Reviewerly/exHarmony huggingface dataset:
Who Is Typing? Automatic Gender Recognition from Interactive Textual Chats Using Typing Behaviour

Gender plays a fundamental role in human–human interaction. The availability of nonverbal behavioural cues such as facial expressions, eye gaze, and gestures in face-to-face interaction allows one to recognise the gender of others easily. In addition, all cultures provide further cues to distinguish between genders, like different clothes, hairstyles, etc. In social psychology, such phenomenon has been extensively investigated in the case of face-to-face interaction. However, when it comes to computer-mediated technologies (e.g. live chats), there is an element of uncertainty regarding whether such mediums comprise nonverbal cues. In the case where this is true, it would be unclear whether such nonverbal cues convey information about the characteristics and identity of individuals. This article investigates whether typing behaviour allows one to automatically recognise the gender of chat participants. The experiments—carried out over a dataset of 30 dyadic text chats involving 60 unacquainted individuals—indicate that several features can be considered as gender makers. Thus, gender tends to leave physical, machine detectable traces in typing behaviour. In addition, the experiments show that there is a difference in typing behaviour between females and males, which is possible to predict using random forests with an accuracy up to 98.8%.
EFRNet: Efficient Feature Reconstructing Network for Real-Time Scene Parsing

In this paper, we introduce a light-weight and powerful convolutional neural network, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient feature reconstructing network</i> (EFRNet), for real-time scene parsing. Our key idea is to decompose the process of learning high-resolution representations into two stages: i) bottom-up codebook/coding matrix learning and ii) top-down feature reconstructing. Specifically, the bottom-up process focuses on learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image-specific</i> codewords (codebook) using deep-layer features and generating a coding matrix with the shallow-layer feature map. In the top-down process, the learned codebook and coding matrix are used to rebuild high-resolution features via a lightweight <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature reconstructing operator</i> (FRO). In addition, our EFRNet is constructed on a new building block, named efficient adaptive abstraction (EAA) block, to further reduce the overall network parameters and achieve a significant speed up. Extensive experiments are conducted on challenging benchmarks, such as CamVid and Cityscapes. The results show that EFRNet demonstrates state-of-the-art performance with an optimal balance between accuracy and speed.
Virtual Reality Technology in Information Teaching

Virtual reality technology is the product of the rapid development of science and technology, and the development of science and technology has also led to the development of education. Nowadays, virtual reality technology is widely used in teaching, which promotes the development of teaching informatization in China. Based on this, this paper studies the application of virtual reality technology in information teaching. In the research, this paper first introduces the virtual reality technology and its commonly used algorithms, and analyzes and summarizes the application of virtual reality technology in information teaching. In order to highlight the application effect of virtual reality technology in information-based teaching, this paper selects two dance classes in our school, using traditional methods and virtual reality technology information teaching respectively, and evaluates the teaching effect. The results show that the average score of dance teaching class in the test is 72.4, and the average score of class using virtual reality technology is 85.8, and each score is higher than that of the other class. Thus, the application of virtual reality technology in information teaching can effectively enhance the teaching effect.
Blockchain-Enabled Verification System

Finance 4.0 (FIN4) enables the incentivization of environmentally friendly behavior. These behaviors can have seemingly unlimited ways of being described. This makes defining ways of verifying them very challenging. We introduce a novel system for positive action verification: A Blockchain-Enabled Verification System. It offers multiple types of verifiers that can be used collectively to offer sophisticated action verification. It is deployed on the Ethereum Blockchain and uses Inter Planetary File System (IPFS) to store proofs that require external files. The system’s various verification services are utilized through a proxy contract that handles all calls directed to it. We believe that this system is a gateway towards secure and transparent verification that, in the FIN4 setting, has the potential of creating a positive impact in the world.
Relation Order Histograms as a Network Embedding Tool

In this work, we introduce a novel graph embedding technique called NERO (Network Embedding based on Relation Order histograms). Its performance is assessed using a number of well-known classification problems and a newly introduced benchmark dealing with detailed laminae venation networks. The proposed algorithm achieves results surpassing those attained by other kernel-type methods and comparable with many state-of-the-art GNNs while requiring no GPU support and being able to handle relatively large input data. It is also demonstrated that the produced representation can be easily paired with existing model interpretation techniques to provide an overview of the individual edge and vertex influence on the investigated process.
Research on Obstacle Avoidance Strategy and Method of UR Manipulator

The obstacle avoidance problem of the robotic arm in a complex working environment is one of the reasons that affect the operating efficiency and capability of the robotic arm. It is of great significance to study the obstacle avoidance strategy and algorithm of manipulator. The six-degree-of-freedom UR robot was simulated through the ROS platform, and the path planning simulation of the robotic arm was performed using Rapidly-Exploring Random Tree (RRT) algorithm and the improved RRT algorithm (RRT-connect, RRT*). The simulation experiment of the algorithm was carried out on the Matlab platform. The results show that in different environments, the RRT algorithm search has a large randomness and is greatly affected by the environment. The RRT* algorithm has a longest search time but the optimal path, and the RRT-connect algorithm has the shortest search time but large path jitter. Mastering the different characteristics of these three algorithms will point out the direction for follow-up research.
Impact of False Positives and False Negatives on Security Risks in Transactions Under Threat

This paper presents a theoretical model, algorithms, and quantitative assessment of the impact of false positives and false negatives on security risks during transaction processing. It is based on analysis of the effect of the parameters of the optimal strategy for planning countermeasures in Partially Observable Markov Decision Processes. The paper reveals the dependency between the false positives and false negatives of the analytics and their impact on the efficacy of the algorithms for detection. Such an analysis is an important component of cybersecurity frameworks, planning countermeasures for mitigating the risks from security threats, but it can be applied to many other business processes which experience device malfunctions or human errors.
What's the Point? How Scores Undermine Written Comments on Open-Ended Work

Scaling assessments typically relies on quantifying work quality, yet written comments are the assessment method of choice for open-ended work. Existing scalable solutions compromise by mapping quality scores to pre-authored comments, but how scores influence interpretation of these comments is not well understood. We report results from a study of how 441 participants authored and revised short stories in response to a score, written comments, both types of feedback, or no feedback. We analyzed data from the story-writing task and two surveys to determine task and feedback satisfaction, revision depth and effort, and improvement between drafts for each participant. We found task satisfaction and task performance were positively correlated among participants who were shown a score. Feedback satisfaction, revision effort, and improvement were highest among participants shown written comments. Either type of feedback prompted more deep revisions than no feedback, but together elicited fewer deep revisions than written comments alone. Our work informs the design of scalable open-ended assessment systems by contributing insights regarding how scores influence perceptions of written feedback and subsequent revision outcomes.
Machine Learning for Streaming Data: Overview, Applications and Challenges

This chapter gives a brief overview of machine learning for streaming data by establishing the need for special algorithms suited for prediction tasks for data streams, why conventional batch learning methods are not adequate, followed by applications in various business domains.
Quantum generative adversarial networks for learning and loading quantum image in noisy environment

Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend quantum generative adversarial networks to the quantum image processing field and show how to learning and loading an classical image using quantum circuits. By reducing quantum gates without gradient changes, we reduced the number of basic quantum building block from 15 to 13. Our framework effectively generates pure state subject to bit flip, bit phase flip, phase flip, and depolarizing channel noise. We numerically simulate the loading and learning of classical images on the MINST database and CIFAR-10 database. In the quantum image processing field, our framework can be used to learn a quantum image as a subroutine of other quantum circuits. Through numerical simulation, our method can still quickly converge under the influence of a variety of noises.
An Improving Noise Immunity Algorithm for Multiple Kernel Boosting Using Noise Detection and Kernel Selection Technique

Abstract Focus on the problem that the Multiple Kernel Boosting(MKBoost) algorithm is sensitive to noise, a Multiple Kernel Boosting algorithm based on weight update and kernel selection is proposed. Firstly, the algorithm use the combined classification error rate of the previously selected classifier and the current classifier to be selected as the selection index of the kernel function in the weak classifier before the kernel of the base classifier is selected in each iteration; Secondly, in the weight update stage, a new weight update method is constructed by fusing the noise-detection and the average of weights in Multiple Kernel Boosting algorithm, which reduce the sensitivity to noise samples. Among the 8 of UCI data sets with varying levels of noise, the algorithm was compared with MKBoost-D1, MKBoost-D2, under the accuracy criteria, it performed better than traditional MKBoost algorithms. Experimental results show that the algorithm is able to effectively reduce the sensitivity of MKBoost to noise, and also has better robustness than traditional MKBoost algorithms.
Development of MySimB’: A Motivational Aspect

Abstract Simpulan Bahasa is one of essential learning subject in Malaysia. Most students learn Simpulan Bahasa by reading books, which considered as normal/manual practice. If student intent to search Simpulan Bahasa , student have to search through books as the dictionary of proverbs. It is however quite ancient and sometimes burdensome for student to bring the dictionary. According to this matter, MySimB’ was developed through mobile learning using ADDIE methodology. The development of MySimB’ inention is to help primary school student to learn a standard Malay Simpulan Bahasa easily and effectively. MySimB’ is interactive website which was developed to ease learning and interactive medium for student, teacher and parent to learn or teach Simpulan Bahasa . MySimB’ provide index using alphabetic, meaning and an example of sentence for each Simpulan Bahasa , searching and learning games to attract student in Simpulan Bahasa learning.
Human activity recognition based on triaxial accelerometer using multi-feature weighted ensemble

Human activity recognition (HAR) has been widely used in some areas such as smart home, health care and so on. However, there are still some low recognition accuracy cases in actual scenarios. In order to improve the accuracy of recognition, we propose a multi-feature weighted ensemble classification method on triaxial accelerometer sensor data. We perform weighted integration on five base classifiers to obtain the final prediction classification label. Among these five base classifiers, three are K-nearest neighbor (KNN) classifiers with different features respectively using three traditional feature extraction methods from original data. Another two are currently popular deep learning models-Attention Mechanisms on Long Short-Term Memory Network (Attention-LSTM) and Convolutional Neural Network (CNN), which can automatically extract features and classify. We demonstrated the feasibility of this ensemble method on a dataset containing eight human daily activities. Comparing experimental results, our method achieved the best recognition effect, with an accuracy of 95.58%.
Curry–Howard–Lambek Correspondence for Intuitionistic Belief

Abstract This paper introduces a natural deduction calculus for intuitionistic logic of belief $$\mathsf {IEL}^{-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>IEL</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:math> which is easily turned into a modal $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math> -calculus giving a computational semantics for deductions in $$\mathsf {IEL}^{-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>IEL</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:math> . By using that interpretation, it is also proved that $$\mathsf {IEL}^{-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>IEL</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:math> has good proof-theoretic properties . The correspondence between deductions and typed terms is then extended to a categorical semantics for identity of proofs in $$\mathsf {IEL}^{-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>IEL</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:math> showing the general structure of such a modality for belief in an intuitionistic framework.
DEVELOPMENT OF WAGONS FOR A FREE ADDRESS OFFICE IN A UNIVERSITY RESEARCH FACILITY

In accordance with the joint development of individual wagons with manufacturers accompanying free-addressing at university research facilities, this study examined the characteristics of each proposal from candidate manufacturers and factors contributing to the user’s wagon selection. Specifically, this study clarified (1) conditions and purposes underlying candidate manufacturers’ proposals and (2) questionnaire respondents’ decisive factors in selecting wagons.
Knowledge Base Question Answering for Intelligent Maintenance of Power Plants

The maintenance of power plants highly relies upon precious knowledge and experience of handling faults, which is often stored in reports such as the event report. Simple string matching is the traditional means of retrieving relevant reports, and there is a failure of such methods in understanding the user&#x0027;s search intention properly. With a focus on improving the accuracy of information feedback, this work aims to develop a system of knowledge base question answering. Specifically, natural language processing is employed to improve question comprehension and information retrieval. The BiLSTM-CRF model and the fine-tuned BERT model are used to capture named entities and relations in the query. And the BM25 algorithm and the fine-tuned BERT model are combined to develop a scheme for better information retrieval. On this basis, the application interface of knowledge base question answering towards intelligent power plant maintenance is developed. With this question answering system, power plant operators can have better interaction with the knowledge base and improve collaboration.
Cross-spectral Gender Classification Using Multi-spectral Face Imaging

Abstract Soft biometrics has been gaining significant attention in biometrics literature for usage in recognition system. Gender classification is one such soft biometric traits that has been studied extensively using visible spectrum range and limited studies beyond visible spectrum. In this paper we present the cross-spectral gender classification using Multi-spectral imaging. We present this study using the facial images captured in nine narrow spectral bands ranging from 530nm to 1000nm. Further, we present an extensive benchmark evaluation based on state-of-the art linear Support Vector Machine (SVM) classifier for facial discriminative features in the cross spectral data in a robust manner. The extensive evaluation results based on 10 fold cross-validation indicates the highest average classification accuracy of 92.84% for cross-spectral study.
Steganographic Methods Resistant to JPEG Compression

The active development of computer systems and information technologies implies an increase in the importance of ensuring the protection and security of information during its transfer or storage. Today, the fulfillment of the necessary data confidentiality requirements tends to the direction of cryptography and steganography. The choice of the applied method of encoding information directly depends on the goals of the user, as well as the available software. The main requirement for the message encoding process is the availability of acceptable computational complexity of the implementation.&#x0D; The article discusses methods of ensuring data confidentiality employing digital steganography, using BMP images as container files, and carrying out their subsequent compression to the JPEG format without losing hidden information. Mathematical methods of constructing a stegosystem for encoding ASCII are studied — a message, each character of which is encoded with exactly one byte. As a result, a computer program was created based on the Microsoft Visual Studio 2017 programming environment and the C# programming language (.NET Framework). The stereotype, in this case, is a sequence of steps between image pixels, into which information is embedded. In this case, the algorithm allows you to memorize not the entire sequence of steps (that is, not the entire keystroke), but only the first five characters of the key or half of the key.
Identifying High-Quality User Replies Using Deep Neural Networks

With the significant expansion of Q&A forums and the increasing need for users to access useful information, identifying quality content in text forums is of particular importance. Previous studies have focused on extracting several types of quality features from text that may be a time and labor-intensive task. To address this problem, in this paper, a long short-term memory (LSTM) deep neural network model is proposed to determine high-quality responses of users in text forums using only raw text of user replies. In the proposed model, embeddings from language models (ELMo) are usesd to represent words in vectors or embeddings. The proposed model is evaluated on two datasets: The TripAdvisor for New York City (NYC) and the Ubuntu Linux distribution online forums. Comparison of the results obtained using the proposed model and support vector machines (SVM), linear regression (LR), artificial neural networks (ANN), and naïve Bayes (NB) algorithms showed that, using only textual features, the accuracy of the proposed model was 43% and 28% higher compared to the highest accuracy obtained by the four traditional machine learning (ML) algorithms on the NYC and the Ubuntu datasets, respectively. This improvement was about 17% and 16% compared to the best results obtained by ML algorithms using both textual and quality dimension features.
Data Augmentation Based on Cannikin Law for Object Detection

Data augmentation is one of the most significant approaches to improve the performance of object detection. Currently, data augmentation is generally used for resolving the long tail and sample imbalance problem. However, there is an imbalance of detection performance in multi-class object detection without the impact of the above-mentioned problems. In this paper, we present an offline data augmentation method based on Cannikin's Law for augmenting the categories far below the average detection accuracy. Specifically, we first apply the statistical method to find the categories with low detection performance. Secondly, a data augmentation following the cut-paste principle is constructed. The instances from the specific categories are segmented and placed into the selected pictures in the training dataset. Lastly, the augmented pictures are selected by the similarity measurement mechanism. Extensive experiments are conducted on the Pascal VOC dataset. For the FCOS detector, the mean Average Precision (mAP) is increased from 79.12% to 83.14%. For the RetinaNet detector, the mAP is boosted from 81.6% to 83.57%. Meanwhile, the detection accuracy of the specific categories is improved by 10% at least. The experimental results show the effectiveness of our data augmentation method.
Highly Effective GPU Realization of Discrete Wavelet Transform for Big-Data Problems

Discrete wavelet transform (DWT) is widely used in the tasks of signal processing, analysis and recognition. Moreover it's practical applications are not limited to the case of one-dimensional signals but also apply to images and multidimensional data. From the moment of introduction of the dedicated libraries that enable to use graphics processing units (GPUs) for mass-parallel general purpose calculations the development of effective GPU based implementations of one-dimensional DWT is an important field of scientific research. It is also important because with use of one-dimensional procedure we can calculate DWT in multidimensional case if only the transform's separability is assumed. In this paper the authors propose a novel approach to calculation of one-dimensional DWT based on lattice structure which takes advantage of shared memory and registers in order to implement necessary inter-thread communication. The experimental analysis reveals high time-effectiveness of the proposed approach which can be even 5 times higher for Maxwell architecture, and up to 2 times for Turing family GPU cards, than the one characteristic for the convolution based approach in computational tasks that can be classified as big-data problems.