Reviewerly/exHarmony huggingface dataset:
Research on the Influencing Factors of Teaching Ability of Textile University Professional Teachers under the Combination of Teaching and Big Data Information--Taking Dalian Polytechnic University as an Example

Nowadays, textile professionals in the era of big data do not just meet a skill requirement. Because the business operation model brought about by big data is very different from the past, different positions are not independent of each other, but are interconnected and interdependent, and jointly serve the enterprise information operation system. In this respect, the professionalism and diversification of teachers' teaching abilities are the core and key to the development of higher education institutions. With the progress of the times, the rapid development of higher education in China, among the many factors that affect the teaching quality of colleges and universities, the teaching ability of professional teachers is an important factor. The teaching ability of college teachers is of great significance to the development of china's education and the growth of students[1]. This research combines the analysis of big data informatization with textile professional teachers as the research object. Through the sorting and analysis of teaching combined big data, the current situation of the teaching ability of textile teachers in my country is found, and the form of data analysis is used to find out the influence of textiles[2]. Influencing factors of professional teachers' teaching ability, scientifically and effectively find effective strategies for the development of professional teachers' teaching ability in my country's colleges and universities.
BDIM: A Blockchain-Based Decentralized Identity Management Scheme for Large Scale Internet of Things

In large-scale Internet of Things (IoT) systems, centralized authentication can be challenging, for example in terms of identity management, authentication overhead, and single point of failure. Distributed identity management has been envisioned as a promising approach for mitigating above all, but the security and performance of the overall solution have not been extensively evaluated. In this article, we proposed a decentralized identity management scheme based on blockchain for tackling large-scale IoT such as VANET. We implement smart contracts to support large-scale user access control, together with design trust management methods for reputation evaluation and credit penalty mechanisms, which can prevent various types of attacks in distributed identification contexts. The experiment results and analysis justified that our scheme is scalable with good performance. Specifically, the system response is on a millisecond scale and all the functions consume within 250 ms. Also, the time consumption of the query maintains a manageable delay within 0.5 ms no matter the remarkable growth of users. Finally, we observe that the throughput of the current blockchain platform could meet the requirement of our scheme with the increasing number of users’ upload request in real-world simulation.
Performance One-step secant Training Method for Forecasting Cases

Abstract The training function used in the ANN method, especially backpropagation, can produce different forecasting accuracy, depending on the method parameters given and the data to be predicted. This paper aims to analyze the ability and performance of one of the training functions in the backpropagation algorithm, namely One-step secant, which can later be used or used as a reference in the case of data forecasting. This method is able to update the values of bias and weights according to the one-step secant method. The analysis process uses a dataset of Foreign Exchange Reserves (US $ Million) in Indonesia 2011-2020. Based on this dataset, the dataset will be divided into two parts. The training data uses the 2011-2014 and 2015 dataset as the training data target. Meanwhile, the test data used 2016-2019 and 2020 as the target test data. The analysis process uses 5 experimental architectures, namely 4-5-1, 4-7-1, 4-9-1, 4-11-1 and 4-13-1. The results of the research based on the analysis obtained the best network architecture 4-11-1 with an MSE Training value of 0.00000012, MSE testing/performance of 0.00115144 (the smallest compared to other architectures) and Epoch 343 Iterations.
An Optimized Tongue Drive System for Disabled Persons

There has been dramatic increase in the number of people with physical disabilities in recent years. Many people with disabilities have substantial difficulties even moving their hands and legs. The disability has thus become a real challenge since most of the physically compromised people need some kind of assistance at all times. However, a tongue in a human body is one critical anatomical part which is very rarely affected by disabilities. To resolve the physical disability problem, various assistive technologies were taken into consideration with the main objective of allowing the disabled people to seamlessly communicate with their surrounding environments. One of the technologies which was made available for use by disabled people was a tongue drive system. But their existing designs involved some mechanical problems besides having accuracy issues. In the subject paper, we strive to implement an optimized tongue drive system for physically disabled persons that enables its users to generate more than eight distinct commands with the aid of artificial intelligence, thereby enabling the users a full command via a computer keyboard and mouse.
Predict the Future: Preventing unanticipated changes is the ultimate challenge for self-adaptive systems

Systems interact with their environment. This might lead to unanticipated events as system models usually cover only to a certain extent the dependencies of a system with the environment. We argue that many of these unanticipated events might become predictable in case we handle current and in particular past behavior of the environment by digital twins. We propose to refine the traditional MAPE-K architecture towards a MAPE-Twin architecture. Thus, the traditional knowledge component becomes an analyzable repository of behavior which allows to predict potential events in the future and to deal with them in a predefined way. Thus, the ultimate challenge for self-adaptive systems are not unanticipated changes, but the prediction of future behavior of a system and its environment.
Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization

In realizing unsupervised pixel-precise anomaly localization by utilizing a generative model, a reference image must be generated (for comparison with an input image) by transforming abnormal patterns of an input image, if any, into normal patterns.In this study, a patch-level operation with adaptive patch control is proposed to improve anomaly localization by generating a better reference image.As a way to exploit a generative model, we divide an image into non-overlapped patches of the same size, generate patch-level reference images, and stitch the patch-level reference images into a single reference image.We then conduct anomaly localization by comparing an input image with the stitched, reconstructed image.To effectively apply the patch-level operation, we propose adaptive patch control to determine the number of non-overlapped patches to be applied.For this, we synthesize defective images using normal images and examine how well the candidate methods with different numbers of patches remove the synthesized defects.In the same way, we utilize adaptive patch control to select a promising model among the candidate generative models.Based on experiments conducted using the MVTec Anomaly Detection dataset, we demonstrate that our method outperforms previous existing methods.Under a real-world scenario, our method shows ROC AUC of 0.926, in contrast to the best value of 0.893 from existing studies.Furthermore, we prove the feasibility of the adaptive patch control by showing that the removal of the synthesized defects and the anomaly localization for real defective images are highly correlated.
Recurring verification of interaction authenticity within bluetooth networks

Although user authentication has been well explored, device-to-device authentication - specifically in Bluetooth networks - has not seen the same attention. We propose Verification of Interaction Authenticity (VIA) - a recurring authentication scheme based on evaluating characteristics of the communications (interactions) between devices. We adapt techniques from wireless traffic analysis and intrusion-detection systems to develop behavioral models that capture typical, authentic device interactions (behavior); these models enable recurring verification of device behavior. To evaluate our approach we produced a new dataset consisting of more than 300 Bluetooth network traces collected from 20 Bluetooth-enabled smart-health and smart-home devices. In our evaluation, we found that devices can be correctly verified at a variety of granularities, achieving an F1-score of 0.86 or better in most cases.
Top-k Community Similarity Search Over Large Road-Network Graphs

With the urbanization and development of infrastructure, the community search over road networks has become increasingly important in many real applications such as urban/city planning, social study on local communities, and community recommendations by real estate agencies. In this paper, we propose a novel problem, namely top-k community similarity search (Top-kCS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), which efficiently and effectively obtains spatial communities that are the most similar to a given query community over road-network graphs. In order to efficiently and effectively tackle the Top-kCS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> problem, in this paper, we will design an effective similarity measure between communities, and propose a framework for retrieving Top-kCS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> query answers. Extensive experiments have been conducted on real and synthetic data sets to confirm the efficiency and effectiveness of our proposed Top-kCS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> approach under various parameter settings.
Privacy protection among three antithetic-parties for context-aware services

The popularity of context-aware services is improving the quality of life, while raising serious privacy issues. In order for users to receive quality service, they are at risk of leaking private information by adversaries that are possibly eavesdropping on the data and/or by the untrusted service platform selling off its data to adversaries. Game theory has been utilized as a powerful tool to achieve privacy preservation by strategically balancing the trade-off between profit (service) and cost (data leakage) for the user. However, most of the existing schemes cannot fully exploit the power of game theory, as they fail to depict the mutual relationship between any two (of the three) parties involved: user, platform, and adversary. Existing schemes are also not always able to provide specific guidance for a user to reduce the impact of the joint threats from the platform and adversary. In this paper, we design a privacy-preserving game to quantify the three parties’ concerns and capture interactions between any two of them. We also identify the best strategy for each party at a fine-grained level, i.e. specific settings, not simply binary choices. We validate the performance of our proposed game model through both a theoretical analysis and experiments.
Laplacian large margin distribution machine for semi-supervised classification

Semi-Supervised Learning (SSL) has attracted much attention in the field of machine learning and data mining. As an extension of Support Vector Machine (SVM), the Semi-Supervised Support Vector Machine (S3VM) was proposed for SSL. Recent studies have disclosed that optimising the margin distribution is more crucial than maximising the minimum margin in generating a better classification. However, the existing S3VM models still follow the idea of maximising the minimum margin. Therefore, this paper proposes a novel Laplacian Large margin Distribution Machine (LapLDM) for SSL to enhance the classification performance. This method can optimise the margin distribution by maximising the first-order (margin mean) and minimising the second-order (margin variance) statistics of margins, and exploit the geometry information of marginal distribution embedded in the unlabelled data through the Laplacian regularizer. Then this paper develops a Preconditioned Conjugate Gradient (PCG) algorithm to solve the nonlinear LapLDM model on those regular-scaled data sets and a Stochastic Gradient Descent with Variance Reduction (SVRG) algorithm to solve the linear LapLDM model on those large-scaled data sets. These algorithms can accelerate the implementing efficiencies of proposed models and make them available for those large-scaled problems. Finally, the numerical results on four artificial and fourteen public benchmark data sets demonstrate that the LapLDM is superior to some well-known S3VM models.
The concept of "mentoring" as one of the main platforms for the disclosure of students' professional competencies

The article considers the concept of professional development of mentoring in higher education institutions. The relevance of this study is to form a closer environment of interaction between students with each other and with the teacher, who points out the necessary professional competencies and forms skills for further cooperation with the employer. The article focuses on two main mentoring platforms: "student-student" and "teacher-student". The analysis of the platforms` participants` portraits, their interaction for the formation of professional competencies of students is carried out. A general strategy for building work for the two mentioned platforms is proposed. The positive and negative factors of these platforms are identified, effective methods, tools and forms of professional development of the student are proposed. Conclusions about the correct and logical organization of the mentoring platform are formulated.
Adaptive Stochastic Gradient Descent for Deep Learning on Heterogeneous CPU+GPU Architectures

The widely-adopted practice is to train deep learning models with specialized hardware accelerators, e.g., GPUs or TPUs, due to their superior performance on linear algebra operations. However, this strategy does not employ effectively the extensive CPU and memory resources – which are used only for preprocessing, data transfer, and scheduling – available by default on the accelerated servers. In this paper, we study training algorithms for deep learning on heterogeneous CPU+GPU architectures. Our two-fold objective – maximize convergence rate and resource utilization simultaneously – makes the problem challenging. In order to allow for a principled exploration of the design space, we first introduce a generic deep learning framework that exploits the difference in computational power and memory hierarchy between CPU and GPU through asynchronous message passing. Based on insights gained through experimentation with the framework, we design two heterogeneous asynchronous stochastic gradient descent (SGD) algorithms. The first algorithm – CPU+GPU Hogbatch – combines small batches on CPU with large batches on GPU in order to maximize the utilization of both resources. However, this generates an unbalanced model update distribution which hinders the statistical convergence. The second algorithm – Adaptive Hogbatch – assigns batches with continuously evolving size based on the relative speed of CPU and GPU. This balances the model updates ratio at the expense of a customizable decrease in utilization. We show that the implementation of these algorithms in the proposed CPU+GPU framework achieves both faster convergence and higher resource utilization than TensorFlow on several real datasets.
Isotropic Maximization Loss and Entropic Score: Fast, Accurate, Scalable, Unexposed, Turnkey, and Native Neural Networks Out-of-Distribution Detection

Current out-of-distribution detection (ODD) approaches require cumbersomeprocedures that add undesired side-effects to the solution. In this paper, weargue that the uncertainty in neural networks is mainly due to SoftMax lossanisotropy. Consequently, we propose an isotropic loss (IsoMax) and a decisionscore (Entropic Score) to significantly improve the ODD performance whilekeeping the overall solution fast, accurate, scalable, unexposed, turnkey, andnative. Our experiments indeed showed that uncertainty is extremely reducedsimply by replacing the SoftMax loss without relying on techniques such asadversarial training/validation, special-purpose data augmentation, outlierexposure, ensembles methods, Bayesian mechanisms, generative approaches, metriclearning, or additional classifiers/regressions. The results also showed thatour straightforward proposal overcomes ODIN, ACET, and is competitive againstthe Mahalanobis approach besides avoiding their undesired requirements andweaknesses. Since IsoMax loss works as a direct and transparent SoftMax lossdrop-in replacement, these techniques may be used combined with our loss toincrease the overall performance even more if their associated drawbacks arenot a concern in a particular use case.
Speech Gesture Generation from Acoustic and Textual Information using LSTMs

This paper presents a gesture generation system developed for the generation and evaluation of non-verbal behaviour for embodied agents (GENEA) challenge 2020. The GENEA challenge provides approximately 4 hours of speech corpus, and 3D full-body human motions called the Trinity speech gesture dataset The gesture generation system's input varies from acoustic speech only, text only, acoustic and text, while the expected output is a video of the corresponding 3D motions.The proposed system consists of 2 steps: pre-processing the data and gesture modeling. In the first step, we pre-process the audio and text and stack them together as an input vector. We then pre-process the output of the 3D motion using a representation learning method. We select only 15 joints in the upper part of the body and represent them with a vector learned from the Denoising Autoencoder Networks (DAEs) model. In the second step, we build the gesture model using the encoder-decoder bidirectional LSTM architecture. This model transforms the acoustic speech and textual information into the DAEs output representation. We then decode the DAE representation to generate the 3D motion output video.The system has been evaluated using the held-out dataset and has been compared among nine systems: five participants, two baseline systems, two natural motions (real video). Two subjective evaluation methods, which are human-likeness and appropriateness, have been measured via a crowdsourced system. Our proposed system gets a fair rating score on both subjective evaluation methods.
Exploring Automatic Fitness Evaluation for Evolutionary Typesetting

The recent popularity of creative coding tools and Computational Creativity approaches are promoting a paradigm shift in the creation, development and production of Graphic Design artefacts. In this work, we present an evolutionary system for the automatic typesetting of typographic posters. This system is inspired by the letterpress typesetting process of the print houses in the earlier 19th century and employs lexicon-based approaches to recognise the semantic meaning of the posters' content. During the evolutionary process, poster designs are automatically created and evaluated according to three objectives: legibility, aesthetics, and semantics. The system allows the users to express their preferences by specifying the intended visual features for the output designs, selecting the preferable fitness assignment strategy, and controlling different aspects of the evaluation strategy. We implemented three automatic strategies to evaluate the fitness of the posters: a multi-criteria hardwired fitness function, a multi-objective optimisation approach, and a hybrid strategy that combines features from the previous two strategies. The experimental results demonstrate the ability of the presented system to generate typographic posters, from scratch, and show the impact of the different evaluation strategies on the evolved poster designs. Overall, this research reveals how Evolutionary Computation approaches can be employed to develop novel co-creative typesetting tools and enable the automatic creation of customised typographic designs.
Hybrid Classifier Based on Binary Neural Network and Fuzzy Ant Colony Optimization Algorithm

Since the last decade, classification methods are useful in a wide range of applications. Classification is a task to segregate the sample into different groups. This capability can be introduced in the computer system by designing various types of classifiers. The methodology known as the neural network in artificial intelligence (AI) offers so many approaches for classification problem. In this aspect, the hybrid classifier is created and tested for the application of the multi-class classification problem on the basis of Binary Neural Network (BNN) and Fuzzy Ant Colony Optimization (FACO). ACO algorithm has proven to be a powerful tool for optimal control of continuous-state dynamic systems. This hybrid Fuzzy ACO algorithm combines the multi-agent optimization and fuzzy partitioning of the state space of the system. This classifier is used to classify any type of data whether it is labelled or unlabeled or both. FACO can monitor the continuous data type and eliminate the complexity and allowing it to outperform the standard algorithm. FACO employs a rule-based feature selection concept to eliminate the least influential features, which increases computational time. To begin, it preprocesses the data set to generate binary values. Then, preprocessed data is used as an input for the hidden layer in order to reduce preparation time across all multiple classes. The hidden layer training is performed in parallel, which employs the geometrical learning principle. This system has been evaluated on a variety of benchmark datasets and compared by varying the learning parameters.
Multi-Topic Labelling Classification Based on LSTM

The necessity for automatic classification of some resources has become extremely important given the fast-increasing number of electronic resources. People's opinions have been extracted from social media sites using Artificial Intelligence (AI). Despite this, the majority of current research focuses on extrapolating features from texts. Multi-label textual data classification is a significant problem in terms of the increasing amount of data available and the growing difficulties of assigning each text piece with one label. Examples include news and email articles. This work focuses on multi-label classification of Arabic texts. After dataset collection; several architectures were tested for this task. Bidirectional Long Short-Term Memory networks (BiLSTM) showed the superior results with F-score equal 86.6 in development set, and F1-score equal 82.24 in leaderboard Mowjaz competition.