Reviewerly/exHarmony huggingface dataset:
Construction of power equipment fault feature model based on unified semantic expression

In view of the poor recognition effect of power equipment fault features in China, a method for building power equipment fault feature model based on unified semantic expression is proposed. The power equipment fault information is identified by combining the unified semantic expression principle. And the phase space reconstruction algorithm is constructed according to the feature semantics of the identified fault information. The power equipment fault feature model is optimized based on the reconstruction results. Finally, it is verified by experiments, the power equipment fault feature model based on unified semantic expression can quickly identify the semantic features of fault information in the process of practical application, and effectively improve the recognition effect.
A Study on the Accuracy of IoT-Sensed Data Using Machine Learning

The booming technology of Internet of Things (IoT) consists of smart devices with embedded systems. The handling of data through IoT devices is not simple, as it involves CPUs, sensors, and communication devices, among other things. Data collection takes place in a variety of environments. In such a case, it is critical that data collected by sensor devices be accurate. A method for processing data from a variety of environments is needed to determine the correctness of data. Machine learning techniques are useful for determining the degree of accuracy of data detected by IoT systems. Machine learning, which uses mathematical principles to generate behavior models without the need for programming, is a boon to use with the IoT. Another important trait is being multidisciplinary, which supports data from different environments that are easy to formulate in the format needed. In this chapter, a sample data set retrieved by the sensor device and stored in the cloud is used to analyze the degree of accuracy of the sensed data by training the data set with machine learning techniques. This chapter not only aids in determining the degree of accuracy of IoT-sensed data, but it also focuses on the efficacy of machine learning algorithms in dealing with IoT.
Cloud Environment Limitations and Challenges

Cloud computing evolved as a huge paradigm in the information technology (IT) industry, which shifts the entire physical infrastructure onto the cloud environment, which gives flexibility, scalability, and on-demand provisioning of resources to the user requests. This paradigm reduces the up-front investment costs for the resources used in physical infrastructure. Apart from the advantages of a cloud environment, there are some limitations and challenges pertaining to security and a lack of control over the applications, which runs in a cloud environment. The main limitation of a cloud environment is that data are to be stored in a third-party environment. The user does not know about the standard of security to be maintained by the cloud provider, but there is a very minimal-level limitation in terms of security. Still there is a chance that a security breach will occur in the cloud environment, and the reason for that is because data are stored in the hands of a third-party cloud provider that lacks skilled employees in IT companies in relation to using the features of cloud environment services, the occurrence of denial-of-service attacks, the hijacking of cloud user accounts, and insecure APIs. These mentioned limitations pose new challenges for cloud providers that they need to address. This chapter covers all the limitations and challenges in the cloud environment in a detailed way.
Lexicalisation of Polish and English word combinations: an empirical study

Abstract One of the main research questions concerning multi-word expressions (MWEs) is which of them are transparent word combinations created ad hoc and which are multi-word lexical units (MWUs). In this paper, we use selected corpus-linguistic and machine-learning methods to determine which lexicalization criteria guide Polish and English lexicographers in deciding which MWEs (bigrams such as adjective+noun and noun+noun combinations) should be treated as lexical units recorded in dictionaries as MWUs. We analyzed two samples: MWEs extracted from Polish and English monolingual dictionaries, and those created by the annotators, and tested two custom-designed criteria, i.e., intuition and paraphrase, also by using statistical methods (measures of collocational strength: PMI and Jaccard). We revealed that Polish lexicographers have a tendency not to include compositional MWEs as lexical entries in their dictionaries and that the criteria of paraphrase and intuition are important for them: if MWEs are not clearly and unambiguously paraphrasable and compositional, then they are recorded in dictionaries. We found that in contrast to Polish lexicographers English lexicographers tend to record also compositional and partly compositional MWEs.
Improvisation in opinion mining using data preprocessing techniques based on consumer’s review

The consumer is one of the assets of every organization.In order to survive in this competitive marketplace, it is mandatory for every organization to assess the sentiments, expectations, and feedback of consumers.For this assessment, it is required to perform the sentiment analysis of data collected from various internet sources.The data collected from various internet sources contain various types of anomalies such as stop words, hypertext markup language (HTML) tags, misspellings, abbreviations, special characters, uniform resource locator (URL), etc. Due to this, it becomes difficult for both humans and machines to get the exact meaning of the sentence.*Author for correspondence Moreover, the presence of this unwanted noise in data increases the dimensions because each word in the text is considered a separate feature and it becomes challenging for classifiers to classify this noisy data.So, to get accurate sentiments it is mandatory to remove this unwanted noise from the data.In previous studies, various preprocessing techniques were used for different types of datasets and all these techniques were evaluated to find the best preprocessing techniques using various classifiers such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), Naïve bayes (NB).Some of the basic preprocessing approaches like removal of stop words, punctuation, tokenization, spell correction [15] stemming/lemmatization were used in various pieces of research.Similarly, various
The Main and Basic Differences between the Google Search and ChatGPT Application

This study aims to focus on two tools used by millions of user, Google Search and ChatGPT (Chat Generative Pre-trained Transformer), Google Search is a web search engine that allows users to search the internet for information, while ChatGPT is a natural language processing tool that allows users to communicate with a Chabot using natural language.
INVESTIGATION OF THE IMPLEMENTATION OF LEADER-FOLLOWER FORMATION CONTROL TO BIPEDAL LOCOMOTION

Robot that uses bipedal locomotion such as humanoid robot has a unique appeal in which the robot can perform many duties/works that cannot be done by wheeled robots due to spatial and environmental constraints. Related literature were about biped robots and robot arm that uses the concept of central pattern generator (CPG). Biped robots that use CPG (technically a neural oscillators) do not need any mathematical model of the robot itself. The controller “exploits” the dynamics of the robot to achieve an efficient way to drive the robot’s joints. The driving frequency from this type of controller is thus heavily influenced by the dynamics of the robot it controls. One disadvantage of this method is that we cannot adjust the walking/movement parameter easily. Inspired by the idea of central pattern generator where one central “brain” controls the movement of the joints of the robot and the fact that the system uses neural oscillators reach some kind of “consensus”, we are interested to study the feasibility of the implementation of leader – follower formation control based on consensus algorithm to bipedal locomotion system. The method studied in this report is to force the dynamic model of the double pendulum to match the kinematic model of the double integrator agent that uses consensus algorithm in forming formation. The walking/movement parameters of the robot using the proposed method are defined by one central “brain”, and then the joints are working in consensus way to achieve the target joints’ trajectories specified by the “brain”. The study also concludes the notion of stability of the system driven by this controller which strongly related to the consensus ability in the context of Multi – Agent System.
Object Tracking Algorithm Based on CNN and Trainable Correlation Filters

Object tracking is considered a challenging problem due to various environmental changes contained in the video sequence. Object tracking is the use of information given in the first frame to estimate the area and trajectory of a target object in a video sequence. In this paper, we propose an algorithm for object tracking using multi-scale feature maps of Resnet-50 and correlation filters. To accommodate changes in object scale, we create an appearance model using different sized feature maps extracted from each block in the network. In order to maintain the robustness of the appearance model, it is adaptively updated according to the peak value of the response map when occlusion occurs due to obstacles. Experiments result using the OTB2015 dataset showed that the proposed algorithm achieved competitive results on several image attribution such as low resolution, scale variation, occlusion and out of view.
Classification of Military Equipments using AI Learning Models and Acoustic Data

Acoustic data is widely used for the development of voice recognition based services, speaker identification system, or environmental sound classification. However, in the defense sector, research on the use of acoustic data and AI learning models has not yet been actively conducted. In addition, collection methods or quality standards that must be considered in order to use acoustic data as training data have not been clearly established. Therefore, this paper attempts to classify six weapons for the first time using four AI learning models and previously collected acoustic data to derive considerations in the process of collecting acoustic data. The learning data used in the experiment is acoustic data related to Army equipment collected through a government-supported project in the second half of 2021. Experimental results show that the classification performance of CNN and LightGBM models is generally high and the best performance is achieved when the data has a medium length. By uniformly re-configuring the number of data per class using SMOTE, the performance can be further improved. Based on these experimental results, we also propose what should be considered when constructing AI training data for classification of military equipment based on acoustic data.
Shuffled Shepherd Optimization Method Simplified for Reducing the Parameter Dependency

Shuffled shepherd optimization algorithm (SSOA) is a newly developed multi-community algorithm inspired by shepherd behavior. Since the SSOA is a parameter-dependent method, the optimum performance requires using the right parameters for each problem. In this chapter, SSOA is modified to become less reliant on parameter adjustment.
An Outlook of Nigeria's Intellectual property Reforms That Will Impact Foreign and Local Businesses from 2023

Every prudent CEO understands that there are two aspects of company assets – tangible and intangible assets (especially intellectual property). These two twin towers must be factored in when planning, executing or amending a company’s business model. Indeed, laws and policies of government impact business models (activities) and assets. This is why many companies spend a lot on regulatory awareness and even politicking. Companies must be abreast of laws and policies before they are passed or come to force. What many ignore is the laws and policies impacting intangible assets. Ignored, these laws or policies in the long run affect tangible assets as well. This is an outlook of Nigeria's intellectual property reforms that will impact foreign and local businesses from 2023.
A Vision and Semantics-Jointly Driven Hybrid Intelligence Method for Automatic Pairwise Language Conversion

In modern society where connections among nations have been more and more frequent, it remains important to realize automatic language conversion methods for the public. Currently, most the existing research works were conducted upon the basis of semantics analysis. But from the perspective of linguistics, the vision characteristics is also a kind of concomitant existence. To deal with such challenge, this paper proposes a vision and semantics-jointly driven hybrid intelligence method for automatic pairwise language conversion. The whole technical framework can be divided into two components: vision sensing part and semantics sensing part. For the former, the virtual reality is introduced for use to capture the visual feature representation for language contents. For the latter, the recurrent neural network model is utilized to capture semantic feature representation for language texts. They are then integrated into a jointly driving framework, so as to improve the conversion efficiency. Taking two dialects (Sichuan dialect and Chongqing dialect) in China as the example, the simulative experiments are conducted on massive real-world training corpus to evaluate the proposal. The results can reflect feasibility of it.
Titolo da definire arriva a fine febbraio

Firenze University Press coined the “Scientific Cloud for Books” label to designate the whole ecosystem surrounding high-quality scientific books, and to fully exploit digital tools with the purpose of ensuring greater visibility and impact of both monographs and collective volumes
CHANGES OF FACIAL PARAMETER “DISTANCE FROM RHENION TO ZYGION” IN BETWEEN JAUNSARI AND GARHWALI ETHNIC GROUPS OF UTTARAKHAND REGION, INDIA.

Background and Objectives: Anthropometry in forensic areas is a very important division of human medical anatomy. Ethical issues are avoided and no use of any invasive methods in this study. 1) To study the distance betwee Objectives: n Rhenion and Zygion in the Jaunsari and Garhwali ethnic groups of the Uttarakhand region of India.; 2) To analyze the gender difference in facial parameters of the Jaunsari and Garhwali Tribe if any; 3) and statistical signicance of the difference in the facial parameters of the Jaunsari and Garhwali Tribe if any. The main Aim is to study facial anthropometric parameters in adult Jaunsari and Garhwali Tribe of District Dehradun, Uttarakhand. This study has been Materials and Methods: carried out in the department of Anatomy, SGRRIMS &amp; HS, ddn, 2021-2022. During this period, 200 subjects i;e- 100 males and 100 females in both ethnic groups, were born and brought up in the Uttarakhand region basically, aged 21 years and above up to 35 years young subjects taken up. Longitude 78.017198°, Lattitude 30.302821°, 437, Chamanpuri, Niranjanpur, Dehradun, Uttarakhand 248001, India. The methodology adopted for the anthropometric measurements was of Singh and Bhasin (1968) and the concerned measurements were done. Statistical data were Result: obtained, observed and analyzed. It provided valuable data for the community for future use. There were signicant variations in all the measured facial parameters distance between Rhenion and Zygion in the male and female populations of the Jaunsari and Garhwali Tribe. The differences in means of facial indices between the two races were statistically weak and nonsignicant (&lt; 0.005). The mean values of facial indices found in the study were 63.70±11.05 in Garhwali females and 77.97±20.47 in Garhwali males. In this Discussion and Conclusion: study, the p-value is 8.08 in Jaunsari females and 2.25 in Jaunsari males; in the case of both males &amp; females the p-value indicates that the result is statistically nonsignicant. On the other hand p- the value is 3.02 in Garhwali females and 7.15 in Garhwali males. In all cases, the p-value indicates that the result is again not statistically signicant. So, we conclude that majority of all genders Conclusion: of Garhwali and Jaunsari ethnic groups; there is not any more difference in their facial indices distance between rhenion and zygion. The result of our study has conrmed that there was sexual dimorphism of facial parameter distance between Rhenion and Zygion. There were no signicant differences in facial parameters distance between Rhenion and Zygion amongst the male and female populations of the Jaunsari and Garhwali Tribe
Indispensable or unnecessary?: a data-driven appraisal of post-cancellation access rights

When breaking out of &lsquo;big deals&rsquo;, some libraries and consortia have found that they can save money by negotiating away post-cancellation access (PCA) to subscribed resources after the subscription concludes. Using subscription data regarding major publisher contracts at several US research libraries, this article reviews options around PCA for libraries and presents a model for assigning a value to PCA content when negotiating a renewal contract.
Lightweight Branching Self-distillation: Be Your Own Teacher

When applying deep neural networks (DNNs) in resource-constrained environments, Lightweight model techniques are often used to modify the model to be less computationally intensive. However, some of these techniques can struggle to balance maintaining their high accuracy against reducing processing costs. Lightweight branching models add early exits to a model for quicker inference, but these earlier exits can suffer a reduced level of accuracy because of the lack of learned knowledge in the preceding layers. We propose the use of a novel form of Knowledge Distillation to assist in the branched model’s training by distilling knowledge from the main classifier exit to the branch exits. This novel technique, entitled branching self-distillation, combines the student-teacher training of knowledge distillation with the effective efficiency optimisation of branching to create a novel lightweight optimisation technique that requires minimal additional training to achieve improvements in both accuracy and processing costs. We demonstrate the effectiveness of the technique and achieve an average of 1.86% increase in accuracy and a 38.02% further reduction in average processing flops per input upon a selection of well-known lightweight model architectures. (Source code and further experiment details can be found online at this work’s repository at https://github.com/SanityLacking/Self-Distillation ).
A Big data Analysis for Demand Forecasting of Multi-variety Small-volume Sales Environment - Focusing on a Case of Selling Electronic Parts

It is very important for companies to find insights that can be used in making strategic decisions through big data analysis. In this paper, we perform an analysis for demand forecasting focusing on the retail industry that handles multi-variety small-volume in an online environment, especially the sales of electronic parts. Through this, we seek the ways to maintain adequate inventory. We propose the introduction of the concept of risk value and risk index to evaluate the impact of each item according to the prediction error. The risk index proposed in this paper is an easy way to evaluate the degree of risk of error and to accurately select items with a high risk index. In addition, we develop a risk index that can be used in the pre-processing. If the risk index is used, a significant improvement in the average error amount can be expected. Our method is expected to be applicable to other multi-variety small-volume environments as well.
Effects of a Cognitive Flexibility Hypertext Learning Environment for University Software Education

This paper analyzed the effects of a cognitive flexibility hypertext learning environment to improve computational thinking skills, and perceptions and attitudes towards software in university software education. The research questions were ‘Is cognitive flexibility hypertext learning environments effective in improving university students’ computational thinking skills?’ and ‘Is cognitive flexibility hypertext learning environments effective in improving university students’ perceptions and attitudes towards software?’. The following conclusions can be drawn based on the results of the study: First, cognitive flexibility hypertext learning environments can be effective in improving university students’ computational thinking skills. In particular, it was found that cognitive flexibility hypertext learning environments can be effective in promoting pattern recognition. Second, cognitive flexibility hypertext learning environments can have no effect in improving university students’ perceptions and attitudes towards software. Based on the discussions, cognitive flexibility hypertext learning environments can be an effective method to develop learners’ computational thinking skills in university software education.
Improvement of SRGAN Performance Using Mutual Distillation

This paper proposes a method to improve the performance of SRGAN, a generative adversarial network (GAN)-based super-resolution neural network model, by applying mutual distillation. The proposed method adds a discriminator for mutual distillation to SRGAN, allowing the generator of each SRGAN to indirectly share the knowledge of the other SRGANs’ generators through adversarial learning with the discriminators added to the other SRGANs. Experiments using two SRGANs were conducted to analyze the performance of the proposed method, and the experiments confirmed that SRGANs produced super-resolution images with improved qualitative and quantitative image quality by applying mutual distillation. In addition, it was confirmed that the performance varies depending on the amount of knowledge transferred through mutual distillation.
An Enhanced Shuffled Shepherd Optimization Algorithm and Application to Space Structures

In this chapter, the Enhanced Shuffled Shepherd Optimization Algorithm (ESSOA) is presented and utilized for optimal design of space structures, Kaveh et al. [1]. Shuffled Shepherd Optimization Algorithm (SSAO) is an optimizer inspired by the herding behavior of shepherds in nature. SSOA may suffer from some disadvantages, including being caught in a local optimum and starting from a random population without previous information. This chapter aims to improve the performance of the SSOA by incorporating two efficient devices.