Reviewerly/exHarmony huggingface dataset:
A Generation Adversarial Network Based on Multi-Condition Confrontation and Gradient Optimization

Aiming at the problem of pattern collapse, this paper starts from the idea of forcing each generator to generate different pattern data in a multi-generator game, and proposes a multi-generator-based generation confrontation network, named improved multi-generator generative adversarial nets (IMGAN). IMGAN uses parameter sharing between multiple generators to speed up training, and at the same time uses the last layer of independent training to weaken the impact of parameter identity; introduces a regular penalty term to make the loss function better satisfy Lipschitz continuousness, which avoids the effect of gradient disappearance to a certain extent; and introduces a hyperparameter to solve the disparity problem caused by multiple loss functions and avoid excessive bias toward one of the gradient directions. At last, we verify the performance of our model through comparative experiments on multiple data sets.
Characterizing the localization of organic C on mineral surfaces: a correlative microscopy/spectroscopy approach

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A low-noise telecom interface for silicon-vacancy quantum network nodes

Fiber-based quantum networks employing atomic memories require an interface between visible and infrared photons. We demonstrate low-noise conversion between silicon vacancy wavelengths and the telecom O-band for use in a deployed quantum network.
Learning interpretable multi-class models by means of hierarchical decomposition: Threshold Control for Nested Dichotomies

The success of Artificial Intelligence at solving real-world problems poses the need for interpretable models, especially in human-centered applications. The multi-class scenario is often present in these environments; however, the majority of research on interpretability has focused on binary classification. In this work, a novel method based on hierarchical decompositions to obtain interpretable multi-class models is introduced. The proposal, named Threshold Control for Nested Dichotomies (TC-ND) method, creates a binary-based hierarchical class structure. Then, it discards meta-classes at each dichotomy of the structure according to a certain level of confidence, pursuing a modular and more comprehensible decomposition of the multi-class problem. The approach presents internal parameters that are optimized using the Differential Evolution algorithm. The goodness of our proposal is assessed using a twofold approach: performance is evaluated by comparing against other state-of-the-art multi-class methods; interpretability is supported by practical examples with counterfactual explanations and a discussion of the advantages that the TC-ND method presents regarding its transparency and auditability.
Research on Software Development Mode and Application of Reusable Framework based on Neural Network Algorithm

With the rapid development of information technology, computer systems and intelligent control systems have become popular in more and more industries and fields. This makes users put forward more diversified and personalized requirements for the software system. In the process of software development, it is not only necessary to pay attention to the functionality of the software, but also to emphasize its reusability. Ensure that the developed primary code can be applied in multiple software after simple modification. While improving the efficiency of software development, it can also save money. Developers only need to build a complete framework based on their commonalities, and then they can include all systems. After that, it is only necessary to make appropriate adjustments to the individual requirements of the software to simplify software development.
Image saliency detection method for safe operation of substation

Aiming at the problem that methods based on background templates in existing substation scenes cannot completely detect salient targets with similar characteristics in some areas and the background, a salient target integrity detection method based on contour detection is proposed. First, extract the initial contours of the input image, use the proposed virtual connection-based contour processing scheme to merge adjacent contours and remove isolated contours, and use the designed shortest path-based closed-loop search scheme to merge the contours with longer distances to obtain Saliency map for contour detection. Then, an adaptive threshold segmentation algorithm is used to process the saliency map based on background template suppression to obtain the binarized saliency map and the salient pixels. By removing the complete area of the contour with the proportion of salient pixels smaller than the specified threshold, an optimized saliency map based on contour detection is obtained. Finally, merge it with the binarized saliency map to obtain a complete saliency map. The results prove that the method in this paper can obtain a better saliency map for images where the saliency target is located at any position.
Exploration and Application of Closed Loop Online Teaching Mode

In order to ensure the suspension of classes and schools during the outbreak of the new epidemic, the online teaching mode was explored and studied. The paper puts forward the teaching objectives and teaching requirements of the closed loop online teaching mode, discusses the problems that need to be considered in the closed loop online teaching mode, and carefully designs the closed loop online teaching mode in view of these problems, explores and forms a relatively perfect closed loop online teaching mode. According to the course of C language programming, the specific implementation process of the closed loop online teaching mode is given. After a semester of exploration and implementation, it ensured that the online teaching not only could be carried out in an orderly manner, but also had been well received by students and the supervisions. It has been agreed that the teaching effect of this teaching mode is good, and it is worth promoting in the future.
On the Performance Analysis of a TensorFlow based Neural Network for Face Mask Detection

Technology has become crucial in applying measures to refrain diseases' spread. Thus, computer vision can be an essential tool in this combat. This paper proposes and demonstrates an application that combines facial detection and artificial intelligence techniques to verify the use of masks by individuals within the range of the camera's vision field. In a pandemic period of COVID-19, it is necessary obtaining better control of mask usage in places where it is mandatory and providing quick identification of someone who will not comply with this rule. With this objective, a Machine Learning system was created to operate in real-time a classification of face mask usage to prevent the transmission of the virus and directly impact suppressing the pandemic.
Classification of MRI's images using the Fractional Fourier transform and the local binary pattern information by artificial neural network

Pattern recognition techniques are widely used in computer vision, classification of radio signals, and voice recognition. The fractional Fourier transform is used to recognize patterns using binary rings masks and segment images. This technique has the characteristic of being invariant to position and rotation and finally obtaining a one-dimensional signature. On the other hand, Neural Networks are used for pattern recognition based on a deep neural network algorithm. It has the characteristic of training large datasets with millions of images. Artificial Neural Networks(ANNs) are used for several applications such as pattern recognition and classification of input data. In particular, the ANN has been used to evaluate medical images from the brain to assess if the image corresponds to Alzheimer's disease. One disadvantage of the neural network is a large amount of time to learn depending on the number of patterns to be identified or classified and the ability to adapt and recognize patterns. Besides, the fractional Fourier transform cannot analyze a large amount of information. In this work, a comparison between the Artificial Neural Network and the Fractional Fourier Transform is presented to determine which will be the best for recognizing a batch of selected medical images. We propose a reconstruction method using both techniques for precise image recognition and the evaluation of their respective metrics such as accuracy, precision, sensitivity, and specificity. The medical images regarding Alzheimer's disease are no dementia, very mild dementia, mild dementia presenting the best performance regarding the receiver operating characteristics and moderate dementia was the worst classified related to the number of images of the dataset.
Hybrid Multiobjective Evolutionary Algorithms for Unsupervised QPSO, BBPSO and Fuzzy clustering

While there has been many new developments in multiobjective evolutionary algorithms, they have not been applied or investigated in clustering problems. In this paper, ten different unsupervised clustering techniques applying different MOEA (SPEA2, IBEA, MOEA/D and MOEA/GLU), PSO (QPSO and BBPSO) and Fuzzy approaches are experimented on ten public datasets. The rationale to apply MOEA is to increase the exploitation capabilities of clustering techniques to further refine the cluster solutions found by fuzzy or PSO clustering. The aim is to investigate in the performance of different types of MOEA applications in clustering, determining whether MOEA Fuzzy clustering outperform MOEA PSO variants. Overall, MOEA/D BBPSO was found to produced the best results. It outperformed MOEA Fuzzy techniques, having tested on datasets with high number of classes, that are imbalanced and/or overlapping classes. IBEA Fuzzy clustering was found to produce the worst results. MOEA/D clustering was found to perform better than other MOEA techniques. In this work, we showed that MOEA/D BBPSO clustering produced the best results on challenging datasets. It was able to use MOEA/D to deepen its exploitation capability while benefiting from the exploratory ability of BBPSO when clustering challenging datasets.
Differentiable Feature Selection, A Reparameterization Approach

We consider the task of feature selection for reconstruction which consists in choosing a small subset of features from which whole data instances can be reconstructed. This is of particular importance in several contexts involving for example costly physical measurements, sensor placement or information compression. To break the intrinsic combinatorial nature of this problem, we formulate the task as optimizing a binary mask distribution enabling an accurate reconstruction. We then face two main challenges. One concerns differentiability issues due to the binary distribution. The second one corresponds to the elimination of redundant information by selecting variables in a correlated fashion which requires modeling the covariance of the binary distribution. We address both issues by introducing a relaxation of the problem via a novel reparameterization of the logitNormal distribution. We demonstrate that the proposed method provides an effective exploration scheme and leads to efficient feature selection for reconstruction through evaluation on several high dimensional image benchmarks. We show that the method leverages the intrinsic geometry of the data, facilitating reconstruction.
Robotic Hand Movement Using If-Statement Technology

Robotic hand systems fall under the areas of artificial intelligence (AI) and machine learning (ML), and are an important field nowadays. Today’s technology shows a great interest in robotic systems, evident in the number of studies being conducted in this area. There are numerous studies in various fields on robotic systems, with the aim to facilitate human life. In this work, a robot hand was designed that is capable of replicating finger movements by relying on flexible sensors attached to a wearable glove. The sensor is able to measure finger angle with high accuracy, albeit inexpensively. Thanks to the manual robotic prototype created in this study using different materials and mechanisms, it can be adopted in areas that are unsafe for human health. Through the gloves, the angle data on the sensors and servo motor position information are transmitted to the robotic handheld system through a digital AI software. Determinations related to the robot hand control are carried out with an Arduino card. Five servo motors, one for each finger, constrain the mechanical hand. These servo engines can be moved if necessary. The Main Control Unit (MCU) is attached to these servo engines. Arduino is used in its MCU. We used the IF statement algorithm to allow the robotic hand’s fingers to move smoothly in a variety of angles, despite the device’s resistance, and at a low cost, so that it could be made available to anyone.
Malicious Twitter Bot Detector

Social media plays a very important role in every person's life today. The user-friendly features of social media have made it gain a large amount of importance in today's world. Social media platforms like Facebook, twitter and Instagram have paved their way to connect people regionally, locally and internationally because distance is not a barrier. Social media has increased its importance by providing employment to people all over the world. However, as every coin has two sides, social media also has a number of disadvantages among which security is the most important one to be considered for the purpose of resolution. Cybercrimes and phishing scams play vital role in obstructing the security on social media wherein it is most commonly found to be carried using twitter platform. Due to the presence of large number of users, hackers use twitter bots in order to circulate malicious URLs which acts as an entry point into the target system. Twitter bots make tweets at a frequency much higher than that of a human account on twitter and hence can be used in order to send the malicious URLs to people. Hence, in order to prevent this spread of malicious URLs among the people we have come up with a system which will initially classify the account as a twitter bot account and then we will check whether it is a malicious bot or not and will report to twitter in case it is malicious in nature.
A Study on Discrete Action Sequences Using Deep Emotional Intelligence

Recognition of emotions from human plays a vital role in our day to day life and is essential for social communication. Automatic emotion recognition is becoming recent research focus on artificial intelligence. A facet of human intelligence is the ability to recognize emotion that is regarded as one of the attribute of emotional intelligence. Although research based on facial expressions or speech is seen in thrive, recognizing emotions from body gestures remains a less explored topic. This chapter proposes a machine learning approach and discussed with deep learning model to achieve emotional intelligence. The block based intensity value (BBIV) feature and the different bin level HoG feature (DBLHoG) are extracted from human body movements and are fed to a supervised learning algorithm. Support vector machine (SVM), k-nearest neighbor (KNN) and random forest classifiers are the supervised learning algorithm used in this chapter. Finally, the pre-trained deep convolutional neural network (DCNN) model is used. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEP) corpus dataset. In this dataset, human body movement expressing the five archetypical emotions likes (anger, fear, joy, pride and sad). In this emotions recognition system, The random forest classifier outperformed better than the SVM and kNN classifier. Finally DCNN model achieve better recognition than random forest classifier. This chapter gives a brief study on achieving emotional intelligence with a DCNN Model.
Implementing AI Techniques to Address the Challenges Surrounding Cryptocurrencies

The research work is focused on examining the role of artificial intelligence (AI) in addressing challenges associated with cryptocurrencies like Bitcoin, Ethereum, etc. The popularity of Bitcoin has sparked the emergence of new alternative cryptocurrencies, commonly referred to as ‘altcoins'. Simultaneously with its growing popularity and public awareness, the Bitcoin system has been branded as a haven for security breaches, selfish mining, money laundering, extreme volatility, and unpredictability of future prices. To address these challenges, stakeholders accepting cryptocurrencies must apply AI techniques to process and analyze large amounts of cryptocurrency data. In this context, this chapter discusses the recent research work to assist the researchers and practitioners in the cryptocurrency domain to make fact-based decisions by using AI techniques. Consequently, the chapter provides a detailed review of the background on fiat currencies, cryptocurrencies, challenges associated with cryptocurrencies, and the role of AI techniques in addressing those challenges.
Research on target detection and tracking method applied to intelligent monitoring system

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.
THE VALUE OF THE NON-USER: DEVELOPING (NON-)USER PROFILES FOR THE DEVELOPMENT OF A ROBOT VACUUM WITH THE USE OF THE (NON-)PERSONA

Abstract This contribution explores non-user integration in a product development context of the development of a vacuum robot in order to analyse users as well as non-users of a product. While user profiles like the persona have been part of product development and product design for years, the non-user has not been widely explored. Within this contribution the known concept of the persona is extended and further developed to the non-persona, a profile that describes non-users and why they do not use a certain product, in this case the vacuum robot. Including the non-user in product development offers the chance of addressing yet unidentified product requirements and therefore opening the product up to a bigger audience. This template works for both users and non-users and can be used to include both sides in a development project.
An Algebraic Approach to Verifying Galois-Field Arithmetic Circuits with Multiple-Valued Characteristics

This study presents a formal verification method for Galois-field (GF) arithmetic circuits with the characteristics of more than two values. The proposed method formally verifies the correctness of circuit functionality (i.e., the input-output relations given as GF-polynomials) by checking the equivalence between a specification and a gate-level netlist. We represent a netlist using simultaneous algebraic equations and solve them based on a novel polynomial reduction method that can be efficiently applied to arithmetic over extension fields $\mathbb{F}_{p^m}$, where the characteristic p is larger than two. By using the reverse topological term order to derive the Gröbner basis, our method can complete the verification, even when a target circuit includes bugs. In addition, we introduce an extension of the Galois-Field binary moment diagrams to perform the polynomial reductions faster. Our experimental results show that the proposed method can efficiently verify practical $\mathbb{F}_{p^m}$ arithmetic circuits, including those used in modern cryptography. Moreover, we demonstrate that the extended polynomial reduction technique can enable verification that is up to approximately five times faster than the original one.
코로나시대 온라인 리듬표현 무용수업의 현황 및 개선방안

This study aims to analyze the present condition, operation methods and problems of online Rhythmic Expressions dance classes, and derive improvement plans through a theoretical model to address the primary causes of the problems. As a result, the conclusion of this study is as follows, analyzing problems according to the contents and status of in-depth interviews of online rhythm expression dance classes and presenting improvement plans. First, given that there are diverse possibilities in online Rhythmic Expressions dance education, we need to contemplate developing various methods, content, and educational environments that enable the realization of the possibilities, rather than treating the online education as a supplementary or alternative to offline education. Second, we need to organize a medium- and long-term online class research team to turn this crisis of coronavirus into an opportunity to open a new era in education. Learning management systems should be enhanced with the functions that ensure systematic delivery of data on the progress of learning and increase interactions between educators and learners to overcome the physical constraints. Third, there are many factors to consider when selecting an online Rhythmic Expressions dance class model, but the priority should be put on the learning of a learner, above all. For example, learners may find a discomfort in an online Rhythmic Expressions dance class model that educators find otherwise. It is a matter of choice to select and operate an online class model. Therefore, we need to grapple to make a good choice, and if a problem occurs from the choice, take a responsive measure to address the problems in a flexible stance. As above, this study demonstrated that online Rhythmic Expressions dance classes can have a different effect depending on learners, educators, lecture content, and system.
Automatic Text Summarization for Malay News Documents Using Latent Dirichlet Allocation and Sentence Selection Algorithm

The proliferation of internet newspapers making an Automatic Text Summarization is now a need to produce a summary that contains most of the important information from the original document. This study focused on the keyword extraction using Latent Dirichlet Allocation and Sentence Selection that used rule based concept approach to produce extractive summary. 100 Malay news documents covering general, sports, health and technology were collected from Utusan Online to evaluate the effectiveness of the system. This study used a single topic from LDA and top 10 words in the selected topic as the keywords. To evaluate, summary generated by the system was compared to summary generated by human expert using Precision Recall formula. The results showed the effectiveness of the summary generated by the system which is the best score 62.7 % that can help people read the Malay news documents in short time as the summary assist the readers to understand the important parts of the document without reading the whole document.