Reviewerly/exHarmony huggingface dataset:
Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems

Deep learning-based Human Activity Recognition (HAR) systems received a lot of interest for health monitoring and activity tracking on wearable devices. The availability of large and representative datasets is often a requirement for training accurate deep learning models. To keep private data on users’ devices while utilizing them to train deep learning models on huge datasets, Federated Learning (FL) was introduced as an inherently private distributed training paradigm. However, standard FL (FedAvg) lacks the capability to train heterogeneous model architectures. In this paper, we propose Federated Learning via Augmented Knowledge Distillation (FedAKD) for distributed training of heterogeneous models. FedAKD is evaluated on two HAR datasets: A waist-mounted tabular HAR dataset and a wrist-mounted time-series HAR dataset. FedAKD is more flexible than standard federated learning (FedAvg) as it enables collaborative heterogeneous deep learning models with various learning capacities. In the considered FL experiments, the communication overhead under FedAKD is 200X less compared with FL methods that communicate models’ gradients/weights. Relative to other model-agnostic FL methods, results show that FedAKD boosts performance gains of clients by up to 20 percent. Furthermore, FedAKD is shown to be relatively more robust under statistical heterogeneous scenarios.
Immersive Journalism: The Effect of System Immersion on Place Illusion and Co-Presence in 360-Degree Video Reporting

Immersive journalism has been promoted as an alternative way of producing content that allows users to experience first-hand the events depicted in the news story. This mixed-method study examines how immersive journalism impacts the user experience of non-fictional narratives in news practices. A between-subject experiment (n = 104) was conducted to inspect the effects of system immersion on place illusion and co-presence. Using a 360-degree video news report produced by the Spanish newspaper El País as a stimulus, two viewing conditions were compared: magic window and 360 degree with a VR headset. The results show that VR technologies determine the effective potential of immersive journalism, namely the experience of place illusion and co-presence and that audiences do not actually explore the so-called whole picture, as a 90–180° movement for exploration prevailed.
Phase space nonclassicality and sub-Poissonianity of deformed photon-added nonlinear cat states: algebraic and group theoretical approach

In this Letter, we introduce four distinct classes of f -deformed photon-added nonlinear cat state. This would be achieved by recalling a nonlinear coherent states approach, as well as a particular class of Gilmore–Perelomov-type of SU (1,1) coherent state and a class of SU (2) coherent state. We then examine the role of photon addition and nonlinearity functions in the phase space structure and sub-Poissonianity of even, odd, and Yurke–Stoler nonlinear cat states. The effect of photon addition, which results in a π phase shift at the origin of the Wigner function toward negativity, interestingly enhances the nonclassicality by means of the Wigner function and Mandel parameter. Furthermore, owing to photon addition, we can observe a deformation in the Gaussian shape of the Wigner function, which may be found to be potentially useful in quantum noise reduction. Moreover, the deformation function shows a remarkable role in revealing the nonclassical behavior and can increase the depth and the domain of nonclassicality.
Absolute phase measurement method based on bidirectional coding patterns

The stair-phase-coding patterns have been widely used to determine the fringe order for phase unwrapping of the wrapped phase in three-dimensional shape measurement. Although the special coding sequence algorithm can achieve with a large number of codewords, it needs the current codeword and its adjacent codewords to jointly determine the fringe order. If any codeword of the grouped adjacent codewords is incorrectly recognized, it will result in many false fringe orders. It increases the probability of fringe order error in the decoding process. And it is challenging to significantly increase the number of codewords. To solve this problem, we propose an absolute phase measurement method based on bidirectional coding patterns. The wrapped phase of the object is obtained by four-step phase-shifting patterns, and the fringe order is obtained by bidirectional coding patterns. When generating the bidirectional coding patterns, we code two groups of stair phase with different frequencies along the horizontal direction, which respectively represent local fringe order and partition information. Then, we alternately repeat the two groups of stair phase along the vertical direction in the whole pattern to obtain the bidirectional coding patterns. Each local fringe order information and the corresponding partition information in a small region jointly determine the fringe order of pixels in this small region. Fringe order errors in a small region do not affect other regions. To verify the effectiveness of our method, we performed simulations and experiments. Simulation and experimental results show that our method is effective for objects with different sizes and isolated objects.
An Optimal Frontier Enhanced “Next Best View” Planner For Autonomous Exploration

This work introduces a novel Optimal Frontier Enhanced “Next Best View” Planner (OFENBVP), which can enable autonomous exploration capabilities in mobile robots. The proposed planner combines a local “Next Best View” (NBV) planner and an optimal frontier-based global enhancement algorithm. The local NBV planner takes charge of exploring a small area surrounding the robot while the optimal frontier-based global enhancement algorithm is in charge of moving the robot to a new unexplored area after a certain number of iterations of the local NBV planner. The proposed method is implemented and validated on the hybrid mobility platform CENTAURO performing an exploration task, demonstrating much less time to complete the exploration of the environment.
Students of Autonomous Learning Using Mobile Assisted Language Learning (MALL) at Senior High School

This research aimed to improve the students’ listening in autonomous learning using Mobile Assisted Language Learning (MALL), this research was conducted in SMAN 18 Bulukumba. The data had been counted by the researcher, then the researcher consider about the implementation of autonomous learning using MALL.In conducted this research, the researcher gave the students autonomous learning and the students work. The data were got using the test for the students, then the data counted by researcher by using Classroom Action Research method of data collection technique, and also through documentation with 32 students in the classroom.The Results of this research showed that autonomous learning using Mobile Assisted Language Learning (MALL) can improve the students’ listening, especially in Respond and Understand of their mobile phone by using several applications podcasts; it proven by the results of the test, where the students’ achievement in listening respond and understand was shown by the students’ scores in D-test (50 and 55.7), Cycle I (60.3 and 60), and Cycle II (81.1 and 71.). Hopefully, the result of this research is able to be replicated by SMAN teachers and students in solving similar problems faced in autonomous learning using Mobile Assisted Language Learning (MALL).
Study on the enhancement method of online monitoring image of dense fog environment with power lines in smart city

In this research, an image defogging algorithm is proposed for the electricity transmission line monitoring system in the smart city. The electricity transmission line image is typically situated in the top part of the image which is rather thin in size. Because the electricity transmission line is situated outside, there is frequently a sizable amount of sky in the backdrop. Firstly, an optimized quadtree segmentation method for calculating global atmospheric light is proposed, which gives higher weight to the upper part of the image with the sky region. This prevents interference from bright objects on the ground and guarantees that the global atmospheric light is computed in the top section of the image with the sky region. Secondly, a method of transmission calculation based on dark pixels is introduced. Finally, a detail sharpening post-processing based on visibility level and air light level is introduced to enhance the detail level of electricity transmission lines in the defogging image. Experimental results indicate that the algorithm performs well in enhancing the image details, preventing image distortion and avoiding image oversaturation.
Robust Federated Learning for execution time-based device model identification under label-flipping attack

Abstract The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks and threats. Among them, device spoofing and impersonation cyberattacks stand out due to their impact and, usually, low complexity required to be launched. To solve this issue, several solutions have emerged to identify device models and types based on the combination of behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques. However, these solutions are not appropriate for scenarios where data privacy and protection are a must, as they require data centralization for processing. In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup. The present work analyzes and compares the device model identification performance of a centralized DL model with an FL one while using execution time-based events. For experimental purposes, a dataset containing execution-time features of 55 Raspberry Pis belonging to four different models has been collected and published. Using this dataset, the proposed solution achieved 0.9999 accuracy in both setups, centralized and federated, showing no performance decrease while preserving data privacy. Later, the impact of a label-flipping attack during the federated model training is evaluated using several aggregation mechanisms as countermeasures. Zeno and coordinate-wise median aggregation show the best performance, although their performance greatly degrades when the percentage of fully malicious clients (all training samples poisoned) grows over 50%.
Performance Analysis of Classification and Detection for PV Panel Motion Blur Images Based on Deblurring and Deep Learning Techniques

Detecting snow-covered solar panels is crucial as it allows us to remove snow using heating techniques more efficiently and restores the photovoltaic system to proper operation. This paper presents classification and detection performance analyses for snow-covered solar panel images. The classification analysis consists of two cases, and the detection analysis consists of one case based on three backbones. In this study, five deep learning models, namely visual geometry group-16 (VGG-16), VGG-19, residual neural network-18 (RESNET-18), RESNET-50, and RESNET-101, are used to classify solar panel images. The models are trained, validated, and tested under different conditions. The first case of classification is performed on the original dataset without preprocessing. In the second case, extreme climate conditions are simulated by generating motion noise; furthermore, the dataset is replicated using the upsampling technique to handle the unbalancing issue. For the detection case, a region-based convolutional neural network (RCNN) detector is used to detect the three categories of solar panels, which are all_snow, no_snow, and partial. The dataset of these categories is taken from the second case in the classification approach. Finally, we proposed a blind image deblurring algorithm (BIDA) that can be a preprocessing step before the CNN (BIDA-CNN) model. The accuracy of the models was compared and verified; the accuracy results show that the proposed CNN-based blind image deblurring algorithm (BIDA-CNN) outperformed other models evaluated in this study.
Los metaversos como herramienta docente en la formación de profesores de educación superior

For years, universities have been making great efforts to introduce improvements and innovations in teaching. To this end, different tools are being incorporated to support teaching methodologies. This article analyzes the application of metaverses in a training session for a group of 47 university professors in a business school using the AltspaceVR tool on a traditional screen. The results obtained in a subsequent survey are analyzed statistically, showing specific differences in the willingness to use them, which is higher among the youngest in the study because they are considered more "digital natives"; among men; among teachers with a Ph.D. degree; and among those with more teaching experience. The conclusions suggest that teachers consider metaverses can be an excellent tool for multiplying the options for remote teaching, both fully online and hybrid, and consider it as a complement, rather than a substitute, in the case of traditional education. However, it is appreciated that implementing this tool requires more technical training.
Design and Implementation of English Teaching Analysis System Based on Data Mining

Driven by the current situation of teaching management, teaching managers at colleges and universities at all levels generally hope to build a more functional teaching analysis system by using advanced information technology, so as to provide more comprehensive historical data analysis functions for teaching managers. The design and implementation of English teaching analysis system based on data mining in this paper improves the management and teaching level of the whole school in the process of English teaching, and finally improves the ability of English subject management in teaching decision-making.
Research on the Application of Data Mining in the Quality Analysis of College English Teaching

In order to give full play to the role of data mining technology, do a good job in analyzing the quality of English teaching in colleges and universities, and provide help for the improvement of teaching quality, this paper will carry out relevant research. This paper first introduces the basic concept of data mining and its relationship with English teaching quality analysis, and then proposes a design scheme of data mining teaching quality analysis system. Through the research, the system can give full play to the role of data mining technology and promote the development of college English teaching quality.
Optimal Data Allocation in the Environment of Edge and Cloud Servers

With the rise in popularity of cloud computing, there is a growing trend toward the storage of data in a cloud environment. However, there is a significant increase in the risk of privacy information leakage, and users could face serious challenges as a result of data leakage. In this paper, we propose an allocation scheme for the storage of data in a collaborative edge-cloud environment, with a focus on enhanced data privacy. Specifically, we first divide the datasets by fields to eliminate as much as possible the correlation between the leaked data. We then evaluate the sensitivity of the data and server trust to calculate the degree of fitting between them and combine the results with the performance score to obtain a server qualification value for the stored fields. Several constraints are also specified, and the E-CARGO model is used to formalize the problem. Based on the qualification value, we can find the optimal allocation using the IBM ILOG CPLEX Optimization (CPLEX) Package. In our experiments, our proposed solution allows the data to be stored in servers that better suit their requirements, while reducing the user overhead.
End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of Moccia Code

The growth of digital libraries has yielded a large number of handwritten historical documents in the form of images, often accompanied by a digital transcription of the content. The ability to track the position of the words of the digital transcription in the images can be important both for the study of the document by humanities scholars and for further automatic processing. We propose a learning-free method for automatically aligning the transcription to the document image. The method receives as input the digital image of the document and the transcription of its content and aims at linking the transcription to the corresponding images within the page at the word level. The method comprises two main original contributions: a line-level segmentation algorithm capable of detecting text lines with curved baseline, and a text-to-image alignment algorithm capable of dealing with under- and over-segmentation errors at the word level. Experiments on pages from a 17th-century Italian manuscript have demonstrated that the line segmentation method allows one to segment 92% of the text line correctly. They also demonstrated that it achieves a correct alignment accuracy greater than 68%. Moreover, the performance achieved on widely used data sets compare favourably with the state of the art.
Multiclass Intent Classification for Chatbot Based on Machine Learning Algorithm

In recent years, the use of Chatbots has grown significantly in various industries, including support systems, education, health care, tourism, entertainment, and banking. Chatbot for education can provide instant feedback in interactions session with students. The responses from Chatbot can be based on machine learning algorithms or multiple heuristics techniques to select responses from the predefined library. The generated response depends on the user's intent of using the Chatbot. There are various classes or categories in the user's intent. However, the user's intent class cannot simultaneously belong to multiple classes. Therefore, this research proposed a multiclass intent classification for the Chatbot based on the machine learning algorithm. The findings of this research showed that Linear SVC is the best machine learning algorithm model for multiclass intent classification. The results of the analysis proved the accuracy of the prediction using Linear SVC.
Teachers’ Digital Competence. The Case of the University System of Galicia

The digital teaching competence of university professors is of vital importance to improving the quality of higher education, and for the strategic positioning of public universities in our country. The purpose of this study is to find out the level of digital competence of Galician university professors, as well as to identify whether there are significant variable-based differences. To this end, a non-experimental design with a descriptive approach and hypothesis contrast is proposed. The sample studied is made up of a total of 610 teachers from the three public universities of Galicia (Spain), who responded to the DigCompEdu Check-In instrument adapted to the Spanish context (Cabero-Almenara &amp; Palacios-Rodríguez, 2020). In general terms, the results show a medium-low level and the existence of significant differences depending on the age, recruitment profile and area of knowledge of the teaching staff. Specifically, the younger teaching and research staff, together with Social and Legal Sciences, have a higher level of competence, whereas tenured teaching and research staff have a lower level of competence with respect to contracted workers. Therefore, training plans should be developed which focus on teaching. Expert advice and personalized methodological support should shift the focus of the intervention radically from technology to teaching.
Classification of some quadrinomials over finite fields of odd characteristic

In this paper, we completely determine all necessary and sufficient conditions such that the polynomial f(x)=x3+axq+2+bx2q+1+cx3q, where a,b,c∈Fq⁎, is a permutation quadrinomial of Fq2 over any finite field of odd characteristic. This quadrinomial has been studied first in [25] by Tu, Zeng and Helleseth, later in [24] Tu, Liu and Zeng revisited these quadrinomials and they proposed a more comprehensive characterization of the coefficients that results with new permutation quadrinomials, where char(Fq)=2 and finally, in [16], Li, Qu, Li and Chen proved that the sufficient condition given in [24] is also necessary and thus completed the solution in even characteristic case. In [6] Gupta studied the permutation properties of the polynomial x3+axq+2+bx2q+1+cx3q, where char(Fq)=3,5 and a,b,c∈Fq⁎ and proposed some new classes of permutation quadrinomials of Fq2. In particular, in this paper we classify all permutation polynomials of Fq2 of the form f(x)=x3+axq+2+bx2q+1+cx3q, where a,b,c∈Fq⁎, over all finite fields of odd characteristic and obtain several new classes of such permutation quadrinomials.
Before Breakdown, after Repair

In science and technology studies, an important amount of research work has described the place of technology in modern societies by focusing on a particular form of failure: breakdowns. Investigated in close relationship with the repair operations that follow them, breakdowns have been extensively analysed as exceptional moments during which sociomaterial assemblages are “opened up,” discussed, and then put back in order. While acknowledging the relevance of such an approach, this chapter focuses on what the binary dynamics of breakdown and repair leave out of the theoretical discussion. It suggests conceptually distinguishing, at least temporarily, the frequently equated notions of maintenance and repair, so as to further explore the mundane situations that come before breakdown and after repair. Thinking with maintenance, the chapter argues, helps us understand material fragility as a common feature rather than a deviation from the norm, and to highlight the repetitive and distributed activities that are undertaken to take care of things. This analytical move leads to a reconsideration of failure itself, which appears not so much as a hiatus or a disruptive event as an everyday part of the relationships between humans and things.
Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach

Sentiment Analysis (SA) is an efficient way of determining people’s opinions from a piece of text. SA using different social media sites such as Twitter has achieved tremendous results. Twitter is an online social media platform that contains a massive amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which are important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment on Twitter have been discussed. There has been an extensive research studies in the field of SA of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable opinion mining techniques based on machine learning and lexicon-based along with their metrics. The proposed work is helpful in informaiton analysis in the tweets where opinions are found heterogeneous, unstructured, polarised negative, positive, or neutral. In order to validate the supremacy of the suggested approach, we have executed a series of experiments on the real-world Twitter dataset that alters to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the SA field and the proposed work’s future scope.