Reviewerly/exHarmony huggingface dataset:
Human activity recognition

With the increasing amount of videos available on the internet and in security settings, human activity recognition has become a highly important topic in machine learning. In this chapter, we cover the topic of deep learning for human activity recognition and the tasks of trimmed action recognition, temporal action localization, and spatiotemporal action localization. Throughout our treatise, we discuss different architectural building blocks including 2D Convolutional Neural Networks (CNNs), 3D-CNNs, Recursive Neural Networks (RNNs), and Spatial-Temporal Graph Convolution Networks (ST-GCNs), and how they were used in state-if-the-art models for human activity recognition. Moreover, we discuss how multiple modalities and data resolutions can be fused via a multistream network topology, and how video-classification models can be extended for usage in (spatio)temporal action localization. Finally, we provide a curated list of data sets for human activity recognition tasks.
Natural Scene Text to Voice Signal Conversion for Visually Impaired using Deep Neural Network

The deep neural network architectures can be utilized to detect and recognize the text in natural or camera images. If this text can be converted to voice signals it will be very helpful for persons with partial or no vision. Selective search-based segmentation along with VGG architecture of Deep neural networks is utilized in this work for text detection. The Py-Tesseract Optimal character recognizer is then utilized for recognizing the detected text, which is then converted to voice signals. The system can beneficial for recognizing road side or corridor boards, thus adding some independence to the life of people with special needs. The system can be modified with a search mode for a particular text in the nearby location.
An Optimized Convolution Neural Network Framework for Facial Expression Recognition

Facial expression recognition (FER) is the simplest way to identify human emotions. Many machine and deep learning methods have been proposed to recognize human emotions from facial expressions. However, conventional machine learning methods suffer from poor feature representation and thus limited in performance. Therefore, deep learning methods have been preferred over them to represent features at micro level. Recently, convolution neural network (CNN) based deep models have gained popularity and widely explored for FER. However, hyperparameters tuning and overfitting avoidance is still challenging. Therefore, in this work, we propose convolution neural network based optimized FER to reduce overfitting by tuning the optimizer using data augmentation. We conducted several experiments to achieve better accuracy and used Adam optimizer for the proposed model. We have performed experiments over JAFFE and CK+ datasets and comparative analysis clearly validate the effectiveness of the proposed method in terms of accuracy, precision, recall and F1-score parameters.
Machine Learning-Based Method for Power System Operation Rules Generation

With continuous expansion of the scale of power systems and the ever-increasing penetration of intermittent renewable energy generation, the uncertainty, complexity, and diversity of power system operation have increased significantly. Hence, traditional power system operation rules which are based on the experience of operators are difficult to be applied to large-scale modern power systems. The development of machine learning provides a new perspective for online formulation of operation rules. Transmission section determination is the basis in formulating operation rules, and in this paper, the Clustering by Fast Search and Find of Density Peak (CFSFDP) algorithm and a cut-vertex search method are adopted to identify key transmission sections of the power system concerned. Then, the Monte Carlo simulation method is used to generate massive training samples, and the Total Transfer Capability (TTC) is calculated for each key transmission section. After feature selection, the Radial Basis Function (RBF) neural network is used to train the model to attain the operation rules. Finally, the IEEE 39-bus power system is employed to demonstrate the proposed method.
Knowledge Graph and GNN-Based News Recommendation Algorithm With Edge Computing Support

The current news information from different media websites has posed a serious problem, i.e., it is very difficult to obtain the satisfactory news contents from the measured data information. There have been some researches on news recommendation to improve the experience of users. In spite of this, they always need the further improvement because the news information has showed the explosive increasing way. Therefore, this paper studies knowledge graph and graph neural network (GNN) based news recommendation algorithm with edge computing consideration. At first, the knowledge graph is used for the knowledge extraction. Then, GNN is used to train the extracted features to complete the news recommendation algorithm. Finally, the edge computing is used to offload the high volumes of traffic to the edge server for the news recommendation computation. Compared with two baselines, the proposed algorithm is more efficient, increasing accuracy rate by 2.73% and 9.94% respectively, and decreasing response time by 84.27% and 87.58 respectively.
The Time aspect in Usability: Case of DHIS2 Program Rule Variable Generator

Time efficiency and design consistency are key measures in evaluating software usability. Literature suggests that usability influences software acceptance. This paper focuses on efforts to improve efficiency and consistency of a configurable software platform that provides functionality for users to define software interface skip-logic. The study was conducted using the District Information Software version 2 (DHIS2), a configurable software platform. In DHIS2, skip-logic is implemented using program rule variables and program rule expressions. In particular, the paper focuses on the development of a custom app for defining program variables, in place of an inbuilt DHIS2 app, whose design was found to be inconsistent with other DHIS2 apps and less time efficient. The inbuilt DHIS2 app restricts users to the creation of one program rule variable at a time. The custom app was evaluated by 11 users to check its efficiency. Users were able to batch-create program rule variables, when using the custom app, reducing development time. An average of 9 minutes 24 seconds was taken to create five program rules variables using the DHIS2 inbuilt app. The same task took an average of 2 minutes and 3 seconds when using our custom app.
Toward On-Device Weight Monitoring from Selfie Face Images Using Smartphones

Obesity is a serious health problem that is on the rise at the global level. Recent studies suggest that BMI can be inferred from facial images using deep learning-based convolutional neural networks (CNNs) for obesity classification with about 85–90% accuracy. However, training and testing these deep learning models involves high computation and storage due to the involvement of millions of parameters. A recent trend is the use of lightweight CNN models to facilitate on-device computation in resource-constrained mobile and wearable devices. In this study, we evaluate several lightweight CNNs such as MobileNet-V2, ShuffleNet-V2, and lightCNN-29 for BMI prediction and obesity classification from facial images captured using smartphones. The comparative analysis is done with heavyweight VGG-16 and ResNet-50-based CNN models. These lightweight models when deployed on smartphones can act as self-diagnostic tool in weight changes and obesity monitoring. These tools can facilitate remote monitoring of patients, obtaining patients’ vital signs, and in improving the quality of care provided. Self-diagnostic tools would also help in keeping users’ health data private, safe, and secure.
Boosting the Transferability of Adversarial Examples with More Efficient Data Augmentation

Abstract Currently, Deep Neural Networks has achieved excellent results on many tasks. But recent studies have shown that these networks are easily influenced by adversarial examples, which are artificially crafted by adding perturbation to original image. Moreover, most of the models to which we can access are black-box, we don’t know the internal structure and parameters of the model. Thus, it is more practical and more challenging to study how to attack these models. In this article, we propose a cam(class activation map)-guided data augmentation attack method, which can improve the transferability of adversarial examples. Specifically, first use the trained network to get the class activation maps for an input image, then binarize the cam to get the mask, finally implement the data augmentation attack method on the masked area of the image. Experiments based on ImageNet prove that our proposed method can generate more transferable adversarial examples, and the attack success rates of our method have a certain improvement compared with the latest methods.
RETRACTED: Construction of ecological environment landscape evaluation system of small towns along the Huaihe River area based on fuzzy cloud model

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the author due to what the author claimed was the imprecise model and inaccurate method applied in the study.
A Sky Image-Based Hybrid Deep Learning Model for Nonparametric Probabilistic Forecasting of Solar Irradiance

Accurate solar irradiance and photovoltaic power forecasting is critical to ensure the secure and economic operation of power systems with rapidly increasing photovoltaic generation. This paper proposes a sky image-based hybrid deep learning model for nonparametric probabilistic forecasting of solar irradiance. The proposed method utilizes variational autoencoder (VAE) to compress sky images autonomously. Long short-term memory (LSTM) is applied to extract temporal information embedded in images and time series. Quantile regression is adopted to estimate the conditional quantiles. Comprehensive case studies are conducted based on actual dataset, which shows the superiority of the proposed method and the potential for practical applications.
Enhanced Super-Resolution via Squeeze-and-Residual-Excitation in Aerial Imagery

Super-resolution (SR) presents us with an outstanding technique of enhancing applications associated with aerial and remote-sensing imagery, hence, tasks like classification, segmentation and object detection can benefit significantly from well-performing SR models. Extensive research is being done in the field of SR for both ground-level and aerial imagery where convolutional neural networks (CNN) have attained incredible progress. Numerous deep CNNs use the attention mechanism in their architectures and one such mechanism is the Squeeze-and-Excitation (SE) inter-channel attention. Although SE block has enhanced the performance of many models, there is no residual mechanism used within its structure. Therefore, in this paper, we propose the Squeeze-and-Residual-Excitation (SRE) attention block. SRE improves upon the SE block by using residual mechanism within its structure to deliver performance gain in the task of SR. Based on our SRE attention mechanism we propose an enhanced SR framework for remote-sensing imagery. We call our model the Squeeze-and-Residual-Excitation Holistic Attention Network (SRE-HAN) that outperforms other attention-based deep SR models for two levels of resolution enhancement: 4x- and 8x-upsampling on two diverse aerial imagery datasets: Satellite Imagery Multi-Vehicles Dataset (SIMD) consisting of 5000 high-resolution (HR) aerial images, and Cars-Overhead-With-Context (COWC). Furthermore, by using YoloV5 object-detection model, we carry out multiple experiments to substantiate the effectiveness of these SR models on the task of object detection on SIMD.
Attention Convolutional U-Net for Automatic Liver Tumor Segmentation

Manual radiology reporting of liver Computed Tomography (CT) volumes is time consuming process, requires expertise and is prone to human errors. Computer aided diagnosis (CAD) of liver tumor has multiple advantages including early diagnosis, treatment planning, tumor volume measurement, and monitoring clinical responses. The challenge in automatic liver segmentation in CT images is due to its heterogeneous size, shape, location of tumor, low contrast among the healthy tissues and tumorous area, and their blurred boundaries. This research focuses on automatic liver tumor segmentation using convolutional neural networks (CNN). A U-Net based attention architecture, i.e., Attention-Convolutional U-Net (ACon-U-Net) is proposed, where the attention high-resolution inception block replaces the sequence of every two convolutional layers. The model was trained, validated, and tested using the LiTS dataset. For tumor segmentation, the proposed model is evaluated in terms of dice coefficient and intersection over union. The dice score for training, validation, and testing was 97 &#x0025;, 69 &#x0025;, and 77 &#x0025;, respectively, from abdominal CT scan images of the liver.
Achieving Transparency Report Privacy in Linear Time

An accountable algorithmic transparency report (ATR) should ideally investigate (a) transparency of the underlying algorithm, and (b) fairness of the algorithmic decisions, and at the same time preserve data subjects’ privacy . However, a provably formal study of the impact to data subjects’ privacy caused by the utility of releasing an ATR (that investigates transparency and fairness), has yet to be addressed in the literature. The far-fetched benefit of such a study lies in the methodical characterization of privacy-utility trade-offs for release of ATRs in public, and their consequential application-specific impact on the dimensions of society, politics, and economics. In this paper, we first investigate and demonstrate potential privacy hazards brought on by the deployment of transparency and fairness measures in released ATRs. To preserve data subjects’ privacy, we then propose a linear-time optimal-privacy scheme , built upon standard linear fractional programming (LFP) theory, for announcing ATRs, subject to constraints controlling the tolerance of privacy perturbation on the utility of transparency schemes. Subsequently, we quantify the privacy-utility trade-offs induced by our scheme, and analyze the impact of privacy perturbation on fairness measures in ATRs. To the best of our knowledge, this is the first analytical work that simultaneously addresses trade-offs between the triad of privacy, utility, and fairness, applicable to algorithmic transparency reports.
Sentiment Analysis in Indonesian on Jakarta Culinary as A Recommender System

In text mining, there is a technique to determine whether the opinion statement is positive or negative, namely sentiment analysis. The positive opinion indicates that it can be used as a reference for recommendations. According to a survey conducted by YouGov of the diet habit which correlates with culinary, Jakarta is one of the healthiest cities in the world according to a study conducted by YouGov of diet habit. Diet has a strong correlation with culinary. Culinary is related to food which is one of the basic human needs. Sentiment analysis in this study processes the Jakarta culinary dataset from Twitter using the Sastrawi library to compare Twitter's sentiments on Jakarta's cuisine with the results of the YouGov survey.y. The results obtained are that Jakarta culinary's recommendations are worth looking for and visiting based on this study's 53% positive sentiment. This result follows the YouGov survey on Jakarta city's dietary habits, proving that Jakarta is one of the healthiest cities in the world.
Three faces of recursion axioms: the case of constructive dynamic logic of relation changers

Abstract This paper proposes an intuitionistic generalization of van Benthem and Liu’s dynamic logic of relation changers, where relation changers are dynamic operators that rewrite each agent’s accessibility relation. We employ Nishimura’s Kripke semantics for a constructive propositional dynamic logic to define the semantics of relation changers. Strong completeness results of Hilbert-style axiomatizations for constructive dynamic logic of relation changers are established by two types of rewriting strategies via recursion axioms: inside-out and outside-in strategies. To establish the semantic completeness by outside-in strategy, we propose the notion of composition of relation changers and an appropriately defined notion of length of a formula. Moreover, we reveal that recursion axioms have additional two faces: semantic and proof-theoretic ones. As for the semantic face, we introduce an alternative semantics for dynamic logic of relation changers to specify the semantic meaning of recursion axioms used in the inside-out strategy. This leads us to a semantic completeness proof of the axiomatization for the original semantics, which does not require a rewriting strategy based on recursion axioms. As for the proof-theoretic face, we transform each of recursion axioms required in the outside-in strategy into two (left and right) inference rules to provide a sequent calculus for dynamic logic of relation changers. Since the resulting sequent calculus is cut-free but semi-analytic, it still enjoys the Craig interpolation theorem by Maehara’s method.
Introduction to the Special Issue on Pattern-Driven Mining, Analytics, and Prediction for Decision Making, Part II

introduction Share on Introduction to the Special Issue on Pattern-Driven Mining, Analytics, and Prediction for Decision Making, Part II Editors: Jerry Chun-Wei Lin Western Norway University of Applied Sciences, Bergen, Norway Western Norway University of Applied Sciences, Bergen, NorwayView Profile , Nachiketa Sahoo Boston University, USA Boston University, USAView Profile , Gautam Srivastava Brandon University, Canada Brandon University, CanadaView Profile , Weiping Ding Nantong University, China Nantong University, ChinaView Profile Authors Info & Claims ACM Transactions on Management Information SystemsVolume 13Issue 3September 2022 Article No.: 23pp 1–3https://doi.org/10.1145/3512468Published:11 February 2022Publication History 0citation105DownloadsMetricsTotal Citations0Total Downloads105Last 12 Months105Last 6 weeks25 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Novel Data Sampling Driven Kalman Filter Is Designed by Combining the Characteristic Sampling of UKF and the Random Sampling of EnKF

In order to improve the performance of the Kalman filter for nonlinear systems, this paper contains the advantages of UKF statistical sampling and EnKF random sampling, respectively, and establishes a new design method of sampling a driven Kalman filter in order to overcome the shortcomings of UKF and EnKF. Firstly, a new sampling mechanism is proposed. Based on sigma sampling with UKF statistical constraints, random sampling similar to EnKF is carried out around each sampling point, so as to obtain a large sample data ensemble that can better describe the characteristics of the system variables to be evaluated. Secondly, by analyzing the spatial distribution characteristics of the obtained large sample ensemble, a sample weight selection and assignment mechanism with the centroid of the data ensemble as the optimization goal are established. Thirdly, a new Kalman filter driven by large data sample ensemble is established. Finally, the effectiveness of the new filter is verified by computer numerical simulation experiments.
Training and Learning in Virtual Reality: Designing for Consistent, Replicable, and Scalable Solutions

The emergence of accessible virtual reality equipment and software have piqued interest in adoption of training based solutions that leverage the affordances of VR. A need exists for effective and efficient protocols/direction for developers to design VR experiences that maximize learner retention. This study offers a framework and generalizable guidelines that prescribe consistent, replicable, and scalable VR training and learning solutions.
Anti‐bloater class restructuring: An exploratory study

Abstract Proper software modularization still poses challenges to developers. One of the symptoms of inappropriate modularization is the large size of object‐oriented classes. In that case, a possible solution would be class restructuring with refactorings, such as Extract Class , Extract Super‐Class , or Move Method . However, class refactoring is challenging because of the possible side effects of improper changes. In this context, more effective decision support systems on which classes are worthwhile for restructuring to improve modularity are still lacking. This work focuses on exploring possible alternatives for supporting decision on class restructuring. A prospective study was performed on selected kinds of restructuring, aiming at determining what types of strategies are typically adopted to restructure bloated classes and which classes developers decided to restructure. Then, we proposed and evaluated a predictive model for indicating which classes to restructure, aiming at delivering a restructuring guide on those classes. Finally, we conducted a qualitative study to evaluate the perception of developers on such guides based on predictions for real software. The results have shown situations in which the proposed predictions could help the restructuring process but also elucidated possible improvements and limitations.
Proactivity as a Factor of Satisfaction with Distance Learning during Lockdown from the Perspective of Students’ Perception and Preferences

During the first wave of COVID-19, universities in many states switched to distance learning. Students faced the challenge of moving quickly from face-to-face to distance learning. This created the prerequisites for analysing this new experience for Russian universities. There are many studies analysing satisfaction with online learning. Nevertheless, they were conducted in the period of voluntary transition to this form of learning and outside the context of proactivity. This paper attempts to address this kind of research gap and answer the question of the correlation between satisfaction with distance learning during the lockdown and students´ attitudes towards proactivity. After switching to distance learning in the spring of 2020, students answered questions in an online questionnaire. 386 people (212 women and 174 men) took part in the survey. The age of participants was 17 - 21 years. The results of the study showed that 1) students do not have negative feelings about distance learning; 2) students still prefer face-to-face learning, and distance (online) learning is perceived as additional or temporary measure; 3) during the lockdown, students showed a mild tendency to react reactively to the situation; 4) proactive attitudes of students affect the degree of their satisfaction with distance learning.