Reviewerly/exHarmony huggingface dataset:
A Compound Optimization Greedy Strategy with Reverse Correction Mechanism

Abstract Greedy strategy is an algorithm thinking with local optimization as the core idea, but only when the problem has no after-effect, the global optimization can be achieved. Therefore, greedy strategy is not the first choice for researchers to solve the problem. Based on the greedy strategy, this paper adds the mechanism of reverse correction thinking, transfers the local optimal solution to the global optimal solution, and puts forward a compound optimal greedy strategy integrating reverse correction thinking. Based on the actual application scenario of blood robot operating costs, the overall “simple greedy strategy model” is constructed and tested based on the greedy strategy as the main modeling basis according to the application needs. On this basis, the interaction relationship between local optimal solutions is deeply analyzed, and the reverse correction mechanism is integrated to optimize the system through the two steps of reverse allocation and reverse merge repair. Gradually improve the model to get the optimized “reverse modified greedy strategy model”, the algorithm can effectively reduce the operating cost. On this basis, in order to test the optimization effect, the effectiveness and stability of the reverse correction mechanism were verified by modifying some parameters of the application scene and randomly generating multiple arrays for re-test, etc., and new parameters were selected to re-run the application scene, and satisfactory verification results were obtained. Compared with other modeling ideas of the same topic, this model weakens the expression of the overall function and emphasizes the change relationship and action mechanism between data, and obtains better operation results. Greedy strategy is very conducive to the analysis of the relationship between requirements, constraints and variables. According to the actual application needs, combined with the mathematical analysis method, the reverse correction mechanism is added to the greedy strategy modeling. In the demand sequence test of 100 groups of simulation, the maximum saving rate can be close to 1.6%, while the lowest saving rate is less than 0.6%, and the average saving rate is 0.9677%. It can save tens of thousands of operating costs for application scenarios.
Decision-Making Training for Students and Managers Using Data Science and Smart Platforms

The purpose of the work is to conduct a systematic analysis of the goals and opportunities for learning adaptive management and decision-making on personnel in the conditions of a multifactorial internal and external environment. The problem of adaptive training of graduate students to the realities of digital business is also touched upon. The methods of system analysis were used, and the relevance of the systems of the classes “Virtual Classroom”, “Open Mass Training”, “Learning on Demand”, “Flexible Learning”, “Mobile Learning” and “Flipped Classroom” was noted. A logistic model of migration in the human resources environment was proposed. The systems of “marketplace” remote and distributed training are studied, considering the efficiency and flexibility of staff training solutions. For example, supported by “meeting rooms”, virtual desktops, machine learning and gamification. The chain of data science management is given, the model (profile) of the client is investigated, and the indexes of his consumer choice are identified.
Comparison of Different Similarity Methods for Text Categorization

Incorporating semantic information into any similarity metric increases its effectiveness and yields findings that may be further analyzed using human interpretation. There will be fewer accurate findings if the similarity is calculated based only on the text’s words. Three alternative approaches are shown in this study, each of which uses a feature vector that combines semantic information from readers and calculates similarities between them. These methods—LSA using word2vec, Explicit Semantic Analysis using Bag-of-Words, and Soft Cosine Similarity using TF-IDF—are based on textual data and knowledge-based methodologies. The technique produces simple-to-read documents that can be used in different information retrieval systems. When comparing commonalities between brief news texts, Latent Semantic Analysis employing Word2Vec Vectors outperformed the other two.
Statistical Significance of Clustering with Multidimensional Scaling

AbstractAbstractClustering is a fundamental tool for exploratory data analysis. One central problem in clustering is deciding if the clusters discovered by clustering methods are reliable as opposed to being artifacts of natural sampling variation. Statistical significance of clustering (SigClust) is a recently developed cluster evaluation tool for high-dimension, low-sample size data. Despite its successful application to many scientific problems, there are cases where the original SigClust may not work well. Furthermore, for specific applications, researchers may not have access to the original data and only have the dissimilarity matrix. In this case, clustering is still a valuable exploratory tool, but the original SigClust is not applicable. To address these issues, we propose a new SigClust method using multidimensional scaling (MDS). The underlying idea behind MDS-based SigClust is that one can achieve low-dimensional representations of the original data via MDS using only the dissimilarity matrix and then apply SigClust on the low-dimensional MDS space. The proposed MDS-based SigClust can circumvent the challenge of parameter estimation of the original method in high-dimensional spaces while keeping the essential clustering structure in the MDS space. Both simulations and real data applications demonstrate that the proposed method works remarkably well for assessing the statistical significance of clustering. Supplementary materials for this article are available online.KEYWORDS: Cluster indexDimension reductionHigh-dimension low-sample size dataPrincipal component analysisUnsupervised learning AcknowledgmentsThe authors are indebted to the editor, the associate editor, and two reviewers, whose helpful suggestions led to a much improved presentation.Supplementary MaterialsAppendix: Proofs of all theoretical results for Section 3 and additional numerical results for Sections 4 and 5. (Appendix.pdf, pdf file)Code and data: Example code and code for each section in the manuscript and the Appendix. Please read the file README contained in the zip file for more details. (code_and_data.zip, zip file)Disclosure StatementThe authors report there are no competing interests to declare.Additional informationFundingThe authors were supported in part by NSF grants DMS-2113662 (Bhamidi and Shen), DMS-1613072 (Bhamidi), DMS-1606839 (Bhamidi), DMS-2134107 (Bhamidi), DMS-2100729 (Liu), SES-2217440 (Liu); ARO grant W911NF-17-1-0010 (Bhamidi); and NIH grant R01-GM126550 (Liu and Shen).
Virtual narratives, physical bodies: Designing diegetic sensory experiences for virtual reality

This article presents a reflection on four virtual reality (VR) research projects to foreground questions about how physical and sensory interactions are incorporated into VR narratives. The four projects explore a range of experiences, from interactions with digitally mediated and found objects, to sensory interactions and outdoor play. Bringing these projects together here offers an opportunity to contribute insight into conceptualizations of diegetic design and the ‘design space’ of VR, providing questions and considerations towards designing sensory experiences for narratives in VR.
紫外・可視・近赤外放射を含む波長別天空日射量の簡易推定法の開発

In this paper, a simplified regression equation that estimates spectral diffuse solar irradiance between 0.3 and 2.5 μm under all-weather conditions from broadband diffuse solar irradiance, is presented. Furthermore, three kinds of correction equations to correct the regression equation are created using diffuse fraction, air-mass, and precipitable water. Using these equations, we calculated the all-weather spectral diffuse solar irradiance and compared it with the measured values. It was found that average of root mean square error for the estimated spectral diffuse solar irradiance between 0.3 and 2.5 μm decreased by 45.5% due to the effect of using all correction equations.
Approximating Bayesian Inference through Internal Sampling

People must often make inferences about, and decisions concerning, a highly complex and unpredictable world, on the basis of sparse evidence. An “ideal” normative approach to such challenges is often modeled in terms of Bayesian probabilistic inference. But for real-world problems of perception, motor control, categorization, language comprehension, or common-sense reasoning, exact probabilistic calculations are computationally intractable. Instead, we suggest that the brain solves these hard probability problems approximately, by considering one, or a few, samples from the relevant distributions. By virtue of being an approximation, the sampling approach inevitably leads to systematic biases. Thus, if we assume that the brain carries over the same sampling approach to easy probability problems, where the “ideal” solution can readily be calculated, then a brain designed for probabilistic inference should be expected to display characteristic errors. We argue that many of the “heuristics and biases” found in human judgment and decision-making research can be reinterpreted as side effects of the sampling approach to probabilistic reasoning.
Research on the Application of Blockchain Technology in the Design of Analgesic Drug Supply Chain

The supply chain of analgesic drugs has many circulation links and trading levels, and the trading channels are complex; Information asymmetry and low logistics efficiency; Wholesale links account for a large proportion of costs. Too many trading links and complicated trading channels make the trading information asymmetric, opaque and disorderly, which will inevitably lead to the loss of efficiency and benefit in the circulation process. In this paper, the application of blockchain technology in the design of analgesic drug supply chain is studied. The analgesic drug data management center is maintained by the pharmaceutical enterprises themselves or the third party, which is mainly responsible for distributing the unique identification code of drugs, backing up all records of the whole cycle of drug production, storage, circulation and sales, and realizing the two-way traceability function of drugs. In the production process, pharmaceutical enterprises attach electronic labels from single products and outer packaging boxes to realize the traceability and anti-counterfeiting of drugs throughout their life cycle. Correspondingly, it also integrates the advantages and disadvantages of self-management and outsourcing. At this stage, enterprises can choose to subcontract non-core logistics business first, so that enterprises can focus on their core business and avoid wasting too much money and energy in logistics.
Students' Attitudes towards Reading Saudi Printed and Electronic Saudi Newspapers

Objectives: The purpose of this study is to determine the attitudes of Saudi students regarding reading printed and electronic newspapers as well as the reasons behind their decisions. Additionally, due to variations in the commitment of each type of newspaper is to this crucial aspect of professional practice, the credibility and professionalism issues in printed and electronic newspapers often lead to the spread of rumors and inaccurate information.&#x0D; Methods: The data for the study were collected using a questionnaire, which was sent to a sample of 360 undergraduate and graduate students at the Department of Media at King Faisal University, KSA.&#x0D; Results: There was no correlation between student's attitudes towards reading electronic newspapers and the future of print journalism. Furthermore, there was no correlation between student's attitudes towards reading electronic newspapers and news credibility. It was found that electronic newspapers have lower credibility than printed newspapers, primarily due to the absence of institutions relying on censorship through gatekeepers, as observed in press institutions.&#x0D; Conclusion: In comparison to printed newspapers, the study found that many electronic newspapers lack professionalism and disseminate misleading news.
TC-CycleGAN: Improved CycleGAN with Texture Constraints for Virtual Staining of Pathological Images

Pathological slides are crucial for the diagnosis of cancer, and their staining is the basis for pathologists to assess the disease. Pathological sections with different staining statuses have different guiding functions for clinical diagnosis. In this paper, we propose a new method based on CycleGAN to improve the conversion from Hematoxylin-Eosin(H&E) sections to immunohistochemical (IHC) sections by adding texture loss and a self-attention mechanism to help the model better capture image details and establish the relationship between features. The proposed method is validated on the BCI dataset. Extensive experimental results demonstrate that the proposed method effectively improves the generation quality of virtual stained sections and reveals the great potential of virtual staining technology in clinical applications.
Research based on improved AD-Census stereo matching algorithm

Aiming at the problem that the synthesis of AD and Census matching costs of AD-Census transformation cannot reflect the weight and central pixel dependence of pixels in the image area, an improved AD-Census stereo matching algorithm is proposed. The algorithm selects the reference pixel values by assigning the distance similarity of the pixels in the support window, and sets the threshold range based on the center pixel difference, and then calculates the cost of AD transformation cost and Census transformation cost composite weight. By using a semi-global aggregation algorithm based on dynamic programming, SGM (Semi-global matching) algorithm, the matching cost information is fused. The results of the Middlebury stereo matching evaluation platform show that compared with the binocular stereo matching performance of the improved AD-Census transform compared with the binocular stereo matching performance based on AD-Census transform, the parallax error of pixels is significantly optimized, and by adding noise interference, it can be seen that the anti-interference performance of the improved AD-Census algorithm is improved.
An image encryption scheme based on a polynomial complex chaotic system

The application of chaotic systems in image encryption and other fields is gradually increasing in recent years. Constructing systems with strong chaotic ergodicity and good long-term periodicity has become a research hotspot. In consideration of some cosine functions having better chaotic properties, the cosine function is transformed into a polynomial function by Taylor expansion in this paper. Then Qin Jiushao algorithm is used to reduce the times of multiplication and improve the efficiency of calculation. On this basis, this paper proposes compounding with the preceding polynomial and a tent map, which is used for chaotic image diffusion encryption. At the same time, it has been experimentally verified by various indicators, which ensures that the system has a better performance optimization in terms of chaotic properties. Experimental results and analysis show that the algorithm has improved encryption speed, key sensitivity, and strong anti-statistical attack capability.
Predicting Synthetic Lethality in Human Cancers via Knowledge Graph Summarization

Synthetic lethality (SL) is of great significance for the development of anticancer drugs. Thanks to the increasing richness of biomedical knowledge graph (KG), machine learning algorithm combined with knowledge graph has been used for SL prediction and achieved good performance. However, how to effectively utilize biomedical KG to learn stronger gene feature representation is an open problem. Therefore, a self-attention based graph summarization scheme is introduced to delete unimportant nodes and edges in the graph. Firstly, the knowledge graph is used to construct the subgraph of the target gene node, and then the self-attention mechanism is used to calculate the edge signal intensities score in the subgraph, so as to remove the entities that are not important to the target gene in the subgraph. Finally, latent features of gene node are learned by graph neural network for SL prediction. The experimental findings demonstrate that our method performs better than the best baseline method, demonstrating the viability of our method.
Design Development and Application of Mobile Epidemic Prevention and Isolation Treatment System

The rapid spread of the virus in the outbreak is alarming. Epidemic prevention is the first front of life safety. Especially, the establishment and improvement of epidemic prevention workstations and facilities such as temporary isolation points can improve the efficiency of epidemic prevention and reduce the risk of infection. In order to solve the problems of strong randomness of parameter selection process, dependence on empirical value, slow convergence speed, and different parameter combinations affecting the convergence speed of the algorithm in the process of searching the optimal path of the mobile isolation treatment system. An ant colony algorithm based on reinforcement learning and artificial potential field is proposed. First, the parameters of ant colony algorithm are configured intelligently by reinforcement learning, that is, reinforcement ant colony algorithm. Secondly, based on the enhanced ant colony algorithm, the local optimization mechanism of artificial potential field algorithm is introduced, and the local path re-planning is carried out for grid maps with different dimensions. The results show that the improved ant colony algorithm can search the optimal path with faster convergence speed and fewer iterations in different dimensional obstacle environments. For the path planning problem in high-dimensional complex obstacle environments, the improved algorithm shows more obvious advantages in convergence speed, iterations, obstacle avoidance ability and path smoothness, providing a new technical route for epidemic prevention.
A Pedestrian Re-identification Method Based on Joint learning and Feature alignment Neural Network

Among the most popular tasks in computer vision, pedestrian re-identification (person re-identification, re-ID) is a technology that uses computer vision knowledge to determine if a particular person is present in an image or video sequence. Person re-ID has attracted substantial interest in the field of computer vision due to the growing demand for intelligent security. Person re-ID is often perturbed by factors such as occluded targets, unconstrained poses, and varying feature scales, and the application of convolutional neural network to extract global features of pedestrians for recognition often fails to produce suitable results. To obtain richer target features, it is advantageous to extract local features; Unfortunately, these features sometimes fail to align, which negatively impacts on the model. In this context, we propose a multi-branch neural network that combines global and local feature learning in order to obtain a more accurate representation of pedestrian features and enhance the recognition capacity of the model. We design a feature alignment module to further improve the performance of the model by implementing a spatial transformation of the feature maps generated by the network at different depths to achieve feature alignment. According to the experiments, the rank-1 and mAP indices on the Market-1501 dataset are 95.32% and 87.64%, respectively. the method achieves results on DukeMTMC-reID dataset with 88.21% rank-1 and 77.62% mAP. The results demonstrate that our method performs more efficiently against the state-of-the-art methods on two widely used person re-ID datasets.
Development of a sign language gesture recognition system based on "RealSense"

With the development of today's society, it is still difficult for deaf mutes and normal people to achieve barrier free communication, and even the communication barriers between them will cause serious contradictions, leading to a strong sense of social belonging that deaf mutes often do not have. If we can use advanced science and technology to achieve rapid and accurate translation of sign language, we will solve the communication barriers between deaf and normal people to a certain extent. Therefore, we will develop a sign language gesture recognition system based on RealSense. So as to realize the recognition of static sign language gestures and dynamic sign language gestures, complete the conversion from body movements to computer algorithms between the deaf and the computer system in the way of sign language, use RealSense camera as the input device to obtain sign language gestures, capture RGB and depth image information, and then use depth information and skin color difference information to segment the captured image information, Then, set up three-dimensional coordinates through RealSense's bone tracking algorithm, so as to obtain the coordinates of hand joints, and deal with them to complete static and dynamic sign language gesture recognition. Combined with software development IDE, set up a sign language gesture recognition system.
Emotional Analysis of Film Criticism Based on RoBERTa

In the classification of emotion analysis, the research results of film review emotion analysis are relatively few. This paper starts with context information. Combining RoBERTA pre-training model and BiLSTM model, we use LSTM, BiLSTM, RoBERta-LSTM, BERT-BiLSTM and RoBERta-BilSTM models to conduct emotional analysis on the IMDB movie data set. The experimental data shows that the adoption of the BiLSTM model is better than LSTM, and RoBERTA is better than BERT. The combination of RoBERTA and BiLSTM has higher accuracy and can be well applied in the emotional analysis of film reviews.
The Effect of Human Resource Allocation on Knowledge-Intensive Project Performance

We investigate the effect of frontloading on project financial performance. Frontloading refers to the practice of concentrating project members’ efforts, on the execution of concurrent tasks, towards the beginning of the project, so that errors are avoided in the earlier stages, avoiding the costly need for rework in later stages. However, not always companies can frontload a project, especially when the concurrent execution of multiple interdependent tasks generates excessive complexity in the early stages. Companies hence face a conundrum when they cannot afford the costs of complexity associated with front-loading, but yet need to reduce the risk of discovering costly mistakes too close to project completion. Building on the literature on project teams, we develop hypotheses to conceptualize how companies can ensure high project performance when low levels of frontloading need to be applied. We test our hypotheses using a proprietary dataset of 413 projects of a high-tech European firm. We find that the positive effect of frontloading is attenuated when team leader, team, or client familiarity is high as the different facets of familiarity allow teams to be better at collaborating and anticipating errors at the beginning of the projects, compared to less familiar teams. The practical and theoretical implication of these moderation effects is that when project managers need to keep frontloading low, project performance can be increased by ensuring that the project team has high team leader, internal and client familiarity. More generally, our findings shed light on the effect of frontloading on project performance—a question that is to date unanswered by empirical research.