Reviewerly/exHarmony huggingface dataset:
Maker Technology and the Promise of Empowerment in a Flemish School for Disabled Children

Maker culture encourages do-it-yourself practices to create, repair, and repurpose technology. In Human-Computer Interaction (HCI) research, it is seen as a means of empowering people, providing affordable and customisable technology with potential to enrich areas such as education or assistive technology. To investigate this alleged potential, we performed an anthropological inquiry at an elementary school for disabled children that lasted one year, participating in everyday activities with students, teachers, and therapists. We observed 'heterogeneity in a fluid environment' and 'creativity in the moment' in an 'endemically underfunded' setting. We saw how technology is 'injecting dependencies', 'reinforcing disability', and 'occupying time and space', changing our view on the role that making can have. Leveraging Empowerment Theory, we highlight how (making) technology risks ignoring the intertwined dynamics between the individual, the organisational, and the community, and articulate points for reflection for technology in schools for disabled children for the HCI research community.
Thermal entanglement in Tavis-Cummings models with Kerr media

In this paper, we investigated the entanglement dynamics between two dipole-coupled qubits not-resonantly interacting with a thermal one-mode field of lossless cavity with Kerr media. We obtained the exact solution for time-dependent density matrix and calculated on its basis the qubit-qubit entanglement parameter – negativity. The results show that Kerr nonlinearity greatly affected the entanglement behavior. More interestingly, that for initial entangled qubits states Kerr media avoids the entanglement sudden death effect.
Video anomaly detection using Cross U-Net and cascade sliding window

As video surveillance exponentially increases, a method that automatically detects abnormal events in video surveillance is essential. Several anomaly detection methods have been proposed to detect abnormal events in video surveillance. Much research has recently used deep learning to obtain high anomaly detection accuracy. Most of the research considered only anomaly detection accuracy, but they do not consider the anomaly detection speed that is essential in video surveillance. In this paper, we propose a Cross U-Net framework that considers anomaly detection accuracy and speed. The Cross U-Net framework uses a newly proposed deep learning model that uses two subnetworks based on U-Net. It makes that every third layer's output in the contracting path combines with the corresponding layer's output in the other subnetwork for use as the next layer's input. This framework also uses a cascade sliding window method, a newly proposed method estimating the anomaly score of a frame. We evaluated the Cross U-Net framework's anomaly detection accuracy and speed using Ped2, Avenue, and ShanghaiTech datasets. We achieved competitive anomaly detection accuracy and real-time anomaly detection in the three datasets.
Density Estimation of Fog in Image Based on Dark Channel Prior

This paper proposes a method and an original index for the estimation of fog density using images or videos. The proposed method had the advantages of convenient operation and low costs for applications in automatic driving and environmental monitoring. The index was constructed based on a dark channel map and the pseudo-edge details of the foggy image. The effectiveness of the fog density index was demonstrated and validated through experiments on the two existing open datasets. The experimental results showed that the presented index could correctly estimate the fog density of images: (1) the estimated fog density value was consistent with the corresponding label in the Color Hazy Image Database (CHIC) in terms of rank order; (2) the estimated fog density level was consistent with the corresponding label in the Cityscapes database and the accuracy reached as high as 0.9812; (3) the proposed index could be used to evaluate the performance of a video defogging algorithm in terms of residual fog.
Single-scan multiple object detection based on template matching using only effective pixels

In this paper, we propose a template matching method that enables multi-class classification by scanning an input image only once, focusing on the classification ability of each pixel in the template image. This ability of each pixel in the template image is represented by using the occurrence frequency of the pixel value. On the basis of this ability, a small number of pixels that are effective for class identification are selected. Experimental results in the task of detecting five kinds of objects from 100 real images showed that the recognition rate of the proposed method was 95.4% and the processing time was 1.9 seconds when 0.6% of all template pixels were used.
Efficient privacy-preserving variable-length substring match for genome sequence

The development of a privacy-preserving technology is important for accelerating genome data sharing. This study proposes an algorithm that securely searches a variable-length substring match between a query and a database sequence. Our concept hinges on a technique that efficiently applies FM-index for a secret-sharing scheme. More precisely, we developed an algorithm that can achieve a secure table lookup in such a way that [Formula: see text] is computed for a given depth of recursion where [Formula: see text] is an initial position, and V is a vector. We used the secure table lookup for vectors created based on FM-index. The notable feature of the secure table lookup is that time, communication, and round complexities are not dependent on the table length N, after the query input. Therefore, a substring match by reference to the FM-index-based table can also be conducted independently against the database length, and the entire search time is dramatically improved compared to previous approaches. We conducted an experiment using a human genome sequence with the length of 10 million as the database and a query with the length of 100 and found that the query response time of our protocol was at least three orders of magnitude faster than a non-indexed database search protocol under the realistic computation/network environment.
Research on Neural Network Machine Translation Model Based on Entity Tagging Improvement

Machine translation, as an efficient tool, can achieve equivalent conversion between different languages while preserving the original semantics. At present, machine translation models based on deep neural networks have become a hot research topic in the fields of natural language processing and image processing. However, the randomness of neural networks leads to the existing neural network machine translation models unable to effectively reflect the linguistic dependencies and having unsatisfactory results when dealing with long sentence sequences. To solve these two problems, a new neural network machine translation model with entity tagging improvement is proposed. First, for the low-frequency word translation problem, UNK entity tags replacement is used to compensate for the weakness of the randomness of neural networks and the encoding/decoding strategy of entity tagging is improved. Then, on the basis of the LSTM translation model, an attention mechanism is introduced to dynamically adjust the degree of influence of the context at the source language end on the target language sequence to improve the feature learning ability of the translation model in processing long sentences. The analysis of the experimental results shows that the translation evaluation index BLEU of the proposed translation model is significantly improved compared with various translation models, which verifies its effectiveness.
Machine learning for the classification of breast cancer tumor: a comparative analysis

The detection and diagnosis of Breast cancer at an early stage is a challenging task. With the increase in emerging technologies such as data mining tools, along with machine learning algorithms, new prospects in the medical field for automatic diagnosis have been developed, with which the prediction of a disease at an early stage is possible. Early detection of the disease may increase the survival rate of patients. The main purpose of the study was to predict breast cancer disease as benign or malignant by using supervised machine learning algorithms such as the K-nearest neighbor (K-NN), multilayer perceptron (MLP), and random forest (RF) and to compare their performance in terms of the accuracy, precision, F1 score, support, and AUC. The experimental results demonstrated that the MLP achieved a high prediction accuracy of 99.4%, followed by random forest (96.4%) and K-NN (76.3%). The diagnosis rates of the MLP, random forest and K-NN were 99.9%, 99.6%, and 73%, respectively. The study provides a clear idea of the accomplishments of classification algorithms in terms of their prediction ability, which can aid healthcare professionals in diagnosing chronic breast cancer efficiently.
Exploring the Effect of Immersive VR on Student-Tutor Communication in Architecture Design Crits

Using digital tools like immersive Virtual Reality (iVR) reduce the carbon footprint by providing collocated and remote communication through virtual design studios.By providing a sense of presence in a digital display, iVR systems impact student-tutor communication during design critiques or crits.Research lacks studies articulating how iVRs change crits' communication to increase the ability to integrate iVRs as educational media and promote a quality education in inter-university studios.To this end, this study explores the cognitive structure of student-tutor communication during collocated architecture crits using iVR and non-immersive media.We employed protocol analysis to analyze divergent thinking by tracking the distribution of First Occurrences of design issues.Combining protocol analysis with Natural Language Processing, we explored the size of the design space generated during the crits.Results from a case study that includes twelve crits from three students show an increase in students' exploration of the design space and divergent thinking in the iVR crits, providing evidence that iVR enhances learners' communication.iVRs can be integrated to support remote design studios without the generation of carbon due to physical travel.
Design and Research of Computer-Aided Translation Teaching Course under the Background of Embedded Microprocessor Wireless Communication

The background of wireless communication is the background of the current society. On this basis, the use of embedded microprocessors for computer-assisted translation teaching courses can well cultivate the practical abilities of college students and enable them to quickly integrate into the society. In the context of wireless communication, this article conducts the design and research of computer-assisted translation teaching courses based on embedded microprocessors, explores the use of high-tech system to train students in colleges and universities, and promotes the reform of college classrooms. This research is to use computers for assisted translation and embedded microprocessors for related calculations in translation teaching courses in colleges and universities, which can improve students’ learning efficiency to a certain extent. The experimental results show that the concentration of students in computer-assisted teaching is 20% higher than that of students in traditional teaching, and other related data are more than 8% higher. The teaching of computer-assisted translation courses benefits students greatly.
Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift

This paper aims to improve the explainability of autoencoder (AE) predictions by proposing two novel explanation methods based on the mean and epistemic uncertainty of log-likelihood estimates, which naturally arise from the probabilistic formulation of the AE, the Bayesian autoencoder (BAE). These formulations contrast the conventional post-hoc explanation methods for AEs, which incur additional modelling effort and implementations. We further extend the methods for sensor-based explanations, aggregating the explanations at the sensor level instead of the lower feature level. To evaluate the performance of explanation methods quantitatively, we test them on condition monitoring applications. Due to the lack of a common assessment of explanation methods, especially under covariate shift, we propose three evaluation metrics: (1) the G-mean of Spearman drift coefficients, (2) the G-mean of sensitivity-specificity of explanation ranking and (3) a sensor explanation quality index (SEQI) which combines the first two metrics, capturing the explanations' abilities to measure the degree of monotonicity and to rank the sensors. Surprisingly, we observe that the explanations of BAE's predictions suffer from high correlation resulting in misleading explanations. This new observation cautions against trusting these explanations without further understanding of when they may fail. To alleviate this, a "Coalitional BAE" is proposed, inspired by agent-based system theory. The Coalitional BAE models each sensor independently and eliminates the correlation in explanations. Our comprehensive experiments on publicly available condition monitoring datasets demonstrate significant improvements of the Coalitional BAEs over the baseline Centralised AEs on the proposed metrics, visualised through critical difference diagrams.
Research on the Algorithm of License Plate Recognition Based on MPGAN Haze Weather

The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.
Designing a Nonlinear Filter of Constant-Gains Type Using Pseudo-Formal Linearization Method

This paper is concerned with the designing a nonlinear filter making use of a pseudo-formal linearization method and Kalman filter approach of constant-gains type for nonlinear systems. We piecewisely linearize the given nonlinear dynamic and measurement equations by a formal linearization method and design linear filters. They are smoothly united into a single nonlinear filter. The efficacy of this filter is shown through numerical examples.
Graph Embedding-Based Sensitive Link Protection in IoT Systems

In the Internet of Things (IoT), massive interconnected intelligent terminal devices constitute diverse networks. Link prediction can serve as a powerful inference attack to speculate the sensitive links in the networks, posing a security threat to entity privacy in IoT. Most antilink prediction methods reduce the prediction ability of link prediction models through link disturbance to hide sensitive links but fail to consider the impact of node attributes on link prediction. This paper proposes a sensitive link protection method based on graph embedding (SLPGE) to combat link prediction attacks. This method is aimed at compressing network topology data into an embedding matrix and lessening private information by combining Variational Graph Autoencoder (VGAE) and Adversarially Regularized Variational Graph Autoencoder (ARVGA). Based on our experiment on two datasets, SLPGE reduces the prediction accuracy of two attack models for sensitive links by up to 30.05% and 15.03% compared to the original data, and the corresponding utility sees a drop of 7.54% and 7.79% at most, which verifies the feasibility of SLPGE—achieving the tradeoff between privacy protection and data utility effectively.
Hierarchical visual interface for educational video retrieval and summarization

With the emergence of large-scale open online courses and online academic conferences, it has become increasingly feasible and convenient to access online educational resources. However, it is time consuming and challenging to effectively retrieve and present numerous lecture videos for common users. In this work, we propose a hierarchical visual interface for retrieving and summarizing lecture videos. Users can utilize the proposed interface to effectively explore the required video information through the results of the video summary generation in different layers. We retrieve the input keywords with the corresponding video layer with timestamps, a frame layer with slides, and the poster layer with summarization of the lecture videos. We verified the proposed interface with our user study by comparing it with other conventional interfaces. The results from our user study confirmed that the proposed interface can achieve high retrieval accuracy and good user experience.
Learnings from Federated Learning in The Real World

Federated Learning (FL) applied to real world data may suffer from several idiosyncrasies. One such idiosyncrasy is the data distribution across devices. Data across devices could be distributed such that there are some "heavy devices" with large amounts of data while there are many "light users" with only a handful of data points. There also exists heterogeneity of data across devices. In this study, we evaluate the impact of such idiosyncrasies on Natural Language Understanding (NLU) models trained using FL. We conduct experiments on data obtained from a large scale NLU system serving thousands of devices and show that simple non-uniform device selection based on the number of interactions at each round of FL training boosts the performance of the model. This benefit is further amplified in continual FL on consecutive time periods, where non-uniform sampling manages to swiftly catch up with FL methods using all data at once.
Research on Full Coverage Path Planning Algorithm of Mobile Robot Based on Astar Improved Algorithm

In order to solve the problems that most robots have in full coverage path planning in the working area, including unreasonable moving paths, long exploration time, and disorderly planning routes in obstacle areas. This paper improves the Astar algorithm and studies a full coverage path planning algorithm for mobile robot path planning. This improved algorithm enables the robot to construct a map in a certain closed area, and use the constructed map to scan the closed space, so that the robot has the ability of autonomous path planning and realizes autonomous navigation and motion control. This algorithm is transplanted into a mobile robot control system, and an experimental study is carried out on this algorithm. Experiments show that the algorithm is stable in time and has strong robustness. Comparing the map established by the robot with the actual experimental area, it is found that the algorithm has a high coverage rate and strong practicability. In addition, a remote video transmission function system is integrated on the mobile robot, and experimental tests such as remote video transmission, robot image recognition and automatic tracking have been carried out, and good results have been achieved.
Optimizing the Training Mode of Swimmers Based on Deep Learning and Data Mining Technology

Data mining is the most popular research direction of information science nowadays. It revolves around all the data and continuously excavates some of the potentially valuable data information. This information has great application value. This is a dynamic and interactive process. Deep learning is an important branch of machine learning, and the introduction of deep learning theory has promoted the development of artificial intelligence. Swimming is a rather special sport. From the perspective of special characteristics, the body is in an unstable state during swimming, there is no fixed fulcrum, and it is necessary to fight against water resistance in the water. In the case of maintaining the best streamlined and reducing resistance as much as possible, through the power forward movement method brought by rowing and kicking, the training of swimming-specific strength is also unique to the project. In this study, the application of deep learning and data mining technology to the optimization of swimming athletes’ training mode is proposed, and algorithms such as neural network and support vector machine are expounded. This paper adopts two different training methods. That is, the experimental group adopts functional training. The control group used traditional training to compare the special performance of the two groups of athletes before and after physical training. In this way, we can understand the changes of traditional training and functional training on the performance of swimmers, compare the methods of the two groups of athletes and provide swimmers with more scientific and effective training methods. The experimental results of this paper show that through the data mining technology experiment, the main item test scores of the two groups of subjects before and after the experiment were <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>=</mo> <mn>0.004</mn> </math> , which was less than 0.01, showing a very significant difference. This shows that the use of deep learning and data mining technology has a very significant effect on swimming athletes in training to improve performance.
A Survey on Human Action Recognition based on Attention Mechanism

Human action recognition has always been an important challenge in the field of computer vision. Human action recognition has a good development prospect for technology applications in many fields such as artificial intelligence and behavior analysis. Based on the collected literature, this article first summarizes the data sets commonly used in the field of human action recognition, and then discusses the common methods of human action recognition based on traditional features and deep learning. Secondly, this paper reviews the principles and concepts of attention mechanism, and the types and applications of attention mechanism. Then this paper reviews the human action recognition methods based on the attention mechanism optimized in recent years and its comparison with other traditional methods on the data sets. Finally, the prospect and summary of the research on this subject are drawn.
The Anatomy of Phishing Attacks and the Detection and Prevention of Fake Domain Names

Internet technology and its infrastructure are getting more and more into our lives. In parallel with this, there is an increase in the number of phishing attacks that rely on fake/deceptive domain names. Web-based phishing attacks aim at obtaining users' (individual/corporate) personal and/or financial information by using fake domain names. Within the scope of this chapter, firstly, phishing attacks are explained. How they are prepared and implemented is examined. Then, the steps to be taken to detect fake domain names to which users are directed are examined.