Reviewerly/exHarmony huggingface dataset:
A hybrid differential evolution and simulated annealing algorithm for global optimization

Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient.
A Blockchain-Based Platform for the e-Procurement Management in the Public Sector

Public procurement represents an important part of the countries’ budgets. It is impossible to effectively solve the problem of public expenditure without establishing a rational and transparent public procurement management system. These markets are an essential government function for the delivery of goods and services to citizens. The overall success of an automated public procurement function leads to progress and economic growth for the country. In this paper, we analyze the potential of blockchain technology to improve the efficiency, ease, and transparency of public procurement in the case of Morocco and identify the current challenges facing public procurement, namely the lack of trust and transparency among critical stakeholders in the procurement process. To solve these problems, we propose a blockchain-based infrastructure to improve the mechanism of public procurement. The use of this technology will reduce the time spent processing documentation, reduce the degree of corruption in the public procurement process, by creating reputation lists accessible to all participants. Also, the use of smart contracts makes it possible to minimize the number of intermediaries in the conclusion of public contracts.
Deep Learning-Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

The effect is tested in various specific scenes of sports videos to complete the multitarget motion multitarget tracking detection application applicable to various specific scenes within sports videos. In this paper, deep neural networks are applied to sports video multitarget motion shadow suppression and accurate tracking to improve tracking performance. After the target frame selection is determined, the tracker uses an optical flow method to estimate the limits of the target sports video multitarget motion based on the sports video multitarget motion of the target object between frames. The detector first scans each sports video image frame one by one, observing the previously discovered and learned image frame subregions one by one until the current moment that is highly like the target to be tracked. The preprocessed remote sensing images are converted into grayscale images, the histogram is normalized, and the appropriate height threshold is selected in combination with the regional growth function to realize the rejection of sports video multitarget motion shadow and establish the sports video multitarget network model. The distance and direction of the precise target displacement are determined by frequency-domain vectors and null domain vectors, and the target action judgment mechanism is formed by decision learning. Finally, comparing with other shadow rejection and precision tracking algorithms, the proposed algorithm achieves greater advantages in terms of accuracy and time consumption.
Sentiment analysis using lexicon-based method with naive bayes classifier algorithm on #newnormal hashtag in twitter

Abstract Back In 2020, World Health Organization (WHO) has announced COVID-19 as a pandemic. From the many public responses, especially those on Twitter regarding the #newnormal campaign, a sentiment analysis process needs to be carried out to find out the perceptions that exist in society through social media. In this study, data were obtained through the crawling process on Twitter using the Twitter API. The method used in the sentiment analysis process is lexicon-based. The lexicon-based method works by labeling words containing sentiments based on a lexicon dictionary that already has weight on each word or doesn’t have weight on words in a lexicon dictionary. The classification results using lexicon-based are also used to make training data in the testing process using the naive Bayes classifier algorithm. In general, the research stages in this sentiment analysis include data crawling, text preprocessing, feature extractions, and the classification process. The sentiment analysis process results showed that the percentage of social media users on Twitter about #newnormal was 33.19% containing negative sentiments and 66.36% containing positive sentiments. Meanwhile, for testing the naive Bayes classifier algorithm in the sentiment analysis process got an accuracy of 79.72%.
Research on construction and innovation new foreign language classroom teaching mode based on the “Internet Plus”

Twenty-first Century is a global information age with rapid development of science and technology, and the information technology represented by multimedia and computer network changes our life and learning mode at extraordinary speed. Nowadays, the quality education is paid attention to by everyone. To further comply with the requirements of the new era curriculum teaching and personnel training, one of the general goals of the “English curriculum standards for senior high schools” promulgated by China in 2003 is to cultivate the “students’ comprehensive use of language skills” and to advocate the basic idea of “concern for students’ emotion”. Relying on the “Internet plus” background, this paper realizes the maintenance of basic data (thesaurus, question bank, article Library) through developing computer assisted foreign language teaching system, and teaching system is mainly to complete the overall design of the teaching system, which not only provides the basic data for the learning and testing of the foreign language assisted teaching system, but also lays a good foundation for the design and realization of the teaching system, and develops the game teaching interface. It has important research value to construct and innovate the new foreign language classroom teaching mode.
Moving Target Localization in Sports Image Sequence Based on Optimized Particle Filter Hybrid Tracking Algorithm

This paper proposes a fusion stadium positioning algorithm, which uses self-optimizing particle filter to integrate the improved athlete dead reckoning and WiFi position fingerprint algorithm for stadium positioning. In order to determine the initial absolute position of the stadium positioning, for athletes entering the stadium from the outside, a seamless switching algorithm outside the stadium is proposed, using the characteristics of high-altitude satellite GPS to find a suitable switching point as the initial absolute position. If in the stadium, WiFi static positioning determines the initial absolute position. Then, aiming at the problem that the poorly diversified particles cannot be better integrated and localized, a self-optimized particle filter algorithm is proposed. After resampling and retaining high-weight particles, the characteristics of low-weight particles are embedded in the copied high-weight particles. This can improve diversity, and we finally carry out fusion positioning. The target tracking algorithm based on Mean Shift has a fixed-scale tracking window, and the tracking effect of variable-size targets is not ideal. In this paper, an affine transformation algorithm is introduced to improve it. First, we iterate the adjacent image frames in reverse Mean Shift to determine the center position of the target and then use the corner matching method to perform template matching on the target to adjust the size of the tracking window. Through simulation verification, it is proved that the optimized particle filter hybrid tracking algorithm can achieve the ideal result when the target size changes. For the image sequence S1, the tracking window of the 20th frame and the 40th frame has a small offset, but the optimal position can be quickly found by Mean Shift iteration. For the image sequence S2, between the 40th frame and the 60th frame, the target occlusion causes the accuracy of the target template to decrease, and the Bhattacharyya coefficient is at a relatively low value. For the image sequence S3, the tracking effect of the optimized particle filter hybrid tracking algorithm meets the requirements.
Same State, Different Task: Continual Reinforcement Learning without Interference

Continual Learning (CL) considers the problem of training an agent sequentially on a set of tasks while seeking to retain performance on all previous tasks. A key challenge in CL is catastrophic forgetting, which arises when performance on a previously mastered task is reduced when learning a new task. While a variety of methods exist to combat forgetting, in some cases tasks are fundamentally incompatible with each other and thus cannot be learnt by a single policy. This can occur, in reinforcement learning (RL) when an agent may be rewarded for achieving different goals from the same observation. In this paper we formalize this "interference" as distinct from the problem of forgetting. We show that existing CL methods based on single neural network predictors with shared replay buffers fail in the presence of interference. Instead, we propose a simple method, OWL, to address this challenge. OWL learns a factorized policy, using shared feature extraction layers, but separate heads, each specializing on a new task. The separate heads in OWL are used to prevent interference. At test time, we formulate policy selection as a multi-armed bandit problem, and show it is possible to select the best policy for an unknown task using feedback from the environment. The use of bandit algorithms allows the OWL agent to constructively re-use different continually learnt policies at different times during an episode. We show in multiple RL environments that existing replay based CL methods fail, while OWL is able to achieve close to optimal performance when training sequentially.
The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora

Detection of occupations in texts is relevant for a range of important application scenarios, like competitive intelligence, sociodemographic analysis, legal NLP or health-related occupational data mining.Despite the importance and heterogeneous data types that mention occupations, text mining efforts to recognize them have been limited.This is due to the lack of clear annotation guidelines and highquality Gold Standard corpora.Social media data can be regarded as a relevant source of information for real-time monitoring of atrisk occupational groups in the context of pandemics like the COVID-19 one, facilitating intervention strategies for occupations in direct contact with infectious agents or affected by mental health issues.To evaluate current NLP methods and to generate resources, we have organized the ProfNER track at SMM4H 2021, providing ProfNER participants with a Gold Standard corpus of manually annotated tweets (human IAA of 0.919) following annotation guidelines available in Spanish and English, an occupation gazetteer, a machinetranslated version of tweets, and FastText embeddings.Out of 35 registered teams, 11 submitted a total of 27 runs.Best-performing participants built systems based on recent NLP technologies (e.g.transformers) and achieved 0.
Using Camshift and Kalman Algorithm to Trajectory Characteristic Matching of Basketball Players

Because of its unique charm, sports video is widely welcomed by the public in today’s society. Therefore, the analysis and research of sports game video data have high practical significance and commercial value. Taking a basketball game video as an example, this paper studies the tracking feature matching of basketball players’ detection, recognition, and prediction in the game video. This paper is divided into four parts to improve the application of the interactive multimodel algorithm to track characteristic matching: moving object detection, recognition, basketball track characteristic matching, and player track characteristic matching. The main work and research results of each part are as follows: firstly, the improved K-means clustering algorithm is used to segment the golf field area; then, HSV is combined with the RGB Fujian value method to eliminate the field area; at last, straight field lines were extracted by Hough transform, and elliptical field lines were extracted by curve fitting, and the field lines were eliminated to realize the detection of moving objects. Seven normalized Hu invariant moments are used as the target features to realize the recognition of moving targets. By obtaining the feature distance between the sample and the template, the category of the sample is judged, which has a good robustness. The Kalman filter is used to match the characteristics of the basketball trajectory. Aiming at the occlusion of basketball, the least square method was used to fit the basketball trajectory, and the basketball position was predicted at the occlusion moment, which realized the occlusion trajectory matching. The matching of players’ track characteristics is realized by the CamShift algorithm based on the color model, which makes full use of players’ color information and realizes real-time performance. In order to solve the problem of occlusion between players in the track feature matching, CamShift and Kalman algorithms were used to determine the occlusion factor through the search window and then weighted Kalman and CamShift according to the occlusion degree to get the track feature matching result. The experimental results show that the detection time is greatly shortened, the memory space occupied is small, and the effect is very ideal.
Application of deep learning in the detection of breast lesions with four different breast densities

This retrospective study evaluated the model from populations with different breast densities and showed the model's performance on malignancy prediction.A total of 608 mammograms were collected from Northern Jiangsu People's Hospital in Yangzhou City. The data from this province have not been used in the training or evaluation data set. The model consists of three submodules, lesion detection (Mask-rcnn), lesion registration between craniocaudal view and mediolateral oblique view, malignancy prediction network (ResNet). The data set used to train the model was obtained from nine institutions across six cities. For normal cases, there were no annotations. Here, we adopted the free-response receiver operating characteristic (FROC) curve as the indicator to evaluate the detection performance of all cancers and triple-negative breast cancer (TNBC). The FROC curves are also shown for mass/distortion/asymmetry and typical benign calcification in two kinds of populations with four types of breast density.The sensitivity to mass/distortion/asymmetry for the four types of breast (A, B, C, D) are 0.94, 0.92, 0.89, and 0.72, respectively, when false positive per image is 0.25, while these values are 1.00, 0.95, 0.92, and 0.90, respectively, for the amorphous calcification lesions. The sensitivity for the cancer is 0.85 at the same false-positive rate. The TNBC accounts for about 10%-20% of all breast cancers and is more aggressive with poor prognosis than other breast cancers. Herein, we also evaluated performance on the TNBC cases. Our results show that Yizhun AI could detect 75% TNBC lesions at the same false-positive level mentioned above.The Yizhun AI model used in our work has good diagnostic efficiency for different types of breast, even for the extremely dense breast. It has a guiding role in the clinical diagnosis of breast cancer. The performance of Yizhun AI on mass/distortion/asymmetry is affected by breast density significantly compared to that on amorphous calcification.
RevMan: Revenue-aware Multi-task Online Insurance Recommendation

Online insurance is a new type of e-commerce with exponential growth. An effective recommendation model that maximizes the total revenue of insurance products listed in multiple customized sales scenarios is crucial for the success of online insurance business. Prior recommendation models are ineffective because they fail to characterize the complex relatedness of insurance products in multiple sales scenarios and maximize the overall conversion rate rather than the total revenue. Even worse, it is impractical to collect training data online for total revenue maximization due to the business logic of online insurance. We propose RevMan, a Revenue-aware Multi-task Network for online insurance recommendation. RevMan adopts an adaptive attention mechanism to allow effective feature sharing among complex insurance products and sales scenarios. It also designs an efficient offline learning mechanism to learn the rank that maximizes the expected total revenue, by reusing training data and model for conversion rate maximization. Extensive offline and online evaluations show that RevMan outperforms the state-of-the-art recommendation systems for e-commerce.
Can Social Agents Efficiently Perform in Automated Negotiation?

In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.
Incorporating Global Information in Local Attention for Knowledge Representation Learning

Graph Attention Networks (GATs) have proven a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data, e.g., Knowledge Graphs (KGs).While such approaches model entities' local pairwise importance, they lack the capability to model global importance relative to other entities of KGs.This causes such models to miss critical information in tasks where global information is also a significant component for the task, such as in knowledge representation learning.To address the issue, we allow the proper incorporation of global information into the GAT family of models through the use of scaled entity importance, which is calculated by an attention-based global random walk algorithm.In the context of KRL, incorporating global information boosts performance significantly.Experimental results on KG entity prediction against the state-of-thearts sufficiently demonstrate the effectiveness of our proposed model.
Issues with Entailment-based Zero-shot Text Classification

The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space.In this opinion piece, we point out a few overlooked issues that are yet to be discussed in this line of work.We observe huge variance across different classification datasets amongst standard BERT-based NLI models and surprisingly find that pre-trained BERT without any fine-tuning can yield competitive performance against BERT fine-tuned for NLI.With the concern that these models heavily rely on spurious lexical patterns for prediction, we also experiment with preliminary approaches for more robust NLI, but the results are in general negative.Our observations reveal implicit but challenging difficulties in entailmentbased zero-shot text classification.
A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations

Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification, style transfer and sentence generation, among others.The existent dominant approaches in the context of text data either rely on training an adversary (discriminator) that aims at making attribute values difficult to be inferred from the latent code or rely on minimising variational bounds of the mutual information between latent code and the value attribute.However, the available methods suffer of the impossibility to provide a fine-grained control of the degree (or force) of disentanglement.In contrast to adversarial methods, which are remarkably simple, although the adversary seems to be performing perfectly well during the training phase, after it is completed a fair amount of information about the undesired attribute still remains.This paper introduces a novel variational upper bound to the mutual information between an attribute and the latent code of an encoder.Our bound aims at controlling the approximation error via the Renyi's divergence, leading to both better disentangled representations and in particular, a precise control of the desirable degree of disentanglement than state-of-the-art methods proposed for textual data.Furthermore, it does not suffer from the degeneracy of other losses in multi-class scenarios.We show the superiority of this method on fair classification and on textual style transfer tasks.Additionally, we provide new insights illustrating various trade-offs in style transfer when attempting to learn disentangled representations and quality of the generated sentence.