Reviewerly/exHarmony huggingface dataset:
Slice position-shifting and multi-scale inter-frame proportional fusion network for video-based pedestrian re-identification

Different from image-based pedestrian re-identification, video-based pedestrian re-identification has more available information because of more temporal information. However, previous methods are unable to extract detailed features adequately. Although the previous methods adopted the row slicing method to extract the detailed features of the image, the slicing method was too simple and the detailed features extracted were too few. We propose a slice position shifting module (SPM), which adopts a variety of ways to slice the image, to extract the feature of each image patches separately. The feature interaction is carried out by making each patches of the image generate position-shifting. How to make time clues in pedestrian re-identification data set useful is also the research focus in this field. We propose a multi-scale inter-frame proportional fusion module (MIPM) to make the features of frame fully fuse with the front and back frames, so as to extract more the temporal clues in the data set. We will form the above two modules into slice position-shifting and multi-scale inter-frame proportional fusion network (SMnet). Full experiments on the MARS dataset show that our algorithm is very useful and can reach the leading level.
Implementing Hybrid Connectivity

Cloud computing unlocks a significant number of capabilities due to its ability to deliver an unprecedented level of computing power at a relatively low cost.
Supply Chain Management Practices and their Impact on Business Performance-A Quantitative Investigation (2020)

Supply chain management practices in determining the performance and success of businesses.Identified key practices that exert a significant impact on business outcomes.The findings unequivocally demonstrate that adept supply chain management practices have a positive effect on various dimensions of business performance.To begin adoption of principles and the implementation of invent enhance segment techniques substantially enhance operational efficiency.Furthermore, the implementation of robust supplier management strategies fosters strong and mutually beneficial relationships with suppliers, thereby ensuring the delivery of superior quality inputs and timely shipments.Consequently, both product quality and customer satisfaction are positively influenced.In addition, the effective utilization of demand forecasting and collaborative planning practices enhances the accuracy of demand prediction, resulting in the minimization of stockouts and excess inventory.Consequently, customer service levels and profitability are significantly improved.Moreover, our research underscores the criticality of integrating technology and sharing information within the supply chain.This, in turn, better decision-making, reduced times, and increased agility in responding to evolving customer demands.The significance of supply chain management practices in driving business is strongly emphasized by our findings.
Meningkatkan Kosa Kata, Pengucapan Kata, dan Motivasi Belajar Bahasa Inggris Siswa Melalui Penerapan Metode Pembelajaran Total Physical Responses [Increasing Vocabulary, Pronunciation, and motivation to Study English Through Implementation of Total Physical Responses]

&lt;div&gt;&lt;p class="1eAbstract-text"&gt;Mastering English language is very important because it is the most spoken language in global interaction. In order to master English, early age linguistic education is required. Therefore, the most suitable method used in this research is the Total Physical Responses (TPR), which emphasizes on vocabulary, pronunciation, and motivation to study English. The purposes of this research are: 1) to increase English vocabulary by using the TPR teaching method in Kindergarten A at Ehipassiko School BSD; 2) to increase English pronunciation by using the TPR teaching method in Kindergarten A Ehipassiko School BSD; 3) to increase motivation to study English by using the TPR teaching method in Kindergarten A Ehipassiko School BSD. This research was conducted in Kindergarten A Ehipassiko School BSD with participants of 18 students, consists of 8 boys and 10 girls. The type of this research is pre-experimental research. The results showed an increased vocabulary, pronunciation, and motivation to study English through the method of TPR. It can be seen from the results of the N-Gain, vocabulary 0.755 was in the high category, the pronunciation in the medium category was 0.45 and 0.54 for motivation which is fall into the medium category. Based on the results of this research can be concluded that the teaching of English through the TPR teaching method can increase vocabulary, pronunciation, and motivation to study English.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;/div&gt;
A Summary View of the Problem and Its Solution

The case study presented in this book highlights the properties and challenges of distinguishing bot and human traffic using weblogs and compares several solutions to this task. We present here, first, the observations related to the methodological side of the potential problem solution.
The Pragmatics of the Data Acquisition and Assessment

In this chapter, we will look in a more detail at the ways in which data were acquired and processed in the framework of the project in question. We will do so against the background of observations and examples already provided in the preceding section, starting with the first section of the present chapter.
Transformer Tracking via Frequency Fusion

Transformer has achieved impressive progress in visual tracking due to their capability of global modeling, which enables them to learn low-frequency features(i.e., high-level semantic information). However, it seems to overlook the high-frequency features(i.e., low-level texture and edge information) which are crucial to identify different intra-class object instances in the tracking task. To address this issue, we propose a transformer based tracker via frequency fusion perspective that investigated whether high-frequency and low-frequency features can be effectively combined to achieve robust tracking. Specifically, we design a simple yet effective two-stage fusion strategy and use an appropriate frequency fusion strategy in tracking process of each stage so as to make full use of frequency domain information. In the feature extraction stage, we use wavelet decomposition of high-frequency subbands to solve the performance loss caused by the transformer’s catastrophic forgetting of high-frequency information. In the prediction head stage, we use a variety of wavelet decomposition subbands to model the multi-frequency information. The two-stage fusion strategy makes our model extract more balanced and beneficial multi-frequency information, enabling it to effectively capture target texture information and local edge information while also being sensitive to global information. Extensive experiments on six challenging benchmarks (i.e., LaSOT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>ext</i></sub> , UAV123, TNL2K, LaSOT, TrackingNet, and GOT-10k) demonstrates the superior performance of our tracker.
Fortifying Data Assets- Safeguarding Cybersecurity in the Era of Big Data

Big Data and artificial intelligence have become preferences of the principal computer industry and this technology brings new challenges in the cyber security environment.As the expansion of data is being going on with its analysis, several cyber security breaches also occur.Today, big data not only helps in analyzing the persistent amount of data but also faces several types of risks.But the question arises that: Is Big data analytics for cyber security is sufficient using AI.Moreover, artificial intelligence is the process of making a machine to enact human actions, especially the two attributes of intelligence and decision-making capability.Today, Artificial Intelligence is being developed to make more human-like machines to reduce problems generated by humans in a working environment.In this going on era many of the tools of Big Data are being used for cyber security to prevent against all possible cyber threats by proper designing and planning to maintain the confidentiality, integrity, and availability to get protected from unauthorized modification, detection and access.
The practice of mixed teaching mode in English teaching

With the deepening reform of educational concepts, many teachers are actively exploring the use of various advanced teaching methods in English teaching practice.In this case, blended teaching is an important approach to the reform of college English classroom in China.Starting with the meaning of mixed teaching, this paper discusses the advantages and structure of mixed teaching mode, and then explores the application of mixed teaching mode in college English teaching, with a view to improving the teaching level of college English and enhancing the interest of college students in learning English courses.
Improving the performance of a two-degree-of-freedom moving solar tracker using fuzzy logic

The energy needed by the operators is considered one of the most important constraints that enter into the design and implementation of alternative photovoltaic systems.Changing loads causes the addition of more equipment and thus an increase in economic costs.In this context, energy reserve problems appear that express the ability of the photovoltaic system to secure energy with changes in loads on it.Where in most cases additional panels and equipment are added to achieve the principle of energy reserve, but in these cases it will require an increase in costs and an increase in the size of the photovoltaic system, The research presents routing for photovoltaic panels using the Arduino development chip that is able to pursue the maximum power point of a moving solar panel with two degrees of freedom so that the largest possible amount of solar energy is obtained, depending on the hybrid fuzzy logic as a decision unit in the routing process so that the hybrid fuzzy logic improves the accuracy of routing and reduces Steering errors in steering systems that use differential techniques to read sensors.The results of the research show the ability of the hybrid fuzzy logic to achieve steering angles that improved the power resulting from the tested board by more than 28%.
Construction of evaluation index system of inquiry teaching in high school mathematics based on analytic hierarchy process

From the perspective of the characteristics of mathematics inquiry learning and experiential learning, it provides innovative ideas for the construction of the evaluation index system of mathematics inquiry teaching in high school.There are three goals in this system: teacher's teaching development, student behavior performance and process factors.And 11 first-level indicators and 31 second-level indicators are constructed.Through the questionnaire survey of experts, the consistency check using the analytic hierarchy process (AHP), it determines the weight of indicators, enriches the development of mathematics inquiry teaching theory, and accumulates experience for the research of mathematics inquiry teaching evaluation in high school.
DNG: Taxonomy Expansion by Exploring the Intrinsic Directed Structure on Non-gaussian Space

Taxonomy expansion is the process of incorporating a large number of additional nodes (i.e., ''queries'') into an existing taxonomy (i.e., ''seed''), with the most important step being the selection of appropriate positions for each query. Enormous efforts have been made by exploring the seed's structure. However, existing approaches are deficient in their mining of structural information in two ways: poor modeling of the hierarchical semantics and failure to capture directionality of the is-a relation. This paper seeks to address these issues by explicitly denoting each node as the combination of inherited feature (i.e., structural part) and incremental feature (i.e., supplementary part). Specifically, the inherited feature originates from ''parent'' nodes and is weighted by an inheritance factor. With this node representation, the hierarchy of semantics in taxonomies (i.e., the inheritance and accumulation of features from ''parent'' to ''child'') could be embodied. Additionally, based on this representation, the directionality of the is-a relation could be easily translated into the irreversible inheritance of features. Inspired by the Darmois-Skitovich Theorem, we implement this irreversibility by a non-Gaussian constraint on the supplementary feature. A log-likelihood learning objective is further utilized to optimize the proposed model (dubbed DNG), whereby the required non-Gaussianity is also theoretically ensured. Extensive experimental results on two real-world datasets verify the superiority of DNG relative to several strong baselines.
Inconsistent Cores for ASP: The Perks and Perils of Non-monotonicity

Answer Set Programming (ASP) is a prominent modeling and solving framework. An inconsistent core (IC) of an ASP program is an inconsistent subset of rules. In the case of inconsistent programs, a smallest or subset-minimal IC contains crucial rules for the inconsistency. In this work, we study fnding minimal ICs of ASP programs and key fragments from a complexity-theoretic perspective. Interestingly, due to ASP’s non-monotonic behavior, also consistent programs admit ICs. It turns out that there is an entire landscape of problems involving ICs with a diverse range of complexities up to the fourth level of the Polynomial Hierarchy. Deciding the existence of an IC is, already for tight programs, on the second level of the Polynomial Hierarchy. Furthermore, we give encodings for IC-related problems on the fragment of tight programs and illustrate feasibility on small instance sets.
A Corpus-Based Study on the Use of High-Frequency Prepositions between Chinese English Learners and Native Speakers

This paper uses corpus-based interlanguage comparative analysis to investigate the similarities and differences in frequency and collocations of ten high-frequency prepositions in native English speakers' corpus and Chinese learners' corpus.The results show that the overall frequency of these ten prepositions in each corpus is on the relative level, but there are phenomena of overuse and underuse when it comes to the use of every single preposition.In addition, it is found that Chinese English learners and native speakers have great differences in the use of prepositional collocations, which is caused by various factors.
Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning

Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the reasoning process. In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning. The learned mathematical knowledge, including word-word relations and word-operator relations, forms an explicit knowledge graph, which bridges the knowledge “learning” and “applying” organically. Moreover, for problem solving, we design a semantics-enhanced module and a reasoning-enhanced module that apply knowledge to improve the problem comprehension and symbol reasoning abilities of any backbone, respectively. We theoretically prove the superiority of LeAp's autonomous learning mechanism. Experiments on three real-world datasets show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process.
An Adaptive Layer to Leverage Both Domain and Task Specific Information from Scarce Data

Many companies make use of customer service chats to help the customer and try to solve their problem. However, customer service data is confidential and as such, cannot easily be shared in the research community. This also implies that these data are rarely labeled, making it difficult to take advantage of it with machine learning methods. In this paper we present the first work on a customer’s problem status prediction and identification of problematic conversations. Given very small subsets of labeled textual conversations and unlabeled ones, we propose a semi-supervised framework dedicated to customer service data leveraging speaker role information to adapt the model to the domain and the task using a two-step process. Our framework, Task-Adaptive Fine-tuning, goes from predicting customer satisfaction to identifying the status of the customer’s problem, with the latter being the main objective of the multi-task setting. It outperforms recent inductive semi-supervised approaches on this novel task while only considering a relatively low number of parameters to train on during the final target task. We believe it can not only serve models dedicated to customer service but also to any other application making use of confidential conversational data where labeled sets are rare. Source code is available at https://github.com/gguibon/taft
Local Path Integration for Attribution

Path attribution methods are a popular tool to interpret a visual model's prediction on an input. They integrate model gradients for the input features over a path defined between the input and a reference, thereby satisfying certain desirable theoretical properties. However, their reliability hinges on the choice of the reference. Moreover, they do not exhibit weak dependence on the input, which leads to counter-intuitive feature attribution mapping. We show that path-based attribution can account for the weak dependence property by choosing the reference from the local distribution of the input. We devise a method to identify the local input distribution and propose a technique to stochastically integrate the model gradients over the paths defined by the references sampled from that distribution. Our local path integration (LPI) method is found to consistently outperform existing path attribution techniques when evaluated on deep visual models. Contributing to the ongoing search of reliable evaluation metrics for the interpretation methods, we also introduce DiffID metric that uses the relative difference between insertion and deletion games to alleviate the distribution shift problem faced by existing metrics. Our code is available at https://github.com/ypeiyu/LPI.
Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers

Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Current attempts to use `general' input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. These features are geometrically correlated, and are computed by accumulating model's gradient information with respect to an unrestricted data distribution. To compute these features, we nudge independent data points over the model loss surface towards the local minima associated by a human-understandable concept, e.g., class label for classifiers. With a systematic projection, scaling and refinement process, this information is transformed into an interpretable visualization without compromising its model-fidelity. The visualization serves as a stand-alone qualitative interpretation. With an extensive evaluation, we not only demonstrate successful visualizations for a variety of concepts for large-scale models, but also showcase an interesting utility of this new form of saliency mapping by identifying backdoor signatures in compromised classifiers.
Research on the Practice Path of English Teaching in Colleges and Universities based on the Background of Big Data

Personalized teaching is an important guide for the reform and innovation of English education in colleges and universities.The application of big data technology in English personalized teaching is conducive to the analysis of the needs of English personalized teaching, further speeding up the construction of English personalized education resources, helping to improve the intelligence level of English personalized teaching, and accelerating the realization of English personalized teaching and multi-field fusion.English teachers in colleges and universities reform English teaching based on the background of big educational data.They need to correctly understand the meaning and characteristics of big educational data and emphasize the significance of English teaching reform under this background.Effective reform of college English teaching under the background of big educational data.
Datafication Analytics

Datafication analytics analyzes data sets to draw a conclusion and gain insights from the data. It uses statistical models and computational techniques to identify data patterns, trends, and relationships.
Datafication Principles and Patterns

Principles are guidelines for the design and development of a system. They reflect the level of consensus among the various elements of your system. Without proper principles, your architecture has no compass to guide its journey toward datafication.
Behavioral Analysis

Behavioral analysis studies behavior patterns to understand why humans act a certain way. It involves collecting and processing behavioral data, such as online activities, social interaction, social behaviors, social media activities, Internet of Things (IoT) sensors, manufacturing machine behavior, and so on.
Transparent Perception and Police Trust Against the Background of Wide Application of Police Portal Websites

The rapid development of e-government has profoundly changed the traditional public relations model.Taking the police portal as the background, this paper explores the internal logic between it and police trust in the context of widespread electronic application from the perspective of public transparency perception, and proposes strategies to enhance public transparency perception and police trust to constitute the research objectives.This paper conducts an empirical test with people's perception of transparency as the main effect, people's satisfaction with e-government as the mediating effect, and people's computer literacy as the moderating effect.It is found that: Firstly, people's perception of transparency is positively related to police trust.Secondly, people's satisfaction with e-government has a partial mediating effect on the relationship between people's perception of transparency and police trust.Thirdly, computer literacy plays a moderating role in people's perception of transparency and police trust.This paper verifies the above influencing factors and provides theoretical support and policy input for the subsequent construction of police trust and the promotion of sustainable development of egovernment.