Reviewerly/exHarmony huggingface dataset:
Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling

User queries for a real-world dialog system may sometimes fall outside the scope of the system’s capabilities, but appropriate system responses will enable smooth processing throughout the human-computer interaction. This paper is concerned with the user’s intent, and focuses on out-of-scope intent classification in dialog systems. Although user intents are highly correlated with the application domain, few studies have exploited such correlations for intent classification. Rather than developing a two-stage approach that first classifies the domain and then the intent, we propose a hierarchical multi-task learning approach based on a joint model to classify domain and intent simultaneously. Novelties in the proposed approach include (1) sharing supervised out-of-scope signals in joint modeling of domain and intent classification to replace a two-stage pipeline and (2) introducing a hierarchical model that learns the intent and domain representations in the higher and lower layers respectively. Experiments show that the model outperforms existing methods in terms of accuracy, out-of-scope recall, and $$F_1$$ . Additionally, threshold-based post-processing further improves performance by balancing precision and recall in intent classification.
Readability Analysis of Texts in College English Textbooks and Reading Passages in CET-6

English textbooks play an indispensable role in classroom teaching and learning.Teachers carry out different activities based on them and students also depend on them to enlarge their knowledge and prepare for an examination.Thus it is of great importance to select textbooks with proper text readability.What's more, College English Tests are a criterion to evaluate students' achievements in English in college.There have been some studies on the text readability of textbooks and test papers, however, there are very few studies on the consistency of the readability of the texts from English textbooks with that of College English Test papers.To make up for the gap, this research makes a comprehensive analysis of the texts in volumes 3 and 4 of New Target College English Integrated Course and the reading passages in College English Test band 6 from July 2020 to June 2022 in terms of text readability.Flesch Reading Ease formula and other five commonly used readability formulas are employed as the detection tools to study the text readability of texts from textbooks and papers.The research aims at finding out the characteristics of the text readability of the two volumes and test papers and the differences between them.Hoping that this study can provide some suggestions for teachers as well as students who use the same textbooks.
Perancangan Sistem Informasi Manajemen Dokumen Pada Kelurahan Keagungan

Letters are a bridge of understanding and communication tools for one person and another. In Kelurahan Keagungan, document management has not been done computerized. The process will experience obstacles such as a lot of paper required. This relates to the security of the letter or information stored because if it is not managed properly the paper can be damaged or lost. The purpose of this research is to build a website-based document management information system. Before creating a system, the author makes several UML diagrams such as Use Case Diagrams, Activity Diagrams, Class Sequence Diagrams, Component Diagrams and Deployment Diagrams. The development method used is the RAD (Rapid Application Development) model where the software development process model is classified as a multilevel technique. This method emphasizes a short, concise and fast development cycle. This information system uses the PHP and MYSQL programming languages ??as its database. This information system was created to make it easier for each incoming and outgoing mail data search quickly, and a solution for the Keagungan Village who needs a web-based information system that can speed up the work in archiving incoming and outgoing letters.
The Existence of Paid Photocopy Services at the Gadjah Mada University (UGM) Library in Era 4.0

The advanced transformation of universities' libraries that is fulfilled by many innovations has offered students an easier way to access collections of sources of information. Even though accessing collections is merely easy, there is a library service area that has restrictions. For instance, reference services frequently include a rule that states that students are only permitted to read materials there and then they are not permitted to borrow collections to read later. Some university libraries offer paid photocopying services despite the limited hours of the library service, which makes it a worthwhile study topic. A qualitative-descriptive research methodology was used for this investigation. In the meantime, the interview approach was employed to obtain the data for this study. The findings demonstrated that students had no other option except to employ copying services in order to fulfill their demands. The availability of these services also fosters a mutually beneficial relationship between users and libraries.For users, there is no need to take notes and go back and forth from the library to refer to the required references. Then for the library, photocopying services provide financial income that supports library development. Its existence, which is still surviving today, helps create a stable cost flow for libraries that can be utilized by library managers and provides time efficiency for users to get the information they need. Although the price of photocopying in the library is more expensive than photocopying outside the library, users are still enthusiastic about using it so that the service continues.
FreshBox App: Fresh Groceries Sales Application Innovation

Buying fresh food is a basic need for a family. Fresh food is needed to cook food / side dishes, buying fresh food is also needed to fill the food so there is no need to buy fresh food needs every day. Along with the development of technology, it is easier for people to access everything including buying basic needs, buying fresh vegetables and meat, now they can use the buying and selling platform, namely "FreshBox". This application deals with the sale of fresh food ingredients such as those sold in traditional markets using online shopping methods. The name of this application is FreshBox, the name "FreshBox" is given because of the process of packaging goods sold using Boxes so that the goods are maintained during the delivery period. This application is useful to make it easier for users who need fresh food ingredients so they don't need to go to traditional markets. This application was developed using the Android application launch application, namely Android Studio. The logo used uses a simple concept and the green color can mean freshness. The freshbox application will be very helpful for someone who wants to buy cooking ingredients but with limited time so that while waiting for the item to do other work, it can also help people with disabilities so they don't need to shop at traditional places. market, This app is very useful in future for everyone. This app is made for all ages.
Basketball Posture Recognition Method in Physical Education Based on Machine Vision

The traditional basketball posture recognition method has large error and the recognition effect decreases. A basketball posture recognition method based on machine vision is designed for physical education teaching. Collect basketball posture information; Extract the characteristics of basketball movement in physical education teaching; Basketball posture calibration based on machine vision; Then realize the recognition of basketball posture. By means of comparative experiments, it is verified that the attitude recognition effect of the new method is better and has great popularization value.
Symbiotic Organisms Search Optimization Algorithm in Cloud Computing: A Nature-inspired Meta-heuristic

In the past few years nature-inspired algorithms are experiencing rapid growth where most optimisation problems in different domains are addressed using it. As a result of this development come the issue of handling a complex optimisation problem within a short period remains very difficult. Symbiotic organisms search (SOS) algorithm is one of the nature-inspired metaheuristics that mimics the symbiotic association of organisms in an ecosystem. This paper proposes to investigate symbiotic organisms search algorithms used in handling various optimisation problems in different fields to bring out strengths and weaknesses of the existing algorithms as well as to point out future directions for the upcoming studies in the domain. To achieve that, studies done in optimisation problems using symbiotic organisms search from 2014 – 2020 that are obtained from some databases (Scopus, ScienceDirect, IEEE Xplore, ACM) were surveyed; where the review of various issues related to SOS such as diversity of solution search space, variants, scalability, and applications of the SOS. Finally, future research directions in the area were recommended.
An Empirical Analysis of Employment Motivation and Career Choice of Graduates of Master of Engineering ——Based on Principal Component Factor Analysis and Binary Logit Regression Model

Taking 205 graduates of Masters of Engineering as the survey objects, their employment motivation, career choice and training evaluation were analyzed by principal component factor analysis and binary logit regression model.The study found that: the average year-end employment rate of the 2015-2020 engineering master students reached 99.31%; Factors, such as the industry background and guidance frequency of the tutor, the development and investment of professional practice, the course setting and teaching method, the patent acquisition situation, satisfaction with the professional level as well as knowledge and skills, have a significant impact on the employment motivation and career choice of graduate students of engineering masters.
Research on Modeling of Adaptive Allocation of Labor Resources Based on Deep Reinforcement Learning

Based on the problem of the unreasonable allocation of labor resources in my country, a modeling method for adaptive allocation of labor resources based on deep reinforcement learning is proposed, combined with deep reinforcement learning algorithms to calculate the distortion of labor resource allocation in my country's primary, secondary, and tertiary industries degree. Analyzed the changing trend of labor resource allocation in urban and rural areas, and proposed an adaptive allocation plan of labor resources based on my country's industrial development structure in recent years to optimize the allocation structure of labor resources. Finally, it was confirmed by experiments that the adaptive allocation model of labor resources based on deep reinforcement learning It has high practicability and can better integrate the actual situation for effective allocation of labor resources.
PERANCANGAN INSTALASI PENERANGAN PADA KAPAL 12.000 TLC FLOATING DOCK PT. M O S

The use of electrical energy in shipping is very important its role as a supporting factor for the smooth operation of the ship itself, for that it needs to be made a very mature design so that the system is able to work effectively and efficiently and be able to overcome the disturbances that occur at any time.&#x0D; Comfort during the process of the operation of the ship and every active'ltas certainly can not be separated from a good lighting system. the main goals and objectives to be achieved in this lighting installation system is that the amount of lighting needed in the operation of the ship can be adequate, sufficient and efficient in its use by not ignoring the safety system both for humans and equipment.
Latent Space-Based Backdoor Attacks Against Deep Neural Networks

The outstanding performance of modern deep learning systems resulted in their widespread adoption in various application domains, which include security-critical applications. However, recent works have shown that these systems are vulnerable to backdoor attacks. This paper proposed a novel approach to perform latent backdoor attacks. Instead of designing the exogenetic trigger backdoor on the pixel space, which has been done by existing works, this paper explored the connection between latent space manipulation and endogenic backdoor trigger generation by utilising deep generative models to generate the backdoor trigger in the latent space. The effectiveness of the proposed attack is demonstrated on several neural network architectures trained on three well-known datasets, which are MNIST, CIFAR-10 and GTSRB. This study is undertaken to provide a new viewpoint for better understanding the endogenic vulnerability of the deep neural networks due to the lack of training data and test data, instead of creating new exogenetic misclassification behaviours for existing backdoor attacks.
Determination of the Factors Influencing Proper Face Recognition in Faces Protected by Face Masks, an Analysis of Their Algorithms and the Factors Affecting Recognition Success

One of the many collateral effects that the entire planet has suffered with the appearance of covid-19 and the declaration of this as a pandemic, has been evidenced in the treatment of facial recognition algorithms and the variety of applications, both commercial and for exclusive use in research for this same purpose. For the time being, there are already reports of effectiveness with respect to the analysis of these algorithms and that are paving the way to understand the degree of affectation that the use of face masks can have on facial recognition processes. In this context, it is important to determine how it is possible that throughout these almost two years of confinement and use of face shields and masks, the human being, regardless of his age, has been able to maintain its advantage over artificial intelligence systems when recognizing the face of a relative, friend or simply an acquaintance; that is why, the present study aims to evaluate some face recognition systems in order to determine the main problems faced by these algorithms when recognizing a face protected with a mask.
Visual odometry based on camera motion and bidirectional long short-term memory network

Most of the deep learning-based visual odometers have defects such as being unable to effectively learn camera motion information directly from RGB images, and low pose accuracy in the face of vehicle turning, high-speed driving and other scenes with dramatic changes in motion. Aiming at the above problems, this paper proposes a monocular visual odometry method that combines the camera's own motion estimation and a bidirectional long-short-term memory network. Firstly, a camera motion based on a gated recurrent convolutional neural network is proposed by introducing a 4D correlation volume of pixel pairs. Feature extraction algorithm to extract the motion features of adjacent frames of the camera; Then, a monocular visual odometry combining motion features and bidirectional long-short-term memory network is proposed. By learning the geometric motion relationship between adjacent frames before and after, more accurate six-degree-of-freedom camera pose information can be obtained. Comparative experiments on the KITTI dataset show that the proposed algorithm can estimate the pose with higher accuracy.
Collaborative Completion of Facial Texture and Displacement in UV Space

When the input face has large pose, it’s hard for single view 3D face reconstruction methods to estimate complete facial texture and geometric details due to self-occlusion. Considering the correlation between the feature distributions of texture and geometric details, TDGAN is proposed, a collaborative completion model with GAN structure which could complete texture and geometric details collaboratively in a uniform framework. Firstly, the texture and geometric details are mapped into the UV space. Then, the collaborative completion is performed in a generative adversarial network which includes a generator, two global discriminators and two local discriminators. Finally, to exploit the common structures of texture and geometric details, a consistency constraint module is incorporated to the framework. The complete and consistent texture and geometric details UV maps are synthesized. Experiments on the currently largest 3D face dataset demonstrate that the proposed collaborative completion method could produce more high quality results than the independent UV completion methods.
A Genetic Algorithm for Causal Discovery Based on Structural Causal Model

With a large amount of data accumulated in many fields, causal discovery based on observational data is gradually emerging, which is considered to be the basis for realizing strong artificial intelligence. However, the existing main causal discovery methods, including constraint-based methods, structural causal model based methods, and scoring-based methods, cannot find real causal relations accurately and quickly. In this paper, we propose a causal discovery method based on genetic algorithm, which combines structural causal model, scoring method, and genetic search algorithm. The core of our method is to divide the causal relation discovery process into the evaluation phase based on the features of structural causal model and the search phase based on the genetic algorithm. In the evaluation phase, the causal graph is evaluated from three aspects: model deviation, noise independence, and causal graph cyclicity, which effectively ensures the accuracy of causal discovery. In the search phase, an efficient random search is designed based on genetic algorithm, which greatly improves the causal discovery efficiency. This paper implements the corresponding algorithm, namely SCM-GA (Structural Causal Model based Genetic Algorithm), and conducts experiments on several simulated datasets and one widely used real-scene dataset. The experiments compare five classic baseline algorithms, and the results show that SCM-GA has achieved great improvement in accuracy, applicability, and efficiency. Especially on the real scene dataset, SCM-GA achieves better results than the state-of-the-art algorithm, with similar SHD (Structure Hamming Distance) value, 40% higher recall rate, and 83.3% shorter running time.
Retrieval-based Statistical Chatbot in a Scientometric Domain

The scope of this research work is to integrate a statistical ontology model of scientometric indicators in a chatbot. Building a chatbot requires the use of Natural Language Processing (NLP) as a capability for recognizing users’ intent and extracting entities from users’ questions. We proposed a method for recognizing the requested indicator and transforming the question expressed in natural language into a query to the semantic model. The chatbot and the ontology model represent a novel framework that can answer questions about Scientometric Indicators. The chatbot is evaluated in terms of Goal Completion Rate (GCR). It measures how many questions the chatbot answered correctly and identifies intent and entity extraction correctly. The second evaluation approach of the chatbot is a survey that focuses on usability, the strictness of language variations, chatbot comprehension, correlation in chatbot responses, and user satisfaction.
Research on Personalized Recommendation of High-Quality Academic Resources based on user Portrait

With the advent of the era of big data, the phenomenon of information overload is becoming increasingly serious.It is difficult for academic users to obtain the information they want quickly and accurately in the face of massive academic resources.Aiming at the optimization of academic resource recommendation services, this paper constructs a multidimensional academic user portrait model and proposes an Academic Resource Recommendation Algorithm Based on user portrait.This paper first, combs the relevant literature and information; Secondly, to obtain the attribute tags of multidimensional user portraits, a set of questionnaires are designed to collect the real information of academic users, and the corresponding academic user portrait model is constructed; Then, the collected data is processed through certain rules, and the user is quantitatively modeled based on the data through mathematical means; Finally, through the construction of the completed academic user portrait model, combined with collaborative filtering algorithm, provide personalized academic resource recommendation services for academic users.Through the verification and analysis of simulation experiments, the Academic Resource Recommendation Algorithm Based on the user portrait proposed in this paper plays a great role in expanding users' interest fields and discovering new hobbies across fields and disciplines.
A Secured Deep Reinforcement Learning Model Based on Vertical Federated Learning

Deep reinforcement learning (DRL) has been widely used in diverse applications, which combines the key technologies of reinforcement learning and deep learning. In this work, a secured DRL based on vertical federated learning (VFL), named VF-DRL, is proposed to protect the model’s information. Specifically, in the proposed VF-DRL, each client trains a partial local model, and uploads the feature embedding cooperatively to a server for global model aggregation. In this way, if one of the client attempts to construct an adversarial attack on the VF-DRL, it cannot be easily succeeded with only its own features of data and local model alone. Meanwhile, the server aggregates the output embedding of each client to accomplish the global model training in a distributed fashion. Extensive experiments are carried out to testify the robustness of VF-DRL. The results show that the VF-DRL achieves the state-of-the-art defensibility compared with base-lines.
Academic User Interest Extraction using Multi-feature TextRank Based on Interest Attenuation

User interest extraction is a bask task in the academic user behavior mining field. This paper proposes a multi-feature TextRank algorithm which combining behavioral characteristics of academic user and word characteristics of academic resource, aiming to extract academic user interests. This paper selects the real user behavior data from Agricultural Sci&Tech Information Resource Co-construction and Sharing Platform to conduct the experiment. Compared with TextRank, multi-feature TextRank improves P value by 4.8%, R value by 3.3%, and F value by 3.7%. And each performance of multi-feature TextRank is better than TF-IDF algorithm. The experiment shows that the multi-feature TextRank improves the accuracy and recall rate of TextRank, and it can be applied to automatic extraction of user interests in the context of multi-source academic resource usage.
Multi-grained Cascade Interaction Network for Temporal Activity Localization via Language

Temporal activity localization via language task aims to localize the target moment in an untrimmed video according to a given language query. A key challenge in this task is how to make an effective semantic alignment between video and language query. Existing methods mainly focus on the single-grained cross-modal interaction and leave the cross-modal interaction in multi-grained manner under-exploited. In this paper, we propose a multi-grained cascade interaction network for temporal activity localization via language. Specifically, a coarse-to-fine cross-modal interaction strategy is exploited to learn an effective query-guided video representation. In addition, we propose a local-global context-aware video encoder to model the temporal relation for video representation. Extensive experiments on two benchmarks verify the effectiveness of our proposed method. Compared with state-of-the-art methods, our proposed method achieves a comparable or superior performance.