Reviewerly/exHarmony huggingface dataset:
Towards Smart Contracts for Verifying DNN Model Generation Process with the Blockchain

In recent years, DNNs (Deep Neural Networks) have been applied into various fields and expected to be deployed into real-world applications. On the other hand, lack of transparency in DNNs makes them unreliable. To ensure transparency in DNN models, it's necessary that model validators can verify the entire learning process and convince third parties with limited resource that the given model is correctly generated. For this purpose, we propose a smart contract that is based on the dispute resolution protocol for verifying DNN model generation process. We divide the entire learning process into layer-based computations. The necessary data for validating each computation, such as the outputs of neurons, weights between layers and their gradients, are uniquely determined by the one-way hash function and the hashes are combined by multiple structured Merkle trees. This enables an honest validator to make a proof that asserts the target model is incorrectly generated, and third parties can check whether the assertion is true by only performing the given computation. Finally, to reveal how our proposal affects the performance degradation during the training, we evaluated running time for deep learning that enables our proofs. This result shows that our proposal can be applied into real applications. For this purpose, we propose a smart contract that is based on the dispute resolution protocol for verifying DNN model generation process. We divide the entire learning process into layer-based computations. The necessary data for validating each computation, such as the outputs of neurons, weights between layers and their gradients, are uniquely determined by the one-way hash function and the hashes are combined by multiple structured Merkle trees. This enables an honest validator to make a proof that asserts the target model is incorrectly generated, and third parties can check whether the assertion is true by only performing the given computation. Finally, to reveal how our proposal affects the performance degradation during the training, we evaluated running time for deep learning that enables our proofs. This result shows that our proposal can be applied into real applications. Finally, to reveal how our proposal affects the performance degradation during the training, we evaluated running time for deep learning that enables our proofs. This result shows that our proposal can be applied into real applications.
Fast Object Detection for Remote Sensing Data Using a Mobile Device

With the rapid development of remote sensing techniques, the quantity and quality of remote sensing data have been greatly promoted. The automatic understanding and semantic analysis of satellite or aircraft remote sensing images are strongly required. In this paper, we design and implement a lightweight CNN based network for the detection task of remote sensing images analysis. Experimental results show that our model is robust to distinguish different object categories with high similarity. At the same time, we deployed the model to mobile devices, which maintain the detection accuracy and prediction speed.
Evaluation of ML-Based Sentiment Analysis Techniques with Stochastic Gradient Descent and Logistic Regression

In recent times, along with the expansion of technology, the Internet also has flourished exponentially. World is more connected today not only through the technology, but also through sharing sentiments to express views, either be constructive or destructive in front of the world through social media. Twitter, Facebook, Instagram, etc., are being used as social media to reach the world. The study of understanding people’s emotions, intentions, attitudes from unstructured data is opinion mining/sentiment analysis. This is an application of NLP or text mining. In this paper, an attempt is made to realize sentiment analysis's multiple dimensions using approaches such as ML and NLP-based technqies like word frequency and TF-IDF. Using ML approach, experiments were conducted, and the performance of the predictions was visualized. Three different datasets are used. A comparison of logistic regression (LR) and stochastic gradient descent (SGD) algorithms are compared using two different document representation. An extensive comparison is carried out using three different types of dataset. Amazon instant video datasets, bank dataset and movie reviews datasets are being used for the same. Analysis of performance is accomplished by using different graphs. The results indicate that logistic regression performs better than stochastic gradient descent for movie review dataset by using word frequency and TF-IDF-based approach.
A Light Transfer Model for Chinese Named Entity Recognition for Specialty Domain

Named entity recognition (NER) for specialty domain is a challenging task since the labels are specific and there are not sufficient labelled data for training. In this paper, we propose a simple but effective method, named Light Transfer NER model (LTN), to tackle this problem. Different with most traditional methods that fine tune the network or reconstruct its probing layer, we design an additional part over a general NER network for new labels in the specific task. By this way, on the one hand, we can reuse the knowledge learned in the general NER task as much as possible, from the granular elements for combining inputs, to higher level embedding of outputs. On the other hand, the model can be easily adapted to the domain specific NER task without reconstruction. We also adopt the linear combination on each dimension of input feature vectors instead of using vector concatenation, which reduces about half parameters in the forward levels of network and makes the transfer light. We compare our model with other state-of-the-art NER models on real datasets against different quantity of labelled data. The experimental results show that our model is consistently superior than baseline methods on both effectiveness and efficiency, especially in case of low-resource data for specialty domain.
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification

Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets.
Text And Sentimental Analysis On Big Data

Big Data has become a very important part of all industries and organizations sectors nowadays. All sectors like energy, banking, retail, hardware, networking, etc all generate a huge amount of unstructured data which is processed and analyzed accurately in a structured form. Then the structured data can reveal very useful information for their business growth. Big Data helps in getting useful data from unstructured or heterogeneous data by analyzing them. Big data initially defined by the volume of a data set. Big data sets are generally huge, measuring tens of terabytes and sometimes crossing the sting of petabytes. Today, big data falls under three categories structured, unstructured, and semi-structured. The size of big data is improving in a fast phase from Terabytes to Exabytes Of data. Also, Big data requires techniques that help to integrate a huge amount of heterogeneous data and to process them. Data Analysis which is a big data process has its applications in various areas such as business processing, disease prevention, cybersecurity, and so on. Big data has three major issues such as data storage, data management, and information retrieval. Big data processing requires a particular setup of hardware and virtual machines to derive results. The processing is completed simultaneously to realize results as quickly as possible. These days big data processing techniques include Text mining and sentimental analysis. Text analytics is a very large field under which there are several techniques, models, methods for automatic and quantitative analysis of textual data. The purpose of this paper is to show how the text analysis and sentimental analysis process the unstructured data and how these techniques extract meaningful information and, thus make information available to the various data mining statistical and machine learning) algorithms.
ADELA

Human activity recognition (HAR) is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. For HAR in a smart environment without privacy and accessibility issues, data streams generated by environmental sensors are leveraged. In this paper, we propose ADELA, a novel activity recognition scheme in a smart collaborative environment where a group of users performs an activity without user identification. ADELA calculates the importance of events from sensor data streams depending on their impact on recognizing activities and finds the best activity recognition base models using attention-based ensemble learning. After the training phase, each base model obtains its weight through weighted majority calculation, and ADELA stores the information to Trained Model Storage to reuse it for inferring. We evaluate ADELA using the data collected from our testbed and CASAS dataset where users perform their tasks daily and validate the effectiveness of ADELA in a real environment. Experiment results show that the proposed scheme performs higher recognition performance than existing approaches.
Learning Scope of Python Coding Using Immersive Virtual Reality

Programming is a highly sought-after technical skill in the job market, but there are limited avenues available for training competent and proficient programmers. This research focuses on evaluating an immersive virtual reality (VR) application that has been introduced in the field of Python learning, which uses the interaction technique and a user interface, allowing the novice to engage in VR learning. 30 participants were recruited for the evaluation purpose and they are divided into two groups–15 for Experiment I, and 15 for Experiment II. A questionnaire to evaluate the user interface was done in Experiment I, and a questionnaire to evaluate the novice’s acceptance of the VR application was given to the participants in Experiment II. Furthermore, interviews were conducted to collect detailed feedback from all the participants. From the results, it can be noted that the implemented interaction designs in this VR application are adequate. However, more interaction techniques can be integrated to increase the degree of immersive experience of the user in the application. Besides, the interface of the application is considered adequate and reasonable. Nevertheless, there is room for improvement in the aspect of usability and provide a higher level user experience. The novices’ acceptance level of the new proposed learning method is low; this might be due to the users’ fear of change– a normal human behaviour in embracing new things in life. Therefore, a larger sample size is proposed to further investigate the novice’s acceptance of the new learning method by using an improved version of the VR application.
TraceSplitter

Realistic experimentation is a key component of systems research and industry prototyping, but experimental clusters are often too small to replay the high traffic rates found in production traces. Thus, it is often necessary to downscale traces to lower their arrival rate, and researchers/practitioners generally do this in an ad-hoc manner. For example, one practice is to multiply all arrival timestamps in a trace by a scaling factor to spread the load across a longer timespan. However, temporal patterns are skewed by this approach, which may lead to inappropriate conclusions about some system properties (e.g., the agility of auto-scaling). Another popular approach is to count the number of arrivals in fixed-sized time intervals and scale it according to some modeling assumptions. However, such approaches can eliminate or exaggerate the fine-grained burstiness in the trace depending on the time interval length.
Decentralized Learning with Virtual Patients for Medical Diagnosis of Diabetes

Abstract Machine learning, applied to medical data, can uncover new knowledge and support medical practices. However, analyzing medical data by machine learning methods presents a trade-off between accuracy and privacy. To overcome the trade-off, we apply the data collaboration analysis method to medical data. This method using artificial dummy data enables analysis to compare distributed information without using the original data. The purpose of our experiment is to identify patients diagnosed with diabetes mellitus (DM), using 29,802 instances of real data obtained from the University of Tsukuba Hospital between 01/03/2013 and 30/09/2018. The whole data is divided into a number of datasets to simulate different hospitals. We propose the following improvements for the data collaboration analysis. (1) Making the dummy data which has a reality and (2) using non-linear reconverting functions into the comparable space. Both can be realized using the generative adversarial network (GAN) and Node2Vec, respectively. The improvement effects of dummy data with GAN scores more than 10% over the effects of dummy data with random numbers. Furthermore, the improvement effect of the re-conversion by Node2Vec with GAN anchor data scores about 20% higher than the linear method with random dummy data. Our results reveal that the data collaboration method with appropriate modifications, depending on data type, improves analysis performance.
Study on face detection based on an improved Gaussian skin color model

Abstract An improved Gaussian skin color model is proposed for face detection applications. It overcomes the issue that the traditional models become ineffective in extracting the skin color region in the presence of diverse skin colors and illuminations. It is a two-dimensional gaussian mixture model based on H-SV (i.e., an improved HSV) and YCbCr color spaces. Experimental results from several face image databases show that the detection accuracy is improved by 35.8% and 6.3%, respectively, compared to a traditional gaussian skin model and Adaboost algorithm.
An Improved Under-sampling Imbalanced Classification Algorithm

To solve the problem of low classification accuracy of minority classes caused by data imbalance, an undersampling classification algorithm based on mean shift clustering for imbalanced data (UECMS) is proposed. The UECMS method uses mean shift clustering and instance selection for the samples of majority classes to complete the under-sampling. The selected samples and all the minority samples from origin data set form a new balanced data set. Also, the bagging-based ensemble learning algorithms are used to classify the balanced data sets. The experimental results show that the UECMS method improves the classification accuracy of minority classes for imbalanced data.
"Internet+" City Sports Stadium Service Platform Design and Application

Internet + stadiums are the core foundation of the new sports business format, and it is of positive significance to promote the construction of urban Internet + stadiums platform information. The functional architecture and design of the urban Internet + sports stadium service platform proposed in this research will make the service management of stadiums more scientific, efficient, standardized and transparent, thereby promoting the informatization process of urban stadiums management. It is suggested that Wuhan City should seize the current opportunity, based on the integration of the stadium network platform, through the development of Internet + stadiums development plan to promote the overall construction of the regional stadium network platform.
Realtime Multi-Person Pose Estimation Based on Android System

Human pose estimation has always been a very popular direction. This technology can be applied to human-computer interaction, abnormal behavior detection, intelligent security and other fields, and it will promote the development of artificial intelligence in the future. Human pose estimation has been developed for a long time, and with the advancement of science and technology, many emerging algorithms have spewed out, but most of these algorithms use a multistage network structure. These network structures have achieved better results in accuracy, but the parameters have become larger and larger. It's a big problem that deploying these networks to mobile and embedded devices with limited system resources. We use the MobileNet and TensorFlow Lite frameworks launched by Google to successfully optimize the complex network model and deploy it to Android phones. The human pose estimation can be quickly realized only by using local calculations, avoiding the delay trouble caused by cloud computing solutions.
SpaceLDA: Topic distributions aggregation from a heterogeneous corpus for space systems

The design of highly complex systems such as spacecraft entails large amounts of documentation. Tracking relevant information, including hundreds of requirements, throughout several design stages is a challenge. In this study, we propose a novel strategy based on Topic Modelling to facilitate the management of spacecraft design requirements. We introduce spaceLDA, a novel domain-specific semi-supervised Latent Dirichlet Allocation (LDA) model enriched with lexical priors and an optimised Weighted Sum (WS). We collect and curate the first large collection of unstructured data related to space systems, combining several sources: Wikipedia pages, books, and feasibility reports provided by the European Space Agency (ESA). We train the spaceLDA model on three subsets of our heterogeneous training corpus. To combine the resulting per-document topic distributions, we enrich our model with an aggregation method based on an optimised WS. We evaluate our model through a case study, a categorisation of spacecraft design requirements. We finally compare our model’s performance with an unsupervised LDA model and with a literature aggregation method. The results demonstrate that the spaceLDA model successfully identifies the topics of requirements and that our proposed approach surpasses the use of a classic LDA model and the state of the art aggregation method.
S-SEJAD Versus DH

Due to the rapid development of computer characteristics and the appearance of quantum ones, and the use of Diffie-Hellmann (DH) as a means of negotiation to establish communication between two users on a transmission channel that is not secure, poses a security problem and increasingly vulnerable. Because there is no mechanism at the DH level to ensure user authentication, the values exchanged during the DH process can be intercepted by a middle man who can interpret them to reconstruct the secret. Several studies have been conducted to overcome this vulnerability, including our S-SEJAD model that secures the exchange of keys using the AES encryption algorithm. In this paper, we make a comparison between DH and S-SEJAD to study the level of reliability for each model and a simulation to evaluate their execution time. The results obtained show the robustness of our solution and that S-SEJAD can be considered as a real alternative to DH.
Research and Implementation of Breast Cancer Intelligent Recognition Algorithm Based on Deep Convolutional Neural Network

Abstract Breast cancer is one of the most dangerous cancers with a particularly high mortality rate for women. In the face of thousands of female pathological pictures, how to use computer related control algorithms to quickly and efficiently intelligently idently female breast cancer images has become an urgent problem in the world. With the development of computer vision and deep convolutional neural networks, this paper applies the convolutional neural network (CNN) algorithm to solve the classification and recognition computer tasks of histological images for breast cancer, aiming to learn from tens of thousands of histological images features for breast cancer, and to assist doctors in diagnosing patients’ disease was found by computer control algorithm. There are many well-known neural network computer algorithms, such as AlexNet, Inception-Net, ResNet. Image recognition for breast is a binary classification problem, and it is not appropriate to use a ResNet network with a large amount of parameters. Therefore, this paper borrowed Alexnet algorithm and redesigned the algorithm, then used it for breast cancer experiments. We compared with the capsule algorithm used by Anupama authors. Our experiment improves the accuracy rate by 2%, and reduces the amount of neural network parameters, which greatly speeds up the training time.
Exploring Language Style in Makassar Cultural Tourism

The purpose of this study is to analyze about language style of Makassar local tourism which tried to find out the kinds of language style that are mostly used in Makassar local tourism. This research focused on Matin Joos’ theory which discussed about language style. Then, it was analyzed by using descriptive approach. The research used six videos on youtube as the instrument to find out more valid data. The result of the study was that Formal style is mostly used by tourist in within conversation the authors found five times, followed by casual style found in fourth, casual style only once, and intimate style twice. This study provides insights into the language styles neccessary to communicate with foreigners which can be a source of material for teaching english to tourism students.
Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis

Elastic Weight Consolidation (EWC) is a technique used in overcoming catastrophic forgetting between successive tasks trained on a neural network. We use this phenomenon of information sharing between tasks for domain adaptation. Training data for tasks such as sentiment analysis (SA) may not be fairly represented across multiple domains. Domain Adaptation (DA) aims to build algorithms that leverage information from source domains to facilitate performance on an unseen target domain. We propose a model-independent framework - Sequential Domain Adaptation (SDA). SDA draws on EWC for training on successive source domains to move towards a general domain solution, thereby solving the problem of domain adaptation. We test SDA on convolutional, recurrent, and attention-based architectures. Our experiments show that the proposed framework enables simple architectures such as CNNs to outperform complex state-of-the-art models in domain adaptation of SA. In addition, we observe that the effectiveness of a harder first Anti-Curriculum ordering of source domains leads to maximum performance.