Reviewerly/exHarmony huggingface dataset:
An Analysis on the Terms and Conditions of the Clickwrap Agreement and the Benefits of Maintaining the Click Wrap Agreement in Cloud Computing

One of the most unique forms of contracting is apparent in cloud computing. Cloud computing, unlike other conventional methods, has adopted a different approach in the formation of binding contract that will be used for the governance of the cloud. This method is namely the clickwrap agreement. Click wrap agreement follows a take it or leave it basis in which the end users are provided with limited to no option in terms of having a say on the contract that binds them during the use of cloud services. The terms found in the contract are often cloud service provider friendly and will be less favourable to the end user. In this article, the authors examine the terms that are often found in the cloud computing agreement as well as study the benefit that is entailed in adopting this contracting method. This chapter has undertaken a qualitative study that comprises interviews of cloud service providers in Malaysia. Hence, this study is a novel approach that also provides insight in terms of the cloud service provider perspective regarding the click wrap agreement.
Evaluation of visual syntax specification techniques: a study of OWL

Diagrams are an integral part of our communication and thus automated processing of diagrams becomes increasingly relevant. One way to realise automated processing is by using declarative specifications of diagrams. These declarative specifications have similarities to domain knowledge modelled in ontologies. The Web Ontology Language (OWL), an open standard of W3C, is a prominent ontology language to model domain knowledge. In this paper, OWL is evaluated for its suitability to specify visual syntax of diagrams. Specifically, reviews of literature are conducted to establish relevant evaluation criteria and to obtain evidence about OWL for the established criteria. The evaluation indicates that OWL is used widely, which could be attributed to its standardisation and expressiveness, and consequently, it has extensive tool support, including reasoners and editors, with noticeable presence of open source tools. The evaluation also indicates that although OWL has been used successfully for diagram specifications, further research is required to understand entirely its strengths and limitations as a visual syntax specification technique.
Benchmarking Virtual Reinforcement Learning Algorithms to Balance a Real Inverted Pendulum

We benchmark common reinforcement learning algorithms on a modified version of OpenAI Gym’s Cartpole: a virtual environment containing a simulation of an inverted pendulum. While Policy Gradient, Actor-Critic, and Proximal Policy Optimization are all able to balance the pendulum, only Policy Gradient and Actor-Critic are able to quickly and consistently learn to balance the pendulum in a simulation. By transferring the trained models to the real world, all of the algorithms are able to satisfactorily balance a real inverted pendulum. On the real pendulum, Actor-Critic is best able to adequately reject disturbances among the algorithms tested.
Text-Based Speaker Identification for Video Game Dialogues

Speaker identification presents a challenging task in the field of natural language processing and is believed to be an important step in building believable human-like systems. Although most of the existing work focuses on utilizing acoustic features for the task, in this paper we propose a text-dependent transformer-based machine learning approach. Our research shows promising results improving on the existing work that utilized textual features for the task, while simultaneously introducing a new dataset acquired from a commercial video game. To our knowledge, this is the first time text-dependent speaker identification was performed in the domain of video games.
Path Planning of Six-Axis Manipulator Based on ROS System

In order to make the six axis manipulator smoothly cross the obstacles and reach the target position, the ROS robot operating system is used to plan the path of the robot arm. This planned model uses SolidWorks to create a six-degree-of-freedom robotic arm prototype model. Configure the robotic arm motion planning toolkit through the C++ related API interface provided by the Moveit! toolkit, then based on the RRT algorithm, plan the path of the robot arm to obtain the optimal path trajectory, and finally, it is concluded that the speed of each joint is relatively stable during the moving process of the robot arm from the initial pose to the target pose, meeting the requirements of general robot arm path error. This provides a certain theoretical reference value for the platform to control the virtual machine or realize the motion of the physical robot arm.
Remote Learning of Speaking in Syntactic Forms with Robot-Avatar-Assisted Language Learning System

To help second language (L2) learners acquire oral communication skills, dialogue-based computer-assisted language learning (DB-CALL) systems are attracting more interest than ever. When robot-assisted language learning (RALL) is used for realizing such systems, L2 learners are provided with a sense of reality and tension similar to that in a real L2 conversation. At the same time, there are increasing demands for remote learning, accelerated in part by the spread of the novel coronavirus. We have therefore developed a robot-avatar-assisted language learning system that simulates a trialogue in English with two robot avatars and a learner for remote learning. The conversation scenarios deal with various daily topics to keep the learner’s interest and the system prompts the learner to acquire oral skills by using specific syntactic forms in conversation. We conducted a six-day remote learning experiment with ten Japanese university students to evaluate the learning effect, using eye gaze as an index of the learners’ degree of concentration. Our findings demonstrated the effectiveness of our system for remote learning and showed that the learners’ eye gaze activities changed between question answering and repeating tasks.
Influence of a Set of Variables on a Boolean Function

The influence of a variable is an important concept in the analysis of Boolean functions. The more general notion of influence of a set of variables on a Boolean function has four separate definitions in the literature. In the present work, we introduce a new definition of influence of a set of variables which is based on the auto-correlation function and develop its basic theory. Among the new results that we obtain are generalizations of the Poincaré inequality and the edge expansion property of the influence of a single variable. Further, we obtain new characterizations of resilient and bent functions using the notion of influence. We show that the previous definition of influence due to Fischer et al. [Proceedings of the 43rd Symposium on Foundations of Computer Science (FOCS 2002), Vancouver, BC, Canada, IEEE Computer Society, 2002, pp. 103–112] and Blais [Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, M. Mitzenmacher, ed., Bethesda, MD, ACM, 2009, pp. 151–158] is half the value of the auto-correlation based influence that we introduce. Regarding the other prior notions of influence, we make a detailed study of these and show that each of these definitions does not satisfy one or more desirable properties that a notion of influence may be expected to satisfy.
Dynamic Hyperparameter Allocation under Time Constraints for Automated Machine Learning

Automated hyperparameter optimization (HPO) is a crucial and timeconsuming part in the automatic generation of efficient machine learning models.Previous studies could be classified into two major categories in terms of reducing training overhead: (1) sampling a promising hyperparameter configuration and (2) pruning non-promising configurations.These adaptive sampling and resource scheduling are combined to reduce cost, increasing the number of evaluations done on more promising configurations to find the best model in a given time.That is, these strategies are preferred to identify the best-performing models at an early stage within a certain deadline.Although these time and resource constraints are significant for designing HPO strategies, previous studies only focused on parallel exploration efficiency using resource awareness.In this study, we propose a novel diversification strategy for HPO, which exploits the dynamic hyperparameter space allocation for a sampler according to the remaining time budget.We provide a simple yet effective method to accelerate the maturity of the sampler that is independent of the sampling algorithm.Compared to previous resource awareness solutions, our solution achieves better performance via both time and resource awareness.We demonstrate the performance gains of our solution on several well-known HPO benchmarks.Furthermore, we implement them to our highperformance computing AI convergence platform.Considering the different types of users, both a fully automated HPO service based on graphic processing unit (GUI) interfaces and HPO job management via python application programming interface (API) on the Jupyterlab are served on the platform, publicly.
SandSlide: Automatic Slideshow Normalization

Slideshows are a popular tool for presenting information in a structured and attractive manner. There exists a wide range of different slideshows editors, often with their own proprietary encoding that is incompatible with other editors. Merging slideshows from different editors and making the slide design consistent is a nontrivial and time-intensive task. We introduce SandSlide, the first system for automatically normalizing a deck of slides from a PDF file into an editable PowerPoint file that adheres to the default slide templates, and is thus able to fully leverage the flexible layout capabilities of modern slideshow editors. SandSlide achieves this by labeling objects, using a qualitative representation to find the most similar slide layout and aligning content from the slide with this layout. To evaluate SandSlide, we collected and annotated slides from different slideshows. Our experiments show that a greedy search is able to obtain high responsiveness on supported and almost supported slides, and that a significant majority of slides fall into this category. Additionally, our annotated dataset contains fine-grained annotations on different properties of slideshows to further incentivize research on all aspects of the problem of slide normalization.
Method of Mathematical and Geoinformation Models Integration Based On Unification of the Ecological Data Formalization

The scientific and methodological basis for the integration of mathematical and geoinformation models based on the unification of the formalization of environmental data has been developed. The ways of integration of the theoretical base of mathematical modeling from information sources about the state of the environment and the processes of its pollution are determined. An algorithm for selecting a modeling object, an algorithm for selecting the rules of recalculation of coordinates, and positioning of the calculation grid on the object are proposed. Systematization and formalization of the main components, quantities, and variables of mathematical models of processes, geoinformation models of systems, and relational models of databases are carried out. The model of relational databases and analogs of methods of data representation in mathematical and geoinformation models are offered. The stages of solving the problem of automating the exchange of data between models of different types in several stages are proposed. Variants of setting problems of automated synthesis of models of different types, depending on the identity of the structure and parameters of these models are considered. The stages of the automated creation of a database management system are determined. The approbation of the developed software is considered on a model example.
Facial depth descend: A generation paradigm for facial depth map

Over the past decades, three-dimensional(3D) face recognition has developed rapidly due to its intrinsic invariance to pose and illumination changes. Yet despite this, deep learning is rarely used in 3D face recognition due to lack of training dat a. Noting that the majority of the recognition processes are based on facial depth map instead of real 3D data, we focus on the generation of facial depth map to surmount the shortage of training data. In this paper, we present a novel paradigm, Facial Depth Descend, which generates facial depth map from existing 3D face data. The key to this generation paradigm is to recombine facial components from existing faces and thereafter generate brand new faces based on them. We then propose a learning framework RPC to generate recognition-friendly faces. First, it extracts three facial components (eyes, nose and mouth) from the 3D data of real faces. Then, with these components as input, a Relative Location Estimator (RLE) is used to predict the relative location among them so that they can be composed in a reasonable manner. Finally, the method feeds a Pix2Pix network with these composed facial components to extrapolate areas surrounding them and output a full facial depth map. We here enforce an identity preserving loss on the generation network to make the facial depth map more discriminative and favorable for recognition tasks. Since we can choose different identities’ different components as input, RPC is theoretically able to generate a vast number of novel 2.5D faces. More specifically, RPC can generate an enlarged face dataset as large as N 3 identities, where N is the number of identities in the original one. Experiments show that, adding the generated depth maps to the training dataset can help improve the recognition rate in 2.5D face recognition.
How flexible must a transformation approach for variability models and custom variability representations be?

A plethora of variability modeling approaches has been developed in the last 30 years. Feature modeling approaches are probably the most common and well-known approaches. All existing variability modeling approaches have their benefits and drawbacks and have been shown to be useful at least in certain use cases. Nevertheless, industry frequently develops their own custom solutions to manage variability because they struggle picking an approach from the (still growing) number of modeling approaches available. Therefore, we work towards a transformation approach, which enables researchers and practitioners alike to compare different (custom) variability modeling approaches and representations and switch in between them at least (semi-)automatically. In this paper, we discuss ongoing challenges for the transformation approach regarding the implementation of the transformations and the expected flexibility of the approach. We present our research agenda towards a flexible and adaptable transformation approach for well-known variability modeling approaches and custom variability representations used in industry.
Learner Model of Intelligent Tutoring System Based on Bayesian Network

The main importance of the education system is the intelligent tutoring system (ITS). An ITS is a computer system that wants to give immediate and modified instructions or feedback to learners, usually without the intervention of a teacher. In the ITS, the terms of acquiring skills and knowledge use the technology of artificial intelligence to bring a lot of help to the learners. In this process, human instructors are not essential to contribute to the organization, to overcome this problem it has been used as Bayesian Network (BN). The beginner learner model is the heart of an ITS. The level of understanding of the ITS can be greatly improved by using a BN with high self-learning potential to build an ITS for the beginner concept. The fundamental theory of an ITS for the beginner concept will be mainly discussed. Then, at this stage, the elements of impact on the learning method of the learners are studied from the perception of the expertise in the teaching of the beginner, mutual with the state of learning and the characteristics of the beginner. Based on the BN the correct probability interval for learners to answer four classified stages like stage U are 0.8 ~ 1.0, stage V are 0. 7 ~ 0.8, stage W are 0.4–0.6, and stage X are 0.1–0.3. The results of the evaluation confirm that the system has a strong analytical capacity. Finally, an ITS for the beginner concept is developed based on the BN. This model can assess the psychological limit of the beginning learner impartially and can conclude the next activity of the beginning learner. Furthermore, the representation is also adapted for e-learning assessment and assessment outcomes are achieved to demonstrate the effectiveness of the modified model.
Self-Efficacy In Relation To Students’ Reading Comprehension

Abstract In this globalization era, students must have the ability to read because reading is the key to student success in learning. Reading has become the language skills to be acquired by the students to support their learning. Reading is quite hard to do for some students because it needs concentration and a supporting environment. Nowadays, students are only read to find specific information and ignore the other information. Many different factors are influencing students in reading, such as students’ self-efficacy, the belief that people can do some tasks at different levels of knowledge and skills. It takes main roles in the academic achievement and reading comprehension of the students. Therefore, this article examines the relationship of self-efficacy and students’ reading comprehension. Keywords: reading comprehension, self-efficacy, reading self-efficacy
The effect of SNS uses on the Formation of Guanxi among Chinese college students

이 연구에서는 중국 이용자의 SNS 이용 동기를 살펴보고 이용행위, 상호작용 요인이 꽌시에 어떻게 영향을 미치는지 분석한다. 이를 위해 SNS 이용 동기, 이용 행위, 상호작용 요인이 꽌시(關係) 형성에 어떻게 영향을 미치는지 검토했다. 요인 분석 결과, SNS 이용 동기는 "자기 표현", "정보 획득", "오락 및 휴식", "학습", "관계 유지", "현실 도피"의 6개 차원으로 구성됨이 확인되었다. 또한 중국 대학생의 꽌시 형성에 영향을 미치는 변인들 중, 자기 표현, 정보 획득과 현실 도피와 이용 행위들 중엔 남긴 댓글 수, 그리고 상호작용 요인들 중엔 지각된 친밀감이 확인되었다. 이 연구는 코로나19로 인해 대면접촉이 제한되는 가운데 SNS가 사람들의 관계 형성에서 어떠한 역할을 하는지 확인했다는 점에서 연구의 의의를 갖는다. This study aims to examine Chinese users" motives for using SNS and to find out how usage behaviors and interaction factors affect Quanxi. Specifically, how motivations for using SNS, using behaviors, and interaction factors affect the formation of Guanxi was investigated. The factor analysis, revealed six dimensions of SNS use motivatioins; "self-expression", "information acquisition", "entertainment and leisure", "learning," "social interaction", and "escapism". It has been confirmed that among the motivations self-expression, information acquisition and escapism, among the using behaviors writing comments, and among the interaction factors perceived intimacy predicted the formation Guanxi. The study is significant in that it identified how SNS affects people"s interpersonal relationship in the absence of face-to-face contact due to COVID-19.
Multi-scale Feature Fusion in Wireless Propagation Model Optimization Algorithms

In order to address the effects of various environmental variables on radio propagation and improve the quality of radio propagation. Inspired by the traditional empirical classification method and based on an artificial environment, this paper improves the experiments on the path well prediction results of the wireless propagation model according to the optimization algorithm with satellite remote sensing images as the input data of the network, and selects RESNet50 with the highest recognition efficiency by comparing the experimental data of eight deep learning networks such as VGG16 and RESNet50 under the self-built wireless transmission environment. By adding a feature pyramid to the network, the recognition accuracy of environmental variables reaches 96.4%, and the recognition time is 1.29 seconds per 1000 samples.
Meta-path based social event recommendation

Event based social networks (EBSNs) provides users with online to offline social services. Users can organize events online and participate in them offline. Particularly, social event recommendation is a hot issue in this field. The existing studies have mainly focused on factors-based or graph-based social event recommendation, which can hardly exploit the semantic information in the social networks thoroughly. To address this problem, a meta-path based social event recommendation method is proposed. The meta-path contains rich semantic information, being an important feature of the heterogeneous information networks. Firstly, a heterogeneous network is constructed, and then various candidate meta-paths are utilized to calculate the correlation matrix between the users and events. On this basis, the weight of each meta-path is solved by optimization method. Accordingly, the social event recommendation is realized in terms of the linear combination of correlation matrix and meta-path weights. The algorithm is validated on the real dataset of Meetup, the most popular event-based social network platform. Meanwhile, various metrics are explored to compare the recommendation methods. The experimental results show that the proposed algorithm outperforms the comparative ones.
Security For Autonomous Vehicle Using Face Recognition for Smart Environment

As we as a whole realize that self-sufficient vehicle growing increasingly more step by step and by fostering the innovation the security issue is that the principle concern with respect to self-ruling vehicles. When a self-ruling vehicle is designed to assemble and store client logs, AI enters the picture to distinguish any irregularities. An assault identification model might be a spot equipped for discovering a signs and fix information got from the skin world through a web organization or ports inside the vehicle. These calculations is wont to identify unusual exercises, correspondence conduct, or undesirable orders like enact the leaving mode while the vehicle is on a street. In this paper, I examine about the ensure of keen vehicle. In which I utilized face acknowledgment calculation utilizing haa rcsacade classifier. we acclimated store the data of different clients or who can go into the vehicle .so that we guarantee about the one that need to go into the vehicle .so that in a specific case, we've all the connected data with respect to the individual .it can keep away from the instance of unapproved access and undesirable vehicles control and In future it will be more worry about the insurance of self-governing vehicle.