Reviewerly/exHarmony huggingface dataset:
A Framework of Blockchain Technology for Public Management in Brazil

Artificial Intelligence and Blockchain will be the most relevant technologies in Brazil within the next 10 years. They may cope with social challenges like corruption, violence and bureaucracy. Blockchain and Smart Contracts are "models of trust" that enable secure data transfer in a peer network. The widespread of Blockchain and Smart Contracts will reduce frauds and errors for all information is registered in the network. They are supposed to improve public services provision, bringing more efficiency, transparency and fairness. This book chapter aims at analyzing the main problems of public services in Brazil, namely, the uncertainty of delivery and lack of universality, by means of a comprehensive ethnography and literature review. Then, the manuscript proposes a scalable Blockchain framework, using as examples the current processes of healthcare and tax refund. This technology framework is based on the Business Process Management (BPM) approach and can be applied in other routines of public services.
A Study on the Effect of the Real-world Backgrounds using Colour Blending Technique on Optical See-through AR User Interface Design

Through the few numbers of formal study that shows the usability of the optical see-through augmented reality (AR) display, especially in the outdoor environment, has been affected by ambient lighting conditions and dynamic real-world backgrounds. They used painted posters which represented a real-world background to examine the effect. Under changing lighting conditions, the colour palette of the environment can also cause considerable problems. For example, specific label colours can be affected by the similarity of the selected colour scheme. Besides, the actual outdoor light source illuminates the actual objects in outdoor AR systems, while the augmented virtual objects are illuminated by a virtual light source. The colour differentials arise from these two light sources, with the actual object and virtual object being visualized as a combination of the colours induced by the two light sources. Furthermore, there is a visible colour difference between the real object and the virtual object. This paper studies the effect of the real-world backgrounds using colour blending technique on optical see-through AR user interface design. It enables AR graphics to be overlaid onto real-world backgrounds as nicely as painted posters. Colour blending effects influenced physical materials as backgrounds are almost the same as their corresponding poster backgrounds, even if each pair of colours are just a metameric match.
SILFA: Sign Language Facial Action Database for the Development of Assistive Technologies for the Deaf

Facial expressions are fundamental in Sign Languages (SLs), which are visuospatial linguistic systems structured on gestures, adopted around the world by deaf people to communicate. Deaf individuals frequently need a sign language interpreter in their access to school and public services. In such scenarios, the absence of interpreters typically results in discouraging experiences. Developments in Automatic Sign Language Recognition (ASLR) can enable newer assistive technologies and change the interaction of the deaf with the world. One major barrier to improving ASLR is the difficulty in obtaining sets of well-annotated data. We present a newly developed video database of Brazilian Sign Language facial expressions in a diverse group of deaf and hearing young adults. Well-validated sentences stimulus were used to elicit affective and grammatical facial expressions. Frame-work ground-truth for facial actions was manually annotated using the Facial Action Coding System (FACS). Also, the work promotes the exploration of discriminant features in subtle facial expression in sign language, a better understanding of the relation between grammatical facial expression class dynamics in facial action units, and a deeper understanding of its facial action occurrence. To provide a baseline for use in future research, protocols and benchmarks for automated action unit recognition are reported.
The Comparison of Distance Measurement for Optimizing KNN Collaborative Filtering Recommender System

Optimizing a recommender system has complexity and challenging things, such as in collaborative filtering. In collaborative filtering recommendation, the similarities are a critical component. This paper uses predictive KNN to perform Collaborative Filtering, and there have some widespread similarity measurements, such as Euclidean, Minkowski, and generalization from both of them called with Minkowski. We compare them to decide which the best similarity measurement to make better recommendation result by considering the prediction performance and execution time. From the experiment that was performed in this paper, the Minkowski has the best performance but two times slower than Euclidean and Manhattan, since manhattan is trying to find the best p (p=1 or p=2) to decide which p was used. On the other hand, Euclidean performance was approaching the Minkowski, so Euclidean was worth considering.
Accelerated Levenberg–Marquardt Algorithm for Radial Basis Function Neural Network

Levenberg–Marquardt (LM) algorithm is commonly used for training radial basis function neural networks (RBFNN) due to its fast convergence. However, its successful application is limited by the vanishing gradient problem in the learning process. To solve this problem, an accelerated LM (ALM) algorithm is designed to improve the learning performance of RBFNN in this paper. First, an error information filtering mechanism (EIFM) is developed to discard training samples information that the RBFNN has already learned, then the same amount of computation can obtain the more error gradient information. Second, a width adjustment mechanism (WAM) is designed to help training samples escape from the saturated region of the activation function to increment gradient value. Finally, some benchmark and real problems are used to exhibition the effectiveness of proposed ALM algorithm. The results demonstrate the proposed ALM-RBFNN can effectively mitigate the vanishing gradient and has good learning performance.
A Comparison of Transformer, Recurrent Neural Networks and SMT in Tamil to Sinhala MT

Neural Machine Translation (NMT) is currently the most promising approach for machine translation. The attention mechanism is a successful technique in modern Natural Language Processing (NLP), especially in tasks like machine translation. The recently proposed network architecture of the Transformer is based entirely on attention mechanisms and achieves a new state of the art results in neural machine translation, outperforming other sequence-to-sequence models. Although it is successful in a resource-rich setting, its applicability for low-resource language pairs is still debatable. Additionally when the language pair is morphologically rich and also when the corpora is multi-domain, the lack of a large parallel corpus becomes a significant barrier. In this study, we explore different NMT algorithms - Long Short Term Memory (LSTM) and Transformer based NMT, to translate the Tamil to Sinhala language pair. Where we clearly see transformer outperforms LSTM by 2.43 BLEU score for Tamil to Sinhala direction. And this work provides a preliminary comparison of statistical machine translation (SMT) and Neural Machine Translation (NMT) for Tamil to Sinhala in the open domain context.
Semantics-Assisted Wasserstein Learning for Topic and Word Embeddings

Wasserstein distance, defined as the cost (measured by word embeddings) of optimal transport plan for moving between two histograms, has been proven effective in tasks of natural language processing. In this paper, we extend Nonnegative Matrix Factorization (NMF) to a novel Wasserstein topic model, namely Semantics-Assisted Wasserstein Learning (SAWL), with simultaneous learning of topics and word embeddings. In Sawl, we formulate an NMF-like unified objective that integrates the regularized Wasserstein distance loss with a context factorization of word context information. Therefore, Sawl can refine the word embeddings for capturing corpus-specific semantics, enabling to boost topics and word embeddings each other. We analyze Sawl, and provide its dimensionality-dependent generalization bounds of reconstruction errors. Experimental results indicate that Sawl outperforms the state-of-the-art baseline models.
Geospatial Assessment of Surface Water Pollution and Industrial Activities in Ibadan, Nigeria

Surface water pollution from industrial, agricultural, municipal, and transportation sources constitutes a serious menace to ecological functioning. In the developing countries of Africa, rapidly emerging urban centers like Lagos, Ibadan Bamako, Niamey, and Nairobi, lack of appropriate wastewater disposal, and treatment facilities feature commonly on prominent river basins. Public health and the adjoining and distal terrestrial and aquatic ecosystems are usually at the receiving end of the resultant malevolent impacts. Several mathematical models have been designed to address surface water pollution problems such as eutrophication, heavy metal toxicity, nutrient contamination, etc. Most of these models are too theoretical and do not consider the multifaceted interactions that are associated with ever increasing anthropogenic activities. This informs the need for geoinformation techniques to model the pattern of pollution along a river that drains an industrial estate with a view of identifying and analyzing risk potential. The industrial basin is found in Ibadan, southwestern Nigeria. The river was sampled around each industry for 14 physicochemical properties using standard laboratory procedures. The diffusion pattern and magnitude of the toxicity was modeled in the geospatial environment while the risk level of the industrial river basin was assessed. Considerable heavy metallic contaminants like As, Pb, Cr, Cd, and Ni were identified at various points along the river. Human settlements and ecosystems within this basin are highly exposed to pollution hazards while settlements downstream of the river are at potential environmental risk. Phytoremediation was considered the most viable method to remediate the situation.
A Smart Home Security System In Low Computing IoT Environment

This work targets design of a face authenticated door-lock system in Raspberry pi and proposes a different neural network model which directly classifies a person's face without considering the euclidean distance. It is to overcome the limitation of handling a large number of stored faces and the cases of variation in angle and illumination in the face image. A set of fully connected layers on the stored face embeddings is trained to predict a person in the last layer. The proposed neural network model, local binary pattern histogram fully connected face authentication (LBPHFCFA), can memorize and generalize to eradicate the flaws of state-of-the-art algorithms. It also tends to work precisely along with the pipeline in Raspberry Pi without much computation overhead as well as better foils the impersonation attack on the system.
A General Framework to Detect Design Patterns by Combining Static and Dynamic Analysis Techniques

Design pattern detection can provide useful insights to support software comprehension. Accurate and complete detection of pattern instances are extremely important to enable software usability improvements. However, existing design pattern detection approaches and tools suffer from the following problems: incomplete description of design pattern instances, inaccurate behavioral constraint checking, and inability to support novel design patterns. This paper presents a general framework to detect design patterns while solving these issues by combining static and dynamic analysis techniques. The framework has been instantiated for typical behavioral and creational patterns, such as the observer pattern, state pattern, strategy pattern, and singleton pattern to demonstrate the applicability. Based on the open-source process mining toolkit ProM, we have developed an integrated tool that supports the whole detection process for these patterns. We applied and evaluated the framework using software execution data containing around 1,000,000 method calls generated from eight synthetic software systems and three open-source software systems. The evaluation results show that our approach can guarantee a higher precision and recall than existing approaches and can distinguish state and strategy patterns that are indistinguishable by the state-of-the-art.
A mobile stereo vision system with variable baseline distance for three-dimensional coordinate measurement in large FOV

A novel mobile stereo vision system (MSVS) with variable baseline distance for three-dimensional (3D) coordinate measurement in large field of view (FOV) is established herein. Each independent mobile camera can rotate in the horizontal and vertical directions, and the position of the cameras are obtained by differential GPS in real time. In order to achieve rapid camera calibration in large FOV, a simplified model of MSVS based on differential GPS is constructed in this paper. A six-point method is proposed to quickly estimate the camera’s initial parameters (i.e. focal length, initial roll, pitch, and yaw angles) in the situation that the approximate value of the initial yaw angle is unknown. Moreover, the camera’s roll angle is equivalent to its approximate value measured by a high-precision inclinometer during the movement, and the unknown extrinsic parameters of each camera are reduced to the pitch and yaw angles after the camera moves. The refined pitch and yaw angles are eventually estimated by only using a single control point, which makes it possible that the 3D coordinate can be measured online after the camera moves. The computer simulation verifies the effectiveness of the extrinsic parameters calibration method using a single control point. The quantitative results demonstrate that the standard deviation of the focal length of the camera does not exceed 0.028 mm, and the mobile camera’s pitch angle and yaw angle estimated by a single control point is extremely close to the reference values. In addition, the average error of the 3D coordinate on each axis after the camera moves is smaller than 0.08 m in the measuring distance of 100 m, and that on X-axis and Z-axis is comparable and does not exceed 0.35 m. The proposed methods are suitable for the occasions that high accuracy is not required in the field of 3D coordinate measurement in large FOV.
Underwater Object Detection With Mixed Attention Mechanism And Multi-Enhancement Strategy

Different from the real-world object detection tasks, underwater object detection has a more complex environment. The underwater environment is often faced with the challenges of the blur, scale variation, color shift, and texture distortion. Most generic object detection algorithms are difficult to maintain real-world robustness in the complex underwater scene. To address these problems, we propose a two-stage object detection method with an attention RPN block and an effective multi-enhancement strategy. For the case of blurred underwater images, our attention RPN improves the characteristic expression of key areas, which helps to gain better proposals in the region proposal stage. Besides, We also introduce a multi-enhancement strategy to improve the quality of the detected pictures. To boost the detection effect, our multi-enhancement strategy works on reducing the domain shift between underwater scene and real scene, especially the images with color shift and distortion. We do extensive experiments in the URPC datasets and MS COCO 2017. Finally, in underwater tasks, our method can gain 1.2 mAP higher than that of results in the baseline.
Exponential Savings in Agnostic Active Learning Through Abstention

We show that in pool-based active classification without assumptions on the underlying distribution, if the learner is given the power to abstain from some predictions by paying the price marginally smaller than the average loss $1/2$ of a random guess, exponential savings in the number of label requests are possible whenever they are possible in the corresponding realizable problem. We extend this result to provide a necessary and sufficient condition for exponential savings in pool-based active classification under the model misspecification.
Efficient functional encryption for inner product with simulation-based security

Abstract Functional encryption (FE) is a novel paradigm for encryption scheme which allows tremendous flexibility in accessing encrypted information. In FE, a user can learn specific function of encrypted messages by restricted functional key and reveal nothing else about the messages. Inner product encryption (IPE) is a special type of functional encryption where the decryption algorithm, given a ciphertext related to a vector x and a secret key related to a vector y , computes the inner product x · y . In this paper, we construct an efficient private-key functional encryption (FE) for inner product with simulation-based security, which is much stronger than indistinguishability-based security, under the External Decisional Linear assumption in the standard model. Compared with the existing schemes, our construction is faster in encryption and decryption, and the master secret key, secret keys and ciphertexts are shorter.
Machine Learning-based Automated Essay Scoring System for Chinese Proficiency Test (HSK)

Automated essay scoring (AES) gains momentum recently in English-based environment. However, the development of Chinese AES system is slow and fruitless. Many foreign students participate in the Chinese Proficiency Test (HSK) so a HSK automated essay scoring system (HSK AES) is in high demand. To develop an effective and reliable HSK AES system, this paper proposes three machine learning and deep learning models that take HSK essays as input. We apply Word2vec and TF-IDF (term frequency-inverse document frequency) methods to extract important features from the original essays. Three machine learning models, including XGBoost, one deep neural network with flatten and dense layer and another deep neural network with LSTM (long short-term memory) and dense layer, are trained. The experimental results show that XGBoost with TF-IDF outperforms the other two models with the lowest MAE (mean absolute error) as 6.7%. We also prove that deep neural networks either with LSTM (long short-term memory) or with flatten perform unsatisfactory on HSK AES.
Towards effective deep transfer via attentive feature alignment

Training a deep convolutional network from scratch requires a large amount of labeled data, which however may not be available for many practical tasks. To alleviate the data burden, a practical approach is to adapt a pre-trained model learned on the large source domain to the target domain, but the performance can be limited when the source and target domain data distributions have large differences. Some recent works attempt to alleviate this issue by imposing feature alignment over the intermediate feature maps between the source and target networks. However, for a source model, many of the channels/spatial-features for each layer can be irrelevant to the target task. Thus, directly applying feature alignment may not achieve promising performance. In this paper, we propose an Attentive Feature Alignment (AFA) method for effective domain knowledge transfer by identifying and attending on the relevant channels and spatial features between two domains. To this end, we devise two learnable attentive modules at both the channel and spatial levels. We then sequentially perform attentive spatial- and channel-level feature alignments between the source and target networks, in which the target model and attentive module are learned simultaneously. Moreover, we theoretically analyze the generalization performance of our method, which confirms its superiority to existing methods. Extensive experiments on both image classification and face recognition demonstrate the effectiveness of our method. The source code and the pre-trained models are available at https://github.com/xiezheng-cs/AFAhttps://github.com/xiezheng-cs/AFA.
Analysis of Social Media Data to Classify and Detect Frequent Issues Using Machine Learning Approach

Social Media (SM) is becoming the next-level journalism as it reflects all the viral contents, social affairs, current conditions of our surroundings. SM site Facebook has achieved the utmost popularity in Bangladesh. Nowadays, people prefer to share their views about a problem on SM rather than any other platform. Considering these consequences, this study (Bangladesh perspective) proposed a system which will analyze Facebook data and will classify and detect the problems people are facing most frequently at a particular time. The problems are categorized into 12 major classes addressing the socio-economic aspects of Bangladesh. The dataset, contains public posts (and comments) from Facebook, is manually collected and labeled with the classes. Performing efficient data preparation, feature vectors are constructed with TF-IDF weights then Chi-square is applied to select the most correlated features. Four machine learning algorithms e.g., Logistic Regression, Support Vector Machine, Multinomial Naive Bayes, Random Forest are employed as supervised classifiers. Besides, hyperparameter tuning of these classifier algorithms is carried out with Grid Search and the best candidate model is selected applying 10-folds cross validation. Finally, the selected `Logistic Regression' model is built with securing classification accuracy of over 93%. The classification report illustrates important information about problems outgrowth.
Serverless distributed learning for smart grid analytics*

The digitization, informatization, and intelligentization of physical systems require strong support from big data analysis. However, due to restrictions on data security and privacy and concerns about the cost of big data collection, transmission, and storage, it is difficult to do data aggregation in real-world power systems, which directly retards the effective implementation of smart grid analytics. Federated learning, an advanced distributed learning method proposed by Google, seems a promising solution to the above issues. Nevertheless, it relies on a server node to complete model aggregation and the framework is limited to scenarios where data are independent and identically distributed. Thus, we here propose a serverless distributed learning platform based on blockchain to solve the above two issues. In the proposed platform, the task of machine learning is performed according to smart contracts, and encrypted models are aggregated via a mechanism of knowledge distillation. Through this proposed method, a server node is no longer required and the learning ability is no longer limited to independent and identically distributed scenarios. Experiments on a public electrical grid dataset will verify the effectiveness of the proposed approach.
Real-time path planning in dynamic environments for unmanned aerial vehicles using the curve-shortening flow method

This article proposes a new algorithm for real-time path planning in dynamic environments based on space-discretized curve-shortening flows. The so-called curve-shortening flow method shares working principles with the well-established elastic bands method and overcomes some of its drawbacks concerning numerical robustness and parameterability. This is achieved by efficiently applying semi-implicit time integration for evolving the path and secondly by developing a methodology for setting the algorithm’s parameters based on physical quantities. Different short- and long-term validation scenarios are performed with three interlinked instances of the curve-shortening flow method each running on an individual industrial control and driving a real or a simulated unmanned aerial vehicle.