Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition using Deep and Machine Learning Algorithms

Activity recognition in humans is one of the active challenges that finds its application in numerous fields such as, medical health care, military, manufacturing, assistive techniques and gaming. Due to the advancements in technologies the usage of smartphones in human lives become inevitable. The sensors in the smartphones help us to measure the essential vital parameters. These measured parameters enable us to monitor the activities of humans, which we call as human activity recognition. In this paper, we have proposed an automatic human activity recognition system that independently recognizes the actions of the humans. Four deep learning approaches and thirteen different machine learning classifiers such as Multilayer Perceptron, Random Forest, Support Vector Machine, Decision Tree Classifier, AdaBoost Classifier, Gradient Boosting Classifier and others are applied to identify the efficient classifier for human activity recognition. Our proposed system is able to recognize the activities such as Laying, Sitting, Standing, Walking, Walking downstairs and Walking upstairs. Benchmark dataset has been used to evaluate all the classifiers implemented. We have investigated all these classifiers to identify a best suitable classifier for this dataset. The results obtained show that, the Multilayer Perceptron has obtained 98.46% of overall accuracy in detecting the activities. The second-best performance was observed when the classifiers are combined together.
Factors affecting social media adoption in small and medium enterprises: evidence from the UAE

The purpose of this study is to identify the various determinants of social media adoption among small and medium-sized enterprises (SMEs) in the United Arab Emirates (UAE). The study addresses the current dearth of literature on social media adoption within UAE-based SMEs. The data were collected through survey questionnaires from 107 SMEs listed at Khalifa Fund for Enterprise Development, a UAE-based entrepreneurial development entity, and correlation analysis was used to test the study hypotheses. The findings revealed that social media adoption within UAE-based SMEs is significantly associated with perceived relative advantage, perceived compatibility, manager's/owner's knowledge and expertise, management characteristics and pressure from customers and competitors. This study enhances understanding about the factors associated with social media adoption within SMEs, and particularly in the UAE. The study findings will help entrepreneurs to promote their businesses via social media, and provide insights that may be useful to policy-makers in designing social media adoption initiatives around technical training for SME owners/managers.
Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing.
Three-Party Secure Semiquantum Summation without Entanglement Among Quantum User and Classical Users

In this paper, a three-party secure semiquantum summation protocol, which can calculate the modulo 2 addition of the private bits from one quantum participant and two classical participants, is constructed by only using single qubits as the initial quantum resource. This protocol needs none of quantum entanglement swapping, the unitary operation or a pre-shared private key. This protocol only requires the quantum participant to perform the Z basis measurements, the X basis measurements and the Bell basis measurements. Compared with the existing only semiquantum summation protocol (Int J Theor Phys, 60 (2021) 3478), this protocol has better performance in quantum measurements for quantum participant; moreover, it may also have higher qubit efficiency.
The effect of virtual reality technology and education on sustainable behavior: a comparative quasi-experimental study

Purpose Sustainability is one of the global challenges, individuals and businesses need to change their behavior and consumption patterns to move towards sustainable development. This is not possible without planning for education and related knowledge transfer. On the other hand, disruptive technologies such as virtual reality (VR) have revolutionized the field of education. The purpose of this study is to evaluate the effect of holding traditional training courses and VR-based training courses on sustainable behavior. Design/methodology/approach It is a quasi-experimental study, in which pretest-posttest design and control group are used. The statistical population includes students of one of the Iranian universities. A total of 105 students were randomly divided into two experimental groups and one control group (35 students in each group). Experimental group 1 underwent a training course using VR and Experimental group 2 received a traditional training course. At first, a pre-test was performed and after completing the eight-session period (two 1-h sessions per week), the post-test was conducted again for the groups. Findings The results of analysis of variance test show that there was a significant difference between the mean scores of sustainable behaviors in the post-test phase in the two experimental groups and the control group. Using Tukey’s test, it was found that the scores of sustainable behavior were different among three groups in pairs. That is, holding a training course as well as using VR has been effective on sustainable behavior. Environmental policymakers and planners can use technologies such as VR to teach environmental issues to create a culture of sustainability and sustainable development, in addition to training and educational courses. Originality/value Contribution of this study shows that the use of VR can be effective in learning sustainable behavior. Also, holding training courses is a way to change the consumption pattern and behavior of people to maintain the environment and sustainability.
Black Swan Events and Intelligent Automation for Routine Safety Surveillance

Effective identification of previously implausible safety signals is a core component of successful pharmacovigilance. Timely, reliable, and efficient data ingestion and related processing are critical to this. The term 'black swan events' was coined by Taleb to describe events with three attributes: unpredictability, severe and widespread consequences, and retrospective bias. These rare events are not well understood at their emergence but are often rationalized in retrospect as predictable. Pharmacovigilance strives to rapidly respond to potential black swan events associated with medicine or vaccine use. Machine learning (ML) is increasingly being explored in data ingestion tasks. In contrast to rule-based automation approaches, ML can use historical data (i.e., 'training data') to effectively predict emerging data patterns and support effective data intake, processing, and organisation. At first sight, this reliance on previous data might be considered a limitation when building ML models for effective data ingestion in systems that look to focus on the identification of potential black swan events. We argue that, first, some apparent black swan events-although unexpected medically-will exhibit data attributes similar to those of other safety data and not prove algorithmically unpredictable, and, second, standard and emerging ML approaches can still be robust to such data outliers with proper awareness and consideration in ML system design and with the incorporation of specific mitigatory and support strategies. We argue that effective approaches to managing data on potential black swan events are essential for trust and outline several strategies to address data on potential black swan events during data ingestion.
Sophia Sophia tell me more, which is the most risk-free plan of all? AI anthropomorphism and risk aversion in financial decision-making

Purpose This research examines whether anthropomorphizing artificial intelligence (AI) chatbots alters consumers' risk preferences toward financial investment options involving differential risks. Design/methodology/approach An experimental approach has been adopted with three studies, all featuring a between-subjects design. Findings Through three studies, the findings document that, in a financial decision-making context, anthropomorphizing AI leads to significantly greater risk aversion in investment decision-making (Study 1). This occurs because AI-enabled chatbot anthropomorphization activates greater psychological risk attachment, which enacts consumers to manifest stronger risk aversion tendency (Studies 2 and 3). Originality/value Anthropomorphizing AI has undeniable relevance in the contemporary marketing landscape, such as humanoid robotics and emotion AI algorithms. Despite of anthropomorphism's significance and relevance, the downstream impact of anthropomorphism remains unfortunately underexplored.
Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments

Homophobia and Transphobia Detection is the task of identifying homophobia, transphobia, and non-anti-LGBT+ content from the given corpus.Homophobia and transphobia are both toxic languages directed at LGBTQ+ individuals that are described as hate speech.This paper summarizes our findings on the "Homophobia and Transphobia Detection in social media comments" shared task held at LT-EDI 2022 -ACL 2022 1 .This shared task focused on three sub-tasks for Tamil, English, and Tamil-English (code-mixed) languages.It received 10 systems for Tamil, 13 systems for English, and 11 systems for Tamil-English.The best systems for Tamil, English, and Tamil-English scored 0.570, 0.870, and 0.610, respectively, on average macro F1-score.
Indoor Positioning System Assisted Big Data Analytics in Smart Healthcare

Thousands of gigabytes of data mostly are now available on the internet, for all intents and purposes gathered from a variety of sources, deliberately or inadvertently. The science of converting all of this data into knowledge that can specifically improve everyone’s life pretty much simpler definitely is known as Big Data. Today, healthcare literally is one of the most important sectors. Our country’s healthcare resources basically are minimal, which particularly is fairly significant. As a result, making resources available to everyone in need becomes extremely difficult in a generally major way. Big Data Analytics can assist us in optimising the usage of these resources so that they particularly are available to anybody who requires them, which basically is quite significant. Keeping track of the resources that kind of are currently in use, those that literally are available, and those that may for all intents and purposes be reassigned is one strategy to optimise the available resources in a subtle way. These systems help us in determining the actually exact location of all resources definitely such that the ones nearest to the patient basically are allocated to him, making the treatment process faster. Indoor positioning systems definitely are like GPS systems but that work inside a building, in this case, hospital, which is fairly significant. This chapter discusses further about the contribution of big data analytics and indoor positioning systems in the evolution of smart healthcare.
GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning

System monitoring and anomaly detection is a crucial task in daily operation. With the rapid development of cyber-physical systems and IT systems, multiple sensors get involved to represent the system state from different perspectives, which inspires us to detect anomalies considering feature dependence relationship among sensors instead of focusing on individual sensor's behavior. In this paper, we propose a novel Graph Relational Learning Network (GReLeN) to detect multivariate time series anomalies from the perspective of between-sensor dependence relationship learning. Variational AutoEncoder (VAE) serves as the overall framework for feature extraction and system representation. Graph Neural Network (GNN) and stochastic graph relational learning strategy are also imposed to capture the between-sensor dependence. Then a composite anomaly metric is established with the learned dependence structure explicitly. The experiments on four real-world datasets show our superiority in detection accuracy, anomaly diagnosis, and model interpretation.
Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation

Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timestamps. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timestamp. And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.
Traffic Response System Based on Data Mining and Internet of Things (Iot) For Preventing Accidents

The study shows that internet of things has now become a vital part of our generation and it has a rapid growth in the field and many people see this as next big thing but they are very much unaware of its norms and benefits it has been estimated that by the end of 2022 to their may be about 50to 60 billion internet of things are going to be installed in our rapid growing world there are numerous benefits of the internet of things in today&#x0027;s fast growing and running world but there are numerous backward points are too which make the internet of things a things that should be handled with care in the right hands basically what are internet of things or the thing we call as IOT so these are the touchable objects which are attached in with sensors, processing abilities, software and other technologies that connect and exchange and process data. so now as our topic is telling us that the numerous attacks has been made on these internet f things so here we will discuss about the how should be the internet of things should be managed and monitored so that the effective monitoring of the Unexpected traffic stops are one of the biggest causes of car accidents, especially on fast-moving roads and motorways with restricted visibility. Other factors such as roadwork, excessive motorised vehicles, particularly during rush hour, and so on can potentially contribute to itFixed traffic sensors put on highways that interact with drivers&#x0027; mobile Apps through the 4Gbps may usually alleviate this problem; however, comparable equipment is not accessible on all main routes. In this study, we propose an alternate strategy for addressing such a problem by directly installing mobile traffic sensors in individual and/or public cars. such as trains, buses, and volunteer cars. Real-time analysis of enormous volumes of transportation data is crucial in this situation for averting accidents.
A study on the adoption of Wireless Communication in Big Data Analytics Using Neural Networks and Deep Learning

The study explores about the Wireless communication which is one of the most rapidly evolving and active technology fields in the communication world. Wireless communication is a means of transmitting data from one point to another without the use of wires, cables, or another physical medium. As a result, commercial network operators have risen at an accelerating rate, bringing in the era of big data. Machine learning has been applied in a range of corporate and academic research contexts as one of the most promising artificial intelligences (AI) methods for deciphering this deluge of knowledge. This study presents a high-level introduction of big data handling and technological advances, as well as their potential applications in next-generation wireless networks (NG). Following that, we employ advanced analytics to estimate mobile users' demands and then use that knowledge to improve the efficacy of “community wireless communication channels.” In specifically, a unified, huge data-aided computer learning framework comprised of feature extraction, data modelling, and prediction/online refinement is provided. The primary benefits of the proposed framework are that we could create the logic, problem formulations, and method of powerful computational models inside the frame of wireless networks by depending on vast data that reflects the both spectrum and other hard demands of users. Via general, information is conveyed from sender to receiver across a certain distance in a communication system. The transmitter may be located anywhere within a few meters using Wireless Communication… We examined currently known approaches and explored their benefits and drawbacks in order to identify new research avenues for future advancements in underwater sensor networks.
Fine-tuning Transformers with Additional Context to Classify Discursive Moves in Mathematics Classrooms

Talk moves" are specific discursive strategies used by teachers and students to facilitate conversations in which students share their thinking, and actively consider the ideas of others, and engage in rich discussions.Experts in instructional practices often rely on cues to identify and document these strategies, for example by annotating classroom transcripts.Prior efforts to develop automated systems to classify teacher talk moves using transformers achieved a performance of 76.32% F1.In this paper, we investigate the feasibility of using enriched contextual cues to improve model performance.We applied state-of-the-art deep learning approaches for Natural Language Processing (NLP), including Robustly optimized bidirectional encoder representations from transformers (Roberta) with a special input representation that supports previous and subsequent utterances as context for talk moves classification.We worked with the publically available TalkMoves dataset, which contains utterances sourced from real-world classroom sessions (human-transcribed and annotated).Through a series of experimentations, we found that a combination of previous and subsequent utterances improved the transformers' ability to differentiate talk moves (by 2.6% F1).These results constitute a new state of the art over previously published results and provide actionable insights to those in the broader NLP community who are working to develop similar transformer-based classification models.
AI from Concrete to Abstract

Artificial intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to contribute to making citizens insightful actors in debates and decisions involving the adoption of AI mechanisms. Currently, existing approaches to the teaching of basic AI concepts through programming treat machine intelligence as an external element/module. After being trained, that external module is coupled to the main application. Combining block-based programming and WiSARD weightless artificial neural networks, this article presents the conceptualization and design of a new methodology, AI from concrete to abstract (AIcon2abs), to endow general people (including children) with a minimum understanding of what AI means. The main strategy adopted was to include AI training and classification primitives as blocks that compose an otherwise conventional computer program. This way, the programmer can use these blocks as he/she uses other programming constructs. In order to achieve this purpose, we also propose BlockWiSARD, a block-based programming environment designed to promote the demystification of artificial intelligence via practical activities related to the development of learning machines, as well as through the observation of their learning process. As a beneficial side effect of BlockWiSARD, the difference between a program capable of learning from data and a conventional computer program becomes more evident. In addition, the simplicity of the WiSARD weightless artificial neural network model enables easy visualization and understanding of training and classification tasks internal realization.