Reviewerly/exHarmony huggingface dataset:
Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI

In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12.
Automatic modulation recognition based on mixed-type features

The existing modulation classification method using instantaneous features is poor for low SNRs, and the high-order cumulant features-based modulation recognition algorithm is only applicable to some types of communication modulation signals. To overcome these problems, we propose a mixed features-based modulation recognition algorithm, which refines instantaneous features and high-order cumulant feature, and the back propagation (BP) neural network is adopted as a classifier to perform experiments. The experimental results show that our proposed mixed features-based modulation recognition method can improve the recognition rate for more kinds of signals.
Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.
Hybrid Ensemble Learning Technique for Software Defect Prediction

The reliability of software depends on its ability to function without error.Unfortunately, errors can be generated during any phase of software development.In the field of software engineering, the prediction of software defects during the initial stages of development has therefore become a top priority.Scientific data are used to predict the software's future release.Study shows that machine learning and hybrid algorithms are change benchmarks in the prediction of defects.During the past two decades, various approaches to software defect prediction that rely on software metrics have been proposed.This paper explores and compares well-known supervised machine learning and hybrid ensemble classifiers in eight PROMISE datasets.The experimental results showed that AdaBoost support vector machines and bagging support vector machines were the best performing classifiers in Accuracy, AUC, recall and F-measure.
Developing Reading Learning Model to Increase Reading Skill for Animal Husbandry Students in Higher Education

The development of teaching and learning activities must be evaluated to find out the success or failure of learning. This evaluation must be done because teaching now will certainly affect the next learning process, especially the reading comprehension model. This study aims to produce a reading comprehension model that is able to improve the reading comprehension skills of second semester students in the English for Professions subject to 2 Semester Credit Systems (SKS) in the department of animal husbandry at the University of Nahdlatul Ulama Blitar. The study approach used was Research and Development by modifying the ten steps of implementing the research strategy into three steps. The research subjects were lecturers and students majoring in animal husbandry at the University of Nahdlatul Ulama Blitar. Data collection techniques used was observation, questionnaires, interviews and tests. Research data analyzed using qualitative and quantitative analysis techniques. Data results show that the use of reciprocal teaching reading strategies combined with peer-teaching methods can significantly improve students' reading comprehension skills. The care giving effect arising from the application of reciprocal teaching to reading comprehension increases social values ​​in second semester students in the English for Professional course.
Design and Implementation of LOD Explorer: A LOD Exploration and Visualization Model

The quantity of data published on the Web according to principles of Linked Data (LD) is increasing intensely. However, this data is still largely limited to be used up by domain professionals and users who understand LD technologies. Therefore, it is essential to develop tools to enhance intuitive perceptions of LD for lay users. The features of LD point to various challenges for an easy-to-use data presentation. In this research, the technical details behind the LOD Explorer, which is a tool for exploring the Web of Data, is presented, the efficiency and scalability of the system has been tested using several different platforms, and the results of experiments indicated an outstanding performance.
Real-Time Traffic Sign Detection and Recognition using CNN

Traffic signs presents on streets and highways have a distinct set of features which may be used to differentiate each one from each other. We propose in this paper a real-time traffic sign detection and recognition algorithm using neural networks. In order to detect traffic sign we used a Faster R-CNN (Region-Based Convolutional Neural Network), and to classify we used a Convolutional Neural Network using two different architectures. Some factors can make it difficult, such as light, occlusion, blurring, and others. This work can be applied in several areas, such as Advanced Driving Assistant System and autonomous cars.
Mapping of Himalaya Leucogranites Based on ASTER and Sentinel-2A Datasets Using a Hybrid Method of Metric Learning and Random Forest

The widely distributed leucogranite belt in the Himalayan orogeny is expected to have excellent potential for developing rare metal mineralization. Finding a way to effectively map the spatial distribution of leucogranites would be a significant contribution to rare metal exploration. Research shows remote sensing technology has long been recognized the significance in geological works, which greatly promoted mineral exploration in a cost-effective manner, especially in the Himalayan orogenic belt with poor natural environment. However, several challenges still exist in relation to the limited spectral band and spatial resolution of remote sensing images, as well as the onerous data processing. In this context, this study sought to resolve these two issues by applying a hybrid approach that comprises image fusion, metric learning, and random forest methods. For the first challenge, multisource and multisensor remote sensing data were integrated to provide more comprehensive spatial texture characteristics and spectral information. To address the second challenge, this study used a hybrid method of metric learning and random forest to promote computing efficiency and classification accuracy. This process is illustrated through a case study of lithological mapping in Cuonadong dome, the northern part of the Himalayan orogeny belt. Seven target lithological units were effectively discriminated with an 85.75% overall accuracy. This provides an important scientific basis for further exploration for rare metal deposits in the Himalayan orogeny belt, and a way of thinking for detecting geological features under harsh natural conditions.
Next-Generation Programming Learning Platform: Architecture and Challenges

With the rapid development of information technology, programming has become a vital skill. An online judge system can be used as a programming education platform, where the daily activities of users and judges are used to generate useful learning objects (e.g., tasks, solution codes, evaluations). Intelligent software agents can utilize such objects to create an ecosystem. To implement such an ecosystem, a generic architecture that covers the whole lifecycle of data on the platform and the functionalities of an e-learning system should take into account the particularities of the online judge system. In this paper, an architecture that implements such an ecosystem based on an online judge system is proposed. The potential benefits and research challenges are discussed.
Deep neural model with self-training for scientific keyphrase extraction

Scientific information extraction is a crucial step for understanding scientific publications. In this paper, we focus on scientific keyphrase extraction, which aims to identify keyphrases from scientific articles and classify them into predefined categories. We present a neural network based approach for this task, which employs the bidirectional long short-memory (LSTM) to represent the sentences in the article. On top of the bidirectional LSTM layer in our neural model, conditional random field (CRF) is used to predict the label sequence for the whole sentence. Considering the expensive annotated data for supervised learning methods, we introduce self-training method into our neural model to leverage the unlabeled articles. Experimental results on the ScienceIE corpus and ACL keyphrase corpus show that our neural model achieves promising performance without any hand-designed features and external knowledge resources. Furthermore, it efficiently incorporates the unlabeled data and achieve competitive performance compared with previous state-of-the-art systems.
Evaluation Model of Adaptive Teaching Ability of College Art Teachers

The existing evaluation models for the teaching ability of college art teachers are unadaptable, unsystematic and incomplete. To solve these problems, this paper puts forward a novel model to evaluate the adaptive teaching ability of college art teachers. Firstly, the teaching demand of college art teachers was analyzed in the knowledge age, highlighting the necessity to evaluate the adaptive teaching ability of college art teachers. Next, an evaluation system was established for the adaptive teaching ability of college art teachers in the knowledge age, and different types of evaluation indices were identified. On this basis, the grey relational analysis (GRA) was introduced to build an evaluation model for the adaptive teaching ability of college art teachers. The GRA-based evaluation model enjoys good operability and feasibility. To sum up, this paper fully integrates the evaluation system and evaluation model for the adaptive teaching ability of college art teachers. The research results have great significance in terms of theoretical innovations and practical applications.
CASCF: Certificateless Aggregated SignCryption Framework for Internet-of-Things Infrastructure

The increasing number of devices in the age of Internet-of-Thing (IoT) has arisen a number of problems related to security. Cryptographic processes, more precisely the signatures and the keys, increase and generate an overhead on the network resources with these huge connections. Therefore, in this paper we present a signcryption framework to address the above problems. The solution highlights the use of aggregate signcryption and certificaless approach based on bilinear pairings. The use of signcryption with aggregation and certificateless authentication reduces the time consumption, overhead and complexity. The solution is also able to solve the key staling problems. Experimental results and comparative analysis based on key parameters, memory utilization and bandwidth utilization have been measured. It confirms that the presented work is efficient for IoT infrastructure.
Deepfake Video Detection Using Convolutional Neural Network

With the advent of new technological enhancements in artificial intelligence, new sophisticated AI techniques are used to create fake videos.Such videos can pose a great threat to the society in various social and political ways and can be used for malicious purposes.These fake videos are called deepfakes.Deepfakes refer to manipulated videos, or other digital representations produced by sophisticated artificial intelligence, that yield fabricated images and sounds that appear to be real.A deep-learning system can produce a persuasive counterfeit by studying photographs and videos of a target person from multiple angles, and then mimicking its behaviour and speech patterns.Detecting these videos is a massive problem because of the increasing developments in more realistic deepfake creation technologies emerging every now and then.The paper aims to solve this problem by proposing a model that analyses the frames of the videos using deep learning approach to detect inconsistencies in facial features, compression rate and discrepancies introduced in the videos while creating them.The model uses a convolutional neural network along with transfer learning to train the model that can catch these instilled errors in the deepfakes.The neural network is trained on these discrepancies induced during deepfake creation around the face.It uses a dataset called "Celeb-DF: A New Dataset for DeepFake Forensics" to train the model.The paper further discusses methods that can be used, in detail, to improve learning by this model.
A Comparative Study on K-Means Clustering and Agglomerative Hierarchical Clustering

Clustering is a well-established unsupervised data mining approach that group data points based on similarities.Clustering entities will give insights into the characteristics of different groups.Clustering results in minimization of the dimensionality of data set when you are dealing with a myriad number of data.The higher the homogeneity within the cluster and the higher the differences between the clusters, the finer the cluster will be.Clusters are mainly of two types: 1) Soft clustering: Based on the probability that a data point will belong to a specific cluster and, 2) Hard clustering: Data points are separated into independent clusters.Among hundreds of clustering algorithms, they can be labeled into one of following models such as connectivity, density, distribution and centroid model.This paper attempts to differentiate two widely used clustering techniques, k-means clustering and hierarchical clustering which belong to the centroid and connectivity models respectively.The comparison will be based on execution time and memory usage of both these algorithms when different sets of a delivery fleet driver data set are manipulated using these algorithms.
Removing the Trusted Third Party in a Confidential Multiparty Registered eDelivery Protocol Using Blockchain

Recently several proposals of blockchain-based solutions for traditional e-commerce applications have been presented, taking advantage of the fact that blockchain is a technology that offers an immutable registry of data. Among these proposals we can find solutions for certified notifications, digital signature of contracts, escrow protocols, fair payments and registered deliveries. In order to execute fair exchanges, most solutions involve trusted third parties, known as TTP, supervising the exchanges in a way or another. Until now, two solutions have been presented for Registered electronic Delivery (eDelivery) services. This service allows a user to prove that he has sent some data to a set of receivers. These protocols differ in the properties achieved and also in the use of trusted third parties. The first protocol is a blockchain-based solution without TTP for the eDelivery of non-confidential data. The second protocol allows also the eDelivery of confidential data. However, this second proposal requires the involvement of a TTP in a non-mandatory resolution phase. In this paper we present a new protocol that achieves the best properties of the previous solutions at the same time. The new protocol doesn't require the involvement of a TTP at any moment while it allows the eDelivery of confidential data, satisfying the security requirements for this service.
Adaptive rule: A novel framework for recommender system

Determining minimum support value on association rule is not easy to users. Hurdles such as data locations and data origins are a mountain that should be overcome by association rule. In addition, rules generated do not necessarily fit user’s needs, thereby, requiring the rules to be put in order based on certain criteria. This paper will discuss a model to produce adaptive rules that originate from a number of different databases, automated threshold determination and rule ranking based on predetermined criteria. The adaptive rule model produced can be implemented in recommender systems in various fields.
Using Context in Neural Machine Translation Training Objectives

We present Neural Machine Translation (NMT) training using document-level metrics with batch-level documents.Previous sequence-objective approaches to NMT training focus exclusively on sentence-level metrics like sentence BLEU which do not correspond to the desired evaluation metric, typically document BLEU.Meanwhile research into document-level NMT training focuses on data or model architecture rather than training procedure.We find that each of these lines of research has a clear space in it for the other, and propose merging them with a scheme that allows a document-level evaluation metric to be used in the NMT training objective.We first sample pseudo-documents from sentence samples.We then approximate the expected document BLEU gradient with Monte Carlo sampling for use as a cost function in Minimum Risk Training (MRT).This twolevel sampling procedure gives NMT performance gains over sequence MRT and maximum-likelihood training.We demonstrate that training is more robust for document-level metrics than with sequence metrics.We further demonstrate improvements on NMT with TER and Grammatical Error Correction (GEC) using GLEU, both metrics used at the document level for evaluations.
Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model

We propose an automatic evaluation method of machine translation that uses source language sentences regarded as additional pseudo references.The proposed method evaluates a translation hypothesis in a regression model.The model takes the paired source, reference, and hypothesis sentence all together as an input.A pretrained large scale cross-lingual language model encodes the input to sentence-pair vectors, and the model predicts a human evaluation score with those vectors.Our experiments show that our proposed method using Crosslingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences.Additionally, using source sentences in our proposed method is confirmed to improve the evaluation performance.
Interference and Generalization in Temporal Difference Learning

We study the link between generalization and interference in temporal-difference (TD) learning. Interference is defined as the inner product of two different gradients, representing their alignment. This quantity emerges as being of interest from a variety of observations about neural networks, parameter sharing and the dynamics of learning. We find that TD easily leads to low-interference, under-generalizing parameters, while the effect seems reversed in supervised learning. We hypothesize that the cause can be traced back to the interplay between the dynamics of interference and bootstrapping. This is supported empirically by several observations: the negative relationship between the generalization gap and interference in TD, the negative effect of bootstrapping on interference and the local coherence of targets, and the contrast between the propagation rate of information in TD(0) versus TD($\lambda$) and regression tasks such as Monte-Carlo policy evaluation. We hope that these new findings can guide the future discovery of better bootstrapping methods.
Max-Margin Incremental CCG Parsing

Incremental syntactic parsing has been an active research area both for cognitive scientists trying to model human sentence processing and for NLP researchers attempting to combine incremental parsing with language modelling for ASR and MT.Most effort has been directed at designing the right transition mechanism, but less has been done to answer the question of what a probabilistic model for those transition parsers should look like.A very incremental transition mechanism of a recently proposed CCG parser when trained in straightforward locally normalised discriminative fashion produces very bad results on English CCGbank.We identify three biases as the causes of this problem: label bias, exposure bias and imbalanced probabilities bias.While known techniques for tackling these biases improve results, they still do not make the parser state of the art.Instead, we tackle all of these three biases at the same time using an improved version of beam search optimisation that minimises all beam search violations instead of minimising only the biggest violation.The new incremental parser gives better results than all previously published incremental CCG parsers, and outperforms even some widely used non-incremental CCG parsers.
CooBa: Cross-project Bug Localization via Adversarial Transfer Learning

Bug localization plays an important role in software quality control. Many supervised machine learning models have been developed based on historical bug-fix information. Despite being successful, these methods often require sufficient historical data (i.e., labels), which is not always available especially for newly developed software projects. In response, cross-project bug localization techniques have recently emerged whose key idea is to transferring knowledge from label-rich source project to locate bugs in the target project. However, a major limitation of these existing techniques lies in that they fail to capture the specificity of each individual project, and are thus prone to negative transfer. To address this issue, we propose an adversarial transfer learning bug localization approach, focusing on only transferring the common characteristics (i.e., public information) across projects. Specifically, our approach (CooBa) learns the indicative public information from cross-project bug reports through a shared encoder, and extracts the private information from code files by an individual feature extractor for each project. CooBa further incorporates adversarial learning mechanism to ensure that public information shared between multiple projects could be effectively extracted. Extensive experiments on four large-scale real-world data sets demonstrate that the proposed CooBa significantly outperforms the state of the art techniques.