Reviewerly/exHarmony huggingface dataset:
Solving The Long-Tailed Problem Via Intra- And Inter-Category Balance

Benchmark datasets for visual recognition assume that data is uniformly distributed, while real-world datasets obey long-tailed distribution. Current approaches handle the long-tailed problem to transform the long-tailed dataset to uniform distribution by re-sampling or re-weighting strategies. These approaches emphasize the tail classes but ignore the hard examples in head classes, which result in performance degradation. In this paper, we propose a novel gradient harmonized mechanism with category-wise adaptive precision to decouple the difficulty and sample size imbalance in the long-tailed problem, which are correspondingly solved via intra- and inter-category balance strategies. Specifically, intra-category balance focuses on the hard examples in each category to optimize the decision boundary, while inter-category balance aims to correct the shift of decision boundary by taking each category as a unit. Extensive experiments demonstrate that the proposed method consistently outperforms other approaches on all the datasets.
International comparison of flatness deviation in areal surface topography measurements

An international comparison of surface topography flatness deviation was carried out. The comparison involved twelve optical surface topography instruments (focus variation instruments, confocal microscopes and coherence scanning interferometers) from six European research laboratories. The results demonstrated the following: (i) The flatness varies orders of magnitude between instruments, dependent on the measurement principle. (ii) The ISO proposed procedure gives meaningful results, especially for instruments based on the confocal principle. (iii) The selection of the measurement artefact is especially important for focus variation measurements, where the flatness deviation is influenced by the surface texture of the used artefact. (iv) A tilt in the used optical flat can significantly influence the measured flatness deviation.
A Personalized<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mfenced open="(" close=")"><mrow><mi>α</mi><mo>,</mo><mi>β</mi><mo>,</mo><mi>l</mi><mo>,</mo><mi>k</mi></mrow></mfenced></math>-Anonymity Model of Social Network for Protecting Privacy

By mining the data published on social network, we can discover the hidden value of information including the privacy of individuals and organizations. Protecting privacy of individuals and organizations on social network has become the focus of more and more researchers. Based on the actual privacy protection need of edge sensitive attribute and vertexes sensitive attribute, we propose a new personalized <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mfenced open="(" close=")"><mrow><mi>α</mi><mo>,</mo><mi>β</mi><mo>,</mo><mi>l</mi><mo>,</mo><mi>k</mi></mrow></mfenced></math> -anonymity technology of privacy preserving to reduce distortion extent of the data in the privacy processing of data of social network. Experimental results of personalized <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><mfenced open="(" close=")"><mrow><mi>α</mi><mo>,</mo><mi>β</mi><mo>,</mo><mi>l</mi><mo>,</mo><mi>k</mi></mrow></mfenced></math> -anonymity algorithm show that <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"><mi>d</mi></math> -neighborhood attack of graph, background knowledge attack, and homogeneity attack can be prevented effectively by using anonymous vertexes and edges, as well as the influence matrix based on background knowledge. The diversity of vertex sensitive attribute can be achieved. Personalized protecting privacy requirements can be met by using such parameter as <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"><mi>α</mi><mo>,</mo><mi>β</mi><mo>,</mo><mi>l</mi><mo>,</mo><mi>k</mi></math> .
Surreptitious Adversarial Examples through Functioning QR Code

The continuous advances in the technology of Convolutional Neural Network (CNN) and Deep Learning have been applied to facilitate various tasks of human life. However, security risks of the users' information and privacy have been increasing rapidly due to the models' vulnerabilities. We have developed a novel method of adversarial attack that can conceal its intent from human intuition through the use of a modified QR code. The modified QR code can be consistently scanned with a reader while retaining adversarial efficacy against image classification models. The QR adversarial patch was created and embedded into an input image to generate adversarial examples, which were trained against CNN image classification models. Experiments were performed to investigate the trade-off in different patch shapes and find the patch's optimal balance of scannability and adversarial efficacy. Furthermore, we have investigated whether particular classes of images are more resistant or vulnerable to the adversarial QR attack, and we also investigated the generality of the adversarial attack across different image classification models.
Stereoscopic deflectometry with a curved screen

Deflectometry has been widely used in topography measurement of specular surface. In deflectometry with a curved screen, the range of the gradient and height field of the measured specular surface can be effectively expanded compared to deflectometry with a plane screen. As stereo deflectometry measures gradient with high accuracy, the specular surface is reconstructed by integrating the gradient. In this paper, a stereo deflectometry with a curved screen is proposed in the aspect of system calibration and measuring principle. A pair of cameras and deflectometric system are calibrated simultaneously to obtain the camera parameters and relationship between two cameras and the curved screen. Then, pairs of rectified fringe patterns are demodulated to obtain two pairs of rectified absolute phase maps and homologous points are searched along epipolar line with the principle of the same normal direction on specular surface. Finally, simulated and actual experiments are carried out and the results show high accuracy and stability.
Firefly algorithm and ant colony algorithm to optimize the traveling salesman problem

Abstract Through the study of ACOTSP, it is found that the previous ant colony algorithm will fall into local optimal when increasing pheromone concentration factor. In order to solve the problem, we through the improved pheromone concentration factor to view your traveling salesman solving process, through the simulation experiments found that due to the pheromone concentration gradually increased with the number of iterations, pheromone concentration and pheromone concentration factor exponential relationship, lead to appear even if the distance is large move also can appear probability is very high. In this design, the ant colony algorithm is optimized by introducing firefly algorithm (FA): the ant colony movement deviation is avoided by adding disturbance factor; and the migration probability caused by excessive pheromone concentration is solved by adding function relation between moving distance and pheromone concentration. Simulation results show that the optimized algorithm has better results and is not easy to fall into local optimum.
Inter-subject registration-based one-shot segmentation with alternating union network for cardiac MRI images

Medical image segmentation based on deep-learning networks makes great progress in assisting disease diagnosis. However, currently, the training of most networks still requires a large amount of data with labels. In reality, labeling a considerable number of medical images is challenging and time-consuming. In order to tackle this challenge, a new one-shot segmentation framework for cardiac MRI images based on an inter-subject registration model called Alternating Union Network (AUN) is proposed in this study. The label of the source image is warped with deformation fields discovered from AUN to segment target images directly. Initially, the volumes are pre-processed by aligning affinely and adjusting the global intensity to simplify subsequent deformation registration. AUN consists of two kinds of subnetworks trained alternately to optimize segmentation gradually. The first kind of subnetwork takes a pair of volumes as inputs and registers them using global intensity similarity. The second kind of subnetwork, which takes the predicted labels generated from the previous subnetwork and the labels refined using the information of intrinsic anatomical structures of interest as inputs, is intensity-independent and focuses attention on registering structures of interest. Specifically, the input of AUN is a pair of a labeled image with the texture in regions of interest removed and a target image. Additionally, a new similarity measurement more appropriate for registering such image pair is defined as Local Squared Error (LSE). The proposed registration-based one-shot segmentation pays attention to the problem of the lack of labeled medical images. In AUN, only one labeled volume is required and a large number of unlabeled ones can be leveraged to improve segmentation performance, which has great advantages in clinical application. In addition, the intensity-independent subnetwork and LSE proposed in this study empower the framework to segment medical images with complicated intensity distribution.
Nonlinear System Identification Using Robust Fusion Kernel-Based Radial basis function Neural Network

In this paper, authors have proposed, a robust Fusion kernel-based Radial basis function (RBF) neural network algorithm is proposed. The objective is to solve dynamic nonlinear system identification problems. The proposed algorithm is a Fusion of both the Gaussian and Cosine, which is capable of updating the weight of kernels by using the gradient descent method. The weight updating process enhances its adaptive learning capabilities. The proposed model is further tested with an ARMA model to prove its superiority. The comparison results illustrate, the proposed method achieves good performance over other models. The performance is evaluated through mean square error (MSE). IN the testing phase the model achieves a very low Mean squared error compared to the existing approach.
Attention-Based Transformer-BiGRU for Question Classification

A question answering (QA) system is a research direction in the field of artificial intelligence and natural language processing (NLP) that has attracted much attention and has broad development prospects. As one of the main components in the QA system, the accuracy of question classification plays a key role in the entire QA task. Therefore, not only the traditional machine learning methods but also today’s deep learning methods are widely used and deeply studied in question classification tasks. This paper mainly introduces our work on two aspects of Chinese question classification. The first is to use an answer-driven method to build a richer Chinese question classification dataset for the small-scale problems of the existing experimental dataset, which has a certain reference value for the expansion of the dataset, especially for the construction of those low-resource language datasets. The second is to propose a deep learning model of problem classification with a Transformer + Bi-GRU + Attention structure. Transformer has strong learning and coding ability, but it adopts the scheme of fixed coding length, which divides the long text into multiple segments, and each segment is coded separately; there is no interaction that occurs between segments. Here, we achieve the information interaction between segments through Bi-GRU so as to improve the coding effect of long sentences. Our purpose of adding the Attention mechanism is to highlight the key semantics in questions that contain answers. The experimental results show that the model proposed in this paper has significantly improved the accuracy of question classification.
Unsupervised Segmentation in NSCLC: How to Map the Output of Unsupervised Segmentation to Meaningful Histological Labels by Linear Combination?

Background: Segmentation is, in many Pathomics projects, an initial step. Usually, in supervised settings, well-annotated and large datasets are required. Regarding the rarity of such datasets, unsupervised learning concepts appear to be a potential solution. Against this background, we tested for a small dataset on lung cancer tissue microarrays (TMA) if a model (i) first can be in a previously published unsupervised setting and (ii) secondly can be modified and retrained to produce meaningful labels, and (iii) we finally compared this approach to standard segmentation models. Methods: (ad i) First, a convolutional neuronal network (CNN) segmentation model is trained in an unsupervised fashion, as recently described by Kanezaki et al. (ad ii) Second, the model is modified by adding a remapping block and is retrained on an annotated dataset in a supervised setting. (ad iii) Third, the segmentation results are compared to standard segmentation models trained on the same dataset. Results: (ad i–ii) By adding an additional mapping-block layer and by retraining, models previously trained in an unsupervised manner can produce meaningful labels. (ad iii) The segmentation quality is inferior to standard segmentation models trained on the same dataset. Conclusions: Unsupervised training in combination with subsequent supervised training offers for histological images here no benefit.
The Influence of Environmental Lighting on Size Variations in Optical See-through Tangible Augmented Reality

Optical see-through head-mounted displays (OST HMDs) are becoming increasingly popular as they get better and smaller. One application area is interaction with virtual content, which is more intuitive when using physical objects as tangibles. Since it is not possible to use a matching replica for each virtual object, it is necessary to identify physical objects that can represent several different virtual objects. As a first step, we investigated to what extent a physical object can differ in size from its virtual counterpart.Since the perception of content in optical see-through Augmented Reality (OST AR) is strongly influenced by the ambient lighting, the illumination intensity was considered in our study. We investigated three indoor lighting conditions and their effects on the perception of seven different size variations between the physical object and its virtual overlay.The results of the study show that there is a decrease in usability and presence with increasing illuminance. However, this cannot be avoided when applications are run under realistic interior lighting conditions. Furthermore, the results demonstrate that the size ranges in which a physical object can deviate from its virtual counterpart without having a strong negative impact on usability, presence and performance increase with increasing environmental illumination. Therefore, it is possible to interact with even smaller and even larger physical props to manipulate the associated virtual content under brighter lighting conditions.
Agile methods used by traditional logistics companies and logistics start-ups: a systematic literature review

An increasing number of companies and start-ups aims to enhance their agility through the use of agile methods and practices in order to cope with raising product development and project complexity and quickly changing requirements (Laanti et al. (2011); Schön et al. (2017); Könnölä et al. (2016)). Especially in the logistics industry, which is known as a slow adaptor to changes in general but regarding new innovations in particular (Cockburn and Highsmith (2001); Beck (2000); Abbas et al. (2008)), it is relevant to see how these companies cope with change. Opposed to that, logistics start-ups seem to be able to create customer value with disruptive products and services. This paper aims to capture the current state of the literature related to the use of agile methods and practices in established logistics companies and logistics start-ups. Of particular interest will be analyzing which methods and practices are used, what specific challenges established logistics companies and logistics start-ups aim to solve with these agile methods and practices, and the difficulties they face in doing so. A systematic literature review (SLR) with an extensive quality assessment of the included nine studies was conducted. After the analysis, insights on the following points were derived: use of agile methods and practices, the challenges that are solved with these methods and practices, and difficulties in the application of these methods and practices. Future research should deepen these findings with, for instance, qualitative data from real-life cases of logistics companies and start-ups. The originality of the SLR presented lies in its contribution to the largely unexplored field of agility in traditional logistics companies and logistics start-ups, as well as its assessment of the state-of-the-art literature analyzed.
Rendering Nighttime Image Via Cascaded Color and Brightness Compensation

Image signal processing (ISP) is crucial for camera imaging, and neural networks (NN) solutions are extensively deployed for daytime scenes. The lack of sufficient nighttime image dataset and insights on nighttime illumination characteristics poses a great challenge for high-quality rendering using existing NN ISPs. To tackle it, we first built a high-resolution nighttime RAW-RGB (NR2R) dataset with white balance and tone mapping annotated by expert professionals. Meanwhile, to best capture the characteristics of nighttime illumination light sources, we develop the CBUnet, a two-stage NN ISP to cascade the compensation of color and brightness attributes. Experiments show that our method has better visual quality compared to traditional ISP pipeline, and is ranked at the second place in the NTIRE 2022 Night Photography Rendering Challenge [13] for two tracks by respective People’s and Professional Photographer’s choices. The code and relevant materials are avaiable on our website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
SFGNet detecting objects via spatial fine-grained feature and enhanced RPN with spatial context

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Design of a fractional-order chaotic secure communication circuit based on sliding mode theory and microcontroller

Purpose With the development of integrated circuit and communication technology, digital secure communication has become a research hotspot. This paper aims to design a five-dimensional fractional-order chaotic secure communication circuit with sliding mode synchronous based on microcontroller (MCU). Design/methodology/approach First, a five-dimensional fractional-order chaotic system for encryption is constructed. The approximate numerical solution of fractional-order chaotic system is calculated by Adomian decomposition method, and the phase diagram is obtained. Then, combined with the complexity and 0–1 test algorithm, the parameters of fractional-order chaotic system for encryption are selected. In addition, a sliding mode controller based on the new reaching law is constructed, and its stability is proved. The chaotic system can be synchronized in a short time by using sliding mode control synchronization. Findings The electronic circuit is implemented to verify the feasibility and effectiveness of the designed scheme. Originality/value It is feasible to realize fractional-order chaotic secure communication using MCU, and further reducing the synchronization error is the focus of future work.
A Large Payload Data Hiding Scheme Using Scalable Secret Reference Matrix

When imperceptibility is an issue, data-hiding techniques typically become limited to small payloads. In this paper, a novel data-hiding scheme is proposed that allows embedding large payloads while maintaining high stego-image quality. The scheme utilizes an N×N sub-block for constructing a reference matrix as a secret key that allows the symmetric embedding and extraction of secret data from a grayscale cover image, where N is a positive integer greater than or equal to two. With proper modification, the proposed method can be directly converted to a symmetric cryptosystem. For cases with acceptable stego-image quality (i.e., PSNR &gt; 34), the experimental results showed that the proposed method can embed, on average, four bits per pixel (bpp), a higher hiding capacity than in previous works, and also offers the ability to adjust the capacity by varying N. The number of solutions for a sub-block reaches the factorial of N2, guaranteeing the security of data embedding and extraction. In addition, the proposed method has low computational complexity and can be implemented in a straightforward manner.
Named Entity Recognition of Ancient Poems Based on Albert-BiLSTM-MHA-CRF Model

The extraction and construction of the knowledge graph related to the entity of ancient poems are helpful to excavate the connection between ancient poems, and it is of great significance to inherit the traditional Chinese culture. This paper proposes an Albert-BiLSTM-MHA-CRF model for entity extraction in ancient poems. Based on the BiLSTM-CRF model, the author introduces the Albert pretraining model and the multihead self-attention mechanism to extract character vectors and enhance the generalization ability of word embedding vectors and the potential semantics between characters of the model, depending on weight and other feature extraction capabilities. The experiment is carried out in the corpus of ancient poetry, and the model is compared with Bert-BiLSTM-CRF, BiLSTM-CRF, and CRF model. The results show that the entity extraction effect of ancient poetry is significantly improved, and the harmonic average value is 97.17%. Compared with Bert model as the pretraining model, Albert model reduces the time by 19.56%.
Two Views of Speech Acts: Analysis and Implications for Argumentation Theory

Argumentation theorists need to command a clear view of the sources of the obligations that arguers incur, e.g., their burdens of proof. Theories of illocutionary speech acts promise to fill this need. This essay contrasts two views of illocutionary acts: one, that they are constituted by rules, the other, that they are constituted by paradigmatic practical calculations. After a general comparison of the two views, the strength of the pragmatic view is demonstrated through an account of the illocutionary act of making an accusation. It is shown that the essential conditions of ACCUSING revealed by conceptual analysis are just what is practically necessary to manage a routine, but complex, communicative problem. The essay closes with remarks on the implications of the pragmatic view of speech acts for argumentation theory generally.