Reviewerly/exHarmony huggingface dataset:
Application of Adversarial Domain Adaptation to Voice Activity Detection

Voice Activity Detection (VAD) is becoming an essential front-end component in various speech processing systems. As those systems are commonly deployed in environments with diverse noise types and low signal-to-noise ratios (SNRs), an effective VAD method should perform robust detection of speech region out of noisy background signals. In this paper, we propose applying an adversarial domain adaptation technique to VAD. The proposed method trains DNN models for a VAD task in a supervised manner, simultaneously mitigating the problem of area mismatch between noisy and clean audio stream in a unsupervised manner. The experimental results show that the proposed method improves robust detection performance in noisy environments compared to other DNN-based model learned with hand-crafted acoustic feature.
Smart cities with blockchain technology

Because urban populations will be the growing faster in years to come, many initiatives including smart city projects have been proposed to meet the needs of urban people and to improve their quality of life. Sustainability, energy efficiency, and a green environment are the major aspirations of smart cities. Many challenges, including air quality, energy efficiency, urban mobility, safety and security, have been faced to make the smart cities concept a reality. However, perhaps in all such things the major challenge faced by the smart cities is data privacy and security. In this paper we have discussed securing smart cities using block chain technology. A smart city uses many sensors and actuators, and it is relying completely on the concept of the Internet of Things (IoT). To maintain sustainability and real-time deployment of smart cities the necessary factors required are secured wireless connectivity and IoT. Being smarter also makes the city more vulnerable to cyber attacks. We have proposed a comprehensive survey about various applications such as healthcare systems, smart vehicles, smart trafficking, and smart economy sharing services in the smart cities, which implements blockchain technology. Case studies of smart Dubai and smart London have also been presented in this paper.
Improving Thai Named Entity Recognition Performance Using BERT Transformer on Deep Networks

Emerging of deep learning and transformer model helps advance many NLP tasks. For Name Entity Recognition (NER), many studies have applied deep transformer architect with Google BERT and ELMO to English language and achieved significant performance improvement compared to traditional embeddings. However, currently there is very little research on applying BERT transformer to Thai NER task, so in this paper we explore two different approaches to apply BERT transform to improve Thai NER which are fine-tuning BERT and using BERT as embedding. We found that Bi-LSTM-CRF network with BERT embedding model achieved a significant performance improvement with averaged F1 Score of 94%, without character embedding feature. This approach yields better performance than using only fine-tuning BERT for Thai NER and better than state of the art on Thai NER from Bi-LSTM with Thai2fit word embedding and character embedding.
Smart Face Recognition with Mask/No Mask Detection

Ever since Covid-19 has affected our lives, the use of masks in public places has taken extreme importance. Be it restaurants, shopping malls, workplaces, airports or any other public place, wearing a mask is essential for the safety of oneself as well as the others. However, there are always some defaulters who will not wear a mask putting all at risk. In this paper, a mask/no mask detector has been implemented to identify such defaulters. Also, a face recognition system has been incorporated to identify those who are not wearing a mask. This setup could be used in offices or any other place of public gathering to ensure the safety of one and all.
Attributed Graph Alignment

Motivated by various data science applications including de-anonymizing user identities in social networks, we consider the graph alignment problem, where the goal is to identify the vertex/user correspondence between two correlated graphs. Existing work mostly recovers the correspondence by exploiting the user-user connections. However, in many real-world applications, additional information about the users, such as user profiles, might be publicly available. In this paper, we introduce the attributed graph alignment problem, where additional user information, referred to as attributes, is incorporated to assist graph alignment. We establish sufficient and necessary conditions for recovering vertex correspondence exactly, where the conditions match for a wide range of practical regimes. Our results recover existing tight information-theoretic limits for models where only the user-user connections are available, and further span the full spectrum between these models and models where only attribute information is available.
Image Reconstruction of Photonics Integrated Interference Imaging System: stablized CLEAN methods

Photonics Integrated Interference Imaging System (PIIIS) is a recently developed far-filed imaging approach aiming at high imaging performance and compact structure. Several sampling lenslet arrays have been proposed to acquire higher imaging quality. These lens configurations provide low-frequency intensive but high-frequency sparse measurements of complex visibility. However, this kind of sampling will induce Gibbs-ringing artifacts in inverse Fourier images. In this article, entropy prior and smoothness prior are proposed to remove the ringing artifacts, and stabilized CLEAN algorithms based on those priors are used to reconstruct images. A simulation experiment using hierarchical multistage sampling lens array is implemented, and the results show that these algorithms can eliminate the ringing artifacts.
Local Path Planning using Distance-Type Fuzzy Reasoning Method in Unstructured and Dynamic Construction Sites

This paper proposes a local path planning method that utilizes the distance-type fuzzy reasoning method for the navigation of mobile robots in unstructured and dynamic environments. By using a distance-based fuzzy reasoning method: (i) both non-sparse and sparse input fuzzy sets representing sensor readings can be properly processed; (ii) benefiting from the modus ponens, when the input exactly matches one of the rules in the fuzzy rule base, the reasoning result is guaranteed to be the same as the consequent of that rule. Furthermore, we propose a behavioral controller that adopts the reasoning results to achieve efficient and safe local path planning. The presented approach has been evaluated by extensive experiments with our newly developed material transportation robots in real construction sites.
Empirical Modelling of Global Solar Radiation in Hatay (Turkey) Province

There are many models used in global solar radiation prediction. However, these prediction models vary according to the climate and geographic characteristics of the region involved. When the prediction model determined for a region is used in a different region, the prediction model may not work. In order to gain absolute and stable results from these models, the studies should be either custom-made or tested. In this study, 7 different models were examined, comparison charts were created and statistical interpretations were made in order to determine the monthly average global solar radiation coming to the horizontal plane taking into account the climatic characteristics of Hatay province, located in the eastern 39.92º latitude and northern longitude 36.16º, covering an area of 5403 km, and has a humid climate with a value of 41.3, based on De Martonne-Gottmann’s drought index and 50.6 based on Erinç's rainfall efficiency index. Under the scope of this study, the most ideal global solar radiation prediction model was selected by making a statistical evaluation according to the parameters of determination coefficient (R²), mean percentage error (MPE), mean deviation error (MBE), root mean square error (RMSE). A general assessment of the graphics and statistical parameters of the predictions obtained by using the models and the measured data has indicated Model 4 as the most successful model by an RMSE value of 0.17306, and the least successful model was Model 3 by an RMSE value of 2.11851. When the models are interpreted according to the determination coefficient, it has been observed that other models are generally successful. However, this situation does not coincide with the comparison charts of the predicted and measured data. For this reason, it was seen that the determination coefficient was not sufficient in the statistical evaluation of the prediction models. In the interpretation of the models, it was observed that the interpretation of root mean square error, mean percentage error and mean deviation error values together gave more accurate results. In conclusion, it has been suggested that Model 4 should be preferred for the global solar radiation forecast of Hatay province.
Real Life Smart Waste Management System [DRY, WET, RECYCLE, ELECTRONIC &amp; MEDICAL]

The problem of real-life smart waste management system can be solved using automatic waste segregation. In particular, the focus of the article is on the problem of detection (i.e., waste classification). In these 5 classes of waste are taken and segregated them into 5 categories namely dry, wet, recycle, electronic and medical. This system will automatically detect the waste object and segregate it into the respective category. The use of machine learning allowed improving the model with more accuracy. Convolutional Neural Networks (CNN) algorithm which is best used for image classification is used for object detection. The models that was trained are ResNet50, VGG16, InceptionV3 and MobileNetV2. Finally, when compared to the results of all these models, MobileNetV2 has given us the best and highest accuracy of about 98% and 99% respectively.
MSAV

With the development of multimedia period, information is always caputred with multiple views, which causes a research upsurge on multi-view learning. It is obvious that multi-view data contains more information than those single view ones. Therefore, it is crucial to develop the multi-view algorithms to adapt the demand of many applications. Even though some excellent multi-view algorithms were proposed, most of them can only deal with the specific problems. To tacle this problem, this paper proposes an unified framework named Multi-view Subspace Analysis with View Consistence (MSAV), which provides an unified means to extend those single-view dimension reduciton algorithms into multi-view versions. MSAV first extends multi-view data into kernel space to avoid the problem caused by different dimensions of the data from multiple views. Then, we introduced a self-weighted learning strategy to automatically assign weights for all views according to their importance. Finally, in order to promote the consistence of all views, Hilbert-Schmidt Independence Criterion is adopted by MSAV. Furthermore, We conducted experiments on several benchmark datasets to verify the performance of MSAV.
Orchestrating an Ubiquitous Learning Situation About Cultural Heritage with Casual Learn

This paper presents a case study that analyzes the orchestration of a ubiquitous learning situation involving a teacher and 89 secondary-school students using Casual Learn. This case study allows us to illustrate how teachers can use Casual Learn to orchestrate ubiquitous learning situations to learn Cultural Heritage. During the case study, Casual Learn played a key role as it enabled to bridge in- and out-classroom activities across physical and virtual learning spaces.
Practice of ERP Cloud Development and Evolution

Based on our practices and experience for component-based ERP cloud developments, three issues, component identification, granularity and evolution, were focused and discussed together. First, according to separation of concerns and best practices, a component-based hierarchy model was proposed to improve the efficiency of ERP cloud development and evolution. And three semantic layers were constructed and in each layer the corresponding types of reusable objects were defined. And one experience/product-based method were given to help identify and extract reusable ERP units. After that, based on the hierarchy model, a directed graph was created to depict CBS. Finally, a graph-based analysis method was proposed to evaluate the ripple effect of component evolution and its propagation.
A logic of interactive proofs

Abstract We introduce the probabilistic two-agent justification logic $\textsf {IPJ}$, a logic in which we can reason about agents that perform interactive proofs. In order to study the growth rate of the probabilities in $\textsf {IPJ}$, we present a new method of parametrizing $\textsf {IPJ}$ over certain negligible functions. Further, our approach leads to a new notion of zero-knowledge proofs.
Combined Channel and Spatial Attention for YOLOv5 during Target Detection

Accuracy target detection can benefit many target detection applications. The latest YOLOv5 method has faster detection speed and better accuracy in target detection. However, there are still insufficient on bounding box positioning and it is difficult to distinguish overlapping objects. This paper proposes an improved Attention-YOLO v5, which adds channel attention and spatial attention mechanisms to the feature extraction. Furthermore, a squeeze and excitation(SE) module is applied to improve the resolution of the input image. Experiments on two public datasets show that our proposed method effectively reduces the positioning error of the bounding box and improves the detection accuracy. The accuracy on INRIA and PnPLO datasets are 97.9% and 96.2%.
Answering Fill-in-the-Blank Questions in Portuguese with Transformer Language Models

Despite different applications, transformer-based language models, like BERT and GPT, learn about language by predicting missing parts of text. BERT is pretrained in Masked Language Modelling and GPT generates text from a given sequence. We explore such models for answering cloze questions in Portuguese, following different approaches. When options are not considered, the largest BERT model, trained exclusively for Portuguese, is the most accurate. But when selecting the best option, top performance is achieved by computing the most probable sentence, and GPT-2 fine-tuned for Portuguese beats BERT.
A Study of Neural Training with Iterative Non-Gradient Methods

In this work, we demonstrate provable guarantees on the training of a single ReLU gate in hitherto unexplored regimes. We give a simple iterative stochastic algorithm that can train a ReLU gate in the realizable setting in linear time while using significantly milder conditions on the data distribution than previous such results. Leveraging certain additional moment assumptions, we also show a first-of-its-kind approximate recovery of the true label generating parameters under an (online) data-poisoning attack on the true labels, while training a ReLU gate by the same algorithm. Our guarantee is shown to be nearly optimal in the worst case and its accuracy of recovering the true weight degrades gracefully with increasing probability of attack and its magnitude. For both the realizable and the non-realizable cases as outlined above, our analysis allows for mini-batching and computes how the convergence time scales with the mini-batch size. We corroborate our theorems with simulation results which also bring to light a striking similarity in trajectories between our algorithm and the popular S.G.D. algorithm - for which similar guarantees as here are still unknown.
Feature point screening for image matching

Generally not all feature points contribute equally to feature matching. Stable, unique and salient feature points are helpful in efficient and accurate image matching. We propose three criteria for screening feature points, which are stability, uniqueness and saliency criterion. Based on stability criterion, robust and stable feature points can be preserved. According to uniqueness criterion, feature points with repeated features would be removed. And points with distinct characteristic information can be retained in accordance with saliency criterion. By eliminating feature points that do not meet these criteria, image matching can be more efficient and accurate. The screening criteria are incorporated into common matching algorithms to verify its effectiveness. Experimental results show that the feature point screening criteria proposed can improve the matching accuracy, repetition rate and matching speed over versatile scenario.
ATLANTIC

Differential privacy promises to enable data sharing and general data analytics while protecting individual privacy. Because the private data is often stored in the form of relational database that supports SQL queries, making SQL-based analytics differentially private is thus critical. However, the existing SQL-based differentially private systems either only focus on specific type of SQL queries such as COUNT or substantially modify the database engine, thus obstructing adoption in practice. Worse yet, these systems often do not guarantee the desired accuracy by the applications. In this demonstration, using the driving trace workload from Cambridge Mobile Telematics (CMT), we show that our ATLANTIC system, as a database middleware, enforces differential privacy for real-world SQL queries with provable accuracy guarantees and is compatible with existing databases. Moreover, using a sampling-based technique, ATLANTIC significantly speeds up the query execution, yet effectively amplifying the privacy guarantee.
Functional Space Consisted by Continuous Functions on Topological Space

Summary In this article, using the Mizar system [1], [2], first we give a definition of a functional space which is constructed from all continuous functions defined on a compact topological space [5]. We prove that this functional space is a Banach space [3]. Next, we give a definition of a function space which is constructed from all continuous functions with bounded support. We also prove that this function space is a normed space.
A Power Grid Fault Center Identification Method Based on Feature Vector Centrality

A power grid fault diagnosis method is proposed based on the graph signal modeling according to the fault recording start information and the network node eigenvector centrality algorithm. Firstly, the fault start information network diagram is constructed from the fault record start information of relay protection and fault information master station. Secondly, the graph smoothness analysis method is used to determine whether the grid fails and identify the fault type. Finally, the fault components are identified based on the feature vector centrality algorithm of network nodes and visualized. The simulation verifies the effectiveness of the proposed algorithm. As a part of the intelligent fault analysis of wave recording, this method helps to quickly grasp the fault situation of power grid, and focus on collecting wave recording data for fault analysis and verification, so as to improve the efficiency of wave recording analysis.
A Framework of Data Fusion Through Spatio-Temporal Knowledge Graph

Data fusion aims to integrate data from different sources that represent the same real-world object from different views. The fusion of multi-source data can reduce the uncertainty and supplement the missing information of single-source knowledge. However, when data is generated from heterogeneous resources, it would be difficult to merge them straightforwardly by traditional data integration methods. So in this paper, we propose a knowledge graph based data fusion framework to integrate spatio-temporal information extracted from two different sources, i.e., travel notes and mobile phone data respectively, which record human travelling behaviors from different views. Firstly, we introduce a method of constructing a path-based spatio-temporal knowledge graph from different sources. Then, a long-path-based knowledge graph embedding is applied to learn entity representations of different knowledge graphs jointly, which can eliminate the heterogeneity of the information from different sources. An attenuation mechanism for modeling the long path relation is proposed in order to improve the representation learning for long path based knowledge graph. Finally, the entities are aligned considering context information to obtain the travelling knowledge in a unified and enhanced form. The experimental results show that compared with the baselines, the long-path-based knowledge graph embedding method is more suitable for the knowledge graph constructed in this paper. And through entity alignment, the two knowledge graphs can be fused to offer more information for subsequent applications.
A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised using deep learning. Afterwards, a heuristic process is applied to obtain the text, orient it and read it with minimal error rates. Finally, a user interface allows the engineer to mark up the corrosion sections based on these findings. Experimental validation shows promising accuracy rates on finding the two shapes of interest and enhance the functionality of optical character recognition when reading the text of interest.
A Survey on Blockchain and Internet of Things

In the age of next-generation computer, the role of the cloud, the internet and smart devices will become stronger. These days we all know the word smart well. This word is often used in our daily lives. The Internet of Things (IoT) will generate a variety of information from a variety of resources. It can store big data in the cloud. Fog computing acts as a signal between cloud and IoT. Fog extensions in this framework apply to material under IoT. IoT devices are called Fog nodes, which can be accessed anywhere within the network range. A blockchain is a novel way of recording in a secure sequence. Creating a new framework in the development of Internet of Things is one of the critical problems of wireless communication where solving such a problem can lead to continued growth in the use and popularity of IoT. Proposed research creates a framework for providing a framework for middleware on the internet of smart devices network for the internet of things using blockchains technology. Our great offering connects new research that integrates blockchains into the Internet of Things and provides secure Internet connection for smart devices. Blockchain (BC) Internet of Things (IoT) is a new technology that works with low-level, distributed, public and real-time leaders to maintain transactions between IoT sites. A blockchain is a series of blocks, each block being linked to its previous blocks. All blocks have cryptographic hash code, previous block hash, and its data. Transactions in BC are the basic components used to transfer data between IoT nodes. IoT nodes are a variety of portable but smart devices with embedded sensors, actuators, systems and the ability to communicate with other IoT nodes. The role of BC in IoT is to provide a process for processing secure data records using IoT nodes. BC is a protected technology that can be used publicly and openly. IoT requires this type of technology to allow secure communication between IoT nodes in different environments. Events in BC can be tracked and monitored by anyone who is certified to communicate within IoT.