Reviewerly/exHarmony huggingface dataset:
Research on ARKFCM Algorithm Based on Membership Constraint and Bias Field Correction in Neonatal HIE Image Segmentation Method

First, this paper presents the algorithm of adaptively regularized kernel-based fuzzy C-means based on membership constraint (G-ARKFCM). Under the idea of competitive learning based on penalizing opponents, a new membership constraint function penalty item is introduced for each sample point in the segmented image, so that the ARKFCM algorithm is no longer limited to the fuzzy index m = 2. Secondly, the multiplicative intrinsic component optimization (MICO) is introduced into G-ARKFCM to obtain the GM-ARKFCM algorithm, which can correct the bias field when segmenting neonatal HIE images. Compared with other algorithms, the GM-ARKFCM algorithm has better segmentation quality and robustness. The GM-ARKFCM algorithm can more completely segment the neonatal ventricles and surrounding white matter and can retain more information of the original image.
Neural Networks with Superexpressive Activations and Integer Weights

An example of an activation function $$\sigma $$ is given such that networks with activations $$\{\sigma , \lfloor \cdot \rfloor \}$$ , integer weights and a fixed architecture depending only on the input dimension d approximate continuous functions on $$[0,1]^d$$ . The range of integer weights required for $$\varepsilon $$ -approximation of Hölder continuous functions is derived, which, together with our discrete choice of weights, allows to obtain the number of networks needed to attain a given approximation rate. Combining this number with the obtained speed of approximation and applying an oracle inequality we get a prediction rate $$n^{\frac{-2\beta }{2\beta +d}}\log _2n$$ for neural network regression estimation of an unknown $$\beta $$ -Hölder continuous function with given n samples. Thus, up to a logarithmic factor $$\log _2n$$ , the attained rate coincides with the minimax estimation rate for the prediction error of $$\beta $$ -smooth functions. As the network sizes are fixed and their weights are integers, the constructed networks are not only easily encodable but they also reduce the problem of finding the best predictor to a simple procedure of minimization over the finite set of candidates.
A road extraction algorithm for reducing shadow effect

The traditional road shadow processing method distinguishes the road from the road shadow, which leads to the reduction of road automation. In this paper, based on the enhancement of the blue component, the brightness component in HSI space is transformed by NDUI shadow exponent transformation, the transformed image is segmented by threshold value, and the road main body is extracted after removing the small area spot and the adaptive structural element morphology closing operation. The test results were evaluated by the detection accuracy and quality, and it was concluded that the detection accuracy of the proposed method in rural roads could reach 96.87% and the detection quality could reach 85.21%. The test results show that this method can effectively eliminate the non-pavement shadow and extract the road with shadow, and has the characteristics of simplicity and high degree of automation.
Dual Loss for Manga Character Recognition with Imbalanced Training Data

Manga character recognition is a key technology for manga character retrieval and verification. This task is very challenging since the manga character images have a long-tailed distribution and large quality variations. Training models with cross-entropy softmax loss on such imbalanced data would introduce biases to feature and class weight norms. To handle this problem, we propose a novel dual loss which is the sum of two losses: dual ring loss and dual adaptive re-weighting loss. Dual ring loss combines weight and feature soft normalization and serves as a regularization term to softmax loss. Dual adaptive re-weighting loss re-weights softmax loss according to the norm of both feature and class weight. With the proposed losses, we have achieved encouraging results on the Manga109 benchmark. Specifically, compared with the baseline softmax loss, our method improves the character retrieval mAP from 35.72% to 38.88% and the character verification accuracy from 87.00% to 88.50%.
Anchor-Free Few-Shot Object Detection for Densely Arranged Small Targets in Drone Images

In many cases, drone is needed to detect objects from high altitude. The lack of training samples of this object category will have a bad impact on the task. In this paper, we design a few-shot detector for drone images. It adopts anchor-free one-stage framework, which lead to more reasonable definition of positive and negative samples and faster speed. We introduce attention mechanism to enable our model match the objects of same categories and distinguish the different class objects and propose a matching score map to utilize the similarity information of attention feature map. The similarity probability of each pixel region for support category is integrated into regression bounding boxes to obtain the similarity probability of each regression bounding box. Finally, through soft-NMS, the predicted detection bounding boxes for support category objects are generated. Compared with YOLOv3 on DOTA dataset, our model is proved to be effective for few-shot detection task of drone images.
Visual Vibration Analysis of Vibrating Object at Low Frequency

The paper explains the use of camera in recording and analyzing the vibration of visible objects. Through spatial and temporal analysis we analyze the elusive changes in the video recorded of object vibrating at low frequency. The information can be used in predicting the physical properties of the object and the force that is responsible for their motion. This paper uses the input video of the simple harmonic pendulum which is vibrating at low frequency. The algorithm developed is used for processing the video and extracting the motion of the pendulum bob using visual vibration analysis. By using cameras for analyzing the vibrations of the object we can present cameras as an low cost vibration sensor with intensely high spatial resolution than the devices that are used from past for vibration measurement. By doing this we give a world a new way to use camera as a powerful tool for vibration analysis.
Optimization of Computer Teaching Strategy Based on Genetic Algorithm

The application of computer technology in the field of education has made great progress. More and more educational problems are solved by using computers. The optimization of teaching strategy is also an important link in the field of education. How to use computer technology to optimize teaching strategy to overcome the disadvantages of subjectivity and randomness of traditional optimization method is a new research aspect of computer application in the field of education. Based on this, this paper studies the optimization of computer teaching strategy based on genetic algorithm. This paper designs an evaluation mechanism of teaching strategy, and on this basis, uses genetic algorithm to optimize teaching strategy, so as to achieve the purpose of continuous evolution and optimization of teaching strategy with the change of teaching content and teaching object. At the same time, the teaching strategy optimization system based on genetic algorithm is designed and implemented. In one academic year, this paper tracks the use of the teaching strategy optimization system by the students of one or two classes in their university and their two teachers in the semester. The experimental group used the system optimization teaching strategy, while the control group did not use the optimization strategy. The results show that the average teaching performance evaluation of the experimental group is 4 points higher than that of the control group. Therefore, genetic algorithm is a kind of parallel optimization algorithm, which is very suitable for solving the problem of computer teaching strategy optimization. The optimization effect of the final generated teaching strategy meets the requirements, has high credibility and achieves good results.
The Effectiveness of Unsupervised Subword Modeling With Autoregressive and Cross-Lingual Phone-Aware Networks

This study addresses unsupervised subword modeling, i.e., learning acoustic feature representations that can distinguish between subword units of a language. We propose a two-stage learning framework that combines self-supervised learning and cross-lingual knowledge transfer. The framework consists of autoregressive predictive coding (APC) as the front-end and a cross-lingual deep neural network (DNN) as the back-end. Experiments on the ABX subword discriminability task conducted with the Libri-light and ZeroSpeech 2017 databases showed that our approach is competitive or superior to state-of-the-art studies. Comprehensive and systematic analyses at the phoneme- and articulatory feature (AF)-level showed that our approach was better at capturing diphthong than monophthong vowel information, while also differences in the amount of information captured for different types of consonants were observed. Moreover, a positive correlation was found between the effectiveness of the back-end in capturing a phoneme's information and the quality of the cross-lingual phone labels assigned to the phoneme. The AF-level analysis together with t-SNE visualization results showed that the proposed approach is better than MFCC and APC features in capturing manner and place of articulation information, vowel height, and backness information. Taken together, the analyses showed that the two stages in our approach are both effective in capturing phoneme and AF information. Nevertheless, monophthong vowel information is less well captured than consonant information, which suggests that future research should focus on improving capturing monophthong vowel information.
Learning Transaction Cohesiveness for Online Payment Fraud Detection

In the online payment fraud detection scenario, transactions are characterized by attributes. A fraud detection system makes use of the attributes to build a binary classifier that tells fraudulent transactions from legitimate ones. The key factor that affects the quality of a fraud detection system is how to extract useful features from transaction attributes. This paper proposes a novel automatic feature learning approach for online payment fraud detection. To begin with, it represents transaction attributes as vectors in a latent vector space. With these vectors, transaction cohesiveness is defined. By maximizing the probability that legitimate transactions have larger cohesiveness than fraudulent ones, the vector representations of attributes can be optimized. Experiments demonstrate that the selected classifiers trained with the cohesive features show superior performance than those with the original attributes.
Facial expression recognition based on multi branch structure

Facial expression recognition (FER) is an important means for machines to perceive human emotions and interact with human beings. Most of the existing facial expression recognition methods only use a single convolutional neural network to extract the global features of the face. Some insignificant details and features with low frequency are easy to be ignored, and part of the facial features are lost. This paper proposes a facial expression recognition method based on multi branch structure, which extracts the global and detailed features of the face from the global and local aspects respectively, so as to make a more detailed representation of the facial expression and further improve the accuracy of facial expression recognition. Specifically, we first design a multi branch network, which takes Resnet-50 as the backbone network. The network structure after Conv Block3 is divided into three branches. The first branch is used to extract the global features of the face, and the second and third branches are used to cut the face into two parts and three parts after Conv Block5 to extract the detailed features of the face. Finally, the global features and detail features are fused in the full connection layer and input into the classifier for classification. The experimental results show that the accuracy of this method is 73.7%, which is 4% higher than that of traditional Resnet-50, which fully verifies the effectiveness of this method.
A Fast and Accurate Object Detector for Handwritten Digit String Recognition

Focusing on handwritten digit string recognition (HDSR), we propose an anchor-free object detector called ChipNet, where a novel encoding method is designed. The input image is divided into columns, and then these columns are encoded by the ground truth. The adjacent columns are responsible for detecting the same target so that it can well address the class-imbalanced problem meanwhile reducing the network computation. ChipNet is composed of convolutional and bidirectional long short term memory networks. Different from the typical detectors, it doesn't use region proposals, anchors or regions of interest pooling. Hence, it can overcome the shortages of anchor-based and dense detectors in HDSR. The experiments are implemented on the synthetic digit strings, the CVL HDS database, and the ORAND-CAR-A & B databases. The high accuracies, which surpass the reported results by a large margin (up to 6.62%), are achieved. Furthermore, it gets 219 FPS speed on 160 × 32 px resolution images when using a Tesla P100 GPU. The results also show that ChipNet can handle touching, connecting and arbitrary length digit strings, and the obtained accuracies in HDSR are as high as the ones in single handwritten digit recognition.
Sequential Adversarial Anomaly Detection with Deep Fourier Kernel

We present a novel adversarial detector for the anomalous sequence when there are only one-class training samples. The detector is developed by finding the best detector that can discriminate against the worst-case, which statistically mimics the training sequences. We explicitly capture the dependence in sequential events using the marked point process with a deep Fourier kernel. The detector evaluates a test sequence and compares it with an optimal time-varying threshold, which is also learned from data. Using numerical experiments on simulations and real-world datasets, we demonstrate the superior performance of our proposed method.
Blockchain enabled UE-assisted multi-connectivity scheme

With the large-scale commercial deployment of 5G network, more and more new applications and services are emerged. These new applications and services put forward higher requirements on the network key performance indicators, which is one or two order of the magnitude of 5G performance. To provide users the Tbps level user experience data rate everywhere, traditional multi-connectivity scheme may not work well especially in the initial 6G network deployment stage. To solve this problem, we proposed a blockchain enabled UE-assisted multi-connectivity solution in this work including the architecture and implementation procedure. In addition to being used for improving the network connectivity, this solution can also be used to facilitate resource sharing among the untrusted nodes, including the computing power resources, storage resources, electric power, useful information, etc. The simulation proved the performance of the proposed scheme in the final part of the work.
A Stage Match for Query-by-Example Spoken Term Detection Based On Structure Information of Query

The state-of-the-art of query-by-example spoken term detection (QbE-STD) strategies are usually based on segmental dynamic time warping (S-DTW). However, the sliding window in S-DTW may separate signal of a word into different segments and produce many illegal candidates required to be compared with the query, which significantly reduce the accuracy and efficiency of detection. In this paper, we propose a stage match strategy based on the structure information of the query, represented with the unvoiced-voiced attribute of the portions in itself. The strategy first locates potential candidates with similar structure against the query in utterances, and further matches the query with Type-Location DTW (TL-DTW), which is a modified DTW with the constraints of pronunciation types and relative positions of paired frames in the voiced sub-segments. Experiments on AISHELL-1 Corpus showed that the proposed approach achieved a relative improvement of 30.51% in AUC against S-DTW and speeded up the retrieval.
On the Minimal Recognizable Image Patch

In contrast to human vision, common recognition algorithms often fail on partially occluded images. We propose characterizing, empirically, the algorithmic limits by finding a minimal recognizable patch (MRP) that is by itself sufficient to recognize the image. A specialized deep network allows us to find the most informative patches of a given size, and serves as an experimental tool. A human vision study recently characterized related (but different) minimally recognizable configurations (MIRCs) [1], for which we specify computational analogues (denoted cMIRCs). The drop in human decision accuracy associated with size reduction of these MIRCs is substantial and sharp. Interestingly, such sharp reductions were also found for the computational versions we specified.
Javadoc Violations and Their Evolution in Open-Source Software

Software quality comprises different and interrelated aspects. One of them is maintainability, which in turn is made up of measurable attributes. Previous studies have shown that documentation, by contributing to the comprehensibility of software, may have a positive effect on maintainability and, hence, software quality. This paper presents a study in which we analyzed Javadoc comments from 163 different open-source projects. Javadoc is the de facto standard for documenting source code files in Java projects, and although its syntax is less strict than in other (programming) languages, documentation written with Javadoc may contain violations. Our study focuses on the detection of different types of Javadoc violations as well as the source code elements affected by them. Also, by utilizing software repository mining techniques, we examined the history of the subject systems to gain further insights into the evolution of Javadoc violations. According to our results, about half of the source code elements have no Javadoc whatsoever. Among the different components of Javadoc comments (if present), the description of exceptions, by far, has the highest average ratio of violations. With regard to the types of affected elements, constructors and methods show very high average ratios. Also, we found that, on average, violations live more than two years.Nowadays, most integrated development environments (IDEs) for Java are capable of detecting missing Javadoc comments as well as comments with syntactic errors. However, our results indicate that the documentation of source code might be considered less important to developers or that these tools alone may not be sufficient for maintaining consistent documentation.
An Extensible Framework for Sentiment Analysis based on Opinion Disambiguation

Sentiment analysis is an extremely well-known application territory of text mining. Deep Sentiment disambiguation is a significant piece of any sentiment analysis or assessment analysis of text mining. This paper chiefly centers around cyberhate order, since the spread of disdain discourse by means of online media will problematically affect social union and lead to territorial and local area strains. The proposed structure tends to the test of an ongoing investigation of Social Media posts and the recognition of self-destruction-related conduct and the cyberhate cycle. To handle the flood of a person's online substance, execute a Fuzzy structure for the order of text, which is generally utilized for the recognition of changes in information stream settings. The contribution to this system is a progression of conduct highlights registered from every Social media post.
Dimensionality Reduction for Data Visualization and Linear Classification, and the Trade-off between Robustness and Classification Accuracy

This paper has three intertwined goals. The first is to introduce a new similarity measure for scatter plots. It uses Delaunay triangulations to compare two scatter plots regarding their relative positioning of clusters. The second is to apply this measure for the robustness assessment of a recent deep neural network (DNN) approach to dimensionality reduction (DR) for data visualization. It uses a nonlinear generalization of Fisher's linear discriminant analysis (LDA) as the encoder network of a deep autoencoder (DAE). The DAE's decoder network acts as a regularizer. The third goal is to look at different variants of the DNN: ones that promise robustness and ones that promise high classification accuracies. This is to study the trade-off between these two objectives - our results support the recent claim that robustness may be at odds with accuracy; however, results that are balanced regarding both objectives are achievable. We see a restricted Boltzmann machine (RBM) pretraining and the DAE based regularization as important building blocks for achieving balanced results. As a means of assessing the robustness of DR methods, we propose a measure that is based on our similarity measure for scatter plots. The robustness measure comes with a superimposition view of Delaunay triangulations that enables a fast comparison of results from multiple DR methods.
Ethico-Juridical Dimension of Artificial Intelligence Application in the Combat to Covid-19 Pandemics

With the dramatic development of information technology, the Covid-19 pandemic period has created the opportunity to access a new phase in terms of the age that is becoming more and more digitized every day. Covid-19 pandemic has emerged as a driving force in changing certain things in the global world order. In order to minimize the effects of the current crisis, countries have entered into a digital transformation process in many different areas, from the education sector to the health sector, judicial practices to monitoring social distance rules. Artificial intelligence (AI), which already existed in our lives, started to be used in certain application areas, especially in this period. Pandemic period once again demonstrated that artificial intelligence and digital technologies have become a big part of our lives. The global world has witnessed a digital transformation with the applications developed within the scope of the Covid-19 fight. However, while using AI-based apps provides many benefits to manage the pandemic process, it also brings with several ethical and legal concerns regarding human rights and fundamental freedoms. Ethics provides several frameworks in the name of the promotion of human values and dignity. Among human rights and ethics, there is a clear and strong connection. The rights and freedom demands that arise based on human rights are ethically based. The legitimacy of human rights-based demands is originally based on ethics. For that reason, using artificial intelligence is directly linked to "human safety, health and safety, liberty, confidentiality, integrity, dignity, autonomy, and non-discrimination," and these also include ethical concerns. Therefore, artificial intelligence practices related to human rights and freedoms that cause some human rights violations during the pandemic period also reveal an ethical violation. Another risk factor is the use of "surveillance technology" in the "new normal" lifestyle, which was effectively used by national governments during the pandemic period. In this extraordinary period, there is concern that artificial intelligence-based practices developed to protect public health will be permanently used as usual in the post-pandemic period. AI has tremendous potency to advance the lives of many people and procure human rights for all. It is necessary to evaluate these potentials and minimize the risks associated with artificial intelligence. Likewise, artificial intelligence also poses deep risks to security, democracy, and human dignity.