Reviewerly/exHarmony huggingface dataset:
Multi-Scale Network Based on Dilated Convolution for Bladder Tumor Segmentation of Two-Dimensional MRI Images

Because of the bladder tumors are often not completely removed during surgery, the recurrence rate after resection is high. In order to separate the bladder tumor from the bladder wall, this paper proposes a multiscale network based on dilated convolution (DMC-Unet). In the DMC-Unet model, we use U-net as the basic network framework. Firstly, the network uses dilated convolutions with different dilation rates to obtain different receptive fields and then realizes the structure of branch downsampling to obtain features of different scales. Secondly, the residual structure is used to extract feature as the sub-module of DMC-Unet, which effectively alleviates the disappearance of the gradient. In addition, a data enhancement strategy is proposed in this paper. We use Gaussian noise, spatial geometric transformation and gamma transformation to amplify the data to avoid overfitting due to the small dataset. Finally, the effectiveness of our method is verified by experiments, high accuracy has been obtained for bladder wall and bladder tumor segmentation aiming at two-dimensional MRI images.
Using the beta distribution technique to detect attacked items from collaborative filtering

A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user’s trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.
Blockchain Applications in the Public Sector: Investigating Seven Real-Life Blockchain Deployments and Their Benefits

This paper analyses the benefits of blockchain technology for the public sector by looking at the outcomes of ongoing experimentation with distributed ledgers by governments. We use an evidence-based approach by analyzing seven pioneering projects in Europe in different stages of implementation, including two services in production phase. The study uses a structured framework for the case study analysis and a horizontal comparison on the functionalities, governance aspects, the usage, the technical aspects and the benefits. The study shows that current blockchain-driven innovation in the public sector mainly focuses on automation and the enforcement of transactions. The ongoing experimentation demonstrates the capacity of blockchain to reduce bureaucracy and costs of administrative processes, like record-keeping or financial management. However, a lack of standards and trusted hosting infrastructure as well as gaps in essential functionality are strong indications that technology has yet to mature. Without addressing scalability, governance and interoperability, blockchain will not become a transformative technology for governments.
Improved SMOTE algorithm for imbalanced dataset

When applying traditional classifiers to imbalanced dataset, the result might be bias towards the majority class, which leads to poor performance of classifiers. Synthetic Minority Oversampling Technique(SMOTE) is a popular algorithm to improve the classifier's performance through generating new minority samples and making dataset balanced. Based on SMOTE, two new over-sampling algorithms DSMOTE and ESMOTE are proposed in this paper. Being different with SMOTE which treats all minority samples equally, the two new over-sampling algorithms mainly synthesize new samples near the easily misclassified samples to improve the classification accuracy of minority class. Experiments show that DSMOTE and ESMOTE could both get better performance than SMOTE.
Evaluation of Source Codes Using Bidirectional LSTM Neural Network

Nowadays, computer programming is a core skill in computer science with software and computing-related disciplines. Thus, the demand for skilled programmers around the world is increasing. Programmers are writing codes to meet the needs of the world. Debugging source codes to search for errors is a time-consuming task, especially for logical errors for both novices and experienced programmers. We propose a language model using bidirectional long short-term memory (BiLSTM) neural network for the source code error evaluation. The BiLSTM neural network model considers both past and future context of input sequences of source codes that produces more accurate results for the error identification as well as prediction. In addition, the proposed BiLSTM language model predicts correction and error position in source codes. We trained and evaluated the proposed BiLSTM model using source codes collected from Aizu Online Judge (AOJ) system. The experimental results demonstrated that the accuracy of the proposed BiLSTM model surpassed other unidirectional RNN and LSTM models in terms of error detection and prediction in source codes. The BiLSTM model in this study can be operated as an intelligent model for source code error detection which is helpful in programming education and software engineering fields.
Integrating AMR to Neural Machine Translation using Graph Attention Networks

Semantic representation is potentially useful to enforce meaning preservation and improve generalization performance of machine translation methods. In this paper, we incorporate semantic information from Abstract Meaning Representation (AMR) semantic graphs into neural machine translation. First, we use Graph Attention Networks (GATs) to encode the AMR graphs into a vector space. Then, we propose an effective way to integrate the semantic representation to the attention-encoder-decoder translation model. The experimental results show the improvements in BLEU scores over the baseline method on the English-Vietnamese language pair.
An Improved Salp Swarm Algorithm Based on Adaptive $$\upbeta $$-Hill Climbing for Stock Market Prediction

Stock market prediction is a tool to maximize the investor’s money and minimize the risk associated with it. This paper proposes a new machine learning based model to predict the stock market price. The proposed model is an improved version of existing Salp Swarm Optimizer (SSO) which is integrated with Least Square-Support Vector Machine (LSSVM). The improved version is a hybrid meta-heuristics algorithm, which is a combination of SSO and Adaptive $$\upbeta $$ -Hill Climbing (A $$\upbeta $$ -HC) algorithm. The proposed model selects the best hyperparameters for LSSVM to avoid over fitting and local minima, and in turn increases the model’s accuracy. It is evaluated on four standard and publicly available stock market datasets, and its result is compared with some popular meta-heuristic algorithms. The results show that our proposed model performs better than the existing models in most of the cases. The source code for our proposed algorithm is available in https://github.com/singhaviseq/SSA-ABHC .
Integrity Auditing with Attribute based ECMRSA Algorithm for Cloud Data Outsourcing

Cloud computing is a vast area within which large amounts of data are exchanged through cloud services and has fully grown with its on-demand technology. Due to these versatile cloud services, sensitive data will be stored on cloud storage servers and it is also used to dynamically control a number of problems: security, privacy, data privacy, data sharing, and integrity across cloud servers. Moreover, the legitimacy and control of data access should be maintained in this extended environment. So, one of the most important concepts of cryptographic techniques in cloud computing environment is Attribute Based Encryption (ABE). In this research work, data auditing or integrity checking is considered as an area of concern for securing th cloud storage. In data auditing approach, an auditor inspects and verifies the data file integrity without having any knowledge about the content of file and sends the verification report to the data owner. In this research, Elliptical Curve Modified RSA (ECMRSA) is proposed along with Modified MD5 algorithm which is used for attribute-based cloud data integrity verification, in which data user or owner uploads their encrypted data files at cloud data server and send the auditing request to the Third-Party Auditor (TPA) for verification of their data files. The Third-Party Auditor (TPA) challenges the data server for ensuring the integrity of data files on behalf of the data owners. After verification of integrity of data file auditor sends the audit report to the owner. The proposed algorithm integrates the auditing scheme with public key encryption with homomorphic algorithm which generates digital signature or hash values of data files on encrypted files. The result analysis is performed on time complexity by evaluating encryption time, GenProof time and VerifyProof Time and achieved improvement in resolving time complexity as compared to existing techiques.
Image Distortion and Rectification Calibration Algorithms and Validation Technique for a Stereo Camera

This paper focuses on the calibration problem using stereo camera images. Currently, advanced vehicle systems such as smart cars and mobile robots require accurate and reliable vision in order to detect obstacles and special marks around. Such modern vehicles can be equipped with sensors and cameras together or separately. In this study, we propose new methodologies of stereo camera calibration based on the correction of distortion and image rectification. Once the calibration is complete, the validation of the corrections is presented followed by an evaluation of the calibration process. Usually, the validation section is not jointly considered with the calibration in other studies. However, the mass production of cameras widely uses the validation techniques in calibrations owned by manufacturing businesses. Here, we aim to present a single process for the calibration and validation of stereo cameras. The experiment results showed the disparity maps in comparison with another study and proved that the proposed calibration methods can be efficient.
English-Marathi Neural Machine Translation Using Local Attention

Machine translation, translates sentences from source language text to target language. NMT (Neural Machine Translation) is a machine translation mechanism based on deep learning. It has improved the weakness of the rule-based and statistical machine translation. In this paper, NMT is applied on parallel corpus pair English – Marathi. We’re proposing an NMT with a local attention mechanism. It is efficient and overcomes the OOV (Out Of Vocabulary) problem. The model performance is evaluated using the BLEU score. The results from the experiment confirm that the proposed model has outperformed the global attention mechanism and also Google translator.
Predicting the Tweet Location Based on KNN-Sentimental Analysis

Geographical information is important in several applications, such as, advertising and recommending. Despite the availability and the existence of social media, especially twitter, the geographical coordinates are often hidden according to privacy reasons. In this paper, a new model is proposed to predict the tweet location based on the KNN-Sentimental Analysis (KNNSA) model. Predicting the tweet location based on the KNN-sentiment analysis (KNNSA) model extracts text features from the tweet in addition to the date and time features. Then, applying sentimental analysis and classifying the data by K-nearest neighbors (KNN) classifier. The (KNNSA) model is evaluated and compared to the previous work and it achieves better performance in terms of root mean squared error (RMSE) and of the mean absolute error (MAE).
Accurate and adversarially robust classification of medical images and ECG time-series with gradient-free trained sign activation neural networks

Adversarial attacks in medical AI imaging systems can lead to misdiagnosis and insurance fraud as recently highlighted by Finlayson et. al. in Science 2019. They can also be carried out on widely used ECG time-series data as shown in Han et. al. in Nature Medicine 2020. At the heart of adversarial attacks are imperceptible distortions that are visually and statistically undetectable but cause the machine learning model to misclassify data. Recent empirical studies have shown that a gradient-free trained sign activation neural network ensemble model requires a larger distortion than state of the art models. We apply them on medical data in this study as a potential solution to detect and deter adversarial attacks. We show on chest X-ray and histopathology images, and on two ECG datasets that this model requires a greater distortion to be fooled than full-precision, binary, and convolutional neural networks, and random forests. We show that adversaries targeting the gradient-free sign networks are visually distinguishable from the original data and thus likely to be detected by human inspection. Since the sign network distortions are higher we expect an automated method could be developed to detect and deter attacks in advance. Our work here is a significant step towards safe and secure medical machine learning.
A Machine Learning Approach to Assess Student Group Collaboration Using Individual Level Behavioral Cues

K-12 classrooms consistently integrate collaboration as part of their learning experiences. However, owing to large classroom sizes, teachers do not have the time to properly assess each student and give them feedback. In this paper we propose using simple deep-learning-based machine learning models to automatically determine the overall collaboration quality of a group based on annotations of individual roles and individual level behavior of all the students in the group. We come across the following challenges when building these models: (1) Limited training data, (2) Severe class label imbalance. We address these challenges by using a controlled variant of Mixup data augmentation, a method for generating additional data samples by linearly combining different pairs of data samples and their corresponding class labels. Additionally, the label space for our problem exhibits an ordered structure. We take advantage of this fact and also explore using an ordinal-cross-entropy loss function and study its effects with and without Mixup.
Convolutional Neural Network Based Smart Door Lock System

The worth of having liberty from any type of hazard, threat, risk, and any other vulnerabilities is known as security. It is the bane of modern-day society. Without security, all will act in a disadvantageous position both psychologically and mentally. A wide variety of locks are used but the most secure one in recent times is the biometric lock system. Moreover, by witnessing the recent situation it is quite evident that the biometric system can become a place of transmission of dangerous viruses as it is seen in the recent COVID-19 pandemic. Due to this reason, the government has banned all biometric systems and in some places, contactless security systems like a lock system using unique identification features like face, retina, and many more are encouraged. To overcome all these difficulties a contactless remote sensing locking system has a far-reaching advantage. In this context, a locking system is designed by collaborating IoT and machine learning techniques. So in this project, a system has been developed which can grant access by simply capturing your face snap. Every human has his/ her facial identification which is unique. Here the extracted feature is used as a passkey and is matched with the database. This project is a part of IoT and Machine Learning. This is developed on a Raspberry Pi and a Pi camera. The system is trained using the Convolutional Neural Network (CNN) approach to recognize the face of the authorized personnel only and to report the unauthorized in case of trespassing.
UAV online path planning technology based on deep reinforcement learning

This paper proposes a method for planning three-dimensional path for unmanned aerial vehicle (UAV) in complex airspace based on interfered fluid dynamical system (IFDS) and deep reinforcement learning. Firstly, the model of unmanned aerial vehicle under various constraints and the mathematical expression of threat zone are established. Secondly, in order to solve the problems of slow calculation speed and difficult to make the global optimal solution existed at present, an intelligent 3D path planning method on the basis of IFDS is proposed, and deep reinforcement learning is used to solve the coefficient of IFDS. The simulation results show that the path planned by the proposed method can avoid the threat zone effectively, meanwhile, the path is smooth, suitable and fuel saving for UAV.
Magnitude Calculation without Saturation from Strong-Motion Waveforms

ABSTRACT After destructive earthquakes, it is a challenge to estimate magnitude rapidly and accurately for dissemination to emergency responders and the public. Here, we propose criteria to calculate peak ground displacement (PGD) from strong-motion records, which can be used to calculate unsaturated event magnitude. Using collocated strong-motion and Global Navigation Satellite Systems observations of five major earthquakes in Japan, we demonstrate the effectiveness and accuracy of our strategy. Our results show that, with the right filtering criteria, PGD estimated from strong-motion acceleration waveforms is consistent with geodetic estimates. The methodology, however, does not allow for calculation of reliable estimates of coseismic deformation or other ground displacement metrics. We demonstrate a simulated real-time magnitude estimation that suggests it is feasible to generate an unsaturated magnitude estimate in real time from near-field strong-motion records. These findings have important implications for early warning and emergency response in seismically active areas, especially where real-time strong-motion data are more widely available than geodetic measurements.
Is a gift on sale “heart-discounted”? Givers’ misprediction on the value of discounted gifts and the influence of service robots

The main purpose of this research is to investigate whether, why, and when givers and recipients perceive the value of a discounted gift differently. The studies provide convergent evidence that givers perceive discounted gifts as less valuable than regular-priced gifts whereas recipients do not perceive them as different. Givers' devaluation of the discounted gift is driven by their concern about the thoughtfulness of a gift. Moreover, the giver-recipient asymmetry is mitigated when the in-store interaction with a salesperson is substituted by technology via a service robot. This research contributes to the gift-giving literature and the growing literature on service robots by revealing how promotional offers influence consumers’ evaluation of gifts and how technological advance in retail may affect the proposed effect. Managerial implications for planning and executing price promotions for gifts are also discussed.
Package Leak Detection Based on Aperiodic Anomaly Extraction

Foods are essential for our society, and they are transported and delivered after packaging to avoid contamination and deterioration of freshness. Nevertheless, there is a package leak problem in automatic packaging processes that would risk impairing human health. In order to protect human health and guarantee the reliability of the industrial automatic food production, automatic leak testing is necessary for all packages. This article developed a package leak tester and proposed an aperiodic anomaly test using the tester to detect the package leaks automatically. The developed package leak tester realized accurate reaction force measurement enabling accurate leak detection, and the proposed aperiodic anomaly test using an aperiodic derivative of the reaction force based on a leaking package model achieved robust leak detection against disturbances. The package leak tester and the aperiodic anomaly test were validated by testing 10 000 package data randomly generated by 388 experimental package data.
Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection

Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error of variational autoencoder (VAE) by maximizing the evidence lower bound. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error and finally arrive at a simpler yet effective model for anomaly detection. In addition, to enhance the effectiveness of detecting anomalies, we incorporate a practical model uncertainty measure into the anomaly score. We show empirically the competitive performance of our approach on benchmark data sets.
Mathematics Lesson using Accelerometer Sensor Interaction in Handheld Augemented Reality Application for Kindergarten

Handheld Augmented Reality (AR) is a combination of the virtual content and real world that gives a chance for the user to interact with virtual objects in real time using handheld device. Recent handheld device equipped with additional sensing like gyroscope, barometer, accelerometer and proximity sensor make possible to explore full potential for interaction in application. This become interesting topic to improve the human perception and to enhance the understanding of complex 3D scenarios. The traditional educational method is not necessarily effective for the children. Sometimes student's incapable to recall the ideas and it seem struggle to solve the problem in learning process. Therefore, the purpose of this project is to develop mathematics lesson using accelerometer sensor interaction in handheld AR application for kindergarten. There are four phases to complete this study. The first phase is preliminary investigation and analyse in handheld application for Mathematics lesson. Then, second phase is to design the details of application operational framework. The third phase is to develop Mathematics lesson application with accelerometer interaction. Last phase is to evaluate the user perception of the application. Unity 3D and Vuforia SDK are used as editors and library respectively through the development process. The evaluation has been done by respondents who are the ages from 6 to 9 years old. The results have proved that the application can help better to improve Mathematics learning skill by using accelerometer interaction through handheld AR application. The application enhances the teaching method for students to learn an addition and subtraction more efficiently and in interactive ways.