Reviewerly/exHarmony huggingface dataset:
Instinctive Intelligence: Our Next AI

Today, our world changes drastically. Yesterday, changes were smooth, so we could differentiate and predict the future. Today, they are sharp. So,we cannot predict the future. And our world was closed with boudnary yesterday, but todayour world becomes open. Thus, yesterday, we could control. Today, datasets become not only complex, but complicated. And materials are getting soft with the progress of material engineering. When things were hard, we could understand what it is and how we should handle it with our eyes alone. But today, we need to interact with them directly. To cope with such drastically changing real world, we need to coordinate all parts of our body. Thus “coordination” is increasing its imortance. Brain is getting wide attention. But most of these studies focus on digital processing. But drastically changing real world needs not only digital processing, but analog procesing, too. Take medical diagnosis for example. Doctors observe blood flow using MRI. Blood is analog. And even after brain death, heart circulates blood around body. And while blood is circulating, we can transplant body parts. In AI, heart is rarely talked about. But heart is deeply associated with emotion. Our need is material-centric at early stage, but with time it shifts to mental and finally to “self-actualization”. We want to demonstrate how capable we are as “self”. Humans can think about the future. Animals live for now. But humans live for tomorrow. This contributed greatly to the development of humans. Many species became extinct because they were not diversified enough. We should realize the big role of instinct. In material-based world, objective and quantitative evalution was important. But to satisfy our emotional expectations, we need to evaluate things subjectively and qualitatively. We need to make decisions what actions we should take by trial and error. AI has been regarded as a man-made tool, and nature has nothing to do with it. But this is a big mistake. Humans are part of nature. The etymology of artifical is related with art. It is associated with “creation”. But who creates? It is us, human and human is part of nature. Babies learn to scrall and walk by themselves. They learn using their instinct alone. So, if we can make the most of our instinct, we can create experience and adapt to the drastically changing world. And “emotion” and “motivation” come from the same Latin word “movere”, i.e. movement. We perceive the environment and situation and we became situationally aware and motivated. And we make decisions what actions to take. If it satisfies our emotional expectation, then it is fine. It not, we repeate the process until we are satisfied. Thus, AI in the next step will be Instinctive Intelligence.
Active Learning Based Labeling Method for Fault Disposal Pre-plans

Power grid companies have vast quantities of data that can be mined for valuable information. However, there is a limited quantity of labeled pre-plans data due to the high labeling cost. This paper proposes an active learning-based sequence labeling method, aiming at achieving good deep learning model performance with only a small amount of labeled data. It proposes a sample selection strategy based on uncertainty and slot, in which the uncertainty and slot respectively represent the informativeness and representativeness of the sample. Meanwhile, to avoid getting into local details in each round of model training, a method for evaluating the performance of data labeling is proposed. Based on these two works, the active learning and data labeling processes are also present. We use four strategies as comparative methods in our experiment, and the results demonstrate that our method outperforms other active learning methods. We conclude that our method is efficient and applicable to tasks involving the labeling of textual data in other contexts.
Dual Multi-Scale Dehazing Network

Single-image haze removal is a challenging ill-posed problem. Recently, methods based on training on synthetic data have achieved good dehazing results. However, we note that these methods can be further improved. A novel deep learning-based method is proposed to obtain a better-dehazed result for single-image dehazing in this paper. Specially, we propose a dual multi-scale network to learn the dehazing knowledge from synthetical data. The coarse multi-scale network is designed to capture a large variety of objects, and then fine multi-scale blocks are designed to capture a small variety of objects at each scale. To show the effectiveness of the proposed method, we perform experiments on a synthetic dataset and real hazy images. Extensive experimental results show that the proposed method outperforms the state-of-the-art methods.
Probabilistic Compositional Semantics, Purely

We provide a general framework for the integration of formal semantics with probabilistic reasoning. This framework is conservative, in the sense that it relies only on typed $$\lambda $$ -calculus and is thus compatible with logical systems already in use. The framework is also presented modularly, in that it regards probabilistic effects (i.e., sampling and marginalization) as side effects, using continuations. We show how our framework may be used to build probabilistic programs compositionally within typed $$\lambda $$ -calculus and then illustrate its use on two applications: semantic learning and pragmatic inference within the Rational Speech Act framework.
A Clustering Strategy-Based Evolutionary Algorithm for Feature Selection in Classification

Feature selection is a technique used in data pre-processing to select the most relevant subset of features from a larger set, with the goal of improving classification performance. Evolutionary algorithms have been commonly proposed to solve feature selection problems, but they can suffer from issues originated from diversity reduction and crowding distance decrease, which can lead to suboptimal results. In this study, we propose a new evolutionary algorithm called clustering strategy based evolutionary algorithm (CEA) for feature selection in classification. CEA combines the clustering mechanism to gather individuals into different clusters, and the crossover operation is dominated by the parents in different clusters, thus enhancing the exploration ability of the algorithm and avoiding the population falling into the local optimal solution space. The performance of CEA was evaluated on 13 classification datasets and compared to four mainstream evolutionary algorithms. The experimental results showed that CEA was able to achieve better classification performance using similar or fewer features than the other algorithms.
A Contrastive Method for Continual Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to train a model that can classify seen and unseen samples based on semantic information. Continual learning, as one of the factors distinguishing artificial intelligence, has received more and more attention in recent years. In this paper, we propose a deep learning model based on a conditional generative model and a contrastive learning framework that can continuously learn from incoming data. In the training phase, knowledge distillation and generative replay mechanisms are used to accumulate past knowledge. In the testing phase, the test samples are mapped to the embedding space to perform generalized zero-shot classification task. Our model not only does not require additional buffers to store data so that it will not cause data leakage problems, but also through experiments show that our model can achieve state-of-the-art results on three benchmark datasets. The source code of the proposed model is available at: https://github.com/liangchen976/CCGZSL.
Remembering Individuals and Remembering Scenes

In the object position of certain intensional transitive verbs (paradigmatically: remember), DPs are semantically ambiguous between individuals and scenes [= scenes that saliently feature these individuals]. This ambiguity cuts across the familiar intensionality-related distinctions (esp. specific/non-specific, referentially transparent/referentially opaque) and cannot be explained at the level of LF. As a result, it poses a challenge for existing semantics for intensional transitive verbs, esp. for Zimmermann’s property-based account, for Stephenson’s situation-theoretic account, and for Moltmann’s truthmaker-semantic account. My paper provides a uniform compositional semantics for ‘individual’- and for ‘scene’-interpretations of remember DP-reports that explains this ambiguity. To do this, it investigates the situations that feature in the proposition-type complement of remember. It finds that, if the referent of the object DP has different properties in these situations, the report receives an individual-interpretation. If the referent has the same properties in all situations, the report can receive an individual-interpretation (next to its scene-interpretation). The resulting semantics captures the intensionality and entailment properties of remember DP-reports and predicts the preferred individual-interpretation of strongly quantificational object DPs.
RSHN: Residual Stacked Hourglass Network for Multi-person Pose Estimation

Multi-person pose estimation is frequently employed in sports analysis, medical assistance, and virtual reality, which consists of key-point localization and classification. Furthermore, it aims to automatically locate all persons’ joints from a single image of visual field. However, multi-object detection and complex interactions between different persons will reduce the accuracy of pose estimation. To meet the challenge, we propose an approach called Residual Stacked Hourglass Network (RSHN). The learning process of the proposed RSHN is divided into two stages. The knowledge extracted by the down-sampling module of the object detector is transferred into the pose estimator. Extensive experiments demonstrate that the proposed RSHN method outperforms state-of-the-art methods, which achieves 91.2 PCKm on the MPII dataset with a competitive computational budget.
Unsupervised Disentanglement Learning via Dirichlet Variational Autoencoder

Unsupervised disentanglement learning is the process of discovering factorized variables that include interpretable semantic information and encode separate factors of variations in the data. It is a critical learning problem and has been applied in various tasks and domains. Most of the existing unsupervised disentanglement learning methods are based on the variational autoencoder (VAE) and adopt Gaussian distribution as the prior over the latent space. However, these methods suffer from a collapse of the decoder weights, which leads to degraded disentangling ability, due to the Gaussian prior. To address this issue, in this paper we propose a novel unsupervised disentanglement learning method based on a VAE framework in which the Dirichlet distribution is deployed as the prior over latent space. In our method, the interpretable factorised latent representations can be obtained by balancing the capacity of the latent information channel and the learning of statistically independent latent factors. The effectiveness of our method is validated through experiments on several publicly available datasets.
A Novel Pipeline to Enhance Question-Answering Model by Identifying Relevant Information

Question-Answering (QA) systems have increasingly drawn much interest in the research community. A significant number of methods and datasets are proposed for the QA tasks. One of the gold standard QA resources is span-extraction Machine Reading Comprehension datasets, where the system must extract a span of text from the context to answer the question. Although state-of-the-art methods for span-extraction QA are proposed, distracting information in the context can be a significant factor that reduces these methods’ performance. Especially, QA in scientific documents has massive contexts whose only a small part contains the relevant information to answer the question. As a result, it is challenging for QA models to arrive at the answer in scientific documents. As an observation, performance can be improved by only considering relevant sentences. This study proposed a novel pipeline to enhance the performance of existing QA methods by identifying and keeping relevant information from the context. The proposed pipeline is model-agnostic, multilingual, and can be flexibly applied to any QA model to increase performance. Our experiments on QA datasets in scientific documents (Qasper) and SQuAD 2.0 show that our approach successfully improves the performance of state-of-the-art QA models. Especially, our detailed comparisons reveal the effectiveness and flexibility of our proposed models in enhancing the current QA systems in low-resource languages such as Vietnamese (UIT-VIQUAD).
Analysis of the Countermeasures for Improving the Management Level of University Laboratories

Improving laboratory testing ability is the basis for building an innovative country.Standardizing the management of external products and services is an important basis for improving laboratory testing ability.Based on the CNAS criteria and taking Xi'an Shiyou University as an example, this paper discusses the specific problems in the management of externally provided products and services in university laboratories, and proposes measures such as building an efficient management process for externally provided products and services, implementing the main responsibilities in management, establishing a supplier evaluation system, establishing a sound supervision mechanism, and improving daily work methods, To achieve the purpose of improving the quality of external products and services.
High Level Talent Training Platform Based on Artificial Intelligence Algorithm

With the rapid development and application of artificial intelligence technology, in the training of high-level talents, more and more needs to be solved is how to transform it into actual productivity.Therefore, based on the current research situation at home and abroad, this paper proposes a set of platform architecture related to AI algorithm.This paper first studies the environment of highlevel talent cultivation, then expounds the importance of high-level talent cultivation and construction, then studies the artificial intelligence algorithm, and based on this, designs a high-level talent cultivation platform.Finally, the performance of the platform is tested by simulation experiments.The test results show that the high-level talent training platform based on intelligent algorithms has short response time and delay time, high software compatibility and security.This shows that the platform has complete functions and good performance.
Replay attack detection based on deformable convolutional neural network and temporal-frequency attention model

Abstract As an important identity authentication method, speaker verification (SV) has been widely used in many domains, e.g., mobile financials. At the same time, the existing SV systems are insecure under replay spoofing attacks. Toward a more secure and stable SV system, this article proposes a replay attack detection system based on deformable convolutional neural networks (DCNNs) and a time–frequency double-channel attention model. In DCNN, the positions of elements in the convolutional kernel are not fixed. Instead, they are modified by some trainable variable to help the model extract more useful local information from input spectrograms. Meanwhile, a time–frequency domino double-channel attention model is adopted to extract more effective distinctive features to collect valuable information for distinguishing genuine and replay speeches. Experimental results on ASVspoof 2019 dataset show that the proposed model can detect replay attacks accurately.
Fast and Low-Complexity Soft-Decision Generalized Integrated Interleaved Decoder

Generalized integrated interleaved (GII) codes are essential to next-generation digital communication and storage systems since they can achieve very high decoding throughput with low complexity. Only hard-decision GII decoding has been considered in previous work. To further improve the error-correcting capability, soft-decision decoding algorithms utilizing the channel probability information need to be developed. The decoding of GII codes constructed based on BCH codes consists of multiple rounds of BCH decoding. Among existing soft-decision decoding algorithms of BCH codes, the Chase algorithm that carries out decoding trials on multiple test vectors can achieve a better trade-off on the coding gain and complexity. Although one-pass Chase algorithms can derive the error-locator polynomials for all the test vectors in one run, the exhaustive Chien search is carried out previously on each error-locator polynomial to decide which one is correct and it leads to long latency. For the first time, this paper proposes an efficient soft-decision GII decoding algorithm. Different methods of incorporating the Chase process into the GII scheme are analyzed and compared to identify the best GII Chase decoding algorithm. Besides, a new error-locator polynomial selection scheme is developed to avoid carrying out the Chien search on each error-locator polynomial by pre-flipping a bit in the received word. Accordingly, the error-locator polynomial can be selected by testing whether it consists of a pre-determined factor. The latency is further reduced by pre-computing short remainder polynomials in our second proposed scheme. In addition, formulas have been developed to estimate the error-correcting performance of the proposed designs. This paper also develops low-complexity hardware architectures to implement the proposed GII-BCH Chase decoders. For an example GII-BCH code with 8 sub-codewords of 4095 bits over GF(212), the proposed GII-BCH Chase decoder can achieve significant coding gain over hard-decision decoder with negligible silicon area overhead. Besides, our proposed designs can reduce the worst-case latency of GII-BCH Chase nested decoding rounds by 54%-80%.
Research and Practice of Intelligent Classroom Teaching Mode in Colleges and Universities Under the Background of Education Informatization

With the concept of wisdom education, under the background of education informatization discusses university wisdom classroom teaching mode, mainly based on linear algebra course, expounds the wisdom classroom information platform, Internet information technology fusion in the concrete practice of classroom teaching, to promote the reform and development of colleges and universities classroom teaching is of great significance.
A Robust Document Localization Solution with Segmentation and Clustering

In the fields of optical character recognition and textual information extraction, document localization is recognized as a potential preprocessing step with a significant impact on accuracy. Despite numerous solutions being presented, localizing documents in images with complicated backgrounds remains an open issue. This paper offers a novel approach to document localization that may successfully handle difficult scenarios with complicated backgrounds. Our strategy blends deep learning with conventional image processing techniques. Specifically, deep learning is applied to determine a rough region of the document’s boundary, while traditional image processing algorithms are exploited to identify the document’s corners. Moreover, to improve model accuracy and mitigate the data-hungry drawback of the deep learning-based approach, we introduce efficient data annotation and augmentation techniques. We perform comprehensive experiments to evaluate the performance of the proposed method on the ICDAR 2015 SmartDoc challenge 1 dataset. The experimental results show that our method achieves higher accuracy while requiring less real training data. Specifically, using only $$20\%$$ of the real training data, our proposal improves the Jaccard Index by $$0.6\%$$ on average and by $$1.7\%$$ concerning the dataset with the most complicated background.
Reducing Reliance on Domain Knowledge in Case-Based Reasoning

Case-based reasoning is an intuitive approach to problem-solving in artificial intelligence that involves reusing existing experience, including solutions to problems or mechanisms to derive them. However, current Case-based reasoning systems suffer from a lack of generality due to their heavy reliance on domain-specific knowledge, and they often struggle with the adaptation process, which is driven by the application domain. In addition, these systems often perform poorly as they treat each step of the Case-based reasoning methodology separately and independently. To address these limitations, this work proposes a domain-independent Case-based reasoning framework that integrates each step of the reasoning process to support other stages of the process. The framework is evaluated in an experimental setting on a sober consumption energy system in buildings, demonstrating its effectiveness.
SC-YOLO: Provide Application-Level Recognition and Perception Capabilities for Smart City Industrial Cyber-Physical System

Logo detection is a crucial task for industrial cyber-physical systems in smart cities, which relies on accurate and efficient analysis of urban environments. In this article, we proposed SC-YOLO, a robust logo detection framework that utilizes an optimized YOLO architecture with several key enhancements. Specifically, this framework encompasses a focus module that enables lossless downsampling and expands the input image channel to enrich feature representation. In addition, we incorporate an attention mechanism that enhances the model's ability to extract and locate critical regions in an image. To facilitate iteration and improve accuracy, we introduce the SIoU loss function that considers angle, distance, and shape costs between targets. The experiments on the LogoDet-3 K dataset demonstrate that SC-YOLO achieves an mAP of 70.84%, outperforming other state-of-the-art deep detection models, including YOLOv7. Overall, our proposed method has a wide range of potential applications in autonomous driving, intelligent transportation, smart city advertising research, and smart city commercial branding, enabling more efficient and safer smart cities.
Parsed Corpus Development with a Quick Access Interface

This paper introduces a web browser based quick access interface for the Treebank Semantics Parsed Corpus (TSPC) as a demonstration of corpus development. The TSPC is a corpus of English with hand worked tree analysis for approaching half-a-million words. The interface provides a ‘live’ view of the corpus as it is worked on, with search functionality and visualisations of the analysis from three different perspectives: Tree, Word, and Graph. The Graph view is especially notable for displaying semantic level relationships to make visually apparent a wider range of the connections that the design of the annotation captures.
Development and Application of First-Class Online Courses Based on Job Competencies

In view of the problems of insufficient "job service" and "vocational guidance" in the construction of online courses of military vocational education, this paper proposes a curriculum development method based on job competency.Because of the relevant standards of national and military online course construction, it constructs a three-stage course development model based on post ability, and proposes a four-link course content development process.The article takes the national excellent online course "Communication Line Construction Safety" as an example to verify the example finally.The application result of the course shows that the method is feasible and effective.It will provide some guidance and reference for military vocational education on-line course construction.