Reviewerly/exHarmony huggingface dataset:
Smart Legal Contracts &amp; Smarter Dispute Resolution

Availability, and efficiency of dispute resolution mechanisms are sine qua non for a healthy and prosperous environment. Regarding the technical difficulties and challenges, however, traditional judiciary mechanisms may not be effective for blockchain-related disputes. In this manner, judicialization of blockchain becomes highly inevitable, especially regarding the vision of creating a “self-sufficient system.” In this context, decentralized dispute resolution platforms have occurred and started to provide solutions to blockchain-based disputes. Yet, they are still far from being perfect and should be improved by legal standards and frameworks, as well as employing technical tools. This paper focuses on on-chain dispute resolution mechanisms for blockchain-related disputes, particularly those derived from smart legal contracts.
EdgeNet for efficient scene graph classification

Scene graph captures rich semantic information of an image by representing objects and their relationships as nodes and edges of a graph. Recent works have demonstrated that scene graph representation improves the performance of various computer vision tasks such as image retrieval, action recognition, visual question answering. Computationally efficient scene graph generation methods are required to leverage scene graphs in various real-world applications (e.g., autonomous driving, robotics). A typical scene graph generation model consists of two modules: (i) object detector and (ii) scene graph classifier. The scene graph classifier module predicts the object category and object-object relationships. The presence of a quadratic number of potential edges poses a major challenge in the scene graph classification task. Detecting the relationship between each object pair using the traditional approach is computationally intensive and non-scalable. To address this issue, we propose a novel module named EdgeNet that directly predicts the set of relevant edges and helps to prune out a significant number of unrelated object pairs, thereby improving the effectiveness and efficiency of the scene graph classifier. The proposed EdgeNet is a generic module and can be plugged into an existing scene graph classifier. Experimental results highlight the effectiveness and efficiency of the proposed approach on the Visual Genome dataset.
MASCA: A Multimodal Abstractive Summarization Model Based on Core Words Fusion Attention

With the rapid development of information technology, automatic summarization research has gradually developed into abstractive summarization. The number of data in various modalities(such as pictures) in practical applications is increasing, but existing summarization methods still focus mainly on textual data and ignore other modal data. At the same time, no attention was paid to the key information contained in original data in past studies. In order to solve the above problems and make full use of multimodal data, this paper proposes a multimodal abstractive summarization model based on core words fusion attention, named MASCA. MASCA adopts a special dual encoder-decoder structure. It adds BART and ResNetl01 to the traditional encoder-decoder model. Firstly, each modality is encoded separately to obtain the corresponding modal representation, and secondly, it is decoded to obtain the intermediate summary in text form. Finally, the core words and semantic information are fused in a second encoder-decoder module that incorporates the attention mechanism to generate the final abstract. The experimental results on the Yelp dataset demonstrate the effectiveness and superiority of the method designed in this paper.
An efficient stereo matching method based on non-local spatial tree filter

Binocular stereo vision is a vital research topic in computer vision and has been widely used in robot navigation, 3d reconstruction and other fields. Stereo matching is the most critical part of binocular stereo vision. In view of the low computational efficiency of local stereo matching methods, non-local methods based on tree structure have attracted much attention of researchers in recent years. In this paper, we propose a new efficient non-local spatial tree filter (NSTF) to aggregate the matching cost. Firstly, in addition to spatial affinity, the internal color similarity is taken as the similarity measure between adjacent pixels. Then, the propagation of cost is carried out recursively in the form of ternary tree. The whole filtering process can be divided into eight different directions. Quantitative experiments on Middlebury benchmark show that NSTF can effectively improve the accuracy of the algorithm, and has better edge-preserving ability than other tree-based non-local methods, especially in weak texture and high texture regions.
Considerations on Evaluation of Practical Cloud Data Protection

Abstract With the continuous growth of enterprises’ digital transformation, business-driven cloud computing has seen tremendous growth. The security community has proposed a large body of technical mechanisms, operational processes, and practical solutions to achieve cloud security. In addition, diverse jurisdictions also present regulatory requirements on data protection to mitigate possible risks, for instance, unauthorized access, data leakage, sensitive information and privacy disclosure. In view of this, several practical standards, frameworks, and best practices in the industry are proposed to evaluate and improve the protection level of cloud data. However, few evaluation models can conduct a comprehensive quantitative evaluation for cloud data protection that includes security, privacy, and even ethical considerations. In this paper, we first make a comprehensive review of cloud data security and privacy issues, especially also including ethical concerns that we consider as a type of specific risks caused by human factors, which refers to acting honorably, honestly, justly, and legally, due diligence, and due care. Then, we propose a novel evaluation model for cloud data protection that can quantitatively assess the protection level. Finally, based on the parallel evaluation between manual assessment by experts and our evaluation model, results show that our evaluation model is consistent with the manual evaluation conclusion.
esign and Application of Optimization Algorithm for Vegetable Planting and Distribution Based on Adaptive Differential Evolution Algorithm

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application.
Federated Twin Support Vector Machine

TSVM is designed to solve binary classification problems with less computational overhead by finding two hyperplanes and has been widely used to solve real-world problems. However, in real scenarios, the data used for learning is always scattered in different institutions or users. At the same time, people pay more attention to the issue of personal privacy leakage. Due to the complex privacy protection issues, simply collecting all the data for model training is no longer acceptable. Federated learning has recently been proposed to solve this problem. It completes model training by sharing model parameter updates in the form of data that remains local. But there is still no algorithm for twin support vector machines under the framework of federated learning. Combining the characteristics of twin support vector machine and federated learning, this paper proposes a federated twin support vector machine algorithm (FTSVM) and extends the twin support vector machine based on stochastic gradient descent into a federated support vector machine. We propose a unique initialization algorithm and integration algorithm to ensure the accuracy of the algorithm and the effectiveness of privacy protection. Accuracy experiments are carried out on five datasets, and the accuracy is basically the same as that of the TSVM based on gradient descent. Ablation experiments show that as the number of participants increases, the accuracy of the FTSVM is significantly higher than the average accuracy of the Stand-alone TSVM. Time experiments show that the time overhead of FTSVM increases linearly with the number of participants. These experiments demonstrate the accuracy, effectiveness, and possibility of application in real-world scenarios of our proposed FTSVM.
Role-Oriented Network Embedding Method Based on Local Structural Feature and Commonality

Role-oriented network embedding has become a powerful technique for solving real-world problems, because it can capture the structures of nodes and make node embeddings better reflect the functions or behaviors of entities in the network. At present, various role-oriented network embedding methods have been proposed. However, most of the methods ignore degree distribution and the commonalities among local structures, resulting in insufficient information of node embeddings, and some methods that preserve commonality always have high time complexity. To address the above challenges, we propose a novel model ReVaC from two aspects of extracting higher-quality local structural features and strengthening the commonalities among local structures in node embeddings. In detail, the degree distribution from node’s 1-hop egonet is incorporated into the extraction process of local structural features to improve traditional ReFeX firstly, and the Variational Auto-Encoder is used to map those features to the local structural embedding space. Then, in the embedding space, we cluster nodes to model the commonalities among local structures. Finally, local structural embeddings and commonalities are fused to get node embeddings. We conduct extensive comparative experiments on real-word networks, and results show that ReVaC has better performance than other state-of-the-art approaches and adapts well to network scale.
Orthoimage Super-Resolution via Deep Convolutional Neural Networks

Using high resolution (HR) images collected from UAV, aerial craft or satellites is a research hotspot in the field forest areas analyzing. In practice, HR images are available for a small number of regions, while for the rest, the maximum density various around 1 px/m. HR image reconstruction is a well-known problem in computer vision. Recently, deep learning algorithms have achieved great success in image processing, so we have introduced them into the field of processing orthoimages. At the same time, we noticed that orthoimages generally have colorful blocks of different sizes. Taking into account this feature, we did not apply the classical algorithms directly, but made some improvements. Experiments show that the effect of proposed method is equivalent to the effect of classical algorithms, however, at the preprocessing stage, it significantly saves time. An approach to the forest areas analyzing, including image segmentation and the tree spices classification is proposed. The results of numerical calculations are presented.
Computer Laboratory Surveillance System: Robbery Scene Detection and Alerting

This paper presents the smart surveillance system using YoLo v3 to detect the robbery scene and send an alert notification through Line application. Our system is capable of detecting the moving objects (CPUs and monitors) and people in computer laboratory. The surveillance system can recognize the authorized persons and captures the suspect who try to steal laboratory's device (system assumption: an unauthorized person is carrying CPU and a monitor out of the laboratory). We used YoLo v3 for CPUs and monitors detection, face detection and recognition, and intersection over union (IOU) technique for the bounding box. The sensitivity, specificity, and accuracy of our system are 0.72, 0.64, and 0.68, respectively.
Comparative Analysis of the Performance of Machine Learning Techniques Applied to Real and Synthetic Fraud-Oriented Datasets

One of the most critical resources today is information, an intangible asset that has become a vital research source. On many occasions, access to data becomes a complex and challenging task. For many organizations sharing information, it is often a risk in terms of security and privacy, especially if the data is sensitive. In response to this problem, synthetic data emerges as a valid alternative, generated by different methods and techniques from an original or real dataset, allowing sharing of information very close to reality. In this work, an experiment is carried out that allows validating the efficiency of synthetic versus real datasets by applying a model that predicts possible fraud cases in a dataset based on machine learning algorithms LDA and Random Forest or Gradient Boosting. We compared the prediction performance of our model over the real and synthetic datasets using metric ROC-AUC curves. Our results show a similar behavior among the data sets in our model, suggesting a promising path in the use of synthetic data sets for this kind of applications.
Hints on designing and running project-based exams for a software engineering course

In this paper, we share our experience in designing and running a project-based exam for a Software Engineering course at the undergraduate level. We underline the teaching objectives and content of the course which aim to prepare students for the industrial environment, e.g., various design perspectives on the project, software quality assurance and evaluation, tools for development and software quality assessment, project management and team work. We present our project-based exam and summarize hints on its design, organization, and evaluation. Project examples are also introduced.
FPGA Realization of a Neural Network based Motor Controller

FPGAs are a hardware solution for applications that require low power usage and real time execution. This work focuses on motor control. We show the design and FPGA implementation of a motor controller based on a small Neural Network (NN) as alternative to a traditional Proportional Integral (PI) controller that was used as reference. Performance metrics are defined from a simulation of the target motor. We investigate different NNs, training, code generation methods and numerical precisions. The best-performing controller is a multilayer NN trained with Reinforced Learning. The controller is implemented and runs on an Intel MAX 10 FPGA. The methodology described in this work is easily applicable to more complex designs when FPGAs become the best implementation platform.
Deep Neural Network Regularization (DNNR) on Denoised Image

Image dehazing in supervised learning models suffers from overfitting and underfitting problems. To avoid overfitting, the authors use regularization techniques like dropout and L2 norm. Dropout helps in reducing overfitting and batch normalization reduces the training time. In this paper, they have conducted experiments to analyze combination of various hyperparameters to have better network performance using deep neural network (DNN) on cifar10 dataset. The qualitative and quantitative study is performed by estimating the accuracy of the model on training and test images using with and without batch normalization. The proposed model performs better and is more stable. The results shows that dropout regularization technique is better than L2 technique containing hidden layers with large neurons. The paper assesses performance of DNN for any denoised model with the techniques like batch normalization and dropout, feature map, and adding more layers to the network. The authors quantitatively identify the value model loss and accuracy with the absence and presence of these parameters.
Neural Inverse Text Normalization with Numerical Recognition for Low Resource Scenarios

Neural inverse text normalization (ITN) has recently become an emerging approach for automatic speech recognition in terms of post-processing for readability. In particular, leveraging ITN by using neural network models has achieved remarkable results instead of relying on the accuracy of manual rules. However, ITN is a highly language-dependent task that is especially tricky in ambiguous languages. In this study, we focus on improving the performance of ITN tasks by adopting the combination of neural network models and rule-based systems. Specifically, we first use a seq2seq model to detect numerical segments (e.g., cardinals, ordinals, and date) of input sentences. Then, detected segments are converted into the written form using rule-based systems. Technically, a major difference in our method is that we only use neural network models to detect numerical segments, which is able to deal with the low resource and ambiguous scenarios of target languages. Regarding the experiment, we evaluate different languages in order to indicate the advantages of the proposed method. Accordingly, empirical evaluations provide promising results for our method compared with state-of-the-art models in this research field, especially in the case of low resource scenarios.
Classification of Contaminants in Glass Recycling Using Hybrid CNN-SVM Model

The sorting process of glass recycling is crucial to determine the grade of glass cullet. Hence, this paper aims to develop an algorithm for the classification of contaminants in glass recycling, presenting a hybrid model integrating two remarkable classifiers: Convolutional Neural Network (CNN) and Support Vector Machines (SVM), which have been proven to achieve the state-of-the-art results in solving classification problem. The proposed approach utilizes pre-trained CNN model as automatic feature extractor and SVM as an output predictor. The hybrid model extracts feature of contaminants from raw images and predicts the corresponding class. A contaminant database consisting around 2000 images is collected with a set of constraints to best portray the realism in practical environment. The performance of three different pre-trained CNN models (i.e. AlexNet, VGG-16 and GoogLeNet) in the hybrid model is tested and evaluated using comparative analysis. Since there is no similar approach used in this research field of interest in the past, the performance of the hybrid models is compared to the fine-tuned CNN models in the aspect of classification accuracy. In this study, the integration of VGG-16 and SVM was improved by 0.17% with SVM as classifier and 0.50% with data augmentation, achieving the highest classification accuracy of 100% among the other models. Among the factors that cause the VGG-16+SVM model with data augmentation to outperform are the depth of network, input size, stride size, size of filters and total number of weights and MACs. The proposed method is more accurate and efficient as compared to current existing method, producing high grade glass cullet at low cost.
Performance Assessment of A Residential Building Integrated Photovoltaic (BIPV) System in Dhaka City

Residential buildings, being an unavoidable part of day-to-day life, can have an impactful subsidy to the Renewable Energy Source (RES). Therefore, in this work, the energy-focused outcome of integrating the BIPV (Building Integrated Photovoltaic) system into a pre-built residential building in Dhaka city has been assessed. To begin with, segmentation of the building into subsystems and incident energy estimation have been considered. The building encounters a total of 391.66 MWh of incident energy over the course of one year. Taking 17% panel efficiency and 32 % system losses into account, the system generates 45.80 MWh of electrical output energy per year without considering cloud impact. It is around 53 % higher than the energy requirement (21 MWh) of the building. The system remained grid independent for more than half a year. It can reduce the emission of carbon by about 20,751 kg. Assuming the longevity of the system to be 20 years, the financial analysis shows that the initial cost can be retrieved within 4 years. It demonstrates that the BIPV system can meet the energy demand of the studied building while reducing grid dependency and carbon emissions. .
Emotion-aware and Intent-controlled Empathetic Response Generation using Hierarchical Transformer Network

Enriching any dialogue systems to exhibit empathy is fundamental for delivering human-like conversations. Empathetic interactions in the form of empathetic dialogue generation has been studied widely in recent times. Existing models either incorporate emotion as a feature at the encoding side or as a latent variable explicitly at the decoder to condition their response on. While understanding speaker emotion is integral to expressing empathy, another aspect of being empathetic necessitates responding with an appropriate emotion (also known as emotional regulating intents) to speaker's mental state. To integrate these multiple aspects, in this paper, we propose a Hierarchical Transformer Network (HTN), an amalgamation of the recently introduced Transformer model and Hierarchical Encoder Decoder (HRED) architecture to capture the speaker emotion and dialogic context. For generating intent controlled empathetic responses, we draw insights from Reinforcement Learning (RL) to optimize rewards implicitly. The proposed approach is demonstrated on two benchmark open-domain empathetic datasets. The empirical evaluation (both automated and manual) demonstrates the system capability by way of outperforming several baselines and the state of the art models.