Reviewerly/exHarmony huggingface dataset:
Survey on Securing Internet of Things through Block chain Technology

Internet of Things (IoT) has found wider acceptance due to its real-time and high efficient collaboration; Wireless Sensor Network aka WSN is core component for supporting the IoT operation. The security issue is one of the major concerns and several research works have been considered. This research review aims to conduct a critical review of the Blockchain-based WSN) domain. Blockchain-WSN aims to focus on the different aspects of security concerns in a wireless sensor network.. Moreover, the novelty of this research review lies in critical analysis of the adoption of blockchain in WSN in comparison with other research reviews. At first, this paper discusses the WSN and its challenges, especially from the security perspective through exposing the vulnerabilities in WSN. Further, this research review work introduces a blockchain approach review and its integration with conventional WSNs. Furthermore, several existing blockchain-WSN mechanism is reviewed and analyzed. At last limitation and restriction of blockchain-WSN is highlighted that open the research gap for designing the new mechanism for WSN security with blockchain
Improving Accuracy of Traffic Sign Detection Using Learning Method

In recent years, the demand for image recognition has been increasing. For example, face recognition can be used to unlock lock screens in smartphones, and it can also be combined with a temperature sensor to detect body temperature. It can also be applied to automatic driving, where it can recognize people, traffic lights, and signs using image recognition to increase safety. Automatic driving requires not only recognition but also object detection to estimate positions of objects at the same time. In addition, it is essential to improve the accuracy of detection to ensure safety. In this study, we focus on the detection of signs and try to improve the detection accuracy.
Energy Optimization Algorithm to Reduce Power Consumption in Cloud Data Center

In the IT industry, Cloud Technology has ushered in a revolution. The sole drawback of a cloud environment is the high power consumption. As a result, to improve the efficiency of the cloud environment, cloud servers must be able to execute more work at the same time while using less power. The majority of existing energy optimization algorithms set an upper and lower threshold for underload and overload detection, lowering energy usage while lowering performance. Using the Energy Optimization Algorithm, we primarily focus on optimizing energy without sacrificing performance in this study (EOA). We identify the overload in our approach by looking at the entire workload utilization of the data center. The performance-to-power ratio is improved with this approach. Reduced live migration of virtual machines is required to achieve high throughput. The primary goal of this EOA is to reduce the number of live migrations while maintaining performance.
Hungarian-Particle Filtering Based Segmentation for On-Road Visual Vehicle Detection and Tracking

This work presents a method for detecting and tracking vehicles in traffic videos collected from urban highways. The method efficiently handles numerous vehicle targets with low computational complexity, making it ideal for real-time driver assisting systems. The method uses segmentation to create a quick bounding box to detect moving vehicles, while a particle filter is used to track these vehicles. The Hungarian matching algorithm is later used to solve the association of tracked vehicles in each video frame. The proposed method and an established vehicle tracking method, Camshift Meanshift tracking with Kalman filtering (CMT-KF), were compared and evaluated on the collected traffic videos. The proposed method resulted in a 52% reduction in tracking failures.
A landscape-based analysis of fixed temperature and simulated annealing

Since the introduction of Simulated Annealing (SA), researchers have considered variants that keep the same temperature value throughout the whole search and tried to determine whether this strategy can be more effective than the original cooling scheme. Several studied have tried to answer this question without a conclusive answer and without providing indications that could be useful for a practical implementation. In this work, we address this question following an experimental approach, relating the characteristics of the algorithms with the characteristics of the landscapes they encounter. We use problem-independent landscape features to study the algorithmic behaviour across different problems. We consider three different objective functions and various instance classes and determine the conditions under which the fixed-temperature variant of SA can outperform its original counterpart and when SA is instead a better choice.
Teaching during COVID-19: reflections of early-career science teachers

The unique circumstances of the COVID-19 pandemic required that instruction be shifted online through asynchronous, synchronous, or hybrid models of instruction. This created a need for many K-12 teachers to dramatically rethink how teaching and learning occurred in their classrooms. In this study, we investigate the experiences of early-career science teachers who were in their first year of teaching when the pandemic struck. Using a comparative case study and an analytical framework focused on technology-related leader practices, we explore the unique opportunities for technology-based leadership that emerged for early-career teachers during the pandemic. We posit that the circumstances of the COVID-19 pandemic presented novel opportunities for early-career teachers to assume leadership roles that were embedded within the classroom teaching experience, which created unique opportunities for innovation and leadership in teaching.
Dynamic Display Design of Cultural and Creative Products Based on Digital Augmented Reality Technology

The development of science and technology has brought about a change in life, and this has also caused people to use technology more and more frequently. In the dynamic display design of cultural and creative products, people are also beginning to look for a technology that can better deal with it. Its characteristics are displayed, which is the research of this article. This article aims to study how to carry out a dynamic display design for cultural and creative products. To this end, this paper proposes a method of digital augmented reality technology, optimizes the visual search technology in augmented reality technology, studies and analyzes its specific digital implementation process, divides and understands the technical principles, and designs relevant experiments to study its specific display effect and carry out the relevant effect analysis in the analysis part. The experimental results of this article show that the improved augmented reality technology can better enhance the consumer’s interactive experience. At the same time, the consumers’ purchase intentions have also been improved by 43%. It can be said that the dynamic effects of cultural and creative products are well demonstrated.
MNE market entry and social investment in battle-weary countries: Evidence from Heineken

To contribute to our understanding of MNE strategy in battle-weary countries, we explore the case of Heineken as it entered and made social investments in these countries prior to 2020. In a first analysis we explore links between host country violence and entry strategy. In a second analysis we compare post-entry social investment in an improving context (Ethiopia) with a worsening context of violence (Myanmar). The case helps to integrate various streams of international business literature pertaining to strategy in conflict zones. It underlines the need to consider violence, entry strategy and subsequent social investment in the same analytical frame.
A Comparative Study of Collaborative Movie Recommendation System

The number of movies available has expanded, making it challenging to select a film that uses current technology to meet users' needs. Following the widespread use of internet services, recommendation systems have become commonplace. The objective for all recommendation systems now is to employ filtering and clustering algorithms to recommend content users are interested in. Suggestions for a media commodity like movies are offered to consumers by locating user profiles of people with comparable likes which makes users' preferences initially determined to allow them to rate movies of their choosing. After a period of use, the recommender system understands the user and offers films that are more likely to receive higher ratings. A comparison study on the existing models helps to understand future scope and improvements for more personalized models for movie recommendation. In comparison to previous models, the MovieLens dataset gives a dependable model that is exact and delivers more customized movie suggestions. In this paper, an approach to do a detailed study and review the user preferences based on item and content of movies has been made to understand the filtering techniques of the collaborative recommendation system to increase accuracy and give highly rated movies as recommendations to the user is carried and based on the results the recommendation system is built with a content-based filtering technique.
Collaborative Learning to Improve the Non-uniqueness of NMF

Non-negative matrix factorization (NMF) is an unsupervised algorithm for clustering where a non-negative data matrix is factorized into (usually) two matrices with the property that all the matrices have no negative elements. This factorization raises the problem of instability, which means whenever we run NMF for the same dataset, we get different factorization. In order to solve the problem of non-uniqueness and to have a more stable solution, we propose a new approach that consists on collaborating different NMF models followed by a consensus. The proposed approach was validated on several datasets and the experimental results showed the effectiveness of our approach which is based on the reducing of standard reconstruction error in NMF model.
Contingency Space: A Semimetric Space for Classification Evaluation

In Machine Learning, a supervised model's performance is measured using the evaluation metrics. In this study, we first present our motivation by revisiting the major limitations of these metrics, namely one-dimensionality, lack of context, lack of intuitiveness, uncomparability, binary restriction, and uncustomizability of metrics. In response, we propose Contingency Space, a bounded semimetric space that provides a generic representation for any performance evaluation metric. Then we showcase how this space addresses the limitations. In this space, each metric forms a surface using which we visually compare different evaluation metrics. Taking advantage of the fact that a metric's surface warps proportionally to the degree of which it is sensitive to the class-imbalance ratio of data, we introduce Imbalance Sensitivity that quantifies the skew-sensitivity. Since an arbitrary model is represented in this space by a single point, we introduce Learning Path for qualitative and quantitative analyses of the training process. Using the semimetric that contingency space is endowed with, we introduce Tau as a new cost sensitive and Imbalance Agnostic metric. Lastly, we show that contingency space addresses multi-class problems as well. Throughout this work, we define each concept through stipulated definitions and present every application with practical examples and visualizations.
Enhancing transparency and fairness in public procurement process with the support of blockchain technology: a smart contract based approach

The possibility of establishing a transparent public procurement process through the inclusion of protected transparent digital technologies or platforms, such as blockchain technology, represents a significant step forward in the field of public procurement. Furthermore, if we add to this idea the Ethereum network and the ability to create applications that run on this network, such as smart contracts, the procurement process can be made much more objective and transparent to all participants in the process. The proposed model presented in this paper consists of two smart contracts. Each one of them has its own basic building blocks used for different phases of the public procurement process, from the definition of security access polices for different parts of procurement contract, public procurement procedure, bidding by interested bidders, evaluation of bids and the final publication of the results. Elements of the proposed system have been analyzed and presented and some of the further development plans have been formulated.
NCCollab: collaborative behavior tree authoring in game development

Game development is a collective process in which a variety of different professionals from different backgrounds collaborate together not only by means of conversational interaction but also collaborative participation, one of which is programming. While collaborative and pair programming solutions exist for text-based programming languages, visual programming has not enjoyed as much attention. These solutions would not only address advanced forms of business communication among team members but could find their use in distance learning, which would have been useful during the pandemic. In our work, we propose a solution for collaborative behavioral animation of NPCs using behavior trees through synchronous and asynchronous modes of collaboration. We conducted a user study with 12 moderately skilled game development university students who were placed in groups of two and engaged in joint fixed behavior tree development tasks using the synchronous and asynchronous modes and auxiliary features of live preview, access and restoration of previous states from behavior tree history, conflict resolution, and instant messaging. Participants also completed a control task where no collaboration was involved and auxiliary features were not available. Feedback form Creativity Support Index, a self-developed questionnaire, and a semi-structured interview were collected. Additionally, task completion times were logged. The results indicate that the two collaborative modes provide expected improvement over the control condition. No significant differences were found between the two collaborative modes. However, the semi-structed interview revealed that the synchronous mode could be useful for quick prototyping, while the asynchronous mode - for most other situations.The online version contains supplementary material available at 10.1007/s11042-022-12307-2.
Fine-grained action segmentation based on complementary Frame-level Classification Model and Action-wise Regression Model

Over-segmentation is a critical issue in fine-grained action segmentation tasks, such as repetitive motion detection and factory construction progress monitoring. To address these problems, we segment the temporal structure of each action instance into three action phases, and propose an innovative framework. This framework consists of a spatial–temporal feature extractor and two paralleled models: the Frame-level Classification Model (FCM) and the Action-wise Regression Model (ARM). We use frames as appearance stream and pixel trajectories as motion stream. The fused features are fed to the FCM for frame-level classification and the ARM for action phase regression. At inference time, the frame-level classification sequence is fine-tuned by regression. In this way, our model reduces over-segmentation and segments actions more accurately. We test the model on three datasets: 50Salads, GTEA, and our self-built dataset in which workers try to drill pipes in a coal mine well. Experimental results show that our method is better than other methods, and at the same time, over-segmentation is significantly reduced after adding the ARM.
Exploring Eye Movement Biometrics in Real-World Activities: A Case Study of Wayfinding

Eye movement biometrics can enable continuous verification for highly secure environments such as financial transactions and defense establishments, as well as a more personalized and tailored experience in gaze-based human-computer interactions. However, there are numerous challenges to recognizing people in real environments using eye movements, such as implicity and stimulus independence. In the instance of wayfinding, this research intends to investigate implicit and stimulus-independent eye movement biometrics in real-world situations. We collected 39 subjects' eye movement data from real-world wayfinding experiments and derived five sets of eye movement features (the basic statistical, pupillary response, fixation density, fixation semantic and saccade encoding features). We adopted a random forest and performed biometric recognition for both identification and verification scenarios. The best accuracy we obtained in the identification scenario was 78% (equal error rate, EER = 6.3%) with the 10-fold classification and 64% (EER = 12.1%) with the leave-one-route-out classification. The best accuracy we achieved in the verification scenario was 89% (EER = 9.1%). Additionally, we tested performance across the 5 feature sets and 20 time window sizes. The results showed that the verification accuracy was insensitive to the increase in the time window size. These findings are the first indication of the viability of performing implicit and stimulus-independent biometric recognition in real-world settings using wearable eye tracking.
Systems Organize Information in Mind and Nature: Empirical Findings of Part-Whole Systems (S) in Cognitive and Material Complexity

Part-whole Systems (S) structure is foundational to a diverse array of phenomena such as belonging and containment, networks, statistics, reductionism, holism, etc. and is extremely similar if not synonymous with sets, sorts, groups, combinations and combinatorics, clusters, etc. In Cabrera (1998), part-whole Systems (S) or “S-rule” is established as one of four universals for the organization of information and thus is foundational to systems and systems thinking as well as the consilience of knowledge. In this paper, seven empirical studies are presented in which (unless otherwise noted) subjects completed a task. Ranging from n = 407 to n = 34,398, the sample sizes vary for each study but are generalizeable to a normal distribution of the US population. With high statistical significance, the results of these studies support the predictions made by DSRP Theory regarding part-whole Systems (a.k.a., “S-rule”) including: the universality of S-rule as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); the internal structures and dynamics of S-rule; S-rule’s mutual dependencies on other universals of DSRP (Distinctions, Systems, Relationships, and Perspectives (i.e., Distinctions, Relationships, and Perspectives); the role S-rule plays in making structural predictions; and, S-rule’s efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of part-whole Systems (S).
Multi-manifold discriminant local spline embedding

Manifold learning reveals the intrinsic low-dimensional manifold structure of high-dimensional data and has achieved great success in a wide spectrum of applications. However, traditional manifold learning methods assume that all the data lie on a common manifold, hence fail to capture the complicated geometry structure of the real-world data lying on multiple manifolds. This paper proposes a novel Multi-manifold Discriminant Local Spline Embedding (MDLSE) algorithm for high-dimensional classification, which considers a more realistic scenario where data of the same class lies on the same manifold. On the basis of this assumption, MDLSE seeks to reconstruct multiple manifolds for different classes of data in the embedding and separate them as apart as possible. In order to preserve the geometry structure of all the manifolds, MDLSE employs thin plate splines to align the local patches within each manifold compatibly in the global embedding. Meanwhile, to separate the different manifolds, MDLSE utilizes discriminative information to ensure the neighboring data from different manifolds to be mapped far from each other. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of MDLSE over the other representative manifold learning algorithms. The advantage of MDLSE is often more obvious on smaller size of training data and in lower embedding dimensions.
Three-state quantum walks on cycles

In this paper, on the basis of constructing a new shift operator [Formula: see text] and choosing the Grover coin [Formula: see text] as coin operator [Formula: see text], we get the standard evolution operator [Formula: see text] on cycles. Using [Formula: see text], we not only got the analytical expression of wavefunction [Formula: see text], but also obtained the conclusion that the limit distribution [Formula: see text] of [Formula: see text], which is not uniform distribution, regardless of [Formula: see text] is odd or even.
Analysis of Breast Cancer Classification using Various Algorithms

Invasive ductal carcinoma has become the most common types of tumors in recent years. The chances of the patient arriving in the case improve if the issue is detected early. The most prevalent classification for breast cancer is binary (benign cancer/malignant cancer), which allows pathologists to come up with a systematic and objective prognosis.The WBCD (Wisconsin Breast Cancer Diagnosis) dataset is used to build the classifier. Before performing the classification, the dataset is preprocessed and explored using various techniques. Four Machine Learning techniques like k-fold cross-validation, pipelining, principle component analysis(PCA), and hyperparameter optimization are compared and analyzed in the project. These techniques are applied to the development of eight classifiers that must distinguish between benign and malignant breast tumors. These techniques were applied on the Adaboost classifier, KNeighbor classifier, DT classifier, RF classifier, SVM classifier, Logistic regression classifier, Gaussian NB classifier, and Gradient boosting classifier. After applying all those algorithms, the SVM classifier has obtained the highest accuracy of 99.1% after applying all those algorithms on the dataset.
Acceleration Study of Two-Stage and Deep-Learning Based Facial Direction Detection on GPU-Based Edge Device

When an intelligent autonomous mover is active, the facial direction messages of pedestrians become important for the use of social-aware navigation in surrounding-crowds environments. To speed-up the performance of the two-stage processing based convolutional neural network (CNN) design for facial direction detection on the cost-effective GPU-based edge device, in this paper, three acceleration methodologies are applied, including TensorRT, Multithreading, and Overclocking technologies, to speed-up the frames per second (FPS) performance on NVIDIA Jetson Nano platform. Compared with the direct two-stage implementation without using accelerations, the accelerated implementation performs more than three times speed-up ratios for facial direction detections of pedestrians.
Project-Based Learning for Teaching the Knapsack Problem in the Course “Telecommunication Security”

The paper presents the implementation of a project-based approach used in the course “Telecommunication Security” for the seventh-semester students-bachelors from the specialty “Internet and Mobile Communications” at the University of Ruse “Angel Kanchev” when teaching the knapsack problem. Working on projects, one of the trends of future education, Education 4.0, allows the development of basic 21st-century skills, such as literacy, numeracy, ICT literacy, creativity, initiative, critical thinking, problem-solving skills, etc.