AI knowledge and can be used to create a set of fuzzy IF-THEN rules along with its corresponding membership functions at ease. The framework also allows users to incorporate prior knowledge during various steps in the framework and generates comprehensive insights summarized from rules and samples, allowing users to identify anomalous rules, feature contributions of each sample and confidence level for each rule. We demonstrate these capabilities and compare our model to other existing rule-based models using various datasets that have been used for rule-based model validations. Validations are then done in terms of performance and whether the rules that are generated by SIM are similar to the rules generated by other more recent rule-based models. • Framework that requires little to no AI knowledge. • Facilitates creation of a set of fuzzy IF-THEN rules. • Allows subject matter experts (SME) to incorporate prior knowledge. • Generates comprehensive insights summarized from rules and samples. • Validation on benchmark datasets to state-of-the art models yields compatible results.
Sentiment Analysis: Twitter Tweets Classification Using Machine Learning Approaches

Analyzing public data from social media could give fascinating results and insights into the realm of public opinion on almost any product, service, or person. Sentiment analysis is a method for analyzing and interpreting Twitter data in order to determine public opinion. Creating sentiment analysis software is a method for measuring Twitter tweet perceptions programmatically. Results classify user’s perspective via tweets into positive and negative, which is represented in result, may include polarity of the tweets. In this paper, we evaluate various machine learning algorithms performance to determine suitable algorithm for twitter datasets classification.
SpaceSaving <sup>±</sup>

In this paper, we propose the first deterministic algorithms to solve the frequency estimation and frequent item problems in the bounded-deletion model. We establish the space lower bound for solving the deterministic frequent items problem in the bounded-deletion model, and propose Lazy SpaceSaving ± and SpaceSaving ± algorithms with optimal space bound. We develop an efficient implementation of the SpaceSaving ± algorithm that minimizes the latency of update operations using novel data structures. The experimental evaluations testify that SpaceSaving ± has accurate frequency estimations and achieves very high recall and precision across different data distributions while using minimal space. Our experiments clearly demonstrate that, if allowed the same space, SpaceSaving± is more accurate than the state-of-the-art protocols with up to logU - 1/ logU of the items deleted, where U is the size of the input universe. Moreover, motivated by prior work, we propose Dyadic SpaceSaving ± , the first deterministic quantile approximation sketch in the bounded-deletion model.
PSIDP: Unsupervised deep hashing with pretrained semantic information distillation and preservation

Blending free annotation cost, low memory usage and high query speed into a unity, unsupervised deep hashing has shown extraordinary talents in image retrieval. In order to address the lack of semantic information in unsupervised scenario, related works leverage models pretrained on large-scale datasets (e.g., ImageNet) to the estimate semantic similarity for specific datasets (e.g., FLICKR-25 K). However, most of them discard the original semantic attribute in the pretrained datasets, which results in hash codes being over-fitted to the estimated semantic similarity information. To alleviate this problem, this paper tries to consider the semantic similarity of the specific dataset and preserve the semantic attribute from the pretrained dataset simultaneously. Meanwhile, to build a reliable semantic similarity matrix for the specific dataset, this paper develops a similarity distiller by jointly measuring the semantic similarity with the distance between deep features, the distance on the local manifold and the distance on the connectivity graph. Moreover, an efficient attribute preserver is also designed to maintain the correspondence between the hash codes and the category attributes from the pretrained dataset based on regeneration criteria. The method proposed in this paper is named PSIDP for simplicity and extensive image retrieval experiments on four benchmark datasets demonstrate the superiority of the proposed PSIDP method when compared with other state-of-the-art unsupervised deep hashing methods. The code is available at https://github.com/reresearcher/PSIDP.