Reviewerly/exHarmony huggingface dataset:
The Fifth Intelligence Layer—Automatized Execution

This chapter aims to explain the fifth intelligence layer of the machine brain—automatized execution, which is realized through information acquisition, management, utilization and then be utilized to interpret machine intelligence associated with the current technologies (the system-level intelligence), emerging technologies (the behavior-level intelligence) and beyond (the thinking-level intelligence). The meta-learning thoughts introduced in Chap. 4 are utilized. Deep RL uses neural networks to approximate the learning data, which develops rapidly and makes the sequential decision making achieve preliminary results. However, deep reinforcement learning is overly dependent on a large amount of training and requires precise rewards. For many problems in the real world, such as robot learning, there is generally no good reward, no infinite training, which requires the ability to learn quickly, so there is meta-learning. This paper proposes a deep reinforcement learning model based on meta-learning, called Meta Q-Network (MQN). The model uses LSTM-based meta-learner to update the Q network. We also present a brief explanation for how to extend the fifth intelligence layer with extenics.
Improvement of MADRL Equilibrium Based on Pareto Optimization

Abstract In order to solve the incalculability caused by the issue of inconsistent objective functions in multi-agent deep reinforcement learning, the concept of Nash equilibrium is introduced. However, a Marko game may have multiple equilibriums, how to filter out a stable and optimal one is worth studying. Besides solution concept, how to keep the balance between exploration and exploitation is another key issue in reinforcement learning. On basis of the methods, which can converge to Nash equilibrium, this paper makes improvement through Pareto optimization. In order to alleviate the problem of over fitting caused by Pareto optimization and non-convergence caused by strategy change, we use stratified sampling in place of random sampling as assistance. What’s more, our methods are trained through fictitious self-play to make full of self-learning experiences. By analyzing the experiment carried out on MAgent platform, the proposed methods are not only far better than traditional methods, but also reaching or even surpassing the state of art MADRL methods.
Nonsmooth Optimization-Based Model and Algorithm for Semisupervised Clustering

Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term expresses the error for data points with must-link (ML) constraints, and the third term represents the error for data points with cannot-link (CL) constraints. This function is nonconvex and nonsmooth. To find its optimal solutions, we introduce an adaptive SSC (A-SSC) algorithm. This algorithm is based on the combination of the nonsmooth optimization method and an incremental approach, which involves the auxiliary SSC problem. The algorithm constructs clusters incrementally starting from one cluster and gradually adding one cluster center at each iteration. The solutions to the auxiliary SSC problem are utilized as starting points for solving the nonconvex SSC problem. The discrete gradient method (DGM) of nonsmooth optimization is applied to solve the underlying nonsmooth optimization problems. This method does not require subgradient evaluations and uses only function values. The performance of the A-SSC algorithm is evaluated and compared with four benchmarking SSC algorithms on one synthetic and 12 real-world datasets. Results demonstrate that the proposed algorithm outperforms the other four algorithms in identifying compact and well-separated clusters while satisfying most constraints.
A Cascade Binary Tagging Joint Extraction Method

Extracting entities and relations from unstructured text has become a significant task in natural processing, especially knowledge graphs. However, the traditional relationship extraction method processes this task in a pipelined manner, i.e., identifying entity first and then recognizing their relations, and rarely consider the relevance and independence of the two subtasks. To solve the problems of error accumulation, we present a jointly learning method to a tagging problem to tackle this problem. Meanwhile, to understand the semantics of sentences, we introduce the pre-training method into our models, such as BERT. To solve the problem of entity overlap, we replace softmax with a sigmoid activation function. In the language and intelligent technology competition, experimental results show that our model outperforms the baseline model.
Report on the 15th round of NII testbeds and community for information access research (NTCIR-15)

This is a report on the NTCIR-15 conference held online in December 2020. NTCIR is a sesquiannual research project designed to evaluate various information access technologies, including information retrieval, information recommendation, question answering, natural language processing, etc. 55 active research groups from 22 countries\regions have participated in one or more of the seven tasks in NTCIR-15. This report introduces the highlights of the conference, describes the scope and task designs of the seven tasks organized at NTCIR-15. Date : 8--11 December, 2020. Website : http://research.nii.ac.jp/ntcir/ntcir-15/.
Recipe Recommendation System with Ingredients Available on User

The sole and main idea behind this project is that we have a plenty of options to find recipes for cooking dishes but getting a recipe according to ingredients available in our kitchen is what all we need. Taking an example of lock-down the hidden cook of everyone comes out but due to lock-down availability of ingredients is limited and we have to do a lot of search to get the recipes according to availability of ingredients. We decided to implement this Idea in form of website so that any user can easily enter the available ingredients and get recipes corresponding to the ingredients available.For website we use HTML and CSS for designing and JavaScript as a scripting language.We use RecipePuppy API[1] for the database of recipes and ingredientsThis website will make our life easier by saving the time of doing research to find the correct recipe according to available ingredients and will suggest some really awesome recipes for the same and we can utilize that time in other works. And If in case you missed some of the Minor ingredients you can that in your shopping list in just one click.
Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images

The increasing availability and accessibility of numerous overhead images allows us to estimate and assess the spatial arrangement of groups of geospatial target objects, which can benefit many applications, such as traffic monitoring and agricultural monitoring. Spatial arrangement estimation is the process of identifying the areas which contain the desired objects in overhead images. Traditional supervised object detection approaches can estimate accurate spatial arrangement but require large amounts of bounding box annotations. Recent semi-supervised clustering approaches can reduce manual labeling but still require annotations for all object categories in the image. This paper presents the target-guided generative model (TGGM), under the Variational Auto-encoder (VAE) framework, which uses Gaussian Mixture Models (GMM) to estimate the distributions of both hidden and decoder variables in VAE. Modeling both hidden and decoder variables by GMM reduces the required manual annotations significantly for spatial arrangement estimation. Unlike existing approaches that the training process can only update the GMM as a whole in the optimization iterations (e.g., a "minibatch"), TGGM allows the update of individual GMM components separately in the same optimization iteration. Optimizing GMM components separately allows TGGM to exploit the semantic relationships in spatial data and requires only a few labels to initiate and guide the generative process. Our experiments shows that TGGM achieves results comparable to the state-of-the-art semi-supervised methods and outperformes unsupervised methods by 10% based on the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores, while requiring significantly fewer labeled data.
Sentiment Polarization in Online Social Networks: The Flow of Hate Speech

The influence of sentiment polarization and ex-change in online social networks has been growing and studied by many researchers and organizations worldwide. For example, the sentiments expressed in a text concerning a topic in the discussion tend to influence a community when a Twitter user retweets the original text, causing a chain of reactions within a network. This paper investigates sentiment polarization in Twitter, focusing on tweets with the hashtags #Coronavirus, #ClimateChange #Immigrants, and #MeToo. Specifically, we collect the tweets mentioned above and classify them into five categories: hate speech, offensive, sexism, positive, and neutral. In this context, we address the problem as a multiclass classification problem by using the pre-trained language models ULMFiT and AWD-LSTM, which achieved a F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">micro</inf> of 0.85. Finally, we use the classified dataset to conduct a case study in which we capture the sentiment orientation during the network evolution.
Comparative Analysis of Some Typical Encryption Algorithms and Hash Algorithms

With the continuous development and progress of information technology and Internet, the struggle against illegal theft and anti-theft of network information has become more and more intense. How to make the information more efficient and secure in the transmission process and how to verify the authenticity of the received information have become today's research topics. Therefore, a variety of encryption algorithms appear in people's vision, such as message digest algorithm, hash algorithm and asymmetric-key encryption algorithm. This paper will mainly discuss the above several types of algorithms, selecting corresponding typical algorithms in each type of algorithm, summarize their working principle, analyzing and comparing their advantages and disadvantages in security and execution speed.
ADR Brief

Alternatives to the High Cost of LitigationVolume 40, Issue 2 p. 31-32 ADR Brief ADR Brief Russ Bleemer, Russ BleemerSearch for more papers by this author Russ Bleemer, Russ BleemerSearch for more papers by this author First published: 29 January 2022 https://doi.org/10.1002/alt.21936Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume40, Issue2February 2022Pages 31-32 RelatedInformation
Accelerate the Convergence Speed of Perceptron Learning Algorithm with Weight

The main problems of the traditional perceptron learning algorithm (PLA) is that there are too many iterations and it is difficult to generate a model quickly, and more iterations are needed when the boundary between the two classes is closed. In this paper, we improve PLA by introducing the current weight into the updating formulation, which can significantly accelerate the iteration. The experiments on different public datasets show that our proposed method can greatly improve the speed of the traditional PLA.
Underwater Diver Image Enhancement via Dual-Guided Filtering

The scattering and absorption of light propagating underwater cause the underwater images to present low contrast, color deviation, and loss of details, which in turn make human posture recognition challenging.To address these issues, this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method.First, the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image.Second, dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details.Four feature weight maps of the two images were then calculated, and two normalized weight maps were constructed for multi-scale fusion using normalization.To better preserve the obtained image details, the fusion image was histogram-stretched to obtain the final enhanced result.The experimental results validated that this method has improved the accuracy of underwater human posture recognition.
Cross-category Virtual Try-on Technology Research Based on PF-AFN

With the universalization of online shopping, virtual try-on based on 2D images has become a popular research direction. The current virtual try-on technology mainly researches the same category of changing clothes, such as originally wearing short sleeves, to virtually try on short sleeves, has not yet realized the virtual try-on from short sleeves to dresses and other cross-category. In order to eliminate the category limitation of virtual try-on, a cross-category virtual try-on method based on PF-AFN is proposed in this paper. We built the corresponding dataset with the example of wearing short sleeves for dresses and trained it with PF-AFN model. Based on this trained model, the user inputs a picture of a person wearing short sleeves and a picture of a target dress, expecting to get a picture of the person trying on the dress. Based on the body type classification and dress style classification, we first find an "agent" in the dataset whose body type and posture are similar to the input body and who is wearing the same dress style as the target dress. After getting the "agent" image, it is input to the trained PF-AFN model together with the target dress image, and the final result of the "agent" trying on the target dress is obtained, so as to provide the user with the reference of trying on the dress. Experiments show that the method in this paper provides a practical idea in solving cross-category try-on and yields good results.
Augmenting Image Aesthetic Assessment with Diverse Deep Features

With the increasing prevalence of digital images, automatically assessing the aesthetic quality of photos could benefit many real-world applications. While many previous methods have produced binary classification results, this paper proposes a model to produce regression results with high accuracy. The proposed model exploits global visual information such as color palette, saturation, and clarity, as well as deep features like blur maps, saliency maps, and scene information to augment the DenseNet architecture. The augmented DenseNet, when evaluated on the AVA dataset, outperformed the current state-of-the-art methods, achieving an accuracy of 88.65% on the 10% subset and a Spearman's rank correlation coefficient of 0.5802 on the full dataset. Comparison of the augmented DenseNet and the DenseNet baseline also demonstrate the effectiveness of the proposed methods of augmentation.
Detecting Anomalous Responses in Demand Response Surveys with Local Outlier Factor

In order to better carry out power demand response to utilize demand-side resources, researchers are conducting survey-based research to develop a quantitative and interpretable demand response behavior model. However, due to the characteristics of the demand response survey, using simple pre-set patterns can only identify a small number of abnormal respondents. This paper investigates the application of local outlier factor algorithm in detecting anomaly responses in demand response survey datasets to improve data quality. We test local outlier factor algorithm through the survey of customers’ electricity consumption habits from the Human and Energy Systems Laboratory, and investigate the results from both practical and theoretical perspectives. Our results reflect that the local outlier factor algorithm is effective. It can not only find abnormal points according to certain attribute pre-set patterns, but also automatically find abnormal points from the complex relationship between attributes, which can eliminate more abnormal responses.
Bitcoin: An Accounting Regime without Accountants

The Bitcoin blockchain is a payment system designed to disintermediate financial transactions and create a financial order free of financiers and accountants. While many professional accounting bodies have sounded the alarm about the potential for blockchain technology to render accountants redundant, few academic articles have been able to transcend high-level conceptual descriptions and dig deeper into the technological nuances of blockchain technology to assess whether or not, and if so, how, blockchain can undermine the accounting profession and its practices. Drawing on a netnography of the early Bitcoin community from the technology’s formation in 2008 through to the disappearance of its founder in 2011, this paper aims to explore the role of accounting in the development of a new financial system. More specifically, this paper aims to address two primary research questions: (1) How does Bitcoin challenge the traditional routines and interpretative schema embedded in accounting systems? and (2) How does Bitcoin challenge the relevance of accountants and their hold on accounting knowledge? We propose that Bitcoin is more than a form of digital currency, but rather a new accounting regime that effectively takes accounting away from accountants. Using sophisticated cryptography and advanced computer programming, the Bitcoin blockchain has created new interpretative schema for measuring and recording financial transactions. Bitcoin relies on new types of experts, namely computer programmers and cryptographers, and a new form of expertise that fuses technological know-how with accounting concepts. We find that Bitcoin programmers are able to co-opt accounting knowledge because these concepts do not constitute a sufficiently esoteric knowledge basis to protect the jurisdictional boundaries of the profession.
Evaluation Model of Piano MOOC Teaching Mode Based on Random Forest Algorithm

Piano course is a basic compulsory course for school music education, and the school is actively carrying out research on piano education reform. “Internet+” brings rich knowledge resources and diversified teaching methods to piano education, and also brings new opportunities for school piano education innovation and talent training. MOOC is the product of educational information-based. This article attempts to combine MOOC with piano teaching in colleges and universities. Based on the analysis of the current problems in piano teaching, the characteristics of MOOC and the random forest model, Random Forest is introduced as a piano. In order to verify the effectiveness of the model, this article uses a questionnaire survey method to survey students majoring in music education in a university to understand the teaching effect of MOO C and students' evaluation of the MOO C teaching model. The survey results show that the evaluation satisfaction rate of piano teaching through MOO C far exceeds the satisfaction rate of piano teaching through the traditional model. The application of this model reflects the actual situation of piano teaching in colleges and universities and achieves the expected results. It shows that the model has played an active role in teaching evaluation.
K-means Image Segmentation Based on Improved Grey Wolf Optimizater

In order to improve the weak global search ability of grey Wolf algorithm and easy to fall into the local optimal solution in the iterative process, an Improved Grey Wolf Optimizer (IGWO) is proposed in this paper. The Improved idea is to adopt the adaptive inertia weight strategy and make the weight change according to the fitness value. Therefore, gray wolves have more diversity when looking for prey, so as to balance the global search and local search capabilities of the algorithm. In order to prove the practicability of the improved algorithm, IWGO optimized k-means algorithm and applied it to image segmentation. In this paper, two groups of images are used for segmentation experiments. By comparing relevant performance indicators, the results show that IWGO has achieved certain results in image segmentation.
To Infect or Not to Infect: A Critical Analysis of Infective Countermeasures in Fault Attacks

As fault-based cryptanalysis is becoming more and more of a practical threat, it is imperative to make efforts to devise suitable countermeasures. In this regard, the so-called “infective countermeasures” have garnered particular attention from the community due to its ability in inhibiting differential fault attacks without explicitly detecting the fault. We observe that despite being adopted over a decade ago, a systematic study of infective countermeasures is missing from the literature. Moreover, there seems to be a lack of proper security analysis of the schemes proposed, as quite a few of them have been broken promptly. Our first contribution comes in the form of a generalization of infective schemes which aids us with a better insight into the vulnerabilities, scopes for cost reduction and possible improvements. This way, we are able to propose lightweight alternatives of two existing schemes. Further, we analyse shortcomings of LatinCrypt’12 and CHES’14 schemes and propose a simple patch for the former.
A Customized Quality Model for Software Quality Assurance in Agile Environment

The agile approach grew dramatically over traditional approaches. The methodology focuses more on rapid development, quick evaluation, quantifiable progress and continuous delivery satisfying the customer desire. In view of this, there is a need for measurement of the agile development process. In this respect, the present research work investigates the inter-relationships and inter-dependencies between the identified quality factors (QF), thereby outlining which of these QF have high driving power and dependence power, working indirectly towards the success of agile development process. This paper proposes a new agile quality model, utilizing an interpretive structural modeling (ISM) approach and the identified factors are classifies using Matriced' Impacts Croise's Multiplication Applique´e a UN Classement (MICMAC) approach. The research findings can significantly impact agile development process by understanding how these QF related to each other and how they can be adopted.
Background Subtraction Algorithm Comparison on the Raspberry Pi Platform for Real Video Datasets

Background subtraction is an advanced method used for video monitoring and is commonly used for indexing of moveable objects. Over the years, several algorithms have been implemented and the implementation of algorithms on the embedded platform can be difficult because the embedded platform has minimal computing resources. The purpose of this study is to conduct a comparative review of background subtraction algorithms available on the embedded platform: Raspberry Pi. The algorithms are compared using a real video dataset based on segmentation accuracy (precision, recall, and f-measure) and hardware efficiency (CPU utilization and time consumption).