Reviewerly/exHarmony huggingface dataset:
Broad learning system based on ensemble learning

Aiming at the problem of poor stability of broad learning system (BLS), combining with the idea of ensemble learning, bagging BLS and stacking BLS algorithms are proposed. Firstly, the data set is sampled to get the data subset, and then the base classifier is trained by the broad learning system. Finally, the prediction results of the whole model are obtained by integrating the base classifiers. Through experiments on multiple data sets, the results show that the proposed algorithms achieve ideal results in image classification, and the two models are better than the single broad learning system in classification accuracy and variance, which shows the effectiveness of the proposed algorithm.
Subspace Capsule Network for Blurred Image Super-Resolution

Super resolution of blurred image can effectively improve image quality and machine recognition accuracy. Existing methods use universal prior information in the data to deblur, and rarely consider image semantic prior information. In the field of deep learning, using more prior information can achieve better results. Inspired by the powerful object semantic information representation ability of the capsule network, in this paper, a super-resolution method based on subspace capsule neural network is proposed. The blurred image and the clear image have different semantic distributions in the subspace capsule feature space. The MNIST data set is de blurred, and the clear image is used as the input of the high resolution self encoder, and the blurred image is used as the input of the low resolution self encoder. A mapping network is used to map the blurred image subspace capsule feature distribution to the clear image subspace capsule feature distribution, then it is input into the decoder of subspace capsule network trained by the original clear image to get the desired super-resolution result. It is proved that the proposed method can effectively improve the image pixel quality.
Training Automatic View Planner for Cardiac MR Imaging via Self-supervision by Spatial Relationship Between Views

View planning for the acquisition of cardiac magnetic resonance imaging (CMR) requires acquaintance with the cardiac anatomy and remains a challenging task in clinical practice. Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible and annotation-free system for automatic CMR view planning. The system mines the spatial relationship—more specifically, locates and exploits the intersecting lines—between the source and target views, and trains deep networks to regress heatmaps defined by these intersecting lines. As the spatial relationship is self-contained in properly stored data, e.g., in the DICOM format, the need for manual annotation is eliminated. Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target view, for a globally optimal prescription. The multi-view aggregation mimics the similar strategy practiced by skilled human prescribers. Experimental results on 181 clinical CMR exams show that our system achieves superior accuracy to existing approaches including conventional atlas-based and newer deep learning based ones, in prescribing four standard CMR views. The mean angle difference and point-to-plane distance evaluated against the ground truth planes are 5.98\(^\circ \) and 3.48 mm, respectively.
Multi-Scale Attention Network Based on Multi-Feature Fusion for Person Re-Identification

As a challenging and high practical research topic in public safety, person re-identification (Re-ID) technology has attracted increasing attention in the field of computer vision. Due to the success of deep learning, Convolutional Neural Networks (CNNs) have become the main techniques to extract discriminative features for person Re-ID++. However, there are many problems in the real scene of pedestrian images, such as pedestrian posture changes, inconsistent shooting perspective and object occlusion, etc. The global features extracted from images by CNNs are easily disturbed by these problems, resulting in the lack of robustness and discrimination, which further leads to low recognition accuracy. To solve these issues, we propose a multi-scale attention network based on multi-feature fusion (MSAN), which adopts a multi-branch deep network structure consisting of a global feature learning branch, two local feature learning branches and a shallow-level feature learning branch. It can sample the features of different depth of the network and get discriminative feature embedding by combining global and local cues, and then the sampled features are fused to predict the pedestrians. We also use the attention mechanism to make the network to focus on the key information of different scale feature maps thus enhance the learning of key parts of the human body and alleviate the interference caused by image changes. Experimental results on three mainstream benchmark datasets Market-1501, DukeMTMC-reID and CUHK03 show that our method can significantly improve the performances and outperform most mainstream methods.
Weighted Ensemble of Neural and Probabilistic Graphical Models for Click Prediction

Predicting user behavior in web mining is an important concept with commercial implications. The user response to search engine results is crucial for understanding the relative popularity of websites and market trends. The most popular way of understanding user interests is via click models that can predict whether a user will click on a search engine result or not, based on past observations. There are two main categories of click models, namely, the neural network based models and the probabilistic graphical models. In this paper, we combine the goodness of both approaches by presenting a weighted ensemble of both types of models. The weighted sum of softmax scores integrates the predictions of the individual models. Assigning higher weights to the neural models is found to improve the performance of the ensemble. The AUC and perplexity scores of our weighted ensemble model are higher than the state of the art, as proved by experiments on the benchmark Tiangong-ST dataset.
Cyber Awareness Training for Election Staff Using Constructive Alignment

Cybersecurity awareness and cyber hygiene trainings are becoming standard practice for employees of election administrations. Election Management Bodies (EMBs) have specific needs: elections are cyclic with regards to the tasks and their associate risks, they are high value targets during a short time window, and they suffer from high turnover of staff making sustainable training difficult. With lots of training methodologies and training programs targeting election observers, officials, etc., there are limited quantifiable measures for the efficiency of this type of training. Evaluating the adequacy of the training objectives and methodologies to the specific needs of election administration is becoming a necessity. We propose to use constructive alignment for designing and evaluating cybersecurity awareness trainings.
A Network-Flow Reduction for the Multi-Robot Goal Allocation and Motion Planning Problem

This paper deals with the problem of allocating goals to multiple robots with complex dynamics while computing obstacle-free motions to reach those goals. The spectrum of existing methods ranges from complete and optimal approaches with poor scalability, to highly scalable methods which make unrealistic assumptions on the robots and/or environment. We overcome these limitations by using an efficient graph-based method for decomposing the problem into sub-problems. In particular, we reduce the problem to a Minimum-Cost Max-Flow problem whose solution is used by a multi-robot motion planner that does not impose restrictive assumptions on robot kinodynamics or on the environment. We show empirically that our approach scales to tens of robots in environments composed of hundreds of polygons.
Short-Term Global Horizontal Irradiance Forecasting Using a Hybrid Convolutional Neural Network-Gate Recurrent Unit Method

Abstract The effectiveness of photovoltaic power generation systems—a clean and renewable use of solar energy—depends on the amount of solar radiation. Consequently, solar radiation forecasting (especially short-term) is crucial for photovoltaic plants. In this paper, a hybrid convolutional neural network (CNN) and gate recurrent unit (GRU) method is proposed for short-term (10-min) solar radiation forecasting based on image and time-series data (i.e., radiation data at different times). The method aims to achieve high performance in solar radiation forecasting, which can be useful for PV plant adjustment. CNN–GRU consists of two branches. One is based on the ResNet-18 structure, which can extract features from sky images. The other is a GRU branch, which consists of three fully connected layers used for meteorological feature extraction. Experiments on a public dataset showed that our method predicts the mean absolute error better than other benchmark models. The ablation experiments demonstrated that the hybrid model performs better than a single model and, therefore, shows promise for application in solar radiation forecasting.
Wi-PW: Inferring Smartphone Password Using Wi-Fi Signals

Privacy security in the era of big data is particularly important, but you are likely to reveal your most important privacy, your password, inadvertently. With the deepening of the Internet of Things (IOT), Wi-Fi is widely used as a bridge to connect smart devices. However, with the popularity of Wi-Fi, people will face more threats. When we were sitting in a cafe and enjoying the fun of online shopping with our own data, maybe our payment password was stolen by the surrounding Wi-Fi devices. In this study, we propose a system using Wi-Fi signals to reproduce the password you entered on the smartphone called Wi-PW. Inspired by the influence of human movement on the Wi-Fi signal, we predict the password by analyzing the CSI information in the received Wi-Fi signal. Due to the existence of multipath reflection, unique interference will be generated when entering the password on the mobile phone and clicking different buttons. We can use this unique interference to infer the input password by LSTM model. Experiments show that the system reaches an accuracy top to 79.0% for recognizing a single keystroke.
Density-Sorting-Based Convolutional Fuzzy Min-Max Neural Network for Image Classification

Traditional image classification methods mostly use offline learning mode, which takes a lot of time when data is updated. In this paper, we propose a density-sorting-based convolutional fuzzy min-max neural network (DCFMNN) for image classification to solve this problem. DCFMNN is realized based on convolutional Neural Network (CNN) and density-sorting-based fuzzy min-max neural network. CNN is applied for image feature extraction. Density-sorting-based fuzzy min-max neural network is used for classification, which includes density-based sorting part and fuzzy min-max (FMM) neural network part. In the part of density-based sorting, patterns are sorted according to the points with the highest density in the same class and two densest points are considered for selection. The purpose is to overcome the influence of the pattern input order in the original FMM on the creation of the hyperbox. In the part of FMM, the fuzzy set classification method is used to enable online learning. Diverse CNN architectures are applied to DCFMNN. The benchmark image datasets were employed for evaluation on DCFMNN. Experimental results show that DCFMNN has high classification accuracy and less network complexity, and its online learning ability reduces the training time.
GrumPy: an automated approach to simplify issue data analysis for newcomers

The development model of open-source projects involves intensive developers' collaborations to develop new features and maintain existing ones. Repositories hosting the projects may offer communication facilities to make partnerships happen in practice systematically. Issue reporting is one of the most employed strategies, as it carries valuable information about the software behavior or the repository itself. Indeed, issues have become the focus of several recent investigations, leading to an increased demand for automated approaches to support issue mining and analysis. The literature has brought a set of solutions for automating issue mining and analysis. However, they do not often provide developers with a user-friendly interface or an easy way to mine, manage or visualize issues' data. Therefore, this paper introduces GrumPy, an open-source environment aimed to provide an automated approach to mine, manage, and visualize statistics about reported issues from open-source repositories. It provides a manageable multi-miner, a module to handle or visualize issues' components, and a dashboard module responsible for showing repository data graphically. Our tool goal is to facilitate issues research work and its components.
Innovation and tradition in metal music production

Contemporary music technology affords limitless potential and has changed the way record producers need to work with metal music, often employing a far more fragmented approach. This article explores technology’s influence on producing metal records through the lived experiences of seven renowned metal music producers, and it is argued that what can be perceived as traditional production processes are production processes that favour capturing performances, embracing the potential of technology. In contrast, the construction of recorded performances through anticipated uses of technology often embodies innovative production methodologies. There are tensions caused by the anticipated use of technology and the participants highlight that commercial and artistic pressures have informed prescriptive and homogenous production methodologies under the guise of innovation.
A study on Quality Assessment of Requirement Engineering Document using Text Classification Technique

The Software Requirement Engineering document is the most important artifacts of the software development life cycle model. In majority of software systems, the Requirements Engineering (RE) Document or SRS (Software Requirement Specification) document has been written in natural language English that are prone to ambiguity. The ambiguous Requirement Engineering document may lead to disastrous results thereby hampering the entire development process and ending up compromising on the quality of a system. The success of any software product depends upon the quality of the Requirement Engineering document. The main reason for software crisis in software Industry is the Ambiguous Requirement Engineering Document. This paper discusses about the types of ambiguity, approaches to handle and providing a level of automatic assistance in order to detect ambiguity in the Requirement Engineering Document. The study also confirms the use of text classification technique to classify a text as “ambiguous” or “Unambiguous” at the syntax level. The key objectives of the work include understanding the presence of ambiguity in any Requirement Engineering document with the help of Machine Learning Techniques and finally minimizing or reducing it.
Kesalahan siswa kelas VII SMP Negeri 19 Mataram Tahun Ajaran 2020/2021 dalam menyelesaikan soal cerita pada materi himpunan ditinjau dari prestasi belajar

This study aims to describe the mistakes made by the VIIA grade students of SMP Negeri 19 Mataram in solving story questions about the set material in terms of learning achievement. With the research subject, namely 6 students of class VIIA which has been selected based on learning achievement. This research is a descriptive qualitative type with research instruments in the form of written test sheets and interview guidelines. The data analysis technique was carried out by the students completed the questions using the Polya analysis or not, if using the Polya analysis, the stages were correct or not, if using the Polya analysis with the correct steps, the students' answers would be analyzed using the Polya analysis. The results of the study were at the stage of understanding the problem, the subject made mistakes 16.66% with a small category, at the stage of choosing the right problem-solving strategy made an error 20% with a small category, at the stage of solving the problem subject made a mistake 51.66% with the high category and at the stage of re-checking subjects made mistakes 64,58% with the very high category.
Teachers and students’ perceptions of Korea’s emergency online learning during the COVID-19 pandemic

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. In Korea, schools and universities begin the first semester in March and it runs through mid-June or July. The second semester begins in September and runs through late December.2. Talking to elders: the Korean language has an honorific system where you have to use a different form of language when speaking to someone who is older. Depending on the interlocutor, the type of Korean is used differently, and it is part of public school education.
Lossless Coding of HDR Color Images in a Floating Point Format Using Block-Adaptive Inter-Color Prediction

This paper proposes a lossless coding method for HDR color images stored in a floating point format called Radiance RGBE. In this method, three mantissa and a common exponent parts, each of which is represented in 8-bit depth, are encoded using the block-adaptive prediction technique with some modifications considering the data structure.
Performance analysis of machine learning libraries

The paper presents results of performance analysis of machine learning libraries. The research was based on ML.NET and TensorFlow tools. The analysis was based on a comparison of running time of the libraries, during detection of objects on sets of images, using hardware with different parameters. The library, consuming fewer hardware resources, turned out to be TensorFlow. The choice of hardware platform and the possibility of using graphic cores, affecting the increase in computational efficiency, turned out to be not without significance.
A Low-Randomness Second-Order Masked AES

We propose a second-order masking of the AES in hardware that requires an order of magnitude less random bits per encryption compared to previous work. The design and its security analysis are based on recent results by Beyne et al. from Asiacrypt 2020. Applying these results to the AES required overcoming significant engineering challenges by introducing new design techniques. Since the security analysis is based on linear cryptanalysis, the masked cipher needs to have sufficient diffusion and the S-box sharing must be highly nonlinear. Hence, in order to apply the changing of the guards technique, a detailed study of its effect on the diffusion of the linear layer becomes important. The security analysis is automated using an SMT solver. Furthermore, we propose a sharpening of the glitch-extended probing model that results in improvements to our concrete security bounds. Finally, it is shown how to amortize randomness costs over multiple evaluations of the masked cipher.
Quantum-Hybrid Neural Vector Quantization – A Mathematical Approach

The paper demonstrates how to realize neural vector quantizers by means of quantum computing approaches. Particularly, we consider self-organizing maps and the neural gas vector quantizer for unsupervised learning as well as generalized learning vector quantization for classification learning. We show how quantum computing concepts can be adopted for these algorithms. The respective mathematical framework is explained in detail.
Unsupervised Learning of Text Line Segmentation by Differentiating Coarse Patterns

Despite recent advances in the field of supervised deep learning for text line segmentation, unsupervised deep learning solutions are beginning to gain popularity. In this paper, we present an unsupervised deep learning method that embeds document image patches to a compact Euclidean space where distances correspond to a coarse text line pattern similarity. Once this space has been produced, text line segmentation can be easily implemented using standard techniques with the embedded feature vectors. To train the model, we extract random pairs of document image patches with the assumption that neighbour patches contain a similar coarse trend of text lines, whereas if one of them is rotated, they contain different coarse trends of text lines. Doing well on this task requires the model to learn to recognize the text lines and their salient parts. The benefit of our approach is zero manual labelling effort. We evaluate the method qualitatively and quantitatively on several variants of text line segmentation datasets to demonstrate its effectivity.
Multi-pose Facial Expression Recognition Based on Unpaired Images

Giving machines the ability to perceive human emotions and enable them to recognize our emotional states is one of the important goals to realize human-computer interaction. In the past decades, facial expression recognition (FER) has always been a research hotspot in the field of computer vision. However, the existing facial expression datasets generally have the problems of insufficient data and unbalanced categories, leading to the phenomenon of over-fitting. To solve this problem, most methods employ the generative adversarial network (GAN) for data augmentation, and achieve good results in facial image generation. But these works focus only on facial identity or head poses, which are not robust for the transformation of facial expression recognition from the laboratory environment to unconstrained scenes. Therefore, we employ the disentangled representation learning to obtain facial feature representation, so as to reduce the impact of pose changes and identity biases on FER. Specifically, the generator uses the encoder-decoder structure to map each face image to two latent spaces: the pose space and the identity space. In each latent space, we disentangle the target attribute from other attributes, and then concatenate corresponding feature vectors to generate a new image with one person’s identity and another person’s pose. Experimental results on Multi-PIE and RAFD datasets show that the proposed method can obtain high quality generated images and effectively improve the recognition rate of facial expressions.
Correlation Power Analysis of Lightweight Block Cipher Algorithm LiCi

Abstract The lightweight block cipher algorithm LiCi processes the input plaintexts into high 32-bits and low 32-bits respectively in Feistel structure, and the corresponding keys of these two parts are not related. Therefore, keys of the first round can be cracked independently, and the computational complexity can be reduced from 2 64 to 2 32 . After studying the key update strategy, this paper proposed that the computational complexity of cracking the second round keys can reduce from 2 64 to 2 13 by using the key obtained in the first round attack. When cracking the sixth round, the complete 128-bit key can be recovered. In this paper, the LiCi encryption algorithm was deployed on FPGA, the power consumption curves were measured with Tektronix MSO5204B oscilloscope which are used to recover the key in the LiCi encryption algorithm. we choose 8 bits keys as attack goal and get the true keys.