Reviewerly/exHarmony huggingface dataset:
Multi-Oriented Text Detection Network Based on Hybrid Feature Enhancement and Shallow Feature Refinement

Scene text detection has achieved impressive progress over the past years. However, there are still two challenges for text detection. The first challenge is the limitation of receptive field on account of the large aspect ratio of texts. The second one is the loss of spatial information due to the long path between lower layers and topmost feature. To address the two problems, we propose an effective text detection network for multi-oriented text. In this paper, we introduce Hybrid feature enhancement module (HFEM) and Low-level feature refinement module (LFRM). HFEM is a multiple parallel branches module to enlarge receptive field and capture multi-scale information for better detection. LFRM is proposed to suppress background noise and strengthen feature propagation. What's more, low-level feature compensation mechanism preserves rich spatial information for the model. Experiments on datasets including ICDAR 2015, MSRA-TD500 and MLT-2017 validate that the proposed method is effective for multi-oriented text. We also provide ablation experiments on ICDAR 2015 to indicate the effectiveness of proposed modules in our network.
Probabilistic Principal Geodesic Deep Metric Learning

Similarity learning which is useful for the purpose of comparing various characteristics of images in the computer vision field has been often applied for deep metric learning (DML). Also, a lot of combinations of pairwise similarity metrics such as Euclidean distance and cosine similarity have been studied actively. However, such a local similarity-based approach can be rather a bottleneck for a retrieval task in which global characteristics of images must be considered important. Therefore, this paper proposes a new similarity metric structure that considers the local similarity as well as the global characteristic on the representation space, i.e., class variability. Also, based on an insight that better class variability analysis can be accomplished on the Stiefel (or Riemannian) manifold, manifold geometry is employed to generate class variability information. Finally, we show that the proposed method designed through in-depth analysis of generalization bound of DML outperforms conventional DML methods theoretically and experimentally.
Problems of Fuzzy – Multiple Control of the “Smart House” System (Review)

A study of the available mathematical models of the “Smart Home” system, as a system that controls electrical loads, lighting, security, climate control, and media devices in households, has been carried out. The article presents an overview of the most relevant devices that carry out the technical configuration of the “Smart Home” system for controlling electrical appliances and the environment, including air temperature, humidity levels, light, noise, as well as air quality, security and alarms, cleaning and cleaning systems. It is noted that the control of all these devices and subsystems, as a rule, is based on fuzzy logic, as well as gamification as the use of fascinating elements that are usually present in games, in real or productive actions. An overview of the most relevant modern works devoted to fuzzy-multiple control of heating, energy saving, gates, lighting, watering, alarms, video surveillance is given; special attention is paid to the model of connected thermostats based on gamification, as well as to complex models of the “Smart Home” system. The second part of the review is devoted to the theoretical foundations of modeling the Internet of Things based on fuzzy-set models.
Adversarial Behaviour Debugging in a Two Button Fighting Game

We introduce the concept of Adversarial Behaviour Debugging (ABD), using intelligent agents to assist in the debugging of human-authored game AI systems, and Bonobo, an ABD system for Unity. To investigate the differences between ABD and traditional playtesting, we collected gameplay data from Bonobo agents and human testers playing against a buggy AI opponent. We present a comparison of the differences in gameplay and an online study on whether these differences affect observers perceptions of the AI opponent. We found that while there were clear differences, ABD compares favourably to human testing and has the potential to offer a distinct perspective.
Performance of single-large simulated sunlight for indoor photovoltaic testing

Recently, there is enormous attention in developing indoor testing-based photovoltaic (PV) modules using simulated sunlight. In this study, the performance of simulated sunlight using a large tungsten halogen lamp was evaluated. The electrical characteristics and non-uniformity performance in the various lamp tilt angles and distances to the irradiated PV were determined and considered according to the standard of solar simulator performance requirements BS EN 60904-9. Results indicated that the present simulated sunlight reached a stable operation after around 10 minutes according to the current response. Meanwhile, the obtained wavelength range of present simulated sunlight indicated quite similar to the visible light. In term of spatial non-uniformity, the present simulated sunlight failed to meet the classification of a typical solar simulator according to BS EN 60904-9 with the non-uniformity level resulted in a higher percentage as the distance of the lamp to the irradiated PV decreased. The dispersion of unevenness value indicated that the configuration of simulated sunlight at 20° could be a reasonable option for performing the test at 0.3 m distance and 90° for distance over 0.3 m.
A Framework for Online Customer Reviews System Using Sentiment Scoring Method

Nowadays, web-based social networking remain intuitive and further easy to use in nature. The scenario of purchasing products online has improved dramatically which has resulted in proportional increase in web users. These web users share their own experiences, pros and cons on these social networking sites. They enable the web clients to provide medium of exchange prejudiced annotations of different reviewers. Reviewers likewise called as raters; the number of reviews expressed by those trusted people should also be minimal. There are enormous number of individuals want to sell or purchase items through online business. Various analysts and business sites exhibit a structure for mining on the web surveys removed. The results were pre-processed with stop word removal & stemming process. Secondly, the reviews clustered using K -Medoid-clustering scheme, which is predominantly better than k-means clustering approach. The overall evaluation done based on the cumulative scores obtained from the set of sentences in the review set, from which recommendation provided for the reviews. The review set also investigated along different timeline.
GNSS Positioning Security: Automatic Anomaly Detection on Reference Stations

Abstract The dependency of critical infrastructures on Global Navigation Satellite Systems (GNSS) keeps increasing over the years. This over-reliance brings concerns as those systems are vulnerable and consequently prone to human-made perturbations, such as jamming and spoofing attacks. Solutions for detecting such disturbances are therefore crucially needed to raise GNSS users’ awareness and protection. This paper suggests an approach for detecting anomalous events (i.e., potentially an attack attempt) based on measurements recorded by Continuously Operating GNSS Reference Stations (CORS). Precisely, the anomaly detection process first consists in modeling the normal behavior of a given signal thanks to a predictive model which combines the Seasonal and Trend decomposition using LOESS and ARIMA algorithms. This model can then be used to predict the upcoming measurement values. Finally, we compare the predictions to the actual observations with a statistical rule and assess if those are normal or anomalous. While our anomaly detection approach is intended for real-time use, we assess its effectiveness on historical data. For simplicity and independence, we also focus on the Carrier-to-Noise Ratio only, though similar methods could apply to other observables. Our results prove the sensitivity of the proposed detection on a reported case of unintentional disturbance. Other anomalies in the historical data are also uncovered using that methodology and presented in this paper.
An Improved Segmentation Algorithm Based on Video Human Motion

The paper proposed an approach to extracting human body from an image without interaction of user. Image segmentation algorithm was an Interactive segmentation algorithm, which was employed to extract the foreground of an image, but it works wrong frequently when the pixels around the foreground are similar to the pixels of the background. We advanced a new cost function to correct the mistake. Using the human detection method, we proposed a human body extraction method from image without interaction. Through experiments, our segmentation algorithm can achieve better segmentation results in two situations. The experiment shows that the size of user input box has less influence on this proposed algorithm.
Interest-aware Item Combination Prediction

One of the new upcoming recommendation scenarios is combinatorial item recommendation, where the recommender system aims to recommend a bundle of items for each user request to maximize the aggregated rewards. In this work, we focus on the item combination prediction task with constraints, i.e., predicting the user’s feedback to the nine exposed items given that if the user wants to buy any item in the next list, she/he must purchase all the three items in the current list. Therefore, the users’ response depends on not only the current item but also the items of the next list. Considering the constraint on the users’ feedbacks, we transform the binary combinatorial prediction into the multi-class prediction. To extract users’ preferences from their historical behaviors, we design the attentive interest network and hierarchical GRU network. The final result of the competition demonstrates the effectiveness of our strategy.
A Texture Synthesis Information Hiding Algorithm based on DCT Transform Coefficients

A texture synthesis algorithm of the coverless information hiding based on DCT transform coefficients was proposed. In this method, several candidate blocks were segmented from source texture images, and then the candidate blocks were divided into core regions and boundary regions. The positive and negative values of DCT coefficients which generated by the DCT transform performed on the core region were used for encoding, and all the candidate blocks with the same code were put in a candidate queue. When embedding secrets, the best candidate block was selected according to the secret information to be embedded, which was the block with the maximal minimum absolute value of DCT coefficient in the candidate queue. Then the best candidate block was pasted to the designated position of the blank canvas generated by the key, and the remaining blank part was filled with texture synthesis method. When extracting secrets, the DCT transform was performed on the core region of the block at the specified position according to the key, and the coefficients of the DCT transform were obtained, and the coefficients were decoded into secret information. The experimental results showed that the proposed method not only provided good visual quality, but also has better performance in embedding capacity and anti-interference ability compared with the previous work.
Multi-Layer Message Signature Scheme with Threshold-based Distributed PKG and Dynamic Leader Selection

The development generally drives the evolution of modern systems towards distributed ones, generally, in blockchain and Distributed Ledger Technology domains. Ensuring the reliable digital signature of messages and the data security of messages signed with a digital signature from unauthorized interference is an essential part of enabling the operation of the paradigms as mentioned earlier. This work proposes a method for signing messages in the distributed system by introducing a two level nodes’ hierarchy, distribution of the Public Key Generator (PKG) master key the nodes of the second level so that a distributed PKG threshold scheme is formed from those. Further, one or more second-layer leaders are selected by the nodes of the first message level in such a way that the aggregate content of the messages determines the value of the current identifier and, thus, the public key are formed by the nodes of the second level forming a distributed threshold PKG, shares of the session secret key corresponding to the identifier and the public key. The session secret key is recovered by one or more second level nodes selected as the leader, and the current message is signed using the corresponding session secret key.
Application of Simulation Technologies for Formation of Safety Related Competences

The formation of safety-related competencies is a difficult task. These specific safety competencies, unlike other competencies, are difficult to fully develop by providing safety learners and students. Simulation Technologies could be the solution to make it easier for students to develop competencies related to human safety. In this thesis, the following methods were used: analysis of scientific literature, projective methods, mathematical methods – in particular registration and ranking, theoretical methods including: analysis, synthesis, generalization, comparison, conclusions, modeling, induction, deduction. The study showed that the use of simulation technologies allows creating conditions for the formation of the necessary competencies without increased risk for applicants and others. One of the most promising technologies for the formation of safety-related competencies is the use of virtual reality. The use of simulation technologies in the educational process will intensify it and improve the quality of education. Simulation technologies should take a special place in the training of specialists whose professional activities are associated with increased risk, as well as with significant negative consequences of erroneous decisions, in particular in the training of rescuers, military specialists, police, operators of complex systems (including transport, nuclear, etc.), medical workers, in the aerospace industry, etc.
Research on performance test analysis method of mechanical structure based on measured error / virtual simulation

Mechanical errors seriously affect the transmission performance of mechanical structure, especially for high-precision mechanical structure. With the development of virtual simulation technology, the performance test of mechanical structure in virtual environment is gradually applied. Under the background, this paper takes the movable tooth reducer as the research object and puts forward a virtual performance test method by combing test errors with virtual simulation technology. The tooth profile errors of key components of the reducer are obtained by optical scanning, and the simulation test is carried out. Meanwhile, the accuracy of the simulation test is verified by the implementation of vibration test. The results show that both the simulated vibration acceleration and the experimental test vibration acceleration show a double-peak variation trend, and the frequency difference between the two peaks is less than 5%. Therefore, the simulation test method proposed in this paper can effectively evaluate the performance of mechanical structures.
Equity‐weighted bootstrapping: Examples and analysis

When faced with severely imbalanced binary classification problems, we often train models on bootstrapped data in which the number of instances of each class occur in a more favorable ratio, often equal to one. We view algorithmic inequity through the lens of imbalanced classification: In order to balance the performance of a classifier across groups, we can bootstrap to achieve training sets that are balanced with respect to both labels and group identity. For an example problem with severe class imbalance—prediction of suicide death from administrative patient records—we illustrate how an equity‐directed bootstrap can bring test set sensitivities and specificities much closer to satisfying the equal odds criterion. In the context of naïve Bayes and logistic regression, we analyse the equity‐weighted bootstrap, demonstrating that it works by bringing odds ratios close to one, and linking it to methods involving intercept adjustment, thresholding, and weighting.
Combining M-MCTS and Deep Reinforcement Learning for General Game Playing

As one of the main research areas in AI, General Game Playing (GGP) is concerned with creating intelligent agents that can play more than one game based on game rules without human intervention. Most recent work has successfully applied deep reinforcement learning to GGP. This paper continues this line of work by integrating the Memory-Augmented Monte Carlo Tree Search algorithm (M-MCTS) with deep reinforcement learning for General Game Playing. We first extend M-MCTS from playing the single game Go to multiple concurrent games so as to cater to the domain of GGP. Then inspired by Goldwaser and Thielscher (2020), we combine the extension with deep reinforcement learning for building a general game player. Finally, we have tested this player on several games compared with the benchmark UCT player, and the experimental results have confirmed the feasibility of applying M-MCTS and deep reinforcement learning to GGP.
Classification Method of Blockchain and IoT Devices Based on LSTM

With the development of the technology, more and more blockchain devices and Internet of Things devices are deployed around us. In order to manage these devices more conveniently, it is necessary to classify the devices according to the flow rate. The existing classification methods of devices are divided into active detection and passive detection. Active detection needs to send messages to them. Passive detection only needs to get the flow information of the devices, and existing passive detection needs to do so. The methods are all based on complete flow. We propose a method based on the previous part of the flow, using LSTM and random forest algorithm to classify. The f1-score of our method is 0.89. Compared with the existing methods, our method improves by 8%–30%, and our method can classify devices more effectively .
A Legal Architecture for Artificial Intelligence: An Herculean Task Ahead. Reflections from a Comparative Perspective

Artificial intelligence (AI) (artificial intelligences) is at the center of an intense institutional reflection, the objective of which is to identify the principles that would protect people from the potentially negative effects of the development of these technologies. In recent years, several institutions have investigated the issue of the legal framework for this technological development, perceived as a need to adapt the legislation. In fact, this reflection questions the categories and traditional legal mechanisms. However, this is not the first time that the law has faced the reception of a completely new technique or practice, and it may be interesting to review the way in which previous developments have been received to learn.
Saliency-based Active Contour Model for Image Segmentation and Region Detection

In this paper, we propose a novel saliency-based active contour model for image segmentation and region detection (SAIR) to overcome the problems of noise and intensity inhomogeneity in image segmentation. The proposed new level set protocol evolves adaptively and eliminates the need for initialization. In the proposed energy function, we formulate an adaptive weight function that adaptively changes the intensity of the internal and external energy functions according to the image. Moreover, according to the inner and outer regions, modulating the signs in the proposed energy function influences the elimination of noise in the image. Finally, SAIR is tested on multiple images with different initial contour positions, intensity inhomogeneity, and noise to demonstrate the robustness of SAIR.
Complex Dynamic Systems Theory and Second Language Development

The field of Applied Linguistics was first introduced to Complex Dynamic Systems Theory by Larsen Freeman (1997), later followed by De Bot (De Botet al, in Bilingualism Lang Cogn 10(1):7–21, 2007).
Fuzzy Data Augmentation for Handling Overlapped and Imbalanced Data

Class imbalance is a serious issue in classification as a traditional classifier is generally biased towards the majority class. The accuracy of the classifier could be further impacted in cases where additionally to the class imbalance, there are overlapped data instances. Further, data sparsity has shown to be a possible issue that may lead to non- invariance and poor generalisation. Data augmentation is a technique that can handle the generalisation issue and improve the regularisation of the Deep Neural Network (DNN). A method to handle both class overlap and class imbalance while also incorporating regularisation is proposed in this paper. In our work, the imbalanced dataset is balanced using SMOTETomek, and then the non-categorical attributes are fuzzified. The purpose of fuzzifying the attributes is to handle the overlapping in the data and provide some form of data augmentation that can be used as a regularisation technique. Therefore, in this paper, the invariance is achieved as the augmented data are generated based on the fuzzy concept. The balanced augmented dataset is then trained using a DNN classifier. The datasets used in the experiments were selected from UCI and KEEL data repositories. The experiments show that the proposed Fuzzy data augmentation for handling overlapped and imbalanced data can address the overlapped and imbalanced data issues, and provide regularisation using data augmentation for numerical data to improve the performance of a DNN classifier.
Existing Research in Video Surveillance System

This chapter provides information on various Gaussian mixture model-based background modeling and its improvements. Also, the chapter gives information on 3D object detection, object tracking, and performance evaluation parameters for the video surveillance system.
Study on application of knowledge graph in TCM pediatric treatment of Disease

The growth of children is a continuous and gradual dynamic process. Although the growth and development of children are rapid, the spleen, stomach and kidney have not yet developed completely. Compared with western medicine, the treatment plan of "treating the pain in the head and treating the pain in the foot"; By adopting the physique identification and diagnosis techniques based on time, place and individual conditions, combined with dietary, acupuncture, massage, massage and other auxiliary health care measures, TCM can achieve the effect of treating both symptoms and root causes. But the knowledge of traditional Chinese medicine is extensive and profound, which is difficult to understand for many parents without any medical background. And the current network spread of massage, acupuncture, cupping and other learning resources mixed, emerge in an endless stream, resulting in parents difficult to distinguish between true and false; When the learning resources are wrong and the treatment techniques are inappropriate, it is easy to aggravate the disease and cause repeated attacks of the disease. This article explores on the carrier of knowledge map AI technology, TCM diagnosis and treatment of pediatric diseases rehabilitation knowledge map construction, to simplify the knowledge of TCM, to at any time to learn, easy to learn the way of sharing to our parents, self-learning medical suit the remedy to the case, play traditional Chinese medicine "cure disease cure not ill" advantage, aftercare children's physique, disease prevention and treatment of diseases, To carry forward and develop traditional Chinese medicine.