Reviewerly/exHarmony huggingface dataset:
Incorporating Reinforcement Learning for Quality-aware Sample Selection in Deep Architecture Training

Many samples are necessary to train a convolutional neural network (CNN) to achieve optimum performance while maintaining generalizability. Several studies, however, have indicated that not all input data in large datasets are informative for the model, and using them for training can degrade the model’s performance and add uncertainty. Furthermore, in some domains, such as medicine, there is insufficient labelled data to train a deep learning model from scratch, necessitating the use of transfer learning to fine-tune a pretrained model in another domain. This paper proposes a transfer learning strategy based on partially supervised reinforcement learning (RL) to address these concerns by selecting the most informative samples while avoiding negative transfers from the dataset. We conducted several experiments on the benchmark image classification databases MNIST, Fashion-MNIST, and CIFAR-10 to create a fair test harness for assessing the performance of the proposed strategy, which can be extended to explore other domains in the future. The results show that the proposed strategy outperforms the classical training methods.
Features of teaching digital competencies to students of economic education

The issues of improving the structure and content of vocational education in the context of the development of digital competencies of graduates are considered. The level of knowledge of the Russian population of modern information technologies is shown and the areas of study that require increased attention in the training of modern economists are highlighted. The results of a selective sociological study on the readiness of teachers for the digital transformation of educational programs are presented. The importance of digital competencies in the system of vocational education as an integral part of training is highlighted and their role in the successful activity of a modern specialist is shown.
A Deep Learning based Feature Entity Relationship Extraction Method for Telemedicine Sensing Big Data

Abstract To solve the problem of inaccurate entity extraction caused by low application efficiency and big data noise in telemedicine sensing data, a deep learning-based method for entity relationship extraction in telemedicine big data is proposed. By analyzing the distribution structure of the medical sensing big data, the fuzzy function of the distribution shape is calculated and the seed relationship set is transformed by the inverse Shearlet transform. Combined with the deep learning technology, the GMM-GAN data enhancement model is built, the interactive medical sensing big data features are obtained, the association rules are matched one by one, the noiseless medical sensing data are extracted in time sequence, the feature items with the highest similarity are obtained and used as the constraint to complete the feature entity relationship extraction of the medical sensing data. The experimental results show that the extracted similarity of entity relations is more than 70%, which can handle overly long and complex sentences in telemedicine information text; the extraction time is the shortest and the volatility is low.
Blockchain Implementations Overview: Bitcoin, Ethereum, and Hyperledger

Bitcoin, introduced in 2008, is considered the first implementation of blockchain. Subsequent implementations of blockchain have made changes to Bitcoin to ease application development, improve scalability, and enhance versatility in terms of the types of applications that can be created.
Rethinking Supervised Depth Estimation for 360° Panoramic Imagery

Depth estimation from a single 360° panorama image is a difficult task. It is an ill-posed problem to estimate depth maps from an RGB panorama image due to the intrinsic scale ambiguity issue. To mitigate the scale inconsistency issue in the ground truth depth map, we propose a simple yet effective method to normalize the depth data based on estimated camera height. In addition, we design a multiple head planar-guided depth network, to provide more geometric constraints for depth estimation. Experimental results show that our relative depth estimation task is more accurate than the absolute depth estimation task, and our proposed model produces state-of-the-art performance on both Matterport3D and Stanford2D3D datasets.
Comparing Graph Data Science Libraries for Querying and Analysing Datasets: Towards Data Science Queries on Graphs

This paper presents an experimental study to compare analysis tools with management systems for querying and analysing graphs. Our experiment compares classic graph navigational operations queries where analytics tools and management systems adopt different execution strategies. Then, our experiment addresses data science pipelines with clustering and prediction models applied to graphs. In this kind of experiment, we underline the interest of combining both approaches and the interest of relying on a parallel execution platform for executing queries.
Semantic IR fused Heterogeneous Graph Model in Tag-based Video Search

With the rapid growth of video resources on the Internet, text-video retrieval has become a common requirement. Scholars handled text-video retrieval tasks with two-broad-category: concept-based methods and neural semantics match networks. Besides deep neural semantics matching models, some scholars mined queries and videos relationships from click-graphs, which express the users’ implicit judgments on relevance relations. However, bad generalization of click-based or concept-based models hardly capture semantic information from short queries, which stunt existing methods to fully utilize the methods to enhance the IR performance. In this paper, we propose a framework ETHGS to combine the abilities of concept-based, click-based and semantic-based models in IR and publish a new video retrieval dataset QVT from a real-world video search engine. In ETHGS, we make use of tags (i.e. concept) to construct a heterogeneous graph to alleviate the sparsity of click-through data. And we also overcome the problem of long-tailed query representation without graph information by fusing tag embeddings to represent queries. ETHGS leverages semantic embeddings to review deviant semantic information from graph nodes information. Finally, we evaluate our model ETHGS on the QVT.
Indoor-Outdoor Tracking and Activity Monitoring System for Dementia Patients

Enabling the elderly to live independently and to maintain their quality of life at home for as long as possible constitutes a major public health challenge, especially in rural and semi-urban environments where the access to formal care may be difficult. Tracking and activity monitoring allows to detect when skills for daily activities are going down to provide timely treatments and avoid future complications. This work presents an IoT system based on a wearable prototype intended for indoor and outdoor tracking and physical activity monitoring of dementia patients in remote locations. For this purpose, the wearable developed makes use of Global Navigation Satellite Systems (GNSS) for outdoor tracking, a developed indoor symbolic location system based on infrared signals, a step detector using an accelerometer and LoRaWAN communications. Positioning information is presented to caregivers or healthcare staff through an accessible web platform, which allows the visualization of the trajectories followed by patients over the last few weeks, together with information about room occupancy in indoor environments and physical activity. These data can be used to detect anomalous changes in daily patients' routines or to know the user's position in cases where the patient may have become disoriented. The proposed system encourages the autonomy of dementia patients and allows information about the disease's progression to be extracted.
The effect of social media marketing on customer buying intention on the context of entrepre-neurial firms: Moderating role of customer involvement

This study aims to provide comprehensive insights into the social media marketing characteristics affecting customer buying intention. The model was theoretically based on and explored using a quantitative approach. A survey strategy was adopted, and a five-point Likert questionnaire was distributed to Jordanian citizens. A total of 237 responses were received. The hypothesis testing followed structural equation modeling using SPSS software. The findings suggest that all social media marketing subcontracts exhibit a significant positive effect on buying intention, whereby accessibility is the highest. Customer involvement also moderates the relationship between social media marketing and customer buying intention. The proposed model provides new insights into social media marketing drivers affecting buying intentions and engagement with an entrepreneurial brand or product. This study reaffirms that social media marketing can significantly influence the success of Jordanian entrepreneurial firms and understanding how to use this tool effectively can significantly change how businesses operate.
Designing for Emerging Aesthetics: Product Semantics Approach to the Design of Smartwatches

Current trends have shown a steep rise in the popularity of smart wearable devices.Inevitably, after mobile phones, the next technology revolution is expected to be in the field of smart wearables.As more and more startups and well-established corporations are venturing into the emerging field of smart wearables, the academic institutes have the responsibility to make students equipped with all the necessary knowledge and skills required to design these futuristic devices for the real world.Moreover, human beings are becoming increasingly dependent on smart devices for their day-to-day tasks.Therefore, a study would be beneficial to understand consumer preferences and perceptions about smart wearable devices.Words express consumers' product preferences, and in design, the terms used to express preferences can be studied using product semantics.The semantic product explorations can be carried out about the aesthetics of products that are to be worn on the body for a prolonged period.In this regard, a study is conducted through a semantic differential questionnaire to get responses from 16 volunteers (8 designers and 8 non-designers) when they are exposed to the images of smartwatches.The ratings obtained in the experimental setting are used to statistically analyze and interpret ordinary consumers' as well as designers' aesthetic preferences and perceptions.Design practitioners can quickly adopt the findings of the study for the smart wearable industry and design students to generate innovative wearable aesthetics.
Data Access Pattern Recommendations for Microservices Architecture

The choice of pattern of data access from database tables is critical for a microservice to maximize benefits of distributed architecture. Traditionally, microservices have been designed using shared table access pattern commonly referred to as CRUD pattern. More recently, there has been a growing interest in applying other patterns like CQRS. In this work, we propose a system that recommends the most suitable pattern for a microservice as per the separation in read and write operations in the transactions performed by the service.
Detection and Classification of Genuine User Profile Based on Machine Learning Techniques

The current era is driven by online social networks which have evolved as a popular virtual communication platform among people. Social media users often share pictures, videos and express their views on various matters through social media posts. Though online social networks have succeeded in helping people to interact and build connections worldwide virtually, it also exposes the user to different kinds of cyber frauds. Hence identifying a genuine user profile on social media has gained significant importance in the lieu of detecting social media users from cyber criminals. With this regard, this paper is focussed at developing a machine learning model that identifies and classifies user profiles as genuine or not genuine categories. The experiment outcome shows that the proposed model achieved an average accuracy of 95% in the classification task considering all three datasets.
The Influence of Sentiment on the Movement of Bank Mandiri (BMRI) Stock Price with Word2Vec Feature Expansion and the Naïve Bayes-Support Vector Machine (NBSVM) Classifier

Sentiment towards a company is suspected of influencing the company's stock price movement. The sentiment is gathered from Twitter, Youtube, Facebook with some news media such as Consumer News and Business Channel (CNBC), Kontan, Detik, Cable News Network (CNN), Stockbit, and Liputan6 which discussed Bank Mandiri. Word2Vec is used to reduce vocabulary errors in sentiment analysis using word embedding. The Word2Vec model was built using the combined corpus of Wikipedia articles and scraped data with a total of 474,277 lines of text data. This study indicates that the correlation between sentiment and stock movements of Bank Mandiri has a positive correlation with a low relationship, indicated by the Spearman Rank test coefficient value of 0.138 and 0.123 for positive and negative sentiment, respectively. The Naïve Bayes-Support Vector Machine (NBSVM) classification model outperforms the Naïve Bayes and Support Vector Machine methods, where the baseline NBSVM gets an accuracy of 64.67%, and after the feature expansion process, the accuracy becomes 70.42%, an increase of 5.75%. This study proves there is a correlation between sentiment and the movement of Bank Mandiri's shares, and Word2Vec feature expansion can increase the model's accuracy.
RA-Swin: A RefineNet Based Adaptive Model Using Swin Transformer for Monocular Depth Estimation

Transformer-based deep learning networks have achieved extraordinary success in the field of natural language processing (NLP) in recent years. However, Transformer faces practical challenges due to the differences in the fields of NLP and visual dense prediction. This paper employs a layered Transformer as a feature extraction encoder for monocular depth estimation to overcome these differences. The encoder takes the original image size as input and performs self-attention computation on non-overlapping local regions of the feature map by shifting the window. It enables the cross-window information to interact. Different variants of the encoder are followed by an adaptable decoder based on the spatial resampling module and Refine Net. The adaptable decoder can better fuse the multi-scale output features of the encoder while keeping the number of parameters low, combined with skip connections. Experiments show that the encoder-decoder structure in this paper, fine-tuned on the NYU Depth v2 dataset, can also yield substantial improvements for monocular depth estimation. The experimental results show that compared with the current advanced Transformer model DPT -Hybrid, the root mean square error (RMS) of the Swin-B and Swin-L based models are reduced by 1.12% and 2.97%, achieving better depth estimation results.
Ingenious Methodology to Detect Disguised Human Faces with Masks using Deep Neural Network

Face recognition is now ubiquitous as an efficient and non-invasive method to verify identity. A facial recognition system works by comparison of a digital image or video frame showing a person's face with a database storing face images. Face masks are considered a required biosafety measure during this COVID-19 pandemic. Use of masks led to various issues to emerge and impact the functioning of earlier facial recognition algorithms and that has motivated our research. The construction of a real-time face recognition system that recognizes faces with and without masks is described in this paper. ResNet10 is used to perform the feature extraction. Then, to perform face detection and recognition, it is paired with a machine learning algorithm such as SVM. Without a mask, the maximum recognition accuracy is 99.40%, while with a mask, it is 98.30%.
LOG-OFF: A Novel Behavior Based Authentication Compromise Detection Approach

Password-based authentication system has been praised for its user-friendly, cost-effective, and easily deployable features. It is arguably the most commonly used security mechanism for various resources, services, and applications. On the other hand, it has well-known security flaws, including vulnerability to guessing attacks. Present state-of-the-art approaches have high overheads, as well as difficulties and unreliability during training, resulting in a poor user experience and a high false positive rate. As a result, a lightweight authentication compromise detection model that can make accurate detection with a low false positive rate is required.In this paper we propose – LOG-OFF – a behavior-based authentication compromise detection model. LOG-OFF is a lightweight model that can be deployed efficiently in practice because it does not include a labeled dataset. Based on the assumption that the behavioral pattern of a specific user does not suddenly change, we study the real-world authentication traffic data. The dataset contains more than 4 million records. We use two features to model the user behaviors, i.e., consecutive failures and login time, and develop a novel approach. LOG-OFF learns from the historical user behaviors to construct user profiles and makes probabilistic predictions of future login attempts for authentication compromise detection. LOG-OFF has a low false positive rate and latency, making it suitable for real-world deployment. In addition, it can also evolve with time and make more accurate detection as more data is being collected.
A Least-Squares Problem of a Linear Tensor Equation of Third-Order for Audio and Color Image Processing

This paper describes a new framework for solving inverse tensor problems for denoising data. This inverse tensor problem can be represented as a least-squares problem of a linear tensor equation of third-order. We use training data to replace knowledge of the noise model by introducing a framework that avoids including a particular noise model. Additionally, we describe a numerical method based on a tensor pseudoinverse to estimate a solution to the least-squares problem. Finally, we present two real-life applications for audio denoising and color image deconvolution. The advantages of the considered technique are discussed and illustrated numerically.
Towards Inertial Human Motion Tracking with Drift-Free Absolute Orientations using only Sparse Sources of Heading Information

Tracking a kinematic chain model with inertial sensors and magnetometers using a Bayesian Filter approach typically one magnetometer per segment is used to compensate for a global heading drift. In this work we present a study showing that using an appropriate modeling, heading information can be propagated from one segment to neighboring segments in a kinematic chain. This implies that the amount of required magnetometers can be much lower than one per segment. In particular we elaborate on recent theoretical results and observe that the absolute orientation of all segments in a kinematic chain can be estimated drift-free with only sparse sources of heading information. Our study consists of two parts. The first part is based on a simulated manipulator consisting of three segments. The second one includes the lower body (seven segments) of subjects performing walking trials. Here the inertial sensor data was generated using position and rotation tracking data from a marker-based optical reference system. We show that under certain circumstances the inclusion of a single source of heading information is enough to capture even under disturbances the absolute orientation of the remaining segments drift-free.
Security Concerns on Social Media Customer-to-Customer Online Transactions. A Case of Namibia Facebook Users

With the rapid use of social media for marketing purposes, a secure online transaction platform on social networking sites has become a serious concern to ensure secure transactions without any fear of privacy breaches. Social media is usually overwhelmed with cases of data breaches or threats to the security of users in terms of secure online transactions. Unfortunately, the factors for enhancing security when transacting on social networks are yet to be fully understood. This paper intends to identify the challenges and risks associated with social media transactions and to propose social media customer-to-customer secure online transaction strategies. To achieve the objective of this paper, quantitative data was collected from a sample of 225 adults in Windhoek, Namibia, using the cluster sampling technique. The research findings reflected that trust, privacy, risk, and security issues still need to be resolved to assure a secure customer-to-customer social media transaction platform.
Blockchain in the Pharmaceutical Industry for Better Tracking of Drugs with Architectures and Open Challenges

The Pharmaceutical Supply Chain (PSC) contains a wide range of components together with immature pharmaceutical dealers, producers, consultants, pharmacies, and patients. The intricacy of the flow of transactions and goods in the PSC needs an operative tracking system to regulate present and previous rights on goods. The digital tracking process offers the great advantage of controlling inaccuracy and guarantees the security of goods. Tracking of drugs via blockchain provides a viable resolution for building a shared data environment for a consistent, responsive, and transparent system for the PSC. The paper highlights the overview of goods tracking problems in the PSC and considers how blockchain technology can offer a solution for operative tracking to reduce fake or unhealthy drugs. The paper discusses the background of blockchain technology with its features. Further, the paper highlights the overview of drug tracking in the healthcare domain. Moreover, the paper explains the required architectures for drug tracking based on blockchain where Hyperledger fabric, Hyperledger besu, and Ethereum architectures have been discussed along with their drug tracking flow. Finally, the paper examines some challenges that can be leveraged to preserve the healthcare amenities.
Fusing User Reviews Into Heterogeneous Information Network Recommendation Model

With the advent of the information epoch and the development of Big Data, users are constantly overwhelmed in massive information online. As an effective method to deal with this dilemma of information overload, recommendation system has become a popular area of research for the past few years. However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model. We try to combine heterogeneous information network with deep learning technology to improve recommendation performance. The working mechanism is to represent the rich auxiliary data by using heterogeneous information network, learning the features of entities through network embedding; in the meantime, by adopting deep learning technology to mine the features of entities from review texts, using the attention mechanism to fuse feature vectors of entities, inputting them into the neural network for recommendation; the last step is that we use Yelp dataset and Douban movie dataset to verify the FHRec model. The experimental results show that the FHRec behaves better than the traditional comparison algorithm on the Yelp dataset and Douban movie dataset.