Reviewerly/exHarmony huggingface dataset:
Blockchain Technology in Construction Supply Chain Management During Pandemic: A Bibliometric Analysis and Systematic Literature Review

The supply chain in the construction industry faces numerous constraints such as a lack of coordination, communication, collaboration, and technological advancement. Supply chain management is critical when products are transferred from one organization to another until they reach end users or consumers without being damaged, counterfeited, or delayed. Despite extensive research, little is known about the factors that will lead to better construction supply chain management. The reason for this examination is to make a bibliometric investigation in light of the verifiable solidification of distributions and branches of knowledge. It intends to recognize research trends, authers, nations, and organizations utilizing VOSviewer. The construction project all over the world have been uncovered in different manner by the COVID-19 pandemic. This paper makes sense of how it is feasible to proceed with development work in this present circumstance. The relevant literature in the field of Blockchain in construction supply chain management is retrieved using the Scopus, Web of Science database as the research object. If the construction work carry on, the economic downturn will be decreased and unemployment will also be decreased. The mapping of Blockchain Construction Supply Chain Management scientific production enabled the visualization of networks of co-occurrence of keywords.
Clustering over Searchable Encrypted Data in Cloud Storage

Due to advancement in Internet Bandwidth and need for more storage capacity, cloud storage has emerged as a better alternative. Although their are some disadvantages of cloud storage but then also consumers are interested in the advantages. Due to privacy reasons some consumers hesitate to use cloud storage. To improve the privacy of data one way is to encrypt the data and then upload it. But encrypting data disables search functionality on the cloud data. For outsourcing data and also have the searching capability with us we find searchable encryption as a good alternative. Specific word searching capabilities is provided by current works but searching semantically related words is still a problem. The importance of semantic search over encrypted data is thus made abundantly evident and to increase efficiency of the process search should be on multi-keywords. Stemming algorithm is used with searchable encryption in this work to match multiple keywords for the queried keyword which in turn helps to find cluster of documents containing those matched words.
Understanding Fusion of Internet of Vehicles with Blockchain : IoV and Blockchain

Smart, autonomous, and roadside units that can communicate wirelessly make up the Internet of Vehicles, aiming to improve transportation services, reduce accidents and improve traffic efficiency. However, the security, privacy, and trust problems with the Internet of Vehicles are significant. With the advent of blockchain technology, services can now be decentralised for increased security without needing responsible third parties. With blockchain, the risks of single points of failure and other attacks are reduced, as well as the advantages of immutability and trustworthiness. In this paper, a brief introduction, advantages and security of the combination of blockchain and the Internet of Vehicles are discussed, and then an analysis is presented on how other technologies are necessary for the secure and efficient development of the network.
Knowledge Base Entity Lookup using Named Entity Recognition: a case study on YAGO

Identification of entities or concepts present in natural language queries is of utmost importance to semantic search applications. Identified entities may then be mapped to structured representations for machine understanding and further processing. Natural Language Processing techniques such as Named Entity Recognition plays a vital role in identification and classification of concepts present in the query. Post recognition, entities could be matched against potential subjects present in knowledge repositories. This paper presents an end to end entity lookup approach for natural language query keywords to structured knowledge base concepts. For case study implementation, Stanford NER library is used in conjunction with web semantics tools such as Apache Jena in order to map the identified entity to potential subjects available in YAGO knowledge base. Illustrations are provided for subject identification from input queries. Detailed setup and configuration of the knowledge base for SPARQL querying is also elaborated with implementation details.
An Effective Phishing Site Prediction using Machine Learning

As the human race is getting advanced with the technology in today’s era, the threat to get damage has also increased. In cyberspace in which we are using the internet and services, there are always some people who try to exploit users by many methods, which can lead to personal to financial loss. Over the past few years, phishing attacks are one which rapidly increased. In this paper, we have proposed API (application programming interface) to predict whether a particular website is malicious or not by providing a strong background to our model with the help of machine learning, regression, and naive Bayes algorithm, our system attains an accuracy of 98% which is better than most of the available systems. We have also presented the research accomplished as of now. Our method outperforms the currently available blacklisting methods.
Anonymity-Enabled Mix Network: Owing to Techniques and Proof of correctness

Anonymous communication channels play a vital role in enhancing privacy and security within communication networks. This research provides the overall survey of Mix Network -an anonymous communication channel. On the basis of the cryptographic operations used, the Mix Network has been classified into different types. The survey has been done to find out the important techniques used for the design of Mix Networks. Upon analysis, it has been seen that the two main techniques to design Mix Networks are decryption and re-encryption. Based on the verification proofs, publicly verifiable mix networks have been identified. Based on proof of correctness, either partially proof-of-correctness or complete-of-correctness mix networks have been identified. In this way, we will find out the robust mix networks designed so far within anonymous communication networks.
Recognition of Indian Sign Language using Hand geometry and Neural Network

Deaf-mute persons communicate using hand gestures, i.e., sign language, making it challenging to share with other persons. A self-effacing Indian sign language recognition system for some real-world gestures is constructed and tested. A dataset consisting of twenty daily-life words used in Indian sign language is created under different variations and lighting conditions. The hand geometrical features are used to estimate hand joints from RGB images. The hand joint coordinates are calculated to determine the distances and angles between the finger’s joints. The proposed model is cost-effective and requires no extra sensors or devices. The experimental results show that the model has a recognizable accuracy of 98.78% on functional gestures.
Boosting Accuracy of Fake Review Prediction Using Synthetic Minority Oversampling Technique

In recent times prior to making a purchase, the vast majority read reviews about that product, and their decision is largely driven by the reviews. Deceitful online sellers often gather fake or spam reviews for their products or services, thereby reducing the effectiveness of online reviews. The review data is often imbalanced such that the fake reviews greatly outnumber the genuine reviews. An imbalance leads to a bias, as the model tends to mostly predict the majority class. To attain a high-quality classification outcome, the issue of imbalanced data should be resolved before applying the classification algorithms. This paper studies the performance of supervised machine learning classifiers pertaining to fake review detection. The approach put forward in this paper aims to improve the prediction accuracy of popular supervised learning classifiers Random - Forest, LightGBM, XGBoost, Naive Bayes, and Decision Tree on an imbalanced review dataset For boosting the accuracy of these classifiers, the Synthetic Minority Oversampling Technique is used for addressing the class imbalance problem. The performance of the classifiers has been studied by changing the oversampling parameters. The application of SMOTE showed a significant improvement in the classifier’s prediction accuracy.
Smart Attendance Tracking and Performance Monitoring System Based on CCTV Camera Input

Attendance through CCTV cameras based on face recognition technique is a standard and efficient approach used by various organizations at present. The main hitch of the system is that it can keep track of the attendance of an individual but cannot monitor the performance. A smart attendance and performance monitoring system without any human engagement is introduced here. The intelligence system is capable enough to visualize all the activity done by every individual in the building. The whole system is sub-divided into three levels, and three servers are designed to fulfill the purpose. Depending upon the analysis, if something is found unsatisfactory, then that information will be forwarded to the higher authority for taking action. DNN-based face detection and face encoding algorithms, along with Euclidean distance, Histogram of Oriented Gradients (HOG), and average hashing is used to establish the whole system.
Futuristic Opportunities and Challenges for Cognitive Tutoring Systems

Across a systematic review of literature, ITS have shown a great potential emerging as systems offering learning to the learner, in a manner that best suits the learner. But the growth rate is affected by specific challenges that are proving to be limiting factors. There are reasonable number of possible mechanisms unexplored as of now, that can be implemented listing opportunities for future directions of research in this field. This paper emphasizes on these limiting factors, opportunities, and describes the existing intelligent feature incorporated in the Intelligent Tutoring System.
RBUE: a ReLU-based uncertainty estimation method for convolutional neural networks

Abstract Convolutional neural networks (CNNs) have successfully demonstrated their powerful predictive performance in a variety of tasks. However, it remains a challenge to estimate the uncertainty of these predictions simply and accurately. Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty accurately, but it is expensive to train and test. MC-Dropout is another popular method that is less costly but lacks the diversity of predictions resulting in less accurate uncertainty estimates. To combine the benefits of both, we introduce a ReLU-Based Uncertainty Estimation (RBUE) method. Instead of using the randomness of the Dropout module during the test phase (MC-Dropout) or using the randomness of the initial weights of CNNs (Deep Ensemble), RBUE uses the randomness of activation function to obtain diverse outputs in the testing phase to estimate uncertainty. Under the method, we propose strategy MC-DropReLU and develop strategy MC-RReLU. The uniform distribution of the activation function’s position in CNNs allows the randomness to be well transferred to the output results and gives a more diverse output, thus improving the accuracy of the uncertainty estimation. Moreover, our method is simple to implement and does not need to modify the existing model. We experimentally validate the RBUE on three widely used datasets, CIFAR10, CIFAR100, and TinyImageNet. The experiments demonstrate that our method has competitive performance but is more favorable in training time.
Behavior Profiling of Role-Playing Game Players Based on Heuristic Event Log Mining

In this paper, we propose a novel player behavior model called the action priority model (APM) for representing player action behaviors. A play log is stored based on the game grammar under analysis, and heuristic filtering rules are applied to eliminate uninteresting events. The proposed model uses sequences and tendencies of event types as the main mining criteria to extract significant player action behaviors. Action sequences reflect the interdependencies of different actions and how a player may use their insights about the game rules to form a (hidden) action priority list (APL) with respect to action types. By mining for significant events from play logs, we aim to produce the APM and APL for different players. In experiments, the fuzzy miner tool was used to analyze a few event logs for the World of Warcraft game to obtain some noticeable discoveries about the structural and timing patterns of effective players. Experimental results show that two players in the same class with similar contributions to encounters will have a similar mined APM and may not have a minimal delay in the timing of actions. Players with a high contribution to an encounter will have an APM like a simulated optimal player along with large thickness edges in the APM to indicate a significant routine of actions.
Stochastic-Gradient-Descent-Based Max-Margin Early Event Detector

Max-margin-based early event detection is first solved by max-margin early event detector (MMED) proposed by Hoai and Torre [10]. In this study, the stochastic gradient descent mechanism is used to replace the quadratic programming solver in [10] to achieve early event detection. Three datasets are tested, including synthetic data, the extended Cohn-Kanade dataset (CK+), and Australian sign language data (ASL). The experimental results show that the proposed approach is feasible, and that the performance is comparable to that obtained in MMED.
AHP-Based Assessment of Developing Online Virtual Reality Services with Progressive Web Apps

The study uses Progressive Web Apps (PWAs) technologies to construct online virtual reality services and utilizes the Analytic Hierarchical Analysis method to evaluate the design and system implementation results. With the development of the metaverse concept, the design and application of online virtual reality services are required. By conducting literature research, semi-structured interviews, and technical application analysis, this study first defines the requirements and framework for developing a prototype virtual reality system with PWAs technologies, following the quality function development analysis and developing the prototype system accordingly. A questionnaire on the prototype system’s integration, usability, and cross-platform technology development was then used to evaluate proposed design elements from digital content planning, design, and front-end and back-end engineering experts. Study findings revealed that cross-platform compatibility was a key feature when integrating PWAs with online virtual reality design, especially among experts from all fields. The results of this study can provide a reference for the development of online virtual reality immersive experiences with PWAs in the future. Further research can focus on collecting and analyzing digital footprints generated by users in progressive web technologies and improving the link between user experience and content in the future.
Evaluation and Analysis of Elderly Mental Health Based on Artificial Intelligence

The purpose is to understand the depression status of the elderly in the community, explore its influencing factors, formulate a comprehensive psychological intervention plan according to the influencing factors, implement demonstration psychological intervention, and evaluate and feedback the effect, so as to provide a reference for improving the mental health of the elderly.In order to make the output of different emotional data in LSTM more discriminative, a method to dynamically filter the output of LSTM is proposed. Combining the methods of Attention-LSTM, time-dimensional AI attention, and feature-dimensional AI attention, the best model in this paper is obtained. The multistage stratified cluster sampling method was used to conduct a questionnaire survey on the elderly aged 60 and above in a certain area, including the general demographic characteristics questionnaire of the elderly, the self-rating scale of mental health symptoms, and the health self-management ability of adults. All data were entered into a database using Excel software, and SPSS 19.0 statistical software was used for statistical analysis. Results/Discussion. The detection rate of depression (GDS ≥ 11 points) among the elderly in a community in a certain area was 39.38%. Multivariate logistic regression analysis showed that family history of mental illness, more negative life events, decreased ability of daily living, living alone, and suffering from physical diseases in the past six months were the risk factors for depression in the elderly. Community health education can partially alleviate depression in the elderly. The detection rate and degree of depression of the elderly in the comprehensive psychological intervention group were significantly lower than those in the control group, and the difference was statistically significant (P < 0.05).
Managing Diagnostic Expectations During a Run

A continuum of investors receive public and private news about the fundamentals of a financial intermediary and must decide whether to withdraw their funds or renew their investment. In the short run (date 1), investors are diagnostic and therefore overreact to both good and bad news when updating their beliefs about the intermediary fundamentals. In the long run (date 2), beliefs revert to rational expectations, as agents can better process the incoming information. An authority, who wants to minimize the size of a run, can prohibit investors from making decisions at date 1, akin to trading halts, the temporary suspension of flows in mutual funds, or the freezing of cryptocurrencies platforms. It is shown that during bad times, when bad public news arrive and/or investment returns are low, such policy actually increases the size of a run, even when almost all investors are receiving bad news and overreacting to it. During good times, the opposite result arises.
Auto Insurance Fraud Detection with Multimodal Learning

ABSTRACT In recent years, feature engineering-based machine learning models have made significant progress in auto insurance fraud detection. However, most models or systems focused only on structural data and did not utilize multi-modal data to improve fraud detection efficiency. To solve this problem, we adapt both natural language processing and computer vision techniques to our knowledge-based algorithm and construct an Auto Insurance Multi-modal Learning (AIML) framework. We then apply AIML to detect fraud behavior in auto insurance cases with data from real scenarios and conduct experiments to examine the improvement in model performance with multi-modal data compared to baseline model with structural data only. A self-designed Semi-Auto Feature Engineer (SAFE) algorithm to process auto insurance data and a visual data processing framework are embedded within AIML. Results show that AIML substantially improves the model performance in detecting fraud behavior compared to models that only use structural data.
Generalized multi-scale image decomposition for new tone manipulation

It is well known that the image multi-scale decomposition plays a foundational role in image processing and a variety of image multi-scale decomposition methods have been derived in various image processing tasks up till now. In many cases, the image decomposition needs to satisfy the conditions: edge-preserving or non-scale-hybridizing. Unfortunately, there has been no such multi-scale image decomposition approach satisfying both the two conditions simultaneously to our best knowledge. In this paper, one novel multi-scale image decomposition approach satisfying both the two conditions is proposed, which achieves different edge-preserving components under different scales. The proposed approach makes full use of well-designed adaptive mean envelope pixels to construct the segmentation envelopes to separate the different scales. Moreover, based on the multi-scale decomposition, an interesting multi-scale tone manipulation scheme and an effective new filtering algorithm for impulse noise are induced. Furthermore, numerical experiments compared with the existent methods are given to demonstrate the state-of-the-art performance of the proposed methods.
Evaluating parallelized support vector regression and nearest neighbor regression with different input variations for estimating daily global solar radiation of the humid subtropical region in China

Abstract Indirectly estimating global solar radiation is strongly nonlinear and needs to be addressed by machine learning. Sequentially developing a machine learning model can be very time consuming. Moreover, whether and how the exogenous meteorological, geographical and temporal variables affect regression accuracy still has not been well understood. This paper evaluates parallelized support vector regression (SVR) and nearest neighbor regression (NNR) models for estimating daily global solar radiation of the humid subtropical region in China using existing Python libraries on a multi-core central processing unit (CPU) and a graphical processing unit (GPU). Seven input variations are studied. Two variations are commonly adopted in literature, four variations contain meteorological, geographical and/or temporal features with bounded Pearson correlation coefficients (PCCs), and the other variation simply include all the available features. Experimental results demonstrate that: SVR and NNR are equally powerful for nonlinear regression, and the variation comprising features with absolute PCCs no less than 0.3 (i.e. just all the meteorological features) is able to achieve most accurate estimation; the GPU-parallelized SVR model can accelerate parameter calibration and prediction; compared with the CPU-parallelized and GPU-parallelized SVR models, the GPU-parallelized NNR model is much more efficient and rather more scalable with the increment of the number of data samples; and the CPU-parallelized NNR model consumes quite less parameter calibration time than the GPU-parallelized NNR model, owing to different methods adopted for determining distances and significant time wasted by the GPU-parallelized NNR model on repeatedly calculating required information during cross-validation.
Experimental Boson Sampling Enabling Cryptographic One-Way Function

Boson sampling is a computational problem, which is commonly believed to be a representative paradigm for attaining the milestone of quantum advantage. So far, massive efforts have been made to the experimental large-scale boson sampling for demonstrating this milestone, while further applications of the machines remain a largely unexplored area. Here, we investigate experimentally the efficiency and security of a cryptographic one-way function that relies on coarse-grained boson sampling, in the framework of a photonic boson-sampling machine fabricated by a femtosecond laser direct writing technique. Our findings demonstrate that the implementation of the function requires moderate sample sizes, which can be over 4 orders of magnitude smaller than the ones predicted by the Chernoff bound; whereas for numbers of photons n≥3 and bins d∼poly(m,n), the same output of the function cannot be generated by nonboson samplers. Our Letter is the first experimental study that deals with the potential applications of boson sampling in the field of cryptography and paves the way toward additional studies in this direction.