Reviewerly/exHarmony huggingface dataset:
Fuzzy C-Means Clustering Algorithms with Weighted Membership and Distance

Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable performance in diverse problems, the current literature does not have studies about how they can improve the clustering quality of partitions with overlapping classes. The better the clustering quality of a partition, the better is the interpretation of the data, which is essential to understand real problems. This work proposes two robust FCM algorithms to prevent ambiguous membership into clusters. For this, we compute two types of weights: an weight to avoid the problem of overlapping clusters; and other weight to enable the algorithm to identify clusters of different shapes. We perform a study with synthetic datasets, where each one contains classes of different shapes and different degrees of overlapping. Moreover, the study considered real application datasets. Our results indicate such weights are effective to reduce the ambiguity of membership assignments thus generating a better data interpretation.
Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).
Video key frame extraction based on scale and direction analysis

Currently, efficient and accurate key frame extraction for massive videos remains a challenge, especially in surveillance applications. To overcome the inaccuracy of traditional key frame extraction, this paper proposes a method for key frame extraction via analyzing scale and direction of motion trajectory on spatiotemporal slice (MTSS). The proposed method employs both local and global motion state changes of object as the metric to extract key frames and meanwhile gives a strategy which integrates coarse extraction with partial fine re-extraction of spatiotemporal slices to capture these state changes. Firstly, coarse extraction of spatiotemporal slices is employed to detect motion segments. Then partial fine re-extraction is performed on the motion segments to obtain MTSS. Finally, the frames at the inflexions of scale and direction of MTSS are extracted as key frames. The experimental results have demonstrated that the proposed method outperforms existing state-of-the-art methods in terms of accuracy for various videos while requiring a comparable or even less computation time.
A Free Simulation Environment Based on ROS for Teaching Autonomous Vehicle Navigation Algorithms

In recent years, engineering degree programs have become fundamental to the teaching of robotics and incorporate many fundamental STEM concepts. Some authors have proposed different platforms for teaching different topics related to robotics, but most of these platforms are not practical for classroom use. In the case of teaching autonomous navigation algorithms, the absence of platforms in classrooms limits learning because students are unable to perform practice activities or cannot evaluate and compare different navigation algorithms. The main contribution of this study is the implementation of a free platform for teaching autonomous-driving algorithms based on the Robot Operating System without the use of a physical robot. The authors present a case study using this platform as a teaching tool for instruction in two undergraduate robotic courses. Students evaluated the platform quantitatively and qualitatively. Our study demonstrates that professors and students can carry out different tests and compare different navigation algorithms to analyze their performance under the same conditions in class. In addition, the proposed platform provides realistic representations of environments and data visualizations. The results claim that the use of simulations helps students better understand the theoretical concepts, motivates them to pay attention, and increases their confidence.
Detection of changes in literary writing style using N-grams as style markers and supervised machine learning

The analysis of an author's writing style implies the characterization and identification of the style in terms of a set of features commonly called linguistic features. The analysis can be extrinsic, where the style of an author can be compared with other authors, or intrinsic, where the style of an author is identified through different stages of his life. Intrinsic analysis has been used, for example, to detect mental illness and the effects of aging. A key element of the analysis is the style markers used to model the author's writing patterns. The style markers should handle diachronic changes and be thematic independent. One of the most commonly used style marker in extrinsic style analysis is n-gram. In this paper, we present the evaluation of traditional n-grams (words and characters) and dependency tree syntactic n-grams to solve the task of detecting changes in writing style over time. Our corpus consisted of novels by eleven English-speaking authors. The novels of each author were organized chronologically from the oldest to the most recent work according to the date of publication. Subsequently, two stages were defined: initial and final. In each stage three novels were assigned, novels of the initial stage corresponded to the oldest and those at the final stage to the most recent novels. To analyze changes in the writing style, novels were characterized by using four types of n-grams: characters, words, Part-Of-Speech (POS) tags and syntactic relations n-grams. Experiments were performed with a Logistic Regression classifier. Dimension reduction techniques such as Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) algorithms were evaluated. The results obtained with the different n-grams indicated that all authors presented significant changes in writing style over time. In addition, representations using n-grams of syntactic relations have achieved competitive results among different authors.
Accurate image segmentation based on adaptive distance regularization level set method

Level set method has been widely applied in the field of image segmentation. However, the level set formulation is inevitably affected by the regularization function, in-homogeneity and weak edge in the process of evolution, which often leads to the instability and inaccuracy of image segmentation results. To solve these problems, a new distance regularization term defined by a double-well potential function is proposed to satisfy more ideal characteristics of signed distance property. In addition, a novel edge indicator function is introduced to segment images with uneven intensity or weak edge. Finally, the adaptive adjustment formulas of distance regularization and area parameters are derived to alleviate the difficulty of parameter adjustment. Experimental results show that the proposed model provides better accuracy and versatility, quantitative experiment on Weizmann segmentation evaluation database achieves mean Dice score (96.87%), IoU (94.38%), Hausdorff distance (3.20[Formula: see text]mm), Recall (97.68%) and Precision (96.32%), respectively.
Text Classification Based on Machine Learning and Natural Language Processing Algorithms

Nowadays, with the development of media technology, people receive more and more information, but the current classification methods have the disadvantages of low classification efficiency and inability to identify multiple languages. In view of this, this paper is aimed at improving the text classification method by using machine learning and natural language processing technology. For text classification technology, this paper combines the technical requirements and application scenarios of text classification with ML to optimize the classification. For the application of natural language processing (NLP) technology in text classification, this paper puts forward the Trusted Platform Module (TPM) text classification algorithm. In the experiment of distinguishing spam from legitimate mail by text recognition, all performance indexes of the TPM algorithm are superior to other algorithms, and the accuracy of the TPM algorithm on different datasets is above 95%.
Continuity, change and challenge: unearthing the (fr)agility of teacher education

In this final article, we bring together the issues raised by authors included in this special issue. We start by describing the current situation in our own countries, partly to highlight the different ways in which nations are responding in the longer term to the pandemic, but also to draw attention to the similarity of experience – of educators using digital technology, of concern with maintaining the supply of teachers, of the challenges relating to lockdowns – during its peak. We then reflect on the systemic issues that have been raised by the authors in this issue: what we call the (fr)agility of the teacher education system, in which educators’ adaptive response to the pandemic and subsequent desire for change can be met by institutional resistance; the multiple questions raised by the use of digital technologies; and the challenges relating to teacher and teacher educator adaptability and/or agility. In the final section, we reflect on what we (might) have learned from the pandemic and consider a future agenda for teacher educators.
Editor's Review and Introduction: Cognition‐Inspired Artificial Intelligence

Cognitive science has much to contribute to the general scientific body of knowledge, but it is also a field rife with possibilities for providing background research that can be leveraged by artificial intelligence (AI) developers. In this introduction, we briefly explore the history of AI. We particularly focus on the relationship between AI and cognitive science and introduce this special issue that promotes the method of inspiring AI development with the results of cognitive science research.
Quality of Measurement in Core Lexicon Measures

Purpose: Core lexicon measures have received growing attention in research. They are intended to provide clinicians with a clinician-friendly means to quantify word retrieval ability in discourse based on normal expectations of discourse production for specific discourse elicitation tasks. To date, different criteria have been used to develop core lexicon measures by groups of researchers. The need for statistical guidance in pursuit of the psychologically robust measure has been recognized. Aims: This study was to investigate the best criterion for accurate measurement. Specifically, we focused on two criteria ( frequency vs. percentage ) that have previously been used for the development of core lexicon measures. Method: Core lexicon measures consisting of five different checklists by word class (verbs, nouns, adjectives, adverbs, and function words) and developed by the two criteria were applied to language samples produced by 470 cognitively healthy adults. Performance in word retrieval ability at the discourse level was modeled as a latent variable based on the observed proportions of the production of core lexicon items in two different sets of core lexicon measures using structural equation modeling. Results: Results indicated that both criterion for core lexicon measures capture word retrieval ability in discourse. Greater residual variances were found in the core lexicon measure established by the percentage criterion compared to the one established by the frequency criterion. This indicates that the measure based on the percentage criterion is more affected by measurement errors. Conclusions: The findings provide evidence that the frequency criterion is better to use for the development of core lexicon measures for core nouns, verbs, adjectives, and adverbs, but not for function words. However, our findings are limited to core lexicon measures based on language samples elicited by wordless picture books. This may not be easily applied to other core lexicon measures that use different discourse elicitation tasks due to the difference in quality and quantity of language samples. Ideally, the same approach should be replicated to evaluate the appropriateness of respective criteria in the development of core lexicon measures. Supplemental Material: https://doi.org/10.23641/asha.20304144
Chronology of self-competition in virtual reality exergames: A historical review

Competing against others with better performances can demotivate and reduce players' performances in Virtual Reality (VR) exergames. Due to this concern, previous studies have found a better solution to this which involves a system of players against themselves in VR exergames. However, the milestone of self-competition in VR exergames has not yet been compiled, compared, and discussed, as there is still room for future improvements. Therefore, this article discusses the evolution of self-competition in VR exergames, and highlights the state of the art for future studies. Related papers were gathered through keyword search strings, handpicking, and snowballing methods. Studies on self-competition in VR exergames have begun since 2007. Two categories of self-competition have been identified and suggested, which are "leaderboard" and "ghost". The "ghost" category of self-competition is better than the "leaderboard" self-competition category in terms of enjoyment and motivation. Among the "ghost" self-competition studies, there have been implementations of a physiological technique called feedforward that has better significant results. The feedforward technique has a promising contribution towards society, especially for sedentary, less fit, and less competitive individuals.
A blockchain-based application for genomic access and variant discovery using smart contracts and homomorphic encryption

Genomic data repositories are rapidly growing due to the decline in the cost of DNA sequencing. This has increased the demand from stakeholders such as researchers to analyze these datasets to advance areas in biomedical research. Genomic datasets are mostly maintained by third-party direct-to-consumer (DTC) genomic companies who operate a business model of collecting DNA data from their customers and selling them to pharmaceutical companies. This puts the privacy of their customers at risk since each individual’s human genome is unique. In addition, customers lose ownership of and access to their genomic data to DTCs and DTCs do not share profits from data sales with them. In this paper, we propose a system based on blockchain technology and homomorphic computation to address the aforementioned problems. We use blockchain transactions and smart contracts to allow genomic data owners (DOs) to have control of their data and sell access to it, and homomorphic computation with secure two-party protocol to enable genomic data users (DUs) to run queries to securely discover DOs of interest. We further optimize the query response time by proposing an approach based on genomic data partitions, binary search trees and bloom filters to reduce the search space. We also propose a blockchain penalty mechanism to encourage parties to behave honestly to avoid malicious behaviors such as uploading fake or non-human genomic data. We conduct our experiments on real genomic datasets and demonstrate that our proposed scheme allows DOs to control access to their data and is feasible and efficient in terms of computation cost, query response time and scalability.
Dilated spatial–temporal convolutional auto-encoders for human fall detection in surveillance videos

Although methods based on supervised learning have demonstrated remarkable performance on fall detection, these existing fall detection algorithms require a substantial quantity of manually labeled training data. In this paper, we combine dilated convolution and LSTM based on auto-encoder, which can be trained on unlabeled data, further saving time and resources, and a novel fall score is computed based on the high-quality reconstructed frame to detect falls. Extensive experimental results indicate that the proposed method further boosts the performance, achieving recognition rate of 97.1%, sensitivity rate of 93.9% and precision rate of 95.1% on the UR dataset.
High-efficient hierarchical federated learning on non-IID data with progressive collaboration

Hierarchical federated learning (HFL) allows multiple edge aggregations at edge devices before one global aggregation to address both issues of non-independent and identically distributed (non-IID) data and communication bottleneck in federated learning (FL). To solve the non-IID issue, most HFL algorithms assume that clients can be assigned to any edge device. In practice, however, these assumptions are always unrealistic. In this paper, we propose a high-efficient HFL algorithm, named FedPEC, which introduces progressive edge collaboration rather than unrealistic client allocation. FedPEC estimates the initial number of collaborators based on our proved convergence upper bound, and then constantly adjusts the estimated number of collaborators according to the characteristics of each stage in the following rounds. Guided by the estimated number of collaborators, each edge device can be assigned an appropriate collaborator set based on an adaptive similarity threshold. Extensive experiments are conducted to investigate FedPEC in terms of accuracy, loss, and convergence speed with various data sets. Our experimental results demonstrate that FedPEC can significantly outperform state-of-the-art FL algorithms.
Trace Oddity: Methodologies for Data-Driven Traffic Analysis on Tor

Traffic analysis attacks against encrypted web traffic are a persisting problem. However, there is a large gap between the scientific estimate of attack threats and the real-world situation. As traffic analysis attacks depend on very specific metadata information, they are sensitive to artificial changes in the transmission characteristics. While the advent of deep learning greatly improves the performance rates of traffic analysis attacks on Tor in research settings, deep neural networks are known for being implicitly vulnerable to artifacts in data. Removing artifacts from our experimental setups is essential to minimizing the risk of evaluation bias. In this work, we study a state-of-the-art end-to-end traffic correlation attack on Tor and propose a novel data collection setup. Our design addresses the key constraint of prior work: instead of using a single proxy node for collecting exit traffic, we deploy multiple proxies. Our extensive analysis shows that in the multi-proxy design (i) end-to-end round-trip times are more realistic than in the original design, and that (ii) traffic correlation attack performance degrades significantly on realistic timings. For a reliable and informative evaluation, we develop a general scientific methodology for replication and comparison of machine and deep-learning attacks on Tor. Our evaluation indicates high relevance of the multi-proxy data collection setup and the novel dataset.
PHISS: Progressive and hierarchical image segmentation-sharing scheme

As a classical secret sharing technique, (k,n) threshold secret image sharing mainly contains the shadow generation phase and the image reconstruction phase. The sensitive secret image is encrypted into n shadows by the dealer in the shadow generation phase, and the secret image can be reconstructed completely by the participants when the number of shadows meet or exceed the threshold k. Previous progressive or hierarchical secret image sharing schemes have some disadvantages (e.g., inflexibility and inefficiency). To solve this problem, we propose a progressive and hierarchical image segmentation-sharing scheme (referred to as PHISS) in this paper. First, the important content region of the sensitive image (called by the target sub-image) is generated by using the proposed image segmentation algorithm. Here the target sub-image refers to the part with the largest amount of information, for instance, the human face in private photo, the pathological region in medical image and the aircraft in military image. Then, in the shadow generation phase, the target sub-image is encrypted into several shadows with hierarchy, and the hierarchies of the generated shadows in the background sub-image are equal. Last, in the reconstruction phase, the background sub-image can be reconstructed completely. The target sub-image has different quality with different number of shadows and different level of shadows. The image can be reconstructed losslessly if and only if all shadows involved. The experimental results and the comparison with similar works demonstrate the feasibility and the superiority of the proposed method.
The importance of e-books in improving students’ skills in physics learning in the 21st century: a literature review

Abstract Along with the development of technology, the education system began to change from face-to-face learning to blended learning. Blended learning uses a combination of print and digital learning media. Most digital learning resources are e-books that replace the function of printed books and the role of the teacher in the classroom. The focus of research in this paper is how e-books can improve student skills in the 21st century. There has been a number of researches related to e-books and their use in high schools. In-depth literature study is needed to get a conclusion on how e-books affect students’ skills, especially after studying Physics. This research provides invaluable insight into how educators were maximizing the use of e-books so as to improve student learning outcomes which in turn impact 21st century skills. These studies describe a lot of data which enables other researchers to understand how teachers use and wish to use e-books. The analysis of these empirical results is used as the basis for developing e-books that have effective content and features that are relevant to e-book literacy.