Reviewerly/exHarmony huggingface dataset:
Emergent Individual Factors for AR Education and Training

As augmented reality (AR) has continued to expand the technology has seen integration into education and training practices. This study uses a comparative approach to address the question does prior experience provide for stronger knowledge retention on mobile augmented reality compared to other training and education modalities? A total of 33 participants were assigned to one of three training modalities (AR, paper manual, or online video). Once the training task was completed, they were tested on their knowledge retention from the experience via a 10-question quiz. While results from this study do not indicate AR provides for a better learning experience than other modalities several results point to the importance of individual characteristics.
BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.
Detection of Rarely Occurring Behaviors Based on Human Trajectories and Their Associated Physical Parameters

The complexity of detecting rarely occurring behaviors through human trajectories is closely related to a lack of data, unclear behavioral characteristics, and complex variations in their related physical parameters (e.g., velocity and orientation angles, etc.). In this context, we propose a methodology to maximize the detection performance of rarely occurring behaviors in public places by investigating the data collection process, trajectory representation based on detected skeleton poses from videos, and the use of 2D (X, Y) trajectory positional data only versus its combination with their associated physical parameters as the input for trajectory learning models. In order to evaluate the proposed method, we studied a rare Japanese behavior in public places called UroKyoro, which is a combination of the two Japanese words Urouro and Kyorokyoro. This behavior includes aimlessly moving while frequently looking in both directions. Since there is a lack of related data from real-life cases, we hired professional actors to role-play the behavior alone or with normal pedestrians moving around. The learning system was trained using limited and augmented data. The trajectory learning system, trained with combined human trajectories and orientation angles following the proposed method, succeeds in detecting the studied behavior with an accuracy of 91.33%, outperforming the accuracy of the trained model using only human 2D (X, Y) trajectories by 4.33%. The results show the effectiveness of the proposed method to detect complex, rarely occurring human behaviors by training the LSTM classifier with a combination of human trajectories and physical parameters. However, the effectiveness of physical parameters on training performance may differ from one case study to another based on behavioral characteristics.
Feasibility Study on Eye Gazing in Socially Assistive Robotics: An Intensive Care Unit Scenario

Recently, there has been an increasing interest in the adoption of socially assistive robots to support and alleviate the workload of clinical personnel in hospital settings. This work proposes the adoption of a socially assistive robot in Intensive Care Units to evaluate the criticality scores of bedridden patients. Within this scenario, the human gaze represents a key clinical cue for assessing a patient’s conscious state. In this work, a user study involving 10 participants role-playing 4 levels of consciousness is performed. The collected videos were manually annotated considering 6 gazing directions and an open-source automatic tool was used to extract head pose and eye gazing features. Different feature sets and classification models were compared to find the most appropriate configuration to detect user gaze in this scenario. Results have suggested that the most accurate gazing estimation is obtained when the head pose information is combined with the eye orientation (0.85). Additionally, the framework proposed in this study seems to be user-independent, thereby encouraging the deployment of appropriate robotic solutions in real assistive contexts.
Impact of Informal Online Learning on the Lives of Tribal Women: An Ethnographic Study from Central India

Technology has a significant impact on our daily lives. Before the COVID-19 outbreak, we were not adequately aware of the use of technology, either in academia or otherwise. After the pandemic, we have been forced to depend on technology for everything. Tribal women in India are deprived of their social, economic, and political rights. Hence, they are in rampant state of poverty and do not have minimum needs to survive. This prevents them from engaging in traditional learning activities. Despite of these limitations, few of them do possess android phones in order to fulfil their essential need of telephonic engagement with their friends and peers. However, it is through these devices that they can most conveniently engage in informal learning through technology and thus, informal technological learning is highly beneficial to tribal women. The article draws its inferences from both primary and secondary sources of data. The primary data is gathered through a field study among the Gond and Panika tribal women from Lalpur village of Pushprajgarh, Anuppur District of Madhya Pradesh. The village is an adjacent to IGNTU campus. The secondary data is gathered from published and unpublished sources, online materials, and other related literature from magazines, newspapers, and reports of government and non-government agencies. The thrust of the article is to explain the use of technology and its benefits to tribal women in their day-to-day life situations.
Graph convolutional network and self-attentive for sequential recommendation

Sequential recommender systems (SRS) aim to provide personalized recommendations to users in the context of large-scale datasets and complex user behavior sequences. However, the effectiveness of most existing embedding techniques in capturing the intricate relationships between items remains suboptimal, with a significant concentration of item embedding vectors that hinder the improvement of final prediction performance. Nevertheless, our study reveals that the distribution of item embeddings can be effectively dispersed through graph interaction networks and contrastive learning. In this article, we propose a graph convolutional neural network to capture the complex relationships between users and items, leveraging the learned embedding vectors of nodes to represent items. Additionally, we employ a self-attentive sequential model to predict outcomes based on the item embedding sequences of individual users. Furthermore, we incorporate instance-wise contrastive learning (ICL) and prototype contrastive learning (PCL) during the training process to enhance the effectiveness of representation learning. Broad comparative experiments and ablation studies were conducted across four distinct datasets. The experimental outcomes clearly demonstrate the superior performance of our proposed GSASRec model.
Finetuning Pretrained Model with Embedding of Domain and Language Information for ASR of Very Low-Resource Settings

This study investigates the effective incorporation of meta-information such as domain and language in finetuning a pretrained model based on self-supervised learning (SSL) for automatic speech recognition (ASR) in very low-resource settings. SSL pretrained models have been shown to achieve comparable or even better performance to conventional end-to-end systems even when we finetune them with a small dataset. However, it still requires the specific target dataset with a considerable amount of labeled data, like 10 h, to achieve satisfactory performance. Thus, we propose to exploit heterogeneous datasets which are partially matched either in language or domain and apply multi-task learning (MTL) or adversarial learning (ADV) using the meta-information. The finetuning comprises (1) domain adaptation, which uses in-domain multi-lingual datasets, and (2) language adaptation, which uses datasets of the same language but different domains. The auxiliary task is domain identification for language adaptation and language identification for domain adaptation. We then embed the output of the auxiliary task into the encoder output of the ASR task. The target dataset is the Khmer corpus of the Extraordinary Chambers in the Courts of Cambodia (ECCC) in various sizes from one hour to 10 h. The experimental evaluations demonstrate that fusing the meta-information in MTL or ADV significantly improves ASR accuracy. Moreover, a two-step adaptation method which first conducts domain adaptation and then language adaptation is the most effective. We also show that the target labeled dataset of only 5 h gives an almost saturated performance.
Dynamic Asymmetric Causality of Bitcoin’s Price-Volume Relation

Bitcoin has attracted incessant attentions in recent times. Studies have completed models to examine the relationship between Bitcoin and other multiple attendant variables. This paper considers a simple and direct price-volume relation. The paper offers causality evidence according to the dynamic asymmetric causality test. Based on available monthly data spanning 2010:M7-2022:M10, the paper shows that Bitcoin price and volume are integrated, both been I(0)’s. Moreover, the paper discloses the short- and long-term price-volume behaviors of Bitcoin using the cointegration test and vector error correction model (VECM). Taken together, the study first confirms long run relations and presents the estimates of the parsimonious VECM. The results show short run evidence of positive price-volume relations, and in the long run, the disequilibria are as well corrective and mean reversing. The outcomes of the Hatemi-J’s causality testing suggest likely evidence of bidirectional causality between the positive and negative fragments of the shocks of Bitcoin price and volume during the periods.
Detection of illegal airstrips in digital images using artificial intelligence techniques

Ecuador is considered a transit country for drug trafficking and smuggling activities. Being a current problem in the northern and coastal border of the national territory, unauthorized airstrips are used for these illegal activities where controlled substances, money, weapons, ammunition, and explosives are transported. The research will be based on the design and development of a methodology using artificial intelligence techniques for the analysis and processing of images that will be obtained from reconnaissance missions carried out by Ecuadorian Air Force airplanes. For the recognition of clandestine airstrips, the YOLO deep learning-based object detection method and segmentation techniques will be used.
Unlocking the Power of Al: A Real-Time Translation of Sign Language to Text

Sign language is an important means of communication for deaf and mute individuals all around the globe. It provides them with the opportunity to interact and socialize with the rest of society without feeling left out. But a large majority of the population does not know Sign Language. This paper proposes a Sign Language Recognition system based on a deep learning model to recognize American sign language alphabets (A-Z). The model uses MobileNet V1 pre-trained model to detect hand signs from the self-captured dataset. Using the Hand tracking module for data collection, the problem of lack of racial diversity was solved. The predicted labels are presented in the form of text. The complete implementation of the system is done on NVIDIA® Jetson Nano™. The model achieved a training accuracy of 99.74%. The system detects American Sign Language gestures in real time and translates them, making communication with the hearing impaired easier for everyone.
A Very Deep Adaptive Convolutional Neural Network (VDACNN) for Image Dehazing

The main challenge faced by the existing methods is that they cannot efficiently eliminate the haze from the dense hazy or foggy images. The haze features of dense hazy images are not effectively learnt by the Deep Neural Networks. To resolve this drawback, a very deep adaptive convolutional neural network model is proposed for efficient image dehazing. The hazy images are first categorized into two categories viz., Less-Haze and High-Haze. Two Very Deep Convolutional Neural Networks (VDCNNs), viz., Less Haze-VDCNN and High Haze-VDCNN are developed separately for the classified images. Then the Less Haze-VDCNN is trained using the input hazy images that are less haze and their transmission maps as output. Similarly, the High Haze-VDCNN is trained separately with high-hazy images and their transmission maps. After the training process, a hazy image can be adaptively dehazed from one of the two trained VDCNNs based on the hazy image categorization. The proposed VDACNN exhibits better results for dense hazy images in comparison to existing approaches.
Impact of Explanations on Transparency in HRI: A Study Using the HRIVST Metric

This paper presents an exploration of the role of explanations provided by robots in enhancing transparency during human-robot interaction (HRI). We conducted a study with 85 participants to investigate the impact of different types and timings of explanations on transparency. In particular, we tested different conditions: (1) no explanations, (2) short explanations, (3) detailed explanations, (4) short explanations for unexpected robot actions, and (5) detailed explanations for unexpected robot actions. We used the Human-Robot Interaction Video Sequencing Task (HRIVST) metric to evaluate legibility and predictability. The preliminary results suggest that providing a short explanation is sufficient to improve transparency in HRI. The HRIVST score for short explanations is higher and very close to the score for detailed explanations of unexpected robot actions. This work contributes to the field by highlighting the importance of tailored explanations to enhance the mutual understanding between humans and robots.
Emoji Based Sentiment Classification Using Machine Learning Approach

Online media platforms like Facebook, Twitter, and Instagram continue to influence our world. People today are more closely connected than ever before, and they exhibit such sophisticated personas. Ongoing studies have revealed a link between excessive social media use and depression. This research focuses on the development of features using Term frequency – Inverse Document Frequency (TF-IDF) and Bag-of-Word (Bow). The work will also put light on the generation of models utilizing a machine-learning technique. The dataset for this research using Twitter API. Only the English context was kept from the Tweets after filtration. It focuses on categorizing users’ mental health at the tweet level. About 20000 reviews make up this dataset. Using these reviews, emoji sentiment classification has been developed. Data is cleaned using a pre-processing approach before BoW and TF-IDF were used to extract features. Following to it, classifier deployment, training and assessment were carried out. Metrics for evaluation are used to gauge classifier accuracy. MultinomialNB (MNB) fared best in the field of Bag-of-words features among the three classifiers used to evaluate the accuracy, but Random Forest (RF) outperformed TF-IDF. In Bag-of-Words, we are able to classify data with an accuracy of 86% using TF-IDF Random Forest and 89% using multinomial NB and BoW.
Interoperability of heterogeneous Systems of Systems: from requirements to a reference architecture

Abstract Interoperability stands as a critical hurdle in developing and overseeing distributed and collaborative systems. Thus, it becomes imperative to gain a deep comprehension of the primary obstacles hindering interoperability and the essential criteria that systems must satisfy to achieve it. In light of this objective, in the initial phase of this research, we conducted a survey questionnaire involving stakeholders and practitioners engaged in distributed and collaborative systems. This effort resulted in the identification of eight essential interoperability requirements, along with their corresponding challenges. Then, the second part of our study encompassed a critical review of the literature to assess the effectiveness of prevailing conceptual approaches and associated technologies in addressing the identified requirements. This analysis led to the identification of a set of components that promise to deliver the desired interoperability by addressing the requirements identified earlier. These elements subsequently form the foundation for the third part of our study, a reference architecture for interoperability-fostering frameworks that is proposed in this paper. The results of our research can significantly impact the software engineering of interoperable systems by introducing their fundamental requirements and the best practices to address them, but also by identifying the key elements of a framework facilitating interoperability in Systems of Systems.
Some combinatorial identities

In this paper, it is shown that a positive integer [Formula: see text] can be written as [Formula: see text]. It is also proved that the two expressions for number of codewords of weight [Formula: see text], obtained in Theorem 1[P. Kumar, M. Sangwan and S. K. Arora, The weight distributions of some irreducible cyclic codes of length [Formula: see text] and [Formula: see text], Adv. Math. Commun. 9 (2015) 277–289] and in Theorem 1 Case (i)[G. Vega, The weight distribution for any irreducible cyclic code of length [Formula: see text], Appl. Algebra Eng. Commun. Comput. 29(4) (2018) 363–370] are same.
Automatic Door Unlocking System Using Facemask Detection and Thermal Screening

The pandemic of the coronavirus disease 2019 (COVID-19) negatively affected the lives and businesses of millions of people in 2022. Everyone is more afraid now, especially those who wish to continue their own activities while the world recovers from the epidemic and gets ready to go back to normal. Studies show that wearing a face mask significantly reduces the danger of spreading the virus and offers the user a sense of security. However, it is difficult to physically check to see whether this rule is being followed. The creation of a face mask detector that can identify face masks is the main objective of this research. Open CV and Tensor flow are used to identify face masks. Face mask recognition is included into both the Raspberry Pi and the USB camera. This encourages the use of face masks, helps catch safety violations, and maintains the workplace safe. MLX90614 is used to determine body temperature without making physical contact. The door is opened if the temperature is below the threshold, or 40 degrees Celsius, and someone is wearing a mask; otherwise, the door must be locked to stop the buzzer from going off.
Cost-Deadline Constrained Robust Scheduling of Workflows Using Hybrid Instances in IaaS Cloud

Cloud Computing has gained popularity due to the on-demand resource allocation in the distributed computing environments and provides resources that are dynamically scalable on the “pay as you go” model. Over the past years, Amazon has started providing a new service called EC2 Spot Instances which provides their idle machines on rent in the spot market at a lower cost. Spot instances are the unused virtual machines which are accessible at almost 75% lower price than their on-demand price to perform compute-intensive tasks. Spot instances will end up till the current spot price is less than the user’s bid price. In this paper for the cloud environment, the IaaS Cloud Partial Critical Paths(IC-PCP) algorithm is expanded with the aim of reducing execution costs while still meeting user-defined deadlines. ICPCP schedules the task by finding a computation service which can execute complete critical path before its latest finish time. The work presented in this paper proposes a workflow scheduling algorithm, IaaS Cloud Partial Critical Paths with robustness(IC-PCPR) uses both on-demand and spot instances to reduce the cost of workflow execution while satisfying a user-defined deadline and making system robust that runs on heterogeneous resources. The proposed work is simulated in MATLAB framework and the experimental results based on three scientific workflows show that the ICPCPR performs much better than ICPCP algorithm.
A Study on the Sources Identification in the Dependency Structure Annotation for Modality in Chinese News Articles

This paper discusses source identification in the dependency structure annotation for modality in Chinese news articles. We updated the annotation scheme and decomposed the modal dependency structure annotation into four subtasks: event identification, source identification, modal superstructure construction and modal strength annotation. One-hundred-thirty documents containing 544 sources have been annotated by three annotators with high levels of agreement. The disagreements that arose between annotators in the past include mislabeling, missing labeling, labeling scope and sources that are not easy to determine. Given the four types of issues, this paper analyzes and concludes the main factors that affect the identification of sources, including the type of sources, the location of sources, and the type of source introducing predicates.