Reviewerly/exHarmony huggingface dataset:
Billiard-ball paradox for a quantum wave packet

Past studies of the billiard-ball paradox, a problem involving an object that travels back in time along a closed timelike curve (CTC), typically concern themselves with entirely classical histories, whereby any trajectorial effects associated with quantum mechanics cannot manifest. Here we develop a quantum version of the paradox, wherein a (semiclassical) wave packet evolves through a region containing a wormhole time machine. This is accomplished by mapping all relevant paths on to a quantum circuit, in which the distinction of the various paths is facilitated by representing the billiard particle with a clock state. For this model, we find that Deutsch's prescription (D-CTCs) provides self-consistent solutions in the form of a mixed state composed of terms which represent every possible configuration of the particle's evolution through the circuit. In the equivalent circuit picture (ECP), this reduces to a binomial distribution in the number of loops of time machine. The postselected teleportation prescription (P-CTCs) on the other hand predicts a pure-state solution in which the loop counts have binomial coefficient weights. We then discuss the model in the continuum limit, with a particular focus on the various methods one may employ in order to guarantee convergence in the average number of clock evolutions. Specifically, for D-CTCs, we find that it is necessary to regularise the theory's parameters, while P-CTCs alternatively require more contrived modification.
An Analytical Study of Administrative Deviations (Functional and Field Guidance) For Volleyball Coaches in Iraq from the Players’ Point of View

The study was concerned with functional deviation and field guidance, which does not use administrative and leadership positions correctly, and their decisions come unlike the public and private interest and come negatively at the level of the players. The study aimed to measure volleyball coaches' administrative and guiding administrative deviation and determine the standard levels of job administrative deviation and thereby the guidance for volleyball coaches. The research problem came with a weakness of the administrative cadres working in the sports sector and the administrator of the volleyball game and increased administrative deviations and field guidance among members of the Central Volleyball Federation. The researchers used the descriptive approach in the survey method due to its suitability for the current nature of the study. The research sample community was chosen by volleyball players from the Premier League clubs (Elite), and the number (114) players by (8) teams, and the research sample was chosen in a random way. The overall sample was divided into two parts, the first was the building sample, with numbered (27) players, and the second section is to the application sample, which numbered (81) players. The conclusions were released, the most important of which was the presence of differences in the levels of functional deviation and field guidance.
Sentiment Analysis of Scraped Consumer Reviews (SASCR) Using Parallel and Distributed Analytics Approaches on Big Data in Cloud Environment

With respect to the rampant growth of data in the form of reviews, feedback from customers, ratings of different products and advertisements, etc. are becoming a part of e-commerce websites on a daily basis. Due to the presence of a large amount of data on such websites, the term big data came into picture which is being used by the merchants to advertise and sell their products to a large number of audiences. On the other hand, the consumers at the same time use it to compare, select, buy, and then provide reviews regarding the product. These reviews are an integral part of new customers who are busy in searching and sorting the best relative products according to their preference. However, reviewers often adapt modern writing styles such as abbreviations, misspelled words, phrases instead of sentences, and emoticons. Hence, automatically detecting the best products as per the customer’s search helps new customers in buying the product with a clear picture of the performance and quality of the product, as well as helps the manufacturing company in keeping track of the user’s opinions. Doing sentiment analyses using parallel and distributed techniques are used for sentiment classification, which checks the polarity of a consumer’s review as positive, negative, and neutral. Furthermore, it is important to calculate the degree of trust of the user who posts a review, and the global reputation of the product. If users get a clear idea about a particular product from the consumer reviews, they can easily decide if they would want to buy the product for themselves. Thus, in this paper we have described a system that will try to help the customer by providing the best possible results for one’s product search based on consumer reviews in the past. The products are then extracted and presented before the consumer from various portals.
Real-time multi-fusion perceptron architecture for autonomous drones

In this paper, a multi-fusion perceptron architecture and real-time algorithms are proposed for AMR (Autonomous Mobile Robot). Traditionally, AGV (Automated Guided Vehicle) has been used in a large number of indoor logistics warehousing, factory automation, and thus, the corresponding sensor environment must be deployed. AMR needs to have various functions, like autonomous navigation, intelligent control, intelligent visual positioning, intelligent detection, etc., to automatically work in different conditions for traditional required fields. In a factory, there are several modes of operations, multi-machine cooperation, heterogeneous machine cooperation, This paper discusses unmanned machines with sensor elements to collaborate and to establish the complementary spatial information lacking in 3D unmanned carriers. A factory-validated multi-aware fusion of small drones is constructed, and a framework with front-end and back-end design is used for multi-computer communication, information analysis, and decentralized collaborative computing. The whole multi-fusion sensor serial platform is proposed and the system consists of a Logitech C270 USB interface Camera, the V4L2 (Video4Linux) universal driver protocol for communication, and a YDLIDAR G4 laser device serial connection. Through the UART communication interface and microcontroller platform, package information exchange and establish the underlying sensor information acquisition with Ethernet/Wi-Fi for remote sensing, algorithm verification, and real-time image confirmation.
Steiner systems $ S(2,4, 2^m) $ supported by a family of extended cyclic codes

<p style='text-indent:20px;'>In [C. Ding, An infinite family of Steiner systems <inline-formula><tex-math id="M2">\begin{document}$ S(2, 4, 2^m) $\end{document}</tex-math></inline-formula> from cyclic codes, <i>J. Combin. Des.</i> 26 (2018), no.3, 126–144], Ding constructed a family of Steiner systems <inline-formula><tex-math id="M3">\begin{document}$ S(2, 4, 2^m) $\end{document}</tex-math></inline-formula> for all <inline-formula><tex-math id="M4">\begin{document}$ m \equiv 2 \pmod{4} \ge 6 $\end{document}</tex-math></inline-formula> from a family of extended cyclic codes. The objective of this paper is to present a family of Steiner systems <inline-formula><tex-math id="M5">\begin{document}$ S(2, 4, 2^m) $\end{document}</tex-math></inline-formula> for all <inline-formula><tex-math id="M6">\begin{document}$ m \equiv 0 \pmod{4} \ge 4 $\end{document}</tex-math></inline-formula> supported by this family of extended cyclic codes. The main result of this paper complements the previous work of Ding, and the results in the two papers will show that there exists a binary extended cyclic code that can support a Steiner system <inline-formula><tex-math id="M7">\begin{document}$ S(2, 4, 2^m) $\end{document}</tex-math></inline-formula> for all even <inline-formula><tex-math id="M8">\begin{document}$ m \geq 4 $\end{document}</tex-math></inline-formula>. Furthermore, this paper also determines the parameters of other <inline-formula><tex-math id="M9">\begin{document}$ 2 $\end{document}</tex-math></inline-formula>-designs supported by this family of extended cyclic codes.
Classification of vertices on social networks by multiple approaches

Due to the advent of the expressions of data other than tabular formats, the topological compositions which make samples interrelated came into prominence. Analogically, those networks can be interpreted as social connections, dataflow maps, citation influence graphs, protein bindings, etc. However, in the case of social networks, it is highly crucial to evaluate the labels of discrete communities. The reason for such a study is the importance of analyzing graph networks to partition the vertices by only using the topological features of network graphs. For each interaction-based entity, a social graph, a mailing dataset, and two citation sets are selected as the testbench repositories. The research mainly focused on evaluating the significance of three artificial intelligence approaches on four different datasets consisting of vertices and edges. Overall, one of these methods so-called "harmonic functions", resulted in the best form to classify those constituents of graph-shaped datasets. This research not only accessed the most valuable method but also determined how graph neural networks work and the need to improve against non-neural network approaches which are faster and computationally cost-effective. Also in this paper, we will show that there is a limit to be accessed by prospective graph neural network variations by using the topological features of trialed networks.
Blockchain and Firm Total Factor Productivity: Evidence from China

This paper creatively constructs blockchain development indicators using geographical characteristics to investigate the influence of blockchain development on the total factor productivity of listed companies. Our findings reveal that local blockchain development can significantly promote the improvement of the firms’ total factor productivity. To alleviate endogeneity, this paper combines exogenous policy and geographic distance to construct instrumental variables. Moreover, the positive influence is more pronounced in non-SOEs, non-excess capacity industries, and samples with high initial productivity. After the robustness test, the results are still valid. The aforementioned results provide practical implications for Chinese listed companies to lay out digital business.
Leveraging Deep Learning and Grab Cut for Automatic Segmentation of White Blood Cell Images

White blood cell image segmentation provides the opportunity for medical experts to objectively diagnose the medical conditions of patients suffering from Leukemia, for example. Due to the rigorous nature of cell image acquisition (staining process and non-uniform illumination) efficient tools must be deployed to achieve the desired segmentation result. In this paper, a deep learning model is proposed together with a grab cut. The developed deep learning model provides an initial coarse segmentation of white blood cell images. However, the objective of this segmentation is to localize or identify regions of interest from white blood cell images. A bounding is generated from the localized cell image and then used to initiate an automatic cell image segmentation using grab cut. Results of the two publicly available datasets of white blood cell images are considered satisfactory on the proposed model.
The Elder Care Robot Based on Panoramic Vision

In order to alleviate the problem of the sharply increase in social pressure caused by the aging of the population, and improve the visual performance of the robots, expand the range of its visual perception. This paper designs an elderly assistance robot system based on panoramic vision. After completing the feasibility analysis and demand analysis, this paper adopts the system design of cloud computing, 5G communication with robots, and focuses on the panoramic image processing algorithm and model compression technology. Finally, the architecture designed in this paper can help realize the panoramic vision function of the robot. Panoramic vision enables the robots to surpass humans in terms of visual perception range and improves the flexibility and functional diversity of robots in real-time detection. In cooperation with other functional modules, the system can complete complex functions such as daily activity monitoring and emergency alarm for the elderly. This can ensure the normal life and safety of the elderly and alleviate the social pressure brought about by the aging population.
Auto-tuning HyperParameters of SGD Matrix Factorization-Based Recommender Systems Using Genetic Algorithm

Recommender systems enable companies to generate meaningful recommendations to users for items or products that might interest them. Stochastic Gradient Descent Matrix Factorization (SGD-MF) is one of the most popular model-based recommender systems. Fractional Adaptive Stochastic Gradient Descent matrix factorization (FASGD-MF) is a subset of SGD-MF-based models that apply fractional calculus in an adaptive way. There are some hyperparameters in these models that impact the quality of the recommender system. However, searching the hyperparameter space to find the best configuration using an exhaustive search is often a time-consuming task. This paper employs a genetic algorithm as a search metaheuristic to tackle this problem. The proposed method is designed based on non-uniform mutation and whole arithmetic crossover. The results indicate that optimizing hyperparameters by the proposed method not only adjusts the values of hyperparameters automatically but also can improve the quality of SGD-MF-based models. Implementing the proposed genetic algorithm on two datasets (MovieLens 100K and MovieLens 1M) verifies the assertion about the performance.
Encoding categorical variables in physics-informed graphs for Bayesian Optimization

The handling of categorical variables is a major challenge while using Bayesian Optimization (BO). Recent publications address this problem by introducing novel BO-frameworks adjusted specifically for categorical inputs. A promising approach, named "Gryffin", highlights the importance of using prior physical knowledge about categorical variables and shows significant performance improvements in comparison to other state of the art frameworks. However, the runtime of this approach increases significantly when the number of categorical choices is large. Therefore, we propose an alternative method for utilizing prior physical knowledge in BO by using physics-informed graphs. Our approach is based on a BO framework named "Combo", which allows handling graph-structures as input variables. In contrast to handling the categorical variables in complete graphs as originally proposed, we present a method for reshaping and simplifying the graph-structures based on prior physical knowledge. Physical similarities of categorical choices are encoded in graph edges, inducing proximity in the search space and enabling more precise predictions of a surrogate model. The optimization performance improves in comparison to the default Combo approach and other state of the art optimization techniques.
Plan and Prepare AVS

Microsoft Azure is one of the most prominent cloud service providers, offering options for IaaS, PaaS, and SaaS that cater to modern enterprises' rapidly changing business requirements.
Appropriate flood control technology in a local trading area: Case study at Pulosari Kiosk Area in Malang City

To date, flood is a problem that often plagues the city of Malang. Floods that occur are mainly caused by poor drainage channels and silting of drainage channels due to people's habits of throwing garbage in rivers. In the Pulosari Kiosk Area of Malang city, the flood was worsened by the capacity of the Pulosari Channel which narrowed due to illegal building. In heavy rain, so much water overflowed into the Pulosari Road, resulting in losses to traders in the area. The purpose of this study was to find a solution to reduce flooding in the Pulosari Channel with the appropriate flood control technology in the area. This study used interviews and questionnaires to investigate the community's response to flood control development efforts. Data analysis was carried out using statistical analysis to obtain a description of the community response to a flood control system implemented by the city government. The research results show that constructing flood diversion channels in the upper area, and educating the community about flood risk management is the best solution in this area to minimize flood losses.
Uso de redes sociales para la prevención del abuso de sustancias durante la cuarentena por COVID-19

Introduction: the COVID-19 pandemic has increased exposure to stressors, which can lead to an increase in drug use as a coping mechanism. Due to the confinement, social networks represent an important means for dissemination of clear, reliable information on ways to prevent drug abuse, and the consequences of said abuse in the general population and among those most vulnerable to acquiring the virus and developing the most serious symptomatology of COVID-19. Objective: to disseminate scientific evidence-based information on alcohol, tobacco and drug use in the context of the COVID-19 contingency in Mexico. Method: we developed messages accompanied by images through the analysis of information on drug use and COVID-19 from a government institution. The messages were disseminated on two social networks to promote healthy behaviors, to increase the knowledge about risks associated to COVID-19 and drug use, and to encourage reflection. Results: the messages reached a total of 851,622 people and had up to 35,819 interactions on Facebook and Twitter. Discussion and conclusions: knowing the interactions in social networks allows to strategically incorporate specific information for different populations at risk. Having information from an institutional social network is a tool to prevent the dissemination of false or inaccurate information.
Analysis of Single Image Super-Resolution Techniques: An Evolutionary Study

Single image super-resolution (SR) is a technique that reconstructs a high-resolution (HR) image from a single low-resolution (LR) input image. The main objective of super-resolution algorithms is to achieve a high-resolution image that is consistent with the input low-resolution image but has enhanced spectral properties. In this review, several research papers and their corresponding algorithms have been reviewed and are classified based on their methodology. The principal objective of this review is to understand the evolution of SISR techniques from basic interpolation techniques to sophisticated convolutional neural networks. This article also presents design considerations for future advancements.
Improvement of YOLOv4 Algorithm and Its Application in Objective Identification of Railway Level Crossing

In order to solve the problem of pedestrians and vehicles ignoring the traffic warning and crossing the tracks in the railway level crossing scene, this paper presents an improved algorithm based on the YOLOv4 model. First, we embed a high-level semantic module for the feature enhancement network of YOLOv4. The leaky ReLU activation function in the YOLOv4 network was then replaced with the parametric rectified linear unit (PReLU) activation function; bilinear interpolation method was also selected to upsample the feature graph in the YOLOv4 network. The YOLOv4 network structure was finally adjusted and optimized. In this paper, the data of car, person, train, bicycle, and motorbike five categories in the VOC 2007 dataset were selected, and the truck category data were expanded to form the experimental dataset. Experimental results show that this method makes AP worth improving compared with the improved YOLOv4 convolutional network model and has good tracking effect on targets appearing in the camera.
A context-action dependency model for spatio-temporal action localization

Understanding the interaction to the context and the co-occurrence of actions is necessary for human action detection. This paper proposes a Context-Action Dependency (CAD) model for spatio-temporal action localization tasks. It leverages context features and action relations through two attention-based modules. A context-dependency module learns the relation between actors and attentive context information. An action-dependency module models each action class separately and explores the relations within co-occurring actions. This paper introduces an action detection framework that integrates the CAD model to improve the precision of action localization. To verify the effectiveness of our solutions, we ran experiments on two datasets AVA and UCF101-24. Our experiment shows that CAD has better performance than the baselines.
Juxtaposing Sampling Techniques for Credit Card Fraud Detection

Abstract: Dealing with the class imbalance issue in the data is a big problem when creating fraud detection systems since valid transactions exceed fraudulent transactions by a large degree. Fraudulent transactions generally make up less than 1% of all transactions. This is a crucial field of research because it can be challenging to discern between a positive instance (a fraudulent case), and it gets tougher when more data are collected and a smaller percentage of such cases are represented. Eight distinct sampling techniques and two classifiers were used in this investigation, and the results of each methodology are reported. The results of this study point to favourable outcomes for sampling methods based on SMOTE. The best F1 score score obtained was with SMOTE sampling strategy on Random Forest classifier at 0.867.
Evaluating Research Dataset Recommendations in a Living Lab

The search for research datasets is as important as laborious. Due to the importance of the choice of research data in further research, this decision must be made carefully. Additionally, because of the growing amounts of data in almost all areas, research data is already a central artifact in empirical sciences. Consequentially, research dataset recommendations can beneficially supplement scientific publication searches. We formulated the recommendation task as a retrieval problem by focussing on broad similarities between research datasets and scientific publications. In a multistage approach, initial recommendations were retrieved by the BM25 ranking function and dynamic queries. Subsequently, the initial ranking was re-ranked utilizing click feedback and document embeddings. The proposed system was evaluated live on real user interaction data using the STELLA infrastructure in the LiLAS Lab at CLEF 2021. Our experimental system could efficiently be fine-tuned before the live evaluation by pre-testing the system with a pseudo test collection based on prior user interaction data from the live system. The results indicate that the experimental system outperforms the other participating systems.