Reviewerly/exHarmony huggingface dataset:
Development of Product Assembly Instructional Video for training purpose

Abstract This work is focused on the development of effective Product Assembly Instructional Videos. Product assembly instructional video is an important element for training, and boost the skills of employees by laying virtual information of any complex assembly. The instructional video is aimed to train employees on “How the product is assembled” with detailed steps to be followed during assembly and with instructional voice-over n a rendered video. It is found from the literature that traditional training often consumes more time and resource for any organization and frequent changes in manpower makes it more difficult for them to spend adequate time to train. So, the instructional videos are helpful to overcome such problems. The method used in this research paper is to develop a framework for product assembly instructional videos. The research s extended via a case study where n these videos are used for training and discuss with the practitioners regarding how they review this model. This paper concludes with a framework for product assembly instructional videos and describing various factors to consider in the creation of industrial training videos. The work also used various software which includes CAD tools, voice recording, impending, and video editing tools to create a training video manual.
Comparative Study of Optimization of Plate Fin Heat Exchanger and Pressure Vessel Design using mTLBO Algorithm

Abstract Teaching Learning Based Optimization (TLBO) algorithm has been proved beneficial in many engineering applications. This algorithm is free from any algorithm specific parameters and can be adapted to all types of design problems. However, there are some drawbacks like convergence to local optimal solution, large computational time and slow convergence rate for complex functions. Some modifications were introduced to overcome these drawbacks in modified Teaching Learning Based Optimization (mTLBO) algorithm. In this paper mTLBO has been applied to optimize plate fin heat exchanger and pressure vessel design. The performance of mTLBO algorithm was compared with original algorithm and other population based techniques such as Particle Swarm Optimization, Generic Algorithm and Artificial Bee Colony. It was found that mTLBO gives the least value of entropy generation units that is 7.22% less than the value obtained using TLBO. Also cost of pressure vessel design using mTLBO is 3.2% lower than that of TLBO design.
Decentralized Collision Avoidance and Motion Planning for Multi-Robot Deformable Payload Transport Systems

We present a decentralized motion planning and collision avoidance algorithm for multi-robot payload transport systems (PTS). A PTS is a formation of loosely coupled non-holonomic robots that cooperatively transport a deformable payload. Each PTS is constrained to navigate safely in a dynamic environment by inter-formation, environmental, and intra-formation collision avoidance. Real-time collision avoidance for such systems is challenging due to the deformability of formations and high dimensional multi-robot non-convex workspace. We resolve the above challenges by embedding workspaces defined by a multi-robot collision avoidance algorithm and multi-scale repulsive potential fields as constraints within a decentralized convex optimization problem. Specifically, we present two main steps to plan the motion of each formation. First, we compute collision-free multi-scale convex workspaces over a planning horizon using a combination of ORCA and repulsive potential fields. Subsequently, we compute the motion plans of formation over a horizon by proposing a novel formulation for collision avoidance, and we leverage a model predictive controller (MPC) to solve the problem. The results validate that our solution facilitates real-time navigation of formations and computationally scales well with an increase in the number of robots and formations used. The algorithm is validated through extensive preliminary simulations, experiments in the gazebo simulator, and a proof of concept using real robots.
WITHDRAWN: Integrated feature selection (IFS) algorithm and enhanced weight based convolutional neural network (EWCNN) for social emotion classification

To design and implement IFS-EWCNN for improve the performance of social emotion classification for twitter reviews. Design a new Integrated Feature Selection (IFS) algorithm, which combines filter and wrapper methods procedure for evaluating semantic gap among source features produced using teaching model and target task characteristics before the application of sparse encoding to transfer learning. Filtering algorithm follows the procedure of the information gain and Pearson’s Correlation. In the wrapper selection algorithm follows the procedure of the Expectation Maximization with Forward Search (EMFS), and Exhaustive feature selection. Then implemented a novel Animal Migration Optimization based Sparse Encoding (AMO-SC) for forming dense high-level features by transforming sparse low-level features and further investigation is required on effectiveness of this method on emotion classification. Finally, for classifications of emotions, proposed an Enhanced Weight based Convolutional Neural Network (EWCNN). It concludes that proposed IFS-EWCNN classifier achieves better performance compared with the existing methods in terms of f-measure, specificity, sensitivity, recall, precision and accuracy. The experiments are carried out in SemEval 2016 Task 4A for sentiment and SemEval 2018 Task 1C emotion classification using Matlab tool. The proposed IFS-EWCNN classifier approaches accurately classify the social emotions.
Further study of planar functions in characteristic two

Planar functions are of great importance in the constructions of DES-like iterated ciphers, error-correcting codes, signal sets and mathematics. They are defined over finite fields of odd characteristic originally and generalized by Y. Zhou [28] in even characteristic. In 2016, L. Qu [23] proposed a new approach to constructing quadratic planar functions over F2n. Very recently, D. Bartoli and M. Timpanella [4] characterized the condition on coefficients a,b such that the function fa,b(x)=ax22m+1+bx2m+1∈F23m[x] is a planar function over F23m by the Hasse-Weil bound. In this paper, using the Lang-Weil bound, a generalization of the Hasse-Weil bound, and the new approach introduced in [23], we completely characterize the necessary and sufficient conditions on coefficients of four classes of planar functions over Fqk, where q=2m with m sufficiently large (see Theorem 1.1). The first and last classes of them are over Fq2 and Fq4 respectively, while the other two classes are over Fq3. One class over Fq3 is an extension of fa,b(x) investigated in [4], while our proofs seem to be much simpler. In addition, although the planar binomial over Fq2 of our results is finally a known planar monomial, we also answer the necessity at the same time and solve partially an open problem for the binomial case proposed in [23].
A Crypto Safe Haven Against Bitcoin

The design of Bitcoin is closely related to gold which has led to the idea that Bitcoin has gold-like features such as being a store of value and a safe haven. However, given Bitcoin's extreme volatility investors may rather need a safe haven against Bitcoin. We hypothesize that stablecoins provide such a safe haven and analyze high-frequency price changes of the largest stablecoins. We find that they act as safe havens, and Tether showing the strongest effect. The results demonstrate that Bitcoin investors seek out stablecoins when Bitcoin experiences extreme negative prices changes. Since stablecoins react to such price changes they are not stable at all times.
The reform of modern education during the COVID-19 pandemic

Abstract Great changes have taken place in modern education methods under the influence of the COVID-19 pandemic. Information science technology has broken through the time and space limits of education, as well as the boundary between physical and virtual objects in learning, and has converted the process of learning from passive to active. These characteristics just meet the needs of education under the challenges of the COVID-19 pandemic. Therefore, the new education mode which integrates information science technology has been developing rapidly during this period of time. This paper summarizes how the development of modern information science, especially network technology, multimedia technology and virtual reality technology, has influenced modern education. Meanwhile, our efforts and work in regard with digital museum will be introduced in this article.
Fine-grained, aspect-based semantic sentiment analysis within the economic and financial domains

The application of sentiment analysis in financial and economic applications has attracted great attention in recent years. News and social media represent a valuable source of information, that is timely available and potentially able to improve the forecast of economic and financial time series. Despite many successful applications of sentiment analysis in these domains, the range of natural language processing techniques employed is still very limited. In this work, we detail the technical presentation of a fine-grained aspect-based semantic sentiment analysis algorithm and check its performance with respect to a humanly annotated data set. The proposed approach is completely unsupervised and relies on a large custom-specific domain lexicon and on a thorough semantic polarity scheme, allowing a better interpretation and explanation of the analysis. Our method shows promising re-suits, with the proposed algorithm assigning a similar sentiment score as human annotators in the large majority of cases.
Arithmetic mean-based decision-making algorithm for obstacle avoidance and multiple intruder detection

This paper demonstrates a decentralized strategy of an arithmetic mean-based navigation algorithm for a group of mobile robots. The aim is to have them navigate through an unknown environment surrounded by obstacles to detect and follow multiple invading intruders. The suggested navigation strategy ensures that the mobile robots safely manoeuvre around surrounding obstacles and avoid collision. Furthermore, a probability of danger algorithm is introduced to ensure that all the intruders present in the environment are followed by at least one robot when they are observed at any time t *. The mobile robots follow the intruders' movements on the basis of their pixel values using a Microsoft Kinect sensor. A low pixel value means that the intruder is far away, and a high pixel value represents the close proximity of the intruder to the mobile robots. All the algorithms and image processing techniques are implemented and tested in a Webots simulation environment using C programming language. The results show the success of the proposed arithmetic mean-based navigation and probability of danger-based intruder-following algorithm.
Compositional Game Theory, Compositionally

We present a new compositional approach to compositional game theory (CGT) based upon Arrows, a concept originally from functional programming, closely related to Tambara modules, and operators to build new Arrows from old. We model equilibria as a bimodule over an Arrow and define an operator to build a new Arrow from such a bimodule over an existing Arrow. We also model strategies as graded Arrows and define an operator which builds a new Arrow by taking the colimit of a graded Arrow. A final operator builds a graded Arrow from a graded bimodule. We use this compositional approach to CGT to show how known and previously unknown variants of open games can be proven to form symmetric monoidal categories.
Real Time Voronoi-like Path Planning Using Flow Field and A

In this paper we introduce a path planning approach for robotic and related applications to reach a target within a known environment. We improve the flow fields based path planning approach by avoiding typical obstacle hugging behavior. Flow field path planning defines the potential at each node of an environment which allows a large number of agents to traverse the environment while avoiding the task of calculating paths for each individual agent. This approach maintains the same benefit of typical flow field path planning with the addition of avoiding paths that are too close to obstacles. This method can be referred to as Valley Field Path Planning. In the general method, this algorithm takes two major steps. Firstly, the obstacle potential field that results in a low potential in regions farthest from the obstacles. The second step of the algorithm is to find potential provided that the goal node corresponds to a higher potential at nodes farther. The weighted sum of these two potential fields results in a flow field that avoids obstacle hugging. To avoid the local minima problem with this approach, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A^{\ast}$</tex> path planning can been introduced with the benefit of a configurable deterministic path at the cost of calculating the path on a per agent basis. The results show that this combination of an obstacle potential field and the A* search algorithm results in an efficient and inexpensive way to generate a configurable path.
Developing a Model of "Artificial Conscience"

The model of artificial conscience as an element of artificial consciousness is described. Proposed paradigm corresponds to previously developed general scheme of artificial intelligence. The functional role of the artificial conscience subsystem is defined and its software prototype is proposed.
A Hybrid Approach to Enhance Underwater Images

Abstract Underwater pictures are barely visible due to effects of dispersion and absorption. Due to poor visibility it is very difficult to study underwater environment. Many Techniques have been used to increase the quality of degraded underwater images. This research paper follows the approach which has two major steps. First step is color correction based on grey world hypothesis. The second step is dehazing technique which is used to eradicate camera haziness. The approach suggested is ideal for use in real time application.
Analysis of Efficiency and Effectiveness in the Implementation of the Flipped Classroom in the Context of Higher Education

The study and investigation around educational models and teaching and learning methodologies is a theme that has long aroused the interest of the academic environment in higher education, both in the period before the advent of digital technology, as in current times in which technology is strongly embedded in the various teaching and learning processes, which involve classroom and distance-learning classes and courses, both in the context of e-learning and b-learning. Understanding how people learn and understand the themes presented in the classroom in face-to-face and e-learning is fundamental for planning and implementing processes that allow teachers to apply teaching and learning methodologies that can be efficient and effective. The main objective is to carry out a critical reflection on b-learning teaching, about the implementation of the teaching and learning methodology of the flipped classroom, one of the variants of b-learning teaching, supported by the results of a field investigation carried out with 152 students (n=152) of higher education.
The influence evaluation of municipal government Website in Guangxi Zhuang Autonomous Region based on link analysis

This study uses Google chrome as a tool, combined with advanced search styles to retrieve related website links, and uses link analysis methods to comprehensively analyze the influence of 14 prefecture-level municipal government portals in Guangxi Zhuang Autonomous Region. At the same time, the analysis results are sorted by using the gray correlation analysis method through EXCEL. The evaluation results show that the relevance of the government portals of prefecture-level cities in Guangxi is generally between 0.7 and 0.9, and improvement strategies are proposed on this basis.
A Framework for Document Delivery Services in an Open Distance e-Learning Environment Library

AbstractDocument delivery service in an open distance environment is one way of taking library services to remote students. However, this service is not without challenges. For example, problems related to document delivery services may include unclaimed library materials by students, which costs the library money, and ‘return to sender’ parcels from the post office. The purpose of this qualitative study was to investigate the provision of document delivery services in an ODeL environment with reference to the University of South Africa (UNISA). Data were collected through document analysis and interviews with purposively UNISA library staff working at the document delivery section. The findings indicated that UNISA library uses mail, courier and e-learning platforms for the provision of document delivery services. It was established that often, courier companies deliver parcels without contacting the students, which sometimes results in the parcel being received by the wrong recipient or returned to the university. It is concluded that even with the use of modern technology in document delivery services at UNISA, delivered documents still get lost. A framework for implementation of document delivery services that can speed up delivery and minimize loss of materials is suggested.Keywords: Document delivery serviceslibrarymaterialopen distance learningUniversity of South Africalibrary
Classification of negative publication in mass media using topic modeling

Abstract The paper proposes a method for evaluating text documents by arbitrary criteria, combining the topic modeling on the text corpora and multiple-criteria decision making. The evaluation is based on an analysis of the corpora as follows: the conditional probability distribution of media by topics, properties and classes is calculated after the formation of the topic model of the corpora. Weights assigned by experts to each topic along with topic model can be applied to evaluate each document in the corpora according to each of the considered criteria and classes. The proposed method was applied to a corpus of 804829 news publications from 40 Kazakhstani sources published from 01.01.2018 to 31.12.2019, in order to classify negative information on socially significant topics. A BigARTM model was calculated (200 topics) and the proposed model was applied. Experiments confirm the general possibility of evaluating the sentiment of publications using the topic model of the text corpora, since ROC AUC score of 0.93 was achieved on the classification task.
Depression Detection Using Optical Characteristic Recognition and Natural Language Processing in SNS

Web is growing apace in mining concerns using depression detection method form sources such as news, blogs and which also includes product reviews considering the earlier cases the responses given are quite difficult to determine. A technique using Natural Language Processing and Optical Character Recognition. Our experiments are to include that an entity scores that are determined using are statistically different from any other approaches and the quality of the approach is completely independent from any other approaches and scores can be determined reports can be provided for health subscription. Many researchers have demonstrated that by user generated content in a context is a method to determining people's mental health levels. The research is to find out the SNS by user post, which helps in classifying the mental health levels of the user.
Numerical Analysis and Optimization Application Trend of Deepfake Video Inspection Technology

Abstract Science and technology can be a double-edged sword. Novel digital technologies not only allow people to enjoy more digital achievements, but also make it increasingly difficult to distinguish the authenticity of digital information. This review begins with a brief introduction of the deepfake technology, followed by an explanation of the possible hazards of deepfake, and finally, an overview of the detection technology and research status of deepfake.
A Generative Model for Zero-Shot Learning via Wasserstein Auto-encoder

Zero-shot learning aims to use the labeled instances to train the model, and then classifies the instances that belong to a class without labeled instances. However, the training instances and test instances are disjoint. Thus, the description of the classes (e.g. text description or class attribute information) is to establish a connection between the training set and the test set to make the model effective. Since real world image annotation requires a lot of manpower and material resources, this setting is very important in the real world. Zero-shot learning can effectively solve the problem of image annotation. Most of the previous methods explored the mapping between visual space and semantic space. Some recent generative methods attempt to generate image features of unseen classes based on auxiliary information, and have achieved good performances. In this paper, we propose to use Wasserstein Auto-encoder (WAE) as a generative model to establish data distribution. Then the generated samples from the generative model are used to classify unseen classes. We test our model on four benchmark datasets including CUB, SUN, AWA2 and aPY, the results of which demonstrate the effectiveness of our model.
Restore DeepFakes video frames via identifying individual motion styles

Recent advance on highly realistic AI-synthesised video approaches makes it hard to distinguish whether a speaker in video is real. Many existing DeepFakes detection approaches can be invalidated by using them as supervision in adversarial training, which in turn improves the performance of DeepFakes. This makes the problem a dilemma. However, human can recognise the identity of familiar persons by observing their motion styles. Inspired by this, the paper proposes a novel method to recognise original speaker's identity clues in DeepFakes videos by learning individual motion styles. As a biological signature, motion styles can neither be used in the training process of DeepFakes nor be modified to deceive detection methods without reducing realistic performance. Also, this paper proposes a novel pipeline to continuously restore the original frames from DeepFakes videos without knowing the DeepFakes approach in advance. Based on above ideas, our scheme makes it possible to restore DeepFakes videos. Through training the cross-modal transfer module, the appearance embedding can be inferred from the identity code. Extensive experiments are conducted to reveal the effectiveness of the method, it shows for the first time that the original persons can be identified automatically and the original video from DeepFakes videos can be generated.