Reviewerly/exHarmony huggingface dataset:
A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APCE-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the target’s appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers.
Face-to-face vs. Online learning in Engineering Courses

After the 2017 earthquake, instruction-at-a-distance was massively adopted at Tecnologico de Monterrey Mexico City Campus (CCM). This tragic experience gave CCM staff, faculty, and students truly online expertise, which is now used due to the pandemic. The pros and cons of in-site classes versus online sessions are presented and discussed for engineering courses. A 25-item survey was designed containing categories such as human interaction, command on oneself, and academic performance, among others. A total sample of 396 engineering students was considered. Most students agree that the face-to-face scenario favors their enthusiasm for class attendance, their learning process as well as teacher and peer-to-peer communication. The lack of human contact, the lack of outdoor activities, and the difficulty in making new friends were also factors against education at a distance. Along the same line, distraction, depression, and stress incremented in the online model. However, from the academic point of view, student performance remained similar in the face-to-face model compared to the online model. The teacher's efforts in order to implement active learning activities through novel apps and simulations in the online model were remarkable.
Learning interlaced sparse Sinkhorn matching network for video super-resolution

How to effectively fuse inter- and intra-frame spatio-temporal information plays a key role in video super-resolution (VSR). Most existing works rely heavily on the accuracy of motion estimation and compensation for spatio-temporal feature alignment. However, they cannot perform well when suffering from large-scale and complex motions. To this end, this paper introduces an efficient and effective Interlaced Sparse Sinkhorn Matching (ISSM) network for VSR, which aligns supporting frames with the reference one in the feature space by learning optimal matching between image regions across frames. Specifically, the ISSM divides the input dense affinity matrix into two sparse block matrixes: one can match long-distance regions while the other can match short-distance regions, and then we leverage an efficient Sinkhorn method on each block to learn optimal matching. Moreover, we insert a residual atrous spatial pyramid pooling module before the ISSM, which can flexibly generate multi-scale features frame by frame to capture the multi-scale context information in images. The aligned features of each adjacent frame are then fed to a bidirectional temporal fusion module to capture the rich temporal information. Finally, the fused features are sent into a frame-wise dynamic reconstruction network to produce an HR frame. Extensive evaluations on three benchmark datasets demonstrate the superiority of our method over the state-of-the-art methods in terms of PSNR and SSIM.
Psychographic traits identification based on political ideology: An author analysis study on Spanish politicians’ tweets posted in 2020

In general, people are usually more reluctant to follow advice and directions from politicians who do not have their ideology. In extreme cases, people can be heavily biased in favour of a political party at the same time that they are in sharp disagreement with others, which may lead to irrational decision making and can put people’s lives at risk by ignoring certain recommendations from the authorities. Therefore, considering political ideology as a psychographic trait can improve political micro-targeting by helping public authorities and local governments to adopt better communication policies during crises. In this work, we explore the reliability of determining psychographic traits concerning political ideology. Our contribution is twofold. On the one hand, we release the PoliCorpus-2020, a dataset composed by Spanish politicians’ tweets posted in 2020. On the other hand, we conduct two authorship analysis tasks with the aforementioned dataset: an author profiling task to extract demographic and psychographic traits, and an authorship attribution task to determine the author of an anonymous text in the political domain. Both experiments are evaluated with several neural network architectures grounded on explainable linguistic features, statistical features, and state-of-the-art transformers. In addition, we test whether the neural network models can be transferred to detect the political ideology of citizens. Our results indicate that the linguistic features are good indicators for identifying fine-grained political affiliation, they boost the performance of neural network models when combined with embedding-based features, and they preserve relevant information when the models are tested with ordinary citizens. Besides, we found that lexical and morphosyntactic features are more effective on author profiling, whereas stylometric features are more effective in authorship attribution.
Artificial intelligence for histological subtype classification of breast cancer: combining multi‐scale feature maps and the recurrent attention model

The aim of this study was to apply a two-stage deep model combining multi-scale feature maps and the recurrent attention model (RAM) to assist with the pathological diagnosis of breast cancer histological subtypes by the use of whole slide images (WSIs).In this article, we propose an integrated framework combining multi-scale feature maps from Inception V3 and the recurrent attention model to classify the six histological subtypes of breast cancer. This model was trained with 194 WSIs, and on 63 validation WSIs the model achieved accuracies of 0.9030 for patch-level classification and 0.8889 for WSI-level classification. In the testing stage, a total of 65 WSIs were used to achieve an accuracy of 0.8462 without any form of data curation. The t-distributed stochastic neighbour embedding showed that features extracted by the feature network of the RAM from WSIs of the same category can cluster together after training, and the visualization of decision steps showed that the decision-making glimpses are focused on the middle tumour area of an example from test WSIs. Finally, the false classification patches indicated that the morphological similarities between tumour tissues of different subtypes or non-tumour tissues and tumour tissues in patches might contribute to misclassification.This model can imitate the diagnostic process of pathologists, pay attention to a series of local features on the pathology image, and summarize related information, in order to accurately classify breast cancer into its histological subtypes, which is important for treatment and prognosis.
Convolutional neural network initialization approaches for image manipulation detection

Nowadays it is common to see image forgeries on almost every media in both professional and personal contexts. From visual retouches to deliberate fake scenes, the technology used to create image forgeries gets easier to use for all users. At the same time, different techniques to assess the authenticity of the content of an image have appeared by taking advantage of the deep learning paradigm. In this paper we propose two initialization approaches for Convolutional Neural Networks (CNNs) used for the detection of image manipulation operations. We focus on the variance stability for the output of a convolutional filter in CNN. Our first proposal is a scaling approach for first-layer convolutional kernels which can cope well with filters generated by different algorithms. Our second proposal is a random high-pass filter initialization approach for CNN's first convolutional layer. The first proposal explicitly computes simple statistical properties of the input signal, while the second approach incorporates the consideration of input statistics in the filter derivation without the need of carrying out explicit computation on the input. Experimental results show the utility of both approaches with improved performance in different image manipulation detection problems and on different CNN architectures.
Path Planning Based on the Improved RRT* Algorithm for the Mining Truck

Planning a reasonable driving path for trucks in mining areas is a key point to improve mining efficiency. In this paper, a path planning method based on Rapidly-exploring Random Tree Star (RRT*) is proposed, and several optimizations are carried out in the algorithm. Firstly, the selection process of growth target points is optimized. Secondly, the process of selecting the parent node is optimized and a Dubins curve is used to constraint it. Then, the expansion process from tree node to random point is optimized by the gravitational repulsion field method and dynamic step method. In the obstacle detection process, Dubins curve constraint is used, and the bidirectional RRT* algorithm is adopted to speed up the iteration of the algorithm. After that, the obtained paths are smoothed by using the greedy algorithm and cubic B-spline interpolation. In addition, to verify the superiority and correctness of the algorithm, an unmanned mining vehicle kinematic model in the form of front-wheel steering is developed based on the Ackermann steering principle and simulated for CoppeliaSim. In the simulation, the Stanley algorithm is used for path tracking and Reeds-Shepp curve to adjust the final parking attitude of the truck. Finally, the experimental comparison shows that the improved bidirectional RRT* algorithm performs well in the simulation experiment, and outperforms the common RRT* algorithm in various aspects.
Whether the Support Region of Three-Bit Uniform Quantizer Has a Strong Impact on Post-Training Quantization for MNIST Dataset?

Driven by the need for the compression of weights in neural networks (NNs), which is especially beneficial for edge devices with a constrained resource, and by the need to utilize the simplest possible quantization model, in this paper, we study the performance of three-bit post-training uniform quantization. The goal is to put various choices of the key parameter of the quantizer in question (support region threshold) in one place and provide a detailed overview of this choice's impact on the performance of post-training quantization for the MNIST dataset. Specifically, we analyze whether it is possible to preserve the accuracy of the two NN models (MLP and CNN) to a great extent with the very simple three-bit uniform quantizer, regardless of the choice of the key parameter. Moreover, our goal is to answer the question of whether it is of the utmost importance in post-training three-bit uniform quantization, as it is in quantization, to determine the optimal support region threshold value of the quantizer to achieve some predefined accuracy of the quantized neural network (QNN). The results show that the choice of the support region threshold value of the three-bit uniform quantizer does not have such a strong impact on the accuracy of the QNNs, which is not the case with two-bit uniform post-training quantization, when applied in MLP for the same classification task. Accordingly, one can anticipate that due to this special property, the post-training quantization model in question can be greatly exploited.
Artificial Intelligence Interactive Design System Based on Digital Multimedia Technology

The current extraction speed of artificial intelligence interactive elements is low, with the low effect, resulting in the poor effect of artificial intelligence interaction. Therefore, a new artificial intelligence interaction method has been developed for digital multimedia technology, performing analysis based on the current background of artificial intelligence, providing a good environment foundation for the interactive place, so that it can integrate artificial intelligence technology after interaction. Aiming at the current problems of digital multimedia technology in the use of artificial intelligence interaction design, innovative exploration of artificial intelligence technology will be carried out based on multimedia technology and innovative thinking. Based on the in-depth analysis of digital multimedia technology, the relationship between artificial intelligence technology and digital multimedia technology is analyzed, and an artificial intelligence interactive design system based on digital multimedia technology is proposed. Finally, the digital multimedia technology is verified on the artificial intelligence interactive design through case analysis.
An Improved Deep Learning Based Cervical Cancer Detection Using a Median Filter Based Preprocessing

Cervical cancer is one of the prevalent type of cancer among women although its treatment success is the highest when compared to other types of cancer once diagnosed.Automatic classification of cervical cancer is essential to accelerate the treatment process and increase the survival rate of the patients.Inadequate awareness, deficiency of medical opportunities, and expensive screening procedures increase the death rates.This common cancer is frequently screened by several imaging tests including Pap smear, cervicography and colposcopy.The decisions are made by the help of these tests, but structural complexities of cervical cells may complicate the decision.Recent developments in neural networks show remarkable achievements in disease diagnosis.Also, transfer learning draws the attention of most of the researchers because of its advantages.This paper presents a transfer learning based cervical cancer detection method for early diagnosis.Pap smear images were preprocessed using median filter before training the deep learning model in order to remove noise from the images for better classification.Cancerous and non-cancerous cervical cells are distinguished through pre-trained networks.Five popular pre-trained networks which are SqueezeNet, VGG-19, AlexNet, ResNet-50 and InceptionV3 have been utilized and compared for the problem.SqueezeNet achieved the best validation accuracy (96.90\%) when compared to other neural structures and this performance makes the proposed method the best among other unsupervised approaches in the literature for cervical cancer diagnosis.Additional experiments also proved the success of the proposed model for the classification of two similar classes, namely Parabasal and Metaplastic cells.The results demonstrate that the proposed approach can provide a confidential, cheap, and fast decision support system for cervical cancer diagnosis.
A Lightweight Authentication Scheme Based on Consortium Blockchain for Cross-Domain IoT

Internet of Things (IoT) has been ubiquitous in both industrial and living areas, but also known for its weak security. Being as the first defense line against various cyberattacks, authentication is even more critical to IoT applications. Moreover, there has been a growing demand for cross-domain collaboration, leading to an increasing need for cross-domain authentication. Recently, certificate-based authentication schemes have been extensively studied. However, many of these schemes are not efficient in computation, storage, and communication, which are highly required in IoT. In this paper, we propose a lightweight authentication scheme based on consortium blockchain and design a cryptocurrency-like digital token to build trust. Furthermore, trust lifecycle management is performed by manipulating the amount of tokens. The comprehensive analysis and evaluation demonstrate that the proposed scheme is resistant to various common attacks and more efficient than competitor schemes in terms of storage, communication, and authentication cost.
Evaluation of the Accuracy of Different PV Estimation Models and the Effect of Dust Cleaning: Case Study a 103 MW PV Plant in Jordan

The estimation of PV production has been widely investigated previously, where many empirical models have been proposed to account for wind and soiling effects for specific locations. However, the performance of these models varies among the investigated sites. Hence, it is vital to assess and evaluate the performance of these models and benchmark them against the common PV estimation model that accounts only for the ambient temperature. Therefore, this study aims to evaluate the accuracy and performance of four empirical wind models considering the soiling effect, and compare them to the standard model for a 103 MW PV plant in Jordan. Moreover, the study investigates the effect of cleaning frequency on the annual energy production and the plant’s levelized cost of electricity (LCOE). The results indicate almost identical performance for the adopted models when comparing the actual energy production with R2 and RMSE (root mean square error) ranges of 0.93–0.98 and 0.93–1.56 MWh for both sub-plants, with a slight superiority of the models that incorporate wind effect. Finally, it is recommended in this study to clean the PV panels every two weeks instead of every three months, which would increase annual energy production by 4%, and decrease the LCOE by 5% of the two PV sub-plants.
Fairness-aware Class Imbalanced Learning

Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups.However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens.In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness.We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases. 1
Chapter 12. Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neural-symbolic models, while being explainable. All source code and datasets are available online. (At https://github.com/uclnlp/ctp)
Network Based Adaptation of Block Chain Technology

Online transactions are growing up day by day due to globalization factors. Threat victims rate also increasing due to lack of privacy protection and identity theft. The number of technologies have been using for maintaining the communication data like Torrent Technology, Databases, etc. Even though threat agents seeking new trends to attack the data. So a technology is required to do a secure transaction in the enterprise society. Recent technology, which can be played a vital role in data security, is Block chain. Once created a block lack of alter function in the block chain will helps to reduce the information attacks/forgery. This paper addresses block chain technology concepts with internal architecture and its applications along with the data communication over a network with block chain objective. The main strength of this work is simulation results with and it’s analysis.
A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.
Injecting Entity Types into Entity-Guided Text Generation

Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG).Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content.To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately.We develop a novel NLG model to produce a target sequence based on a given list of entities.Our model has a multistep decoder that injects the entity types into the process of entity mention generation.Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.