Reviewerly/exHarmony huggingface dataset:
E-Learning Course Design Concept for Engineering Students on the Basis of Competence Approach

In the current pandemic situation distance learning has become the most important for learning and knowledge was the only possible way of education during the quarantine. The issue of distance learning implementing for the engineering specialties students which during their training, must acquire applied (professional, special) competencies, be able to perform the relevant types of professional activity is particularly important for now. These students mainly acquire competencies aimed at solving subject-mental and symbolic-practical problems. To develop an effective online course (OC), the research results presented in the article obtained on the basis of the use of such general scientific tools as a hypothetical-deductive method and bases of the activity-based approach in the understanding of competencies and the learning process. The article considers developing an online course in a fundamental professional academic course based on a competency-based approach. In order to test the concept (hypotheses) provisions the online course on fundamental professional academic course (FPAC) was developed.
Linguistic multi-attribute group decision-making method based on similarity measurement of cloud model

As for the defects of the existing cloud similarity, a novel similarity measurement approach based on the location and shape similarity is proposed and applied to linguistic multi-attribute group decision-making in this paper. Firstly, the framework of integrated similarity measurement of cloud model is established by combining the location and shape similarity. The advantages of the new method are analyzed on comparison with other methods. Then by converting the linguistic information to cloud model, the proposed similarity measurement is used to linguistic multi-attribute group decision-making problem. And an improved TOPSIS method is proposed. Finally, the feasibility and effectiveness of the proposed method are verified by numerical example and comparative analysis.
Augmented Reality for Managerial Tasks: Review and Implications for Engineering and Operations Management

Augmented Reality (AR) has advanced rapidly, and a breadth of applications can be found in industry. While research suggests that these tools are effective in improving operational or worker performance, applications related to engineering and operations management functions, such as monitoring work practices and process control, are less common and reportedly experience additional challenges due to the nature of these tasks. This article reviews research focused on AR applications for management-related tasks exploring progress in this area, identifying key challenges, and defining opportunities for future research. Augmented Reality technology has the potential to support engineering and operations management by creating Immersive Performance Environments; however, there are many challenges to the successful adoption of this technology that must be overcome to bring the potentially powerful benefits of this technology to practice.
In-Depth Opinion Mining and Sentiment Analysis of Twitter Data

In today’s high-tech world, opinion mining and sentiment analysis have become a major topic that is growing day by day and is utilized in many real-world applications. Nowadays, instead of gathering feedback from friends and family for purchasing a specific item, we determine the opinions of various individuals across the world by microblogging data. A very popular social media platform where we can express our opinions and interact with other people is Twitter. The tweets posted on Twitter based on their emotional content can be categorized into positive, negative, or irrelevant with the help of various classifying algorithms. In this paper, a sentiment classifier has been trained which can be used in accomplishing various tasks like social media monitoring customer support management and analyzing customer feedback.
An Improved Recommendation Model Based on Matrix Factorization

Matrix factorization techniques has become a dominant methodology within collaborative filtering recommenders. It has proven to be efficient in recommender systems when predicting user preferences from known user-item ratings. One of the matrix factorization benefits is very good at discovering the potential interests of the users. In a movie recommendation system, a common phenomenon is often to recommend you some movies that you have already seen. Unlike music recommendations, users may listen to the same music repeatedly, but they hardly watch the same movie repeatedly. Recommending a lot of movies to a user that user has already seen is a serious problem. In this paper, we improve the Latent Factor Model to reduce this situation. At the same time, high-quality films increase the chances of being recommended. Finally, we introduce how to build the movie recommendation system with Apache Spark. The model we proposed reduced the problems described above by reducing the prediction score of popular films and increasing the prediction score of high-quality films which can improve user experience.
High-efficient Semantic Segmentation for Internet-of-Things-enabled Smart Cameras

Even in the era of the Internet of Things (IoT), existing smart cameras still cannot efficiently run complex neural networks of image segmentation due to restricted storage and computing capacities. To address this issue, we proposed a high-efficient semantic segmentation for IoT-enabled smart cameras. The experiment results show that the accuracy of the proposed semantic segmentation model can improve the classwise accuracy by 4%, given that the model is reduced by 12.5% and even runs faster than prior studies.
Cryptanalysis of Two White-Box Implementations of the SM4 Block Cipher

The SM4 block cipher has a 128-bit block length and a 128-bit user key, formerly known as SMS4. It is a Chinese national standard and an ISO international standard. White-box cryptography aims primarily to protect the secret key used in a cryptographic software implementation in the white-box scenario that assumes an attacker to have full access to the execution environment and execution details of an implementation. Since white-box cryptography has many real-life applications nowadays, a few white-box implementations of the SM4 block cipher has been proposed, in particular, in 2009 Xiao and Lai presented the first white-box SM4 implementation based on traditional way, which has been attacked with the lowest currently published attack complexity of about \(2^{32}\) using affine equivalence technique; and in 2020 Yao and Chen presented a white-box SM4 implementation based on state expansion, and got the lowest attack complexity of about \(2^{51}\) among a variety of attack techniques. In this paper, we present collision-based attacks on Yao and Chen’s and Xiao and Lai’s white-box SM4 implementations with a time complexity of about \(2^{23}\) for recovering a round key, and thus show that their security is much lower than previously published.
Changes and Challenges: a Study on the Application of Artificial Intelligence Technology in College English Teaching

The artificial intelligence technology has experienced a relative long-time development and exerted a great influence in the fields of data, communication and computing. It is not only the inevitable trend of future education development but also the internal demand of education to take advantage of artificial skills and technology in education. College English teaching is also facing changes and challenges under this condition. On the one hand, artificial intelligence technology promotes the realization of personalized and precise education. On the other hand, there still appear many problems when artificial intelligence technology are taken use of in teaching. Schools, teachers and students are expected to make joint efforts to explore a scientific and reasonable model to put artificial intelligence into college English teaching practice.
Speech to Text System for Noisy and Quiet Speech

Abstract This paper examines one of the available and simple methods to develop speech recognition systems capable of recognizing speech from noisy or silent recordings. Such systems improve the automated operation of call centers, and also bring us closer to creating speech recognition models capable of ignoring the speech deficiencies of speakers.
Visual Navigation via Reinforcement Learning and Relational Reasoning

We, as humans, can quickly navigate to localize a target object, even in novel scenes. We argue that the ability is mainly due to the incorporation of a scene prior knowledge and visible object-relational reasoning. In this study, we propose a target-driven model by incorporating ground truth prior knowledge to navigate in a human-inspired way. We realize this approach by constructing a scene graph, which provides scene object spatial relationships through object relation detection. To imitate human reasonability, we propose to incorporate the prior with Markov model for predicting next best action and concatenate it into policy network which improves model generalization in seen and unseen scenes. Moreover, we perform experiments on the AI2THOR virtual environment [1] and outperform the current most state-of-the-art both in success rate and SPL (Success weighted by Path Length) on average.
Emergence of biased errors in imperfect photonic circuits

We numerically investigate the impact of imperfections and biased errors in integrated photonic circuits. Our analysis connects and deepens previous studies along the same direction, revealing potential criticalities for high-precision tests and optical implementations of machine learning.
Linear-time approximation scheme for k-means clustering of axis-parallel affine subspaces

In this paper, we present a linear-time approximation scheme for k-means clustering of incomplete data points in d-dimensional Euclidean space. An incomplete data point with Δ>0 unspecified entries is represented as an axis-parallel affine subspace of dimension Δ. The distance between two incomplete data points is defined as the Euclidean distance between two closest points in the axis-parallel affine subspaces corresponding to the data points. We present an algorithm for k-means clustering of n axis-parallel affine subspaces of dimension Δ that yields an (1+ϵ)-approximate solution in O(nd) time. The constants hidden behind O(⋅) depend only on Δ,ϵ and k. This improves the O(n2d)-time algorithm by Eiben et al. (2021) [7] by a factor of n.
Non-Interactive VDF Client Puzzle for DoS Mitigation

Denial of Service (DoS) attacks pose a growing threat to network services. Client puzzles have been proposed to mitigate DoS attacks by requiring a client to prove legitimate intentions. Since its introduction, there have been several constructions of client puzzles. Nevertheless, most of the existing client puzzles are interactive, where a server constructs a puzzle for a client request and asks the client to solve it before giving access to a resource. Additionally, most existing client puzzles do not provide desirable properties such as fairness, non-parallelizability, or non-interactivity. In this work, we propose a non-interactive client puzzle that achieves all these desired properties through a verifiable delay function (VDF). In a non-interactive puzzle, the client generates a puzzle and sends its solution along with the puzzle to access a resource of the server. We present different methods to generate verifiable client puzzles to prevent puzzle forgery and attacks from the client side. Further, we exhibit a transformation of the client puzzle into a DoS-resistant protocol. We also demonstrate the applicability of the DoS-resistant protocol in different contexts of the blockchain ecosystem.
Deep Learning for Safe Human-Robot Collaboration

Recent advances in computer vision and deep learning have lead to implementations in different industrial applications such as collaborative robotics, making robots able to perform harder tasks and giving them consciousness of their environment, easing interaction with humans. With the objective of eliminating physical barriers between humans and robots, a security system for industrial collaborative robots based on computer vision and deep learning is proposed, where an RGBD camera is used to detect and track people located inside the robot’s workspace. Detection is made with a previously trained convolutional neural network. The position of every detection is fed to the tracker, that identifies the subjects in scene and keeps record of them in case the detector fails. The detected subject’s 3D position and height are represented in a simulation of the workspace, where the robot’s speed changes depending on its distance to the manipulator following international safety guidelines. This paper shows the implementation of the detector and tracker algorithms, the subject’s 3D position, the security zones definition and the integration of the vision system with the robot and workspace. Results show the system’s ability to detect and track subjects in scene, and the robot’s capacity to change its speed depending on the subject’s location.
Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network

Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm, called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the “dagger technique”. First, the paper proposes to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for making good predictions in text classification tasks. Next, a greedy search algorithm, called the Backward Dropping Algorithm, is proposed to handle long-term dependencies in the dataset. Moreover, the paper proposes a novel engineering technique called the “dagger technique” that fully preserves the relationship between the explanatory variable and the response variable. The proposed techniques can be further generalized into any feed-forward Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), and any neural network. A real-world application on the Internet Movie Database (IMDB) is used and the proposed methods are applied to improve prediction performance with an 81% error reduction compared to other popular peers if I-score and “dagger technique” are not implemented.
Research on Application of Somatosensory Interactive Technology in Teaching

As a new human–computer interaction technology, somatosensory interaction technology enables people to reach a new height in the way of controlling multimedia. People do not need to directly touch the equipment, but operate multimedia equipment through natural language such as body movements. At present, somatosensory technology has some frontier explorations and experiments in the field of teaching applications. It is used in the classroom as a teaching tool to make up for the shortcomings of the traditional teaching model. It can not only help teachers create a more realistic teaching environment, but also optimize the sense of experience of students. This article focuses on the teaching application cases of LeapMotion equipment development and design and analyzes the form of somatosensory interaction technology used in teaching, and comparisons are made in terms of teaching environment, teaching methods, teaching subjects, and teaching content. It also provides ideas for the subsequent research on somatosensory technology in teaching.
The Optimization of Classroom Teaching in Colleges and Universities Based on Network Topology

In order to improve the teaching effect of programming courses, this article applies network topology to the construction of programming course teaching platform system and constructs a teaching framework combined with intelligent teaching mode. In addition, this article takes the opportunistic social network composed of mobile smart terminal devices as the application background and briefly introduces the concept, characteristics, and application scenarios of the opportunistic social network. In addition, the typical message forwarding algorithm currently studied in this paper is analyzed. Taking into account the social attributes and forwarding willingness of nodes, in order to increase the message transmission rate and reduce message transmission delay and network overhead, two message transmission strategies are proposed to improve the operating effect of the programming course teaching platform. Finally, this paper verifies the effectiveness of the network topology in the teaching of the algorithm and programming of the intelligent teaching mode through experimental research.
Generating Team Quality Formula to Predict Product Quality in Software Engineering Project of College Students

In the CHAOS report 2020 released by the Standish Group, “a good team” is an essential factor in determining the success of a project. Therefore, team quality assessment becomes significant for team management. Furthermore, it has believed that the quality of the product developed represents the quality of the development team. For this reason, it is necessary to measure the quality of the team at any point, so we can take mitigation steps to improve the quality. The study has two aims: (1) to build a team quality measurement model to prove that team quality positively correlates with product quality and (2) to produce a team quality formula to predict product quality. We conducted the study on ten teams with 57 students who attended the Software Development Workshop class. The students applied self-assessment to assess the quality of the team. In addition, software development practitioners carried out product quality assessments. The method used to obtain a relationship between the two is the Pearson correlation. We generated a team quality measurement formula using the Brute Force method. This study proves that the quality of the development team has a positive correlation with the quality of the product developed by 0.87. Meanwhile, the team quality formula can be used as a reference to predict the quality of the product developed
Asymmetric Error Control Under Imperfect Supervision: A Label-Noise-Adjusted Neyman–Pearson Umbrella Algorithm

Label noise in data has long been an important problem in supervised learning applications as it affects the effectiveness of many widely used classification methods. Recently, important real-world applications, such as medical diagnosis and cybersecurity, have generated renewed interest in the Neyman–Pearson (NP) classification paradigm, which constrains the more severe type of error (e.g., the Type I error) under a preferred level while minimizing the other (e.g., the Type II error). However, there has been little research on the NP paradigm under label noise. It is somewhat surprising that even when common NP classifiers ignore the label noise in the training stage, they are still able to control the Type I error with high probability. However, the price they pay is excessive conservativeness of the Type I error and hence a significant drop in power (i.e., 1 - Type II error). Assuming that domain experts provide lower bounds on the corruption severity, we propose the first theory-backed algorithm that adapts most state-of-the-art classification methods to the training label noise under the NP paradigm. The resulting classifiers not only control the Type I error with high probability under the desired level but also improve power.
A Novel Two-Stage Game Model for Pricing Cloud/ Fog Computing Resource in Blockchain Systems

Cloud/ fog computing resource pricing is a new paradigm in the blockchain mining scheme, as the participants would like to purchase the cloud/ fog computing resource to speed up their mining processes. In this paper, we propose a novel two-stage game to study the optimal price-based cloud/ fog computing resource management, in which the cloud/fog computing resource provider (CFP) is the leader, setting the resource price in Stage I, and the mining pools act as the followers to decide their demands of the resource in Stage II. Since mining pools are bounded rational in practice, we model the dynamic interactions among them by an evolutionary game in Stage II, in which each pool pursues its evolutionary stable demand based on the observed price, through continuous learning and adjustments. Backward induction method is applied to analyze the sub-game equilibrium in each stage. Specifically in Stage II, we first build a general study framework for the evolutionary game model, and then provide a detailed theoretical analysis for a two-pool case to characterize the conditions for the existence of different evolutionary stable solutions. Referring to the real world, we conduct a series of numerical experiments, whose results validate our theoretical findings for the case of two mining pools. Additionally, the impacts from the size of mining block, the unit transaction fee and the price of token on the decision makings of participants are also discussed.
A Novel Method for Analyzing Best Pages Generated by Query Term Synonym Combination

A lot of concern is presently given on developing best pages w.r.t requirements of a user. Even a normal search engine results are not that convincing when it comes to user finding very specific information. The other eye raising aspect is the return of too many pages bringing more confusion into the scene. To address the problem of finding the best page, a method of using similar meaning words forming terms in the query will definitely help. A collection of such semantically similar words when delivered by a user and fired to a search engine has proved to return more relevant and less number of pages. In a number of cases, it has given better pages than an individual search engine returned for a query. The present paper concentrates on novel approaches to analyze these pages generated by combining the crossed synonyms of the query terms given by a user and sending these combinations to a search engine. The pages returned when analyzed by novel techniques are found to be fewer, more relevant, and at a better ranking as compared to those returned by individual search engines.
EcoBot: Virtual assistant for e-commerce of ecological bricks based on Facebook Messenger

The number of buyers in e-commerce is around 1500 million, which represents 63% of Internet users, a percentage that has been growing and taking relevance in all sectors, particularly in the construction industry, which represents 10% of world GDP and 7% of employment, with brick being the product that is most consumed, specifically, the ecological brick is the one that has been gaining importance, due to its low negative impact on the environment. However, the lack of guidelines to facilitate the purchase, factors such as mistrust and the need to see the product, and the timely visualization of sales indicators for management, limit its growth. In this work, EcoBot is proposed, a chatbot with Facebook Messenger to make more attractive and reliable the e-commerce of ecological bricks, in addition, a dashboard on sales indicators, for making quick and timely decisions. A case study in a brick company in Lima shows an increase of 14% of transactions, 15% in sales in the first use of EcoBot, in addition, a survey of 42 customers shows that usability and user experience is very positive.