Reviewerly/exHarmony huggingface dataset:
Specular Reflections Removal for Endoscopic Images Based on Improved Criminisi Algorithm

Specular reflections always exist in endoscopic images, and they can severely disturb surgeons' observation and judgment. This paper proposes a exemplar-based inpainting algorithm based on the adaptive search range to remove the endoscopic specular reflections. First, a binary thresholding algorithm based on b-channel and morphological dilation operation is used to automatically locate specular reflection regions, and then an improved algorithm based on the Criminisi algorithm is proposed, which uses the method of sequentially repairing highlight regions to reduce the edge contour priority calculation times, and then adopt an adaptive search range strategy to reduce the amount of calculation in the process of searching for the best matching patch, while searching in the additional search area provided by the best frame to improve the accuracy of matching. Experimental results demonstrate that the proposed method can automatically and correctly not only locate but also remove specular reflection regions in endoscopic images. In addition, because the improved method takes much less time than existing methods, and the PSNR and SSIM are higher than existing methods, the specular reflections removal scheme in this paper is better than existing methods.
CS2S-NCG: Cycle-Seq2Seq Model for Open Domain Neural Conversation Generation

Neural conversation generation model has attracted more and more attention in open domain conversation generation. Even though NCG-based conversation generation methods obtain promising results, they tend to generate short, generic and uninformative responses for most inputs. Moreover, training NCG models heavily relies on a large amount of quality paired dialogue corpus which tends to be expensive and difficult. In this paper, a unified Cycle-Seq2Seq model is proposed for fully data-driven Neural Conversation Generation (CS2S-NCG) in open domain. CS2S-NCG has ability to simultaneously learn the relevance of paired dialogue corpus, utilize feedback about the quality of the two generation models and reduce dependency on the amount of paired training dataset. Meanwhile, a joint learning strategy supports enhanced generation by better usage of massive unpaired dialogue corpus. An alternating Joint-EM algorithm is developed to solve this joint model. A series of experiments are conducted to evaluate CS2S-NCG on two automated metrics, as well as with human evaluation. The results demonstrate that CS2S-NCG significantly improves the relevance and diversity of generation results, compared to traditional neural conversation generation models. Furthermore, quantitative results also reveal that conversation generation quality are enhanced by leveraging unpaired dialogue corpus for not only CS2S-NCG but also traditional NCG.
Angular joint loss for image classification

Current CNN-based deep feature learning methods for image classification are mainly focused on learning with two types of loss functions: the softmax loss and its variants with class-level labels, or the metric-learning losses with pairwise labels. The former regards the weight vectors of the last fully connected layer of CNNs as representations of the class centres and focuses on the similarity between a single feature and each weight vector. The latter aims at seeking appropriate distance metrics between features pairs to boost the CNNs by learning a discriminative embedding space. In this work, we propose a simple yet effective new loss function called angular joint loss (AJL) to exploit the best of both worlds. By integrating feature-weight angles with feature-feature angles, AJL inherits the simplicity of softmax loss and the power of pair-wise metric learning and enhances both the intra-class compactness and the inter-class separability. The proposed AJL is simple to implement and has no time-consuming mining of pair or triple samples. On some popular image classification datasets (CIFAR-10, CIFAR-100 and ImageNet), extensive experimental results show that the proposed AJL can achieve the state-of-the-art performance.
Flex Force Smart Glove for Therapy Treatment Using Arduino and Raspberry Pi

The process of rehabilitation of a stroke patient is long and complex, yet it is necessary to recover the flexibility of muscle movement and reduce the risk of a recurrent stroke. Thus, this paper uses flex force smart gloves to assist physicians to remotely monitor the rehabilitation process of stroke patients via data collection gloves using flex sensors. In this work, Raspberry Pi is used to connect to the Internet and, in conjunction with the Arduino platform, to interface with the electronic components of the system. Performance analysis on Arduino and Raspberry Pi has shown that Arduino is capable of output data at an average interval of 1,785 ms at a baud rate of 115,200 baud. However, communication between Arduino and Raspberry Pi reduces performance to an average of 5,421 ms. In addition, this paper also demonstrated the ability of Raspberry Pi to perform complex processes without significant performance degradation in this application. The final system has shown its ability to read, process, record and relay data from the data gloves.
Mobile Net Convolutional Neural Networks for Video Classification

Current data and correspondence advances give the foundation to send bits anyplace, however don't dare to deal with data at the semantic level.s This paper researches the utilization of video content investigation and high- light extraction and bunching strategies for additional video semantic arrangements. This system can be applied to the applications, for example, on-line video ordering, separating and video synopses, and so forth. Grouping recordings as indicated by content semantics is a significant issue with a wide scope of uses. In this paper, we propose a combination significant learning structure for video request, which can show static spatial information, transient development, similarly as momentary snippets of data in the chronicles. Specifically, the spatial and the transient development features are extricated freely by two Convolutional Neural Networks (CNN). The standard commitment of this work is the cream learning framework that can show a couple of huge pieces of the video data. We in like manner show that joining the spatial and the flashing development highlights in the regularized combination arrange is better than direct grouping and combination utilizing the CNN with a delicate max layer, and the sequence-based LSTM is profoundly corresponding to the conventional characterization technique without considering the transient casing orders. Recordings can be characterized by utilizing MobileNet Classification.
Automatic Vehicle Detection from Satellite Images Using Deep Learning Algorithm

Attributable to global economic development and rapid urbanization, traffic explosion has been observed. This situation becomes very prominent as the traditional traffic control systems are incapable to efficiently monitor and control the traffic. Therefore, this paper presents automatic vehicle detection from satellite images using deep learning approaches. For this purpose, two most renowned and widely used detection algorithms (YOLOv4 and YOLOv3) have been employed to develop satellite image vehicle detector using publicly available DOTA dataset. This work confirms the supremacy of YOLOv4 over YOLOv3 by large improvements in mAP, IOU, precision, recall, F1-score with increase of 45%, 20%, 11.1%, 45.9%, and 29.5%, respectively. Therefore, these investigational results verify the strength of YOLOv4 algorithm for satellite image vehicle detection and recommend its use for the development of an intelligent traffic control system.
A Graph-Based Reputation Assessment System for Online Review Communities

One major concern regarding the operation of online communities is the trustworthiness of the users, and a reliable way for estimating it is by using reputation systems. These systems aim to provide reliable rankings of users, items and businesses based on feedback provided by the rest of the users in the online community. However, a reliable reputation system should be robust against different types of attacks orchestrated by malicious users. In this paper, we propose a robust graph-based reputation assessment system for online review communities. This system is based on the construction of a weighted bipartite graph that includes both users' and businesses' clusters as nodes. After the users and the businesses of the online community have been grouped into clusters and the bipartite graph has been constructed, the reputation of businesses based on user ratings and the topology of the graph is estimated. The proposed system was evaluated in terms of robustness against malicious attacks using a real-world review dataset, and compared with the simple-average reputation system. Preliminary results indicate that the proposed system yields more robust behaviour against a specific type of malicious attack compared to the simple-average reputation system.
SCHOOL COURSE OF INFORMATICS IN THE ERA OF DIGITAL TRANSFORMATIONS: THE PRIORITY DIRECTION IS THE DEVELOPMENT OF DIGITAL LITERACY

The purpose of the article is to identify possible directions for the development of the content of the school informatics course in the era of digital transformations for the formation of digital literacy of students. The study is based on the results of the analysis of approximate basic educational programs of secondary general education in the aspect of the planned personal, metasubject and subject learning outcomes, including in the study of informatics, a review of the content of school textbooks on informatics, scientific developments in the field of teaching informatics by domestic and foreign authors, generalization existing pedagogical experience in teaching informatics in different countries. The authors define digital literacy as an interdisciplinary educational result and substantiate the leading role of informatics in its development. The authors indicate the directions for the development of informatics in schools in various areas of digital literacy (hardware and software fundamentals, information literacy, digital content creation, security, problem solving, career competencies). The authors talk about a new section of school informatics, "Fundamentals of digital literacy and cybersecurity". which consistently and systematically demonstrates to students the changes caused by digital innovation. The authors indicate the possible content of this section. This is a new, valuable and practically significant material for domestic and foreign school education. A detailed description of digital reality in a school informatics course allows us to model the development of students' digital literacy and establish criteria and indicators for its assessment in various situations: from motivation to mastering digital literacy, acquiring digital skills with a general way of performing educational actions and on the subject content of a school informatics course to meaningful and the systematic practical use of digital skills.
Comparative Study on Data Embedding Techniques in Image Text-Based Steganography

The network usage is increasing day by day not only for common communication among the individuals but also the confidential information between the organizations, and military and secret agencies to secure their national interests. In this scenario, securing confidential information is of higher priority that can be done through steganography. Yet, the major concern is which kind of methodology suitable for which format of the image. So, this article mainly deals with the comparative study of various popular image-text based steganographic methodologies with effective to the various image formats such as JPEG, PNG, and BMP. For such a study, the methodologies are classified based on the image format as well as the corresponding effective domain for the image-text based steganography. The conclusion withdrawn from this comparative study with the aid of PSNR and SSIM is that the image formats JPEG and BMP are effective image formats for image text-based steganography.
FLOCK: Fast, Lightweight, and Scalable Allocation for Decentralized Services on Blockchain

Many decentralized services have recently emerged on top of blockchain, offering benefits like privacy, and allowing any node in the network to share its resources. In order to be a competitive alternative to their central counterparts, their performance needs to match up. Specifically, service allocation remains a performance bottleneck for many decentralized services.In this paper we present FLOCK, an allocation system which is highly scalable, fast, and lightweight. Furthermore, it allows nodes to indicate their preference for clients/sellers without needing to submit bids by using stable matching algorithms. We decouple the price discovery and outsource this function to a smart contract on the blockchain.Additionally, another smart contract is used to orchestrate the allocation and take care of service discovery, while trusted execution environments securely compute allocation solutions, and off-chain payment networks are used to send rewards.Evaluation of FLOCK shows that gas costs are manageable and improve upon other solutions which leverage auctions, and that our instance of the stable matching algorithm greatly improves run-time and throughput over auction counterparts. Finally, our discussion outlines practical improvements to further increase performance.
Analysis of an Efficient Reduction Algorithm for Random Regular Expressions Based on Universality Detection

In this article we study a very simple linear reduction algorithm that is specific to regular expressions. It aims to detect, in a bottom-up fashion, universal subtrees in regular expressions trees, and replace them by the smallest equivalent to $$\varSigma ^*$$ . Of course, this does not detect every universal subtree, as the universality problem is PSPACE-complete. However, we prove that for the uniform random tree model, this simple algorithm detects a large proportion of universal trees. Furthermore, we prove that on average this algorithm reduces uniform regular expressions to a constant size that is very small, and that can be computed efficiently. For example, for two letters the limit expected size is $$\sim $$ 77.8. Our theoretical constants are backed-up by the experimental evidence. This confirms the phenomena reported in [13], and further it completely discards the usefulness of the uniform distribution on regular expressions.
A Feature-Based Machine Learning Approach for Mixed-Criticality Systems

Driven by the recent technological advancements in the field of artificial intelligence, machine learning has emerged as a promising representation learning and decision-making method in many technological domains. Inspired by impressive these results, now machine learning techniques are also being applied to address the decision-making and control problems in the area of cyber-physical systems. For instance, some of these systems fall under the category of safety-critical systems such as chemical plants, autonomous vehicles, surgical robots, and modern medical equipment. One of the major performance issues related to the applicability of machine learning with safety-critical systems is related to the probability-based prediction nature of machine learning components used within such systems. This particular characteristic of machine learning makes it extremely difficult to guarantee safety as directed by standards such as ISO 26262. More importantly, the non-transparent and complex nature of machine learning algorithms make both the reasoning as well as formally establishing the safety aspects of the underlying system extremely difficult. The objective of this research work is to investigate on this key issue, and further on propose an efficient machine learning methodology based on the mixed-criticality approach feasible to safety-critical systems.
A Novel Microblog Sentiment Classification Method Based on Top-k Pooling

Microblog is a popular social media platform for information sharing and dissemination. Recently, microblog sentiment classification has received a lot of attention in many real-world applications, such as stock price prediction, public opinion monitoring, and crisis management. Existing microblog sentiment classification methods identify the sentiment polarity mainly based on the textual content. As microblog is usually short and contains a lot of noisy, it is very challenge to learn powerful representation only relied on the textual information. In this paper, we argue that the multi-head self-attention, which can capture complicated interactions information between words in sequence, would inevitably to introduce lots of noisy information. Moreover, the user sentimental polarity also plays an important role in judging the sentiment of text. Therefore, we propose a novel microblog sentiment classification method, called Multi-head Self-attention Based on Top-k pooling (MSBT). Specifically, we design a Top-k pooling layer to alleviate the issue caused by the multi-head self-attention network, and then incorporate user historical sentiment tendency in the loop of microblog sentiment classification. Extensive experiments conducted on a real-word dataset MDUHI demonstrates that our proposed approach MSBT can significantly improve the performance of microblog sentiment classification, e.g., the F1 score is 0.98% higher than the optimal benchmark method NPA, reaching 93.39%.
Intelligent Decision Making System for Solving Traveling Salesman Problems: MPSO

Multi swarm Particle swarm optimization (MPSO) is a variant of Particle Swarm Optimization (PSO) where various sub-swarms involved instead of single swarm, thereby balancing the exploitation and exploration. The progression of gbest is achieved by passing the best fitness value obtained from the child swarm and further progression of gbest is achieved by using gbest of parent swarm. TSP is an NP hard problems and its objectives is to find the minimum distance for a given cities and the constraints is to visit all the cities exactly ones to reach the final destination. In this paper we proposed MPSO approach to solve combinatorial optimization problem using BVA techniques comprising (Region_bc(Rg), Replicate_bc(Rp) and Evade_bc(Ev)), the performance of the algorithm is evaluated in terms of computation time, optimal fitness, error rate, convergence rate, convergence diversity and average convergence diversity for particle 30, the proposed techniques outperformance well with minimum computation time for optimal fitness value and error rate. In future we plan to implement the proposed technique in cloud computing environment for job scheduling problems.
Evaluate of The Reproductive Efficiency of Cows With Fuzzy Logic

Fuzzy Logic (Fuzzy Logic) is a branch of science based on thinking like human beings and solving them with mathematical functions. Fuzzy logic theory is a mathematical theory. Based on fuzzy set theory, it also uses intermediate values. The fuzzy logic that emerged in 1965 is used in many fields. In the production of pacemakers, in the production of artificial organs, in many electronic devices, company efficiency estimation, etc. situations are used. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. With the help of machines, people-specific data and experiences are studied using the fuzzy logic approach. In this study, by using Matlab Fuzzy Toolbox, it was aimed to design a system that gives information about the breeding performances of cows. The expert system was designed based on the optimal values under the ideal conditions specified in the literature. The architecture of the system presented in this paper is designed as three input parameters and one output. The designed system was tested with 100 sample values. Afterwards, expert results were evaluated and system decisions were compared. The success of the decision support system was 94%. As a result, the reproductive efficiency of cows can be determined with this designed system. With this determination, the handling or disposal of cows can be determined.
Investigating standardized value for Human Activity Recognition in Machine Learning

The study aimed to identify whether there is a difference in accelerometer and gyroscope readings along the x, y and z-axis for human activities such as Walking, Walking Downstairs, Sitting, Standing and Laying. Further to identify the standard value of accelerometer and gyroscope reading along x, y and z-axis. The data was collected from 30 subjects aged between 19 and 48 years old performing one of five standard activities (Walking, Walking Downstairs, Sitting, Standing and Laying) while wearing a smartphone on the waist that recorded the movement data. From the result, it is understood that the accelerometer sensor is more effective than the gyroscope sensor in human activity recognition along the x, y and z-axis plane. It was also found that there is a significant difference in reading along the x and z-axis for various activities such as Walking, Walking Downstairs, Sitting, Standing and Laying. But along the y-axis of the accelerometer and gyroscope sensor, there is no significant difference in mean score with a 95% confidence interval.
Research on Software Defined Programmable Strategy of Fireworks Model Oriented to Edge Computing Nodes

In cloud computing, since the program only runs in the cloud, it can be written in a programming language and compiled in a specific target platform. Due to the heterogeneous nature of the edge node platform, many tasks are migrated from the cloud to the edge terminal. So it is not easy to realize the edge computing programming, and the maintenance cost is also high. In order to solve this problem, this paper designs a set of software definable programming strategy through Hypertext Marked Language (HTML) format (independent of the operating system) file, and realizes the programming of the fireworks node of Internet of things (IoT) gateway under the edge computing, which is applied in the remote automatic monitoring system. Compared with the same system under the cloud computing, it is convenient for the user's programming, enhances the real-time performance, and greatly. It reduces the communication data flow, saves the data transmission cost, relieves the overall pressure of the system, and reduces the power consumption due to data transmission data storage.
The Algorithms for Word Segmentation and Named Entity Recognition of Chinese Medical Records

A complete inpatient electronic medical record contains a lot of information. In recent years, numerous researchers have carried out research on word segmentation of medical texts. Since the medical record text is written by free text, structuring the medical record text is an important part of the medical record intelligent analysis when these medical named entities are recognized. At present, named entity recognition methods are mainly divided into lexicographical and rule-based methods and machine learning-based methods. The machine learn-based approach takes the named entity recognition task as the annotation problem of sequence data, mainly considering the context information. The features commonly used in feature construction are contextual feature and dictionary features. BERT+LSTM+CRF were used to train the named entity recognition model. Open source CRF++ was adopted as the tool we relied on. We trained the LSTM+F model using the results of the original word segmentation and the information in the context as features. We carried out a 5-fold cross validation. The results showed that the overall F-1 score (MICRO-F) of named entity recognition reached 0.92, which confirmed that the model could accurately complete the task of medical named entity recognition.