Reviewerly/exHarmony huggingface dataset:
A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

Purpose Wheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments. Design/methodology/approach Given a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR. Findings The authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments. Originality/value In this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.
Intelligent metasurfaces can recognize objects

An on-chip optical neural network is built using metasurfaces, which can recognize objects with high accuracy.
Combating False Data Injection Attacks on Human-Centric Sensing Applications

The recent prevalence of machine learning-based techniques and smart device embedded sensors has enabled widespread human-centric sensing applications. However, these applications are vulnerable to false data injection attacks (FDIA) that alter a portion of the victim's sensory signal with forged data comprising a targeted trait. Such a mixture of forged and valid signals successfully deceives the continuous authentication system (CAS) to accept it as an authentic signal. Simultaneously, introducing a targeted trait in the signal misleads human-centric applications to generate specific targeted inference; that may cause adverse outcomes. This paper evaluates the FDIA's deception efficacy on sensor-based authentication and human-centric sensing applications simultaneously using two modalities - accelerometer, blood volume pulse signals. We identify variations of the FDIA such as different forged signal ratios, smoothed and non-smoothed attack samples. Notably, we present a novel attack detection framework named Siamese-MIL that leverages the Siamese neural networks' generalizable discriminative capability and multiple instance learning paradigms through a unique sensor data representation. Our exhaustive evaluation demonstrates Siamese-MIL's real-time execution capability and high efficacy in different attack variations, sensors, and applications.
Profiling Hungarian K12 teachers based on their techno-pedagogical skills: State of affairs and development possibilities amid COVID-19

Abstract Covid-19-triggered emergency remote teaching shed light on the discrepancies of the long-desired digital transformation of education. To learn more about Hungarian K12 (primary and secondary) teachers' techno-pedagogical skills, this study aimed to measure how they rate the components of the Technological Pedagogical Content Knowledge (TPACK) framework. The observed mean values gave grounds for clustering Hungarian K12 teachers based on their existing techno-pedagogical skills as well as proposing possible directions for development. It was found that among teachers who participated in the study ( N = 216), 20% belong to the group of Beginners, 40% are Independent, and 40% are Advanced users of techno-pedagogical tools and methods. The groups are rather homogenous as gender, age, qualification and teaching experience are not predictors of techno-pedagogical knowledge. Beginners need help on the very operational levels of technology, Independent users transform traditional teaching methods in the online space, while Advanced users plan face-to-face and online classes differently but also include techno-pedagogy in their everyday classroom teaching practices. It was further observed that teachers in all groups are generally motivated in preparing for their online lessons, but the perceived motivation of their learners is much lower, and teachers do not generally consider online teaching effective.
Lifecycle in CS1

In practice, programmers start with informal requirements proposed by a customer, make the requirements precise, and implement them in a program that meets the customers needs. It is difficult for a CS1 course to convey a sense of this journey to students, since they need to understand the notional machine, technological artifacts, and the informal to formal transition at the same time. In this paper we show how to sequence the teaching of programming ideas in such a way that in a short two-month course, novice students learn to develop simple programming projects from requirements in English to a working program. We start by showing that programs are an extension of school algebra using Pugofer, a pedagogical Haskell-like language. Then we present list comprehensions, which lets us create simple programs with lists and tuples as handy "databases''. Next we teach requirements analysis for information processing situations such as library management, and create programs that represent domain concepts using data, and behaviors using functions and comprehensions. Students follow this process in their own projects. We end by teaching recursion, motivating it as a way to implement comprehensions. We have taught courses in this style three times; each time students have delivered projects as well as standard CS1 exams, and rated the course highly. It has been easy to move to imperative programming in later courses. We believe that our approach can be readily replicated, giving students the benefit of experiencing key elements of the software lifecycle early in their CS education.
A Quantum-Inspired Tensor Network Algorithm for Constrained Combinatorial Optimization Problems

Combinatorial optimization is of general interest for both theoretical study and real-world applications. Fast-developing quantum algorithms provide a different perspective on solving combinatorial optimization problems. In this paper, we propose a quantum-inspired tensor-network-based algorithm for general locally constrained combinatorial optimization problems. Our algorithm constructs a Hamiltonian for the problem of interest, effectively mapping it to a quantum problem, then encodes the constraints directly into a tensor network state and solves the optimal solution by evolving the system to the ground state of the Hamiltonian. We demonstrate our algorithm with the open-pit mining problem, which results in a quadratic asymptotic time complexity. Our numerical results show the effectiveness of this construction and potential applications in further studies for general combinatorial optimization problems.
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR

Graph-based temporal classification (GTC), a generalized form of the connectionist temporal classification loss, was recently proposed to improve automatic speech recognition (ASR) systems using graph-based supervision. For example, GTC was first used to encode an N-best list of pseudo-label sequences into a graph for semi-supervised learning. In this paper, we propose an extension of GTC to model the posteriors of both labels and label transitions by a neural network, which can be applied to a wider range of tasks. As an example application, we use the extended GTC (GTC-e) for the multi-speaker speech recognition task. The transcriptions and speaker information of multi-speaker speech are represented by a graph, where the speaker information is associated with the transitions and ASR outputs with the nodes. Using GTC-e, multi-speaker ASR modelling becomes very similar to single-speaker ASR modeling, in that tokens by multiple speakers are recognized as a single merged sequence in chronological order. For evaluation, we perform experiments on a simulated multi-speaker speech dataset derived from LibriSpeech, obtaining promising results with performance close to classical benchmarks for the task.
Substation Operation Information Maintenance Based on Intelligent Data Mining

In order to solve the problems of redundancy of substation operation information, diversification of monitoring system versions, and heterogeneous data, the author proposes a remote-based intelligent data mining system for substation automation. Use data mining technology to classify and standardize the original alarm data, and associate it with the equipment ledger system to obtain a data warehouse of equipment parameters, status information, and related historical fault information. On this basis, the improved Apriori algorithm is used to extract strong association rules that satisfy the minimum trust threshold from the data warehouse, which provides decision-making basis for the operation and maintenance management of substation equipment. Experimental results show that using the improved Apriori algorithm to perform data mining on the substation operation alarm information in 143 intervals of a power supply bureau substation, the following strong association rules are obtained: association rule 1, “handcart test position ∩ device running alarm ∩ alarm signal has been reset interval debugging and maintenance”, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>γ</mi> </mrow> <mrow> <mtext>support</mtext> </mrow> </msub> <mo>=</mo> <mn>57</mn> <mi>%</mi> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>γ</mi> </mrow> <mrow> <mtext>confidence</mtext> </mrow> </msub> <mo>=</mo> <mn>69</mn> <mi>%</mi> </math> , and association rule 2, “Protection action∩ switch sectional position ∩ reclosing action ∩ total accident (hold) ∩ switch sectional signal has been reset ∩ reclosing action signal has been reset ∩ total accident (hold) signal has been reset Temporary equipment failure”, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>γ</mi> </mrow> <mrow> <mtext>support</mtext> </mrow> </msub> <mo>=</mo> <mn>41</mn> <mi>%</mi> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msub> <mrow> <mi>γ</mi> </mrow> <mrow> <mtext>confidence</mtext> </mrow> </msub> <mo>=</mo> <mn>79</mn> <mi>%</mi> </math> . The effectiveness of the proposed data mining method is verified by a field case.
Optimizing subgraph matching over distributed knowledge graphs using partial evaluation

Abstract The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of partial evaluation: if the volume of intermediate results is large, a large number of repeated partial matches will be generated; and the assembly computation handled by the master would be a bottleneck. In this paper, we propose an optimal partial evaluation algorithm and a filter method to reduce partial matches by exploring the computing characteristics of partial evaluation and assembly framework. (1) An index structure named inner boundary node index (IBN-Index) is constructed to prune for graph exploration to improve the searching efficiency of the partial evaluation phase. (2) The boundary characteristics of local partial matches are utilized to construct a boundary node index (BN-Index) to reduce the number of local partial matches. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.
Network Visualization and Assessment of Student Reasoning About Conditionals

Understanding the thought processes of students as they progress from initial (incorrect) answers toward correct answers is a challenge for instructors, both in this pandemic and beyond. This paper presents a general network visualization learning analytics system that helps instructors to view a sequence of answers input by students in a way that makes student learning progressions apparent. The system allows instructors to study individual and group learning at various levels of granularity. The paper illustrates how the visualization system is employed to analyze student responses collected through an intervention. The intervention is BeginToReason, an online tool that helps students learn and use symbolic reasoning-reasoning about code behavior through abstract values instead of concrete inputs. The specific focus is analysis of tool-collected student responses as they perform reasoning activities on code involving conditional statements. Student learning is analyzed using the visualization system and a post-test. Visual analytics highlights include instances where students producing one set of incorrect answers initially perform better than a different set and instances where student thought processes do not cluster well. Post-test data analysis provides a measure of student ability to apply what they have learned and their holistic understanding.
Design of Exercise Grading System Based on Text Similarity Computing

Text similarity, as an important basis for scoring subjective items in the examination, directly determines the examination results of candidates and the work efficiency of teachers. Therefore, this paper first introduces the theoretical basis of text similarity computing and compares different calculation methods. Then, the text-similarity algorithm is designed, where by conceptualizing text terms, the computing methods based on corpus and knowledge base are combined. Then, according to the similarity computing model of text terms, an automatic grading system for exercises is designed, including the design of technical architecture, the design of functional modules, and the realization process of comprehensive grading. Among them, the grading module is the core module of the system and the key to automatic grading. The systematic test results show that the problem scoring system designed in this paper has little difference from manual marking and can achieve good scoring results.
IRVILAB

Information retrieval (IR) evaluation can be considered as a form of competition in matching documents and queries. This paper introduces a learning environment based on gamification of query construction for document retrieval, called IRVILAB (Information Retrieval Virtual Lab). The lab has modules for creating standard evaluation settings, one for topic creation including relevance assessments and another for performance evaluation of user queries. In addition, multilingual Wikipedia online collection enables a module, where relevance assessments are translated to other languages. The underlying game utilizes IR performance metrics to measure and give feedback on participants' information retrieval performance. It aims to improve participants' search skills, subject knowledge and contributes to science education by introducing an experimental method. Distinctive features of the system include algorithmic relevance assessments and automatic recall base translation.
Predictive Subdata Selection for Computer Models

An explosion in the availability of rich data from the technological advances is hindering efforts at statistical analysis due to constraints on time and memory storage, regardless of whether researchers employ simple methods (e.g., linear regression) or complex models (e.g., Gaussian processes). A recent approach to overcoming these limits involves information-based optimal subdata selection and Latin hypercube subagging. In the current study, we develop a novel subdata selection method for large-scale computer models based on expected improvement optimization. Numerical and empirical analysis using real-world data are used to select subdata by which to derive accurate predictions. During the optimization procedure, the proposed scheme employs the geometry of the input feature region as well as information related to output values. The data points associated with the largest improvement in prediction accuracy are combined in the construction of a subdataset that can be used to formulate predictions with affordable computing time. Supplementary materials for this article, including proofs of theorems and additional numerical results, are available online.
High Precision Analog Gauge Reader Using Optical Flow and Computer Vision

This paper aims at developing algorithms to automate the process of reading analog gauges at different operational industries, healthcare sector and automobiles using Artificial Intelligence (AI) techniques. Proposed algorithms start with video stabilization using optical flow followed by image processing which performs segmentation using HSV color space and morphological operations. Next, Hough transform is applied to determine the dial region and pointer. Using the coordinates locating the pointer in the frame, angle made with minimum valued scale mark is calculated using trigonometry and evaluated reading is displayed in the form of text and a progress bar. The proposed algorithms for Analog Gauge Reader (AGR) is executed using open-source libraries such as OpenCV, NumPy, PIL with Python programming and it is developed in the PyCharm environment. The experimental results show significant decrease in the error with the introduction of image preprocessing and the generalizability of the algorithm to different types of analog gauges captured in different conditions of illuminance. The accuracy and precision of the algorithms is 99% to 100% in the experimental studies.
Development of Interactive Teaching of Physical Dance Based on Dynamic Time Reversion Technique

In order to cultivate students’ dance expression, in physical dance teaching, emphasis should be placed on cultivating students’ interest in physical dance, strengthening the training of movement skills and musical rhythm, training of cultural cultivation, and training of coordination and cooperation between male and female partners. In this paper, we design a dance teaching aid based on DTW (Dynamic Time Warping) movement similarity evaluation algorithm. The evaluation of movement similarity is difficult because each movement is multidimensional, there is a lot of noise when the movement data are collected, and there is high variability in the performance of different individuals, and the real-time requirement of movement similarity evaluation. In the teaching practice, we should pay attention to the cultivation of students’ dance expression, so that students can match the music and make coordinated sports dance movements, realize the integration of music and dance, and improve the professional standard.
A Residual Neural Network for Modulation Recognition of 24 kinds of Signals

With the development of wireless communication technology and the updates of communication equipment, the modulation of signal becomes more complex, and modulation recognition is becoming more and more difficult. Traditional signal modulation recognition methods rely on human experience, its feature extraction process is complex, and the empirical threshold is difficult to find. The recognition method combined with manual feature extraction and deep neural network can achieve better recognition accuracy, but it is still limited by the process of feature extraction. Compared with the above, automatic modulation recognition method based on deep learning is more efficient in complicated open environment. In this paper, a residual neural network for automatic modulation recognition was designed, and the experiment had achieved remarkable results. When SNR is 10dB, we got an accuracy of 95.3% faced to 24 kinds of signals, and when SNR is 12dB, we got an accuracy of 96.3%. Compared with existing models, this model reduces the network parameters, greatly shortens the training time, and lower the hardware requirements. This model shows a good result on the recognition of high-level modulation signal. When SNR is 10dB, the recognition accuracy of 128APSK, 128QAM and 256QAM is 97%, 88% and 88%.
Outdoor Clothing Design for Traffic Safety Based on Big Data and Artificial Intelligence

With the development of technologies in various fields, more and more technologies have been applied to safety clothing, which has led to the rapid development of safety clothing. The improvement of living standards is accompanied by the change of consumption concepts. Consumers’ requirements for clothing products have become more artistic, healthier, and more ecological, and they look forward to more and better safety clothing to meet their health needs. In this context, this article studies traffic safety outdoor clothing design based on big data (BD) and AI. This article introduces the design method of outdoor safety clothing for traffic based on BD and machine learning in artificial intelligence (AI) and did two experiments. To this end, this paper adopts a Deep Belief Network (DBN), which is trained layer by layer through Restricted Boltzmann Machine (RBM), and successfully solves the problems of lack of a large number of labeled samples and easy to fall into local optimum. The first experiment is to test the accuracy of various machine learning algorithms for clothing size measurement. The results obtained are as follows: the predicted value of the DBN neural network is the closest to the actual value, the average prediction accuracy of DBN for the cuff size is 90%, and the average prediction accuracy for the neck circumference is 91.5%. The second experiment is to investigate the functional needs and performance concerns of children and outdoor workers. The results of the experiment are as follows: for children, 79.9% of people want clothing to have a positioning function, which accounts for the highest proportion. For outdoor workers, the most important clothing function they need is eye-catching style, and 90.1% of those choose this option. In terms of clothing performance concerns, most people choose to care very much, and the second most people choose to care about comfort.
Putting the disability in DEI through inclusive imagery

Disability representation---both the presence of people with disabilities and images depicting disability---is increasingly integrated into computing. But how do we make sure the processes we have for developing inclusive imagery are themselves inclusive?
Password Security: What Are We Doing Wrong?

Passwords are used consistently in our everyday lives. Whether it’s from logging in to our smart phones, our computers, accessing our bank, etc. We need to use passwords to protect everything important to us, and to keep our information safe. If our passwords are known by a malicious party, we could lose not only access to our personal data, but potentially lose everything through identity theft. While it’s important to change our passwords occasionally, could requiring changes too frequently actually make our passwords worse. This paper highlights the weakest links of cyber security, the user’s mistakes when choosing their passwords. Additionally, this paper provides different ways of password security in both personal, and business use.