Reviewerly/exHarmony huggingface dataset:
Do Brelfies Help to Normalize Breastfeeding in Public: Lewd or Food?

Often, lack of support results in early termination of breastfeeding compromising infant health. Lactivists argue one way to normalize public breastfeeding is by posting brelfies on social network sites. An online experiment tested a 2 × 2 between-subjects factorial design. Participants (N = 296) were randomly assigned to one of the four experimental conditions varying the valence of comments or the control where no user comments were present. Most participants posted supportive comments. Compared to participants who only viewed brelfies, those who read any type of comment posted by other users showed negative attributions and emotions to brelfies. A number of other variables were also measured. Identification with other social media users was associated with approval for posting brelfies and supporting mothers. Emotional arousal and cognitive appraisal may be two sources of stigma about comments for brelfies even though people recognize that breastfeeding is an important health issue.
Arbitrary-oriented ship detection with RetinaNet embedded angle vector module

As an important part of object detection, rotated object detection plays a great role in many visual applications involving ship detection in remote sensing images, oriented scene text spotting, and face detection etc. Since the different definitions of the angle and the periodicity of the numerical angle values, the angular boundary discontinuity will happen during the model training which decreases the detection accuracy. To address that, we propose an arbitrary-oriented ship detection with RetinaNet embedded angle vector module (RetinaNet-AVM), which adds a new angle vector module (AVM), improves the boxes localization loss function and applies a new boxes angle loss function. At first, the numerical angle values will be encoded into angle vectors by the AVM before data training. After the training, the angle vectors will be decoded into numerical angle values to generate the bounding boxes of the final results by the AVM. On the HRSC2016 dataset, RetinaNet-AVM could detect the arbitrary-oriented, large aspect ratio and densely arranged ships with the rotated bounding boxes, and the precision is 90.76%, the recall is 89.66% and the average precision (AP) is 86.71%. Compared with the RetinaNet-H, our model has improved accuracy, recall and AP by 4.88, 1.47, and 3.9, respectively. And compared with R2 CNN, RRPN and RRD, our model is still competitive.
Blockchain Applications in Smart Grid

With the fast growth of the internet industry, particularly information technology, the smart grid concept has been developed. The smart grid is an attempt to enhance and rejuvenate the secondary sector (the traditional power sector). In the contemporary context, the goal of constructing smart grids is to create a fair and healthy competitive energy market environment. Simultaneously, doing so can help to safeguard the environment and achieve carbon neutrality. With the advent of blockchain, new ideas for smart grid solutions have been developed and implemented. From the standpoint of blockchain types, this article details the aims and contributions made by state of the art technologies, particularly in the field of security. Further, we summarize the shortcomings of existing technologies and the relatively understudied security topics. Then we propose recommendations and describe what the future smart grid should achieve in terms of security.
Deriving and evaluating a fault model for testing data science applications

Abstract Data science (DS) applications not only suffer from traditional software faults but may also suffer from data‐specific and model‐related faults. Fault models play an important role in evaluating and designing tests for testing DS applications. The existing fault models do not consider DS specific faults. In this study, we built a fault model DS applications. We investigate the faults by using diverse approaches: (i) a multi‐vocal literature survey of published literature, (ii) semi‐structured interviews of industry experts. The Multi‐vocal study allows us to synthesize the existing knowledge from researchers and practitioners. Qualitative data from semi‐structured interviews provide us with insights into the nature of faults encountered by practitioners. We combine the results of (i) and (ii) to derive a detailed fault model. The developed fault model is further validated through a quantitative survey of industry practitioners, and the respondents were asked to identify the faults from our proposed fault model that they have experienced and classify those faults based on their severity as perceived by practitioners and its frequency. The results show that practitioners consider prediction bias and model decay as the most severe faults while data sampling and splitting faults along with feature engineering faults are the most frequent.
Marketing SeeLigg, a Private Electric Chargers Sharing Network

Two factors that slow down the switch to electric vehicles are the fear of losing autonomy due to limited coverage of the public electric charger network, and cross border compatibility of the cards used for payment of the electric charges. Many private entities deployed in-premises electric chargers to charge their vehicle fleet of one or more vehicles, these chargers are sometimes in places with public access and are part-time idle, thus could be used by others if there was a way to know when the charger was available for use and a straightforward way of making payments that ideally should work across country borders. Potential solutions to these problems have been implemented that take care of payment and availability information but they are limited in scope and usually do not work across borders. We propose SeeLigg, a blockchain-based solution that is marketed to the potential user’s community as open and transparent, trying in this way to achieve critical mass for the network effect to kick-in and to become worthwhile being part of the SeeLigg community.
MIAM: Motion information aggregation module for action recognition

In the field of action recognition based on RGB videos, it is infeasible to train deep networks on dozens or hundreds of frames because of limits on computational complexity and memory. Previous works commonly adopted a sparse sampling strategy, which unfortunately leads to missing crucial frames and insufficient modelling for short-range motions. In this letter, an effective motion information aggregation module (MIAM) that utilises convolutional neural networks to aggregate motion information from multiple frames into one single frame is proposed. This allows to train deep networks end-to-end with densely sampled frames efficiently. The MIAM enables the model to gather motion information at every instant of action, which avoids missing subtle movements. Experiments on the NTU RGB+D 60 and NTU RGB+D 120 datasets verify that the MIAM significantly improves the recognition accuracy with very limited extra computational cost and exhibits unique advantages for recognising subtle actions.
Research on image enhancement based on fuzzy theory

Since human visual properties have ambiguity, the neighborhood information of pixels in an image also has ambiguity. Using fuzzy theory for image enhancement allows mathematics to learn the advantages of the human brain in recognition and judgment of ambiguous phenomena, and use precise mathematical methods to deal with things that cannot be accurately represented by mathematics. In the article, the basic concepts of fuzzy theory, several common fuzzy distributions are introduced, and image transform domain enhancement algorithm based on fuzzy theory are studied. It is shown that the fuzzy theory algorithm can improve the contrast of useful information of images and facilitate people to get useful information more quickly.
Hate-Speech Bans are at Odds with the Central Principles of Liberalism

In line with my 2021 book Freedom of Expression as Self-Restraint – albeit in a much shorter compass – this essay will argue against the moral defensibility of hate-speech laws like those in the United Kingdom and Canada and the Antipodes and most countries of western Europe. Such laws contravene the moral principle of freedom of expression, and therefore contravene one of the central principles of liberal democracy. After expounding the principle of freedom of expression, this essay explains why some hateful utterances can be prohibited in full compliance with that principle and without any hate-speech legislation. It then recounts how a system of governance should deal with hateful utterances that cannot properly be prohibited. Finally, the essay argues that hate-speech laws are profoundly degrading for any society in which they are adopted.
Home Activities Sequence Pattern Analysis

The elderly population in most of the countries has risen from year to year. Number of elderlies that staying alone at home also increase accordingly since the number of children in one family is decreasing. The cost of hiring caregivers is normally expensive and might cause financial burden to a family in the long-term. Fortunately, with advance technology, alternative approaches such as Global Monitoring System, smart clothing as well as embedded health care system in smart phone and smart watch could be the alternative approaches to monitor the health of some elderly that is able to live alone. The most feasible and cost-saving approach for in-house monitoring should be setting up sensors at different locations in a smart home for independent elderly that stay alone. Daily activities recorded through the sensors can be collected and analyzed to detect if there is any anomaly found. In this paper, analysis of staying alone elderly’s daily activities and behaviors are performed. This analysis is important to further developing suitable model that can be used to detect the anomalies in their routine home activity patterns.
On the number of minimal codewords in codes generated by the adjacency matrix of a graph

Minimal codewords have applications in decoding linear codes and in cryptography. We study the number of minimal codewords in binary linear codes that arise by appending a unit matrix to the adjacency matrix of a graph.
Successful Research in Today's Library

The author of this chapter, an academic teaching librarian, lays out a path for successful researching in the academic library of the 2020s: familiarity with a college/university’s online resources, identifying information formats, developing successful search terms and research questions, selecting appropriate search tools, managing and citing sources, and making effective use of campus library services. The chapter also provides information on accessing resources germane to music research, such as collected and critical score editions, music industry publications, audio & video resources, and more.
An Integrated AMPSO-CLSTM Model for Photovoltaic Power Generation Prediction

To address the challenges of volatile and intermittent nature in photovoltaic power (PV) generation forecasting, a new convolutional long short-term memory network (CLSTM) prediction model optimized by adaptive mutation particle swarm optimization (AMPSO) is proposed. In this model, the local sensing ability of the convolutional kernels in the CNN is used to extract high-dimensional features from the variable influential factors of PV power generation, and a mapping between time series data and PV is established by the memory ability of the gate control unit in LSTM. The AMPSO algorithm is introduced to optimize the network structure and weights of CLSTM simultaneously. The performance of the model is verified by two different two data sets. The results show that compared with that of the CLSTM, Auto-LSTM, LSTM and recurrent neural network models, the root mean square error (RMSE) of the AMPSO-CLSTM model decreases by 1.92–6.53% and 6.23–31.10%, the mean absolute error (MAE) decreases by 6.92–16.87% and 11.71–48.84%, and the mean absolute percentage error (MAPE) decreases by 13.24–31.75% and 12.22–49.00%, respectively. Compared with those of the CLSTM model, the number of channels in the convolutional layer of the AMPSO-CLSTM is reduced by 51.76–71.09% and 61.72–86.72%, respectively, and the number of hidden neurons in LSTM is reduced by 32–60% and 53–84%, respectively.
Studying the commonalities, mappings and relationships between non-functional requirements using machine learning

Studying the interactions and relationships between non-functional requirements (NFRs) is important when developing software applications. This is because that a specific NFR may conflict, overlap or complement other NFRs. Moreover, one NFR may include characteristics and features of other NFRs. Most of the current studies focused on classifying NFRs into distinct different categories without considering the common characteristics or the relationships between these categories. This paper provides a study on how the results of various machine learning (ML) classifiers can be visualized and analyzed with the help of confusion matrices and principal component analysis (PCA) tools in order to examine the commonalities, mappings and relationships between NFRs. And to define which NFRs are mostly distinct and which NFRs are mostly overlapping. The paper also defines which ML classifier achieves the best performance through various classifications of NFRs. The experiments were performed using five different ML classifiers on the newly expanded PROMISE dataset and the achievements of these classifiers are evaluated based on precision, recall and F1-Score metrics. The results of this research help practitioners and researchers in the field of software engineering to pay more attention on the behavior of various NFRs.
Multiclass Classification for Large Medical Data using Adaptive Random Forest and Improved Feature Selection Methods

A Classification method stands out as a reliable data mining technique applied in medical sciences. We observe multi-classification problem afflicting many recent applications that includes social network analysis, biology, anomaly detection and computer vision. However, such classification techniques usually struggle while dealing with features of data that is generated from multiple classes. Moreover, in case of large medical data, it is observed that the dimension of the data poses the biggest challenge while applying classification technique to them. In order to overcome such problems, we have proposed an adaptive random forest classifier method that uses ensemble feature selection technique for better information gain (IG), improved correlation (IC) and gain ratio (GR). Also, it seeks to solve the class imbalance problem by applying bootstrap resampling for medical data. The result of the proposed method proved that adaptive RF (Random Forest) classifier offers better accuracy, precision and F-score values than standard Random Forest and KNN classification algorithms. The overall performance of algorithms was tested over five real datasets. The result analysis shows performance of the proposed classifier is promising in all real datasets as compared to standard methods.
Special Issue “Natural Language Engineering: Methods, Tasks and Applications”

Natural language engineering includes a continuously enlarging variety of methods for solving natural language processing (NLP) tasks within a pervasive number of applications [...]
Tracking Industrial Assets Using Blockchain Technology

There are several methods to track the movement of goods using different custom developed software applications. This paper presents the implementation of blockchain technology for tracking the movement of assets/equipment within the organization effectively and to share the asset/equipment without any formal authorizations. In this paper, software application is developed to implement transactions. Usage of blockchain technology for this application makes it superior than the other applications in terms of data security and immutability of data. Asset transfer can be made transparent or private to all the users in the chain subject to the application. Once after the submission of the data, it can never be overridden or countermanded. This application allows us to work with improved accuracy and without involvement of human verification.
Smart Voice Assistance for Speech disabled and Paralyzed People

People who are paralyzed, confront numerous challenges in meeting their basic necessities on a daily basis. It is very difficult to understand the speech of people with dysarthria, amyotrophic lateral sclerosis (ALS) and similar conditions. Automatic speech command recognition system will enhance the lifestyle of people with voice disorder like dysarthria and paraplegics. The proposed work will convert the speech command of paralyzed people into text and send it to the care taker&#x0027;s mobile with the help of Twilio message services. Algorithms like Support Vector Machine (SVM) and Convolutional Neural network (CNN) model is used for speech command identification and speech to text conversion. CNN model yields an accuracy of 90.62&#x0025;, whereas the SVM algorithm gives a very low accuracy. The developed TensorFlow model is deployed in the flask server.
EXPLODE – a new model of exploratory learning environment for neural networks to improve learning outcomes

The paper proposes EXPLODE, a new model of exploratory learning environment for teaching and learning neural networks. The EXPLODE model is about pedagogically instrumenting a software development environment to transform it into an exploratory learning environment for neural networks. Such an environment is particularly aimed for students who are skilled in programming and meets typical challenges in teaching and learning neural networks, related to the lack of prerequisite knowledge in mathematics. By providing such students with a familiar learning environment and allowing them to programmatically experiment with neural networks, the EXPLODE model aims at improving the students' learning outcomes and learning experience. The effectiveness of the model was evaluated in an experimental study with 77 final-year IT students. The results have shown that the students from the experimental group, that is, those who were exposed to the EXPLODE model, scored higher on the knowledge test. Furthermore, the perceived learning experience of the experimental group was better than that of the control group. The results have also suggested that the proposed model helps students better understand learning topics that require practical experience and facilitates a deeper understanding of the internal operations of neural networks.
Ecological Message Design Strategies Based on Narrative Evidence from Immigrants and Refugees in Diverse Socio-Cultural Contexts

This chapter discusses how to apply an ecological model to a health message design based on cultural narrative evidence from immigrants and refugees. The core idea of the ecological model is contextual embeddedness where each level of influence functions within the operation of another level or context. Discussion and recommendation are centered on how the embeddedness of the ecological approach can be applied to message design by focusing on intrapersonal factors, interpersonal and community factors, and structural limitations. More specifically, this chapter suggests agency-based message design, using trusted sources and role model stories, and participatory message design/production. The conclusion of this chapter suggests integrative messaging strategies based on both ecological approaches and theoretical robustness for future research.
Video streaming using light-weight transcoding and in-network intelligence

In this paper, we introduce a novel approach, LwTE, which reduces streaming costs in HTTP Adaptive Streaming (HAS) by enabling light-weight transcoding at the edge. In LwTE, during encoding of a video segment in the origin server, a metadata is generated which stores the optimal encoding decisions. LwTE enables us to store only the highest bitrate plus corresponding metadata (of very small size) for unpopular video segments/bitrates. Since metadata is of very small size, replacing unpopular video segments/bitrates with their metadata results in considerable saving in the storage costs. The metadata is reused at the edge servers to reduce the required time and computational resources for on-the-fly transcoding.
3D human pose estimation for martial arts analysis through graph convolutional networks

This paper presents a human pose estimation method for martial arts video analysis using a Semantic Graph Convolutional Network (SemGCN) instead of an ordinary convolutional neural network (CNN). The inputs for the model are videos from the Human3.6M dataset, in addition to the ones from Martial Arts, Dancing and Sports (MADS) dataset. A data unification process is described so that MADS joints can be adapted to the Human3.6M base setting. The performance of the model when only uses Human3.6M for training is compared to training with both Human3.6M and MADS datasets, resulting in a lower mean per-joint position error (MPJPE) for the latter. Finally, performance indicators such as the vertical position of the center of mass, balance and stability, are calculated for the MADS sequences in order to provide insights regarding martial arts execution.
Local Binary Pattern Regrouping for Rotation Invariant Texture Classification

This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The analysis of LBP’s two hundred fifty-six patterns has led us to propose a new organization of uniform and no uniform patterns into twenty-eight groups; each group assembled a number of patterns varied according to specific terms. The principal idea is to preserve the low complexity of LBP and simultaneously increase the method robustness against quality degradation caused by image operations like rotation, grey level changes, illumination and mirror effects. The experiments are done with the two texture databases Outex and Brodatz; the tests are proving the robustness of Local Binary Pattern Regrouping (LBPG) under circumstances.
Predictive analysis and supervised detection for fraudulent cases in healthcare

Healthcare is perhaps one of the most crucial industry for humanity and it has been, and it will always be a growing industry. This increases the risk of being exploited. In our paper, we performed and analyzed different trends for the suspicious/fraudulent medical activities. We categorized different groups of patients involved and analyzed their distribution on the basis of multiple factors. Healthcare is a massive and widely distributed sector with a numerous entities and stakeholders involved. Limited connectivity within these distributed management create various loopholes that people try to exploit. We performed multiple analysis for suspicious and fraudulent activities in the healthcare/Medicare industry and tried to look for popular trends people opt to exploit the system. We also tried various supervised machine learning algorithms to see how they behave with our dataset and what is the accuracy of their detection. This can be helpful in choosing the best model while building a solution to deal with various use cases involved in fraudulent activity detection. Healthcare is one of the sectors that will always be relevant to living beings and should be taken special care of.