Reviewerly/exHarmony huggingface dataset:
An Improved Oversampling Method Based on Neighborhood Kernel Density Estimation for Imbalanced Emotion Dataset

Classification problem of imbalanced dataset is one of the main research topics. Imbalanced dataset where majority class outnumbers minority class is more difficult to handle than balanced dataset. The ADASYN approach has tried to solve this problem by generating more minority class samples for a few samples around the border between two classes. However, it is difficult to expect good classification with ADASYN when the imbalanced dataset contains noise samples instead of real minority class samples around the border. In this study, to overcome this problem, a new oversampling approach deals with the probability that a minority class sample belongs to dangerous set, not noise samples by using kernel density estimation. The proposed method generates appropriate synthetic samples to train well the learning model for minority class samples. Experiments are performed on ECG dataset collected for emotion classification. Finally, the experimental results show that our method improves the overall classification accuracy as well as recall rate for minority class.
Classification of Emotions Using a 2-Channel Convolution Neural Network

This paper focuses on facial emotion recognition with the help of a 2-channel convolution neural network. The first channel in the CNN model contains a standard network and the other layer is made up of local features of the images. Information from both channels is connected to a fully connected layer which is used for classification using a log loss function. The designed scheme is tested on FER2013 dataset. The results obtained from the designed system are compared with standard CNN and SVM using accuracy, precision and Fl score. The results reveal that the 2-channel CNN is far better than the other models in terms of recognizing emotions correctly. It also revealed that providing local feature descriptors of images using CNN helps in improving classification by a significant margin in low-resolution images.
LUAI Challenge 2021 on Learning to Understand Aerial Images

This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV’2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 [7] and GID-15 [35] datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images. This challenge received a total of 146 registrations on the three tasks. Through the challenge, we hope to draw attention from a wide range of communities and call for more efforts on the problems of learning to understand aerial images.
Side-Scan Sonar Imaging: Real-Time Acoustic Streaming

Most data collected with autonomous underwater vehicles (AUVs) can only be reviewed and analyzed post-mission. This limitation is due to the low-bandwidth acoustic links used for communication between AUV and surface, which are typically devoted to monitoring a few mission-critical parameters. In this work, we propose a plug-and-play solution that would enable live streaming of side-scan sonar data on such low-bandwidth modems with no additional hardware requirements. Using principles from image super resolution, we first downsample and compress sonar images on the sensor(AUV)-side and then estimate the full high-quality sonar image on the receiver(surface)-side. The bandwidth requirements for this livestreaming are below 1.87 kbit/s while still providing reconstructions difficult to distinguish from the originals. Our complete end-to-end system has been implemented using EvoLogics underwater modems and successfully tested in our laboratory and harbor basin.
On the Robustness of Intrusion Detection Systems for Vehicles Against Adversarial Attacks

Because connected cars typically have several communication capabilities (through 5G, WiFi, and Bluetooth), and third-party applications can be installed on the cars, it would be essential to deploy intrusion detection systems (IDS) to prevent attacks from external attackers or malicious applications. Therefore, many IDS proposals have been presented to protect the controller area network (CAN) in a vehicle. Some studies showed that deep neural network models could be effectively used to detect various attacks on the CAN bus. However, it is still questionable whether such an IDS is sufficiently robust against adversarial attacks that are crafted aiming to target the IDS. In this paper, we present a genetic algorithm to generate adversarial CAN attack messages for Denial-of-Service (DoS), fuzzy, and spoofing attacks to target the state-of-the-art deep learning-based IDS for CAN. The experimental results demonstrate that the state-of-the-art IDS is not effective in detecting the generated adversarial CAN attack messages. The detection rates of the IDS were significantly decreased from 99.27%, 96.40%, and 99.63% to 2.24%, 11.59%, and 0.01% for DoS, fuzzy, and spoofing attacks, respectively.
AI based Segmented Anomaly Detection

This paper aims at targeting to solve the problem of anomaly detection by proposing a system that would be efficient in detecting anomalies and also being region-specific to narrow the target of detecting anomalies in a particular environment. The system elaborates on leveraging the power of convolutional autoencoders, LSTMs to detect anomalies of various kinds in an unsupervised manner, hence not being limited to the scope of labels of pre-trained modules. Furthermore, the architecture is enhanced by an ensemble of various such modules to improve the overall performance of the system. The obtained results are further analysed to provide region-specific based anomaly detection, this would help in prioritising and investigating those anomalies that would be of greater risk. With the region-specific anomaly detection module, anomalies in an environment are prioritised according to the risk it poses to that specific environment, hence making the system robust and adaptable to the specific environment it is working on. The final obtained results are then used to enforce the necessary contingencies, precautions and prevention methods to combat the same. The outcome of the system is to prepare humans to be take precautions and hence be vigilant to various anomalies/risks that one is subjected to. Preventing such risks would help humans become more intrepid and hopeful about their bright future.
RLGC: residual low rank group sparsity constraint for image denoising

Image denoising is an important topic in the field of image processing. With the application of nonlocal similarity in sparse representation, the work of image denoising began to be performed on similar patch groups. The sparse representations of patches in a group will be learned together. In this paper, we propose a novel image denoising model by combining group sparsity residual with low-rankness. Firstly, motivated by the relationship between low rank and sparsity, a low rank constraint is imposed on the sparse coefficient matrix of each similar patch group to enhance the sparsity. Secondly, since 𝛾-norm can most closely match the true rank of a matrix, it is applied for rank approximation in our model. Finally, in view of the fact that numerous iterations are required in the group sparse representation (GSR) model, we develop an efficient algorithm based on the Majorize-Minimization (MM) optimization. It greatly reduces the computational complexity and the number of iterations. Experimental results show that our model makes great improvements in image denoising and outperforms many state-of-the-art methods.
Geometrical Based Trajectory Calculation for Autonomous Vehicles in Multi- Vehicle Traffic Scenarios

A computationally cheap method for computing collision-free trajectories with multiple moving obstacles is proposed here while meeting comfort and safety criteria. By avoiding search in the trajectory calculation and instead using a geometrical set to calculate the trajectory, the calculation time is significantly reduced. The geometrical set is calculated by solving a support vector machine problem and solving the SVM problem characterizes maximum separating surfaces between obstacles and the ego vehicle in the time-space domain. The trajectory on the separating surface might not be kinematically feasible. Therefore, a vehicle model and a Newton-Raphson based procedure is proposed to obtain a safe, kinematically feasible trajectory on the separating surface. A roundabout scenario and two take-over scenarios with different configurations are used to investigate the properties of the proposed algorithm. Robustness properties of the proposed algorithm is investigated by a large number of randomly initiated simulation scenarios.
A Novel TRNG Based on Traditional ADC Nonlinear Effect and Chaotic Map for IoT Security and Anticollision

In the rapidly developing Internet of Things (IoT) applications, how to achieve rapid identification of massive devices and secure the communication of wireless data based on low cost and low power consumption is the key problem to be solved urgently. This paper proposes a novel true random number generator (TRNG) based on ADC nonlinear effect and chaotic map, which can be implemented by traditional processors with built-in ADCs, such as MCU, DSP, ARM, and FPGA. The processor controls the ADC to sample the changing input signal to obtain the digital signal DADC and then extracts some bits of DADC to generate the true random number (TRN). At the same time, after a delay based on DADC, the next time ADC sampling is carried out, and the cycle continues until the processor stops generating the TRN. Due to the nonlinear effect of ADC, the DADC obtained from each sampling is stochastic, and the changing input signal will sharply change the delay time, thus changing the sampling interval (called random interval sampling). As the input signal changes, DADC with strong randomness is obtained. The whole operation of the TRNG resembles a chaotic map, and this method also eliminates the pseudorandom property of chaotic map by combining the variable input signal (including noise) with the nonlinear effect of ADC. The simulation and actual test data are verified by NIST, and the verification results show that the random numbers generated by the proposed method have strong randomness and can be used to implement TRNG. The proposed TRNG has the advantages of low cost, low power consumption, and strong compatibility, and the rate of generating true random number is more than 1.6 Mbps (determined by ADC sampling rate and processor frequency), which is very suitable for IoT sensor devices for security encryption algorithms and anticollision.
Design and Implementation of Sensitive Information Detection Algorithm Based on Deep Learning

With the continuous progress of science and technology, people get information on the Internet more and more quickly, the way is more and more convenient. People are more willing to use mobile devices, computers and other electronic products than to obtain information from paper books, newspapers and other channels. While using these electronic products, there will be many behaviors of storing or spreading bad information. Traditional OCR technology can be widely used in the detection and recognition of ordinary single background level text detection and recognition, but for the natural scene pictures with a certain Angle of the text detection and recognition effect is not good. With the development of deep learning technology, text detection and text recognition are mostly completed by deep learning architecture. In this paper, the popular EAST algorithm and CRNN algorithm are combined to detect and identify the text information in the natural scene.
Improving the Cyber Security by Applying the Maturity Model Case: Electricity Industry of Iran

Continuity and reliability of electricity supply in a country are vital to the operational continuity of the critical infrastructure and government. High levels of cyber security are required to achieve such key measures in the electricity industry. We propose solutions and models on how to improve the cyber security using the maturity model in order to achieve continuity of electricity supplies. Maturity levels start from the relative level (level 1) and continue to the adaptive level (level 4). A total of 10 domains are defined in the model. The practices of cyber security are clustered into domains. The model is used for assessment of the strength and weakness factors for the organizations from the model. The case to demonstrate the concepts is from Iran.
SeeQuery

Ontology authoring is a complicated and error-prone process since the knowledge being modeled is expressed using logic-based formalisms, in which logical consequences of the knowledge have to be foreseen. To make that process easier, competency questions (CQs), being questions expressed in natural language are often stated to trace both the correctness and completeness of the ontology at a given time. However, CQs have to be translated into a formal language, like ontology query language (SPARQL-OWL), to query the ontology. Since the translation step is time-consuming and requires familiarity with the query language used, in this paper, we propose an automatic method named SeeQuery, which recommends SPARQL-OWL queries being translations of CQs stated against a given ontology. It consists of a pipeline of transformations based on template matching and filling, being motivated by the biggest to date publicly available CQ to SPARQL-OWL datasets. We provide a detailed description of SeeQuery and evaluate the method on a separate set of 2 ontologies with their CQs. It is, to date, the only automatic method available for recommending SPARQL-OWL queries out of CQs. The source code of SeeQuery is available at: https://github.com/dwisniewski/SeeQuery.
Assessment of Deep Learning Models for Human Activity Recognition on Multi-variate Time Series Data and Non-targeted Adversarial Attack

Human activity recognition (HAR) is one of the leading research fields in ubiquitous computing working to integrate seamless technologies in our daily lives. The researches in this field focus on the technological advancement of fine activity recognition through using minimal technological deployments along with consideration of human factors. The domain specific knowledge of human activity recognition has shed significant importance on the activity data processing, due to the innate random nature of the activity data. The necessity for improved prediction accuracy of activities within limited infrastructural support and cost is necessary for the research works to be conducted at large. The data set utilized in this study is modified through tree-based feature-engineering method, which has significantly impacted the intrinsic time-series pattern associated with the original data set acquired from UCI machine learning repository. In this paper, the primary focus lies on improving classification accuracy of the human activities included in the data set through utilizing deep learning classifier models. The particular modified pattern of the data devoid of time-series pattern to major extent has resulted in lower classification accuracy by benchmark time-series classification models namely Keras-LSTM and RNN-LSTM. CNN based networks have demonstrated consisted improved training performance on the data set. The state-of-the-art classifier ResNet introduced by Microsoft Research in 2015 has achieved 99.9% classification accuracy on the data set of this study. Another important aim of this study is to infer the risk attached with adversarial attack on machine learning models. The high vulnerability of machine learning models in face of adversarial attacks has emerged as security concern of the deployment pipeline of machine learning models. To the author’s best knowledge, there has been no prior study to find out the high insecurity associated with the human activity recognition data set and concerned benchmark models. In this study, two prevalent adversarial approaches namely Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) have been employed to carry out non-targeted attack on the pre-trained ResNet model. The accuracy loss by the adversarial attack over a small range of added perturbation is tremendous, nearly 96% accuracy is diminished in the process. Finally, we have demonstrated the comparative performance by FGSM and BIM on reducing the classification accuracy of pre-trained model. The findings shed light on the nature of the data influencing the classification accuracy and the resiliency of state-of-the-art classifier model on facing adversarial attacks.
Bit-Parallel Systolic Architecture for &lt;i&gt;AB&lt;/i&gt; and &lt;i&gt;AB&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; Multiplications over &lt;i&gt;GF&lt;/i&gt;(2&lt;i&gt;&lt;sup&gt;m&lt;/sup&gt;&lt;/i&gt;)

In this paper, we present a scheme to compute either AB or AB2 multiplications over GF(2m) and propose a bit-parallel systolic architecture based on the proposed algorithm. The AB multiplication algorithm is derived in the same form as the formula of AB2 multiplication algorithm, and an architecture that can perform AB multiplication by adding very little extra hardware to AB2 multiplier is designed. Therefore, the proposed architecture can be effectively applied to hardware constrained applications that cannot deploy AB2 multiplier and AB multiplier separately.
End-to-End Detection and Recognition of Arithmetic Expressions

The detection and recognition of handwritten arithmetic expressions (AEs) play an important role in document retrieval [] and analysis. They are very difficult because of the structural complexity and the variability of appearance. In this paper, we propose a novel framework to detect and recognize AEs in an End-to-End manner. Firstly, an AE detector based on EfficientNet-B1 [] is designed to locate all AE instances efficiently. Upon AE location, the RoI Rotate module [] is adopted to transform visual features for AE proposals. The transformed features are then fed into an attention mechanism based recognizer for AE recognition. The whole network for detection and recognition is trained End-to-End on document images annotated AE locations and transcripts. Since the datasets in this field are rare, we also construct a dataset named HAED, which contains 1069 images (855 for training, and 214 for testing). Extensive experiments on two datasets (HAED and TFD-ICDAR 2019) show that the proposed method has achieved competitive performance on both datasets.
Assessment of the Used Mobile Applications by Small-Scale Farmers at Miono and User Requirements in the Present and Future Mobile Apps

The revolution of smartphone usage in agriculture by small-scale farmers has improved productivity by establishing a reliable agricultural knowledge information system. Despite the development achieved by the smartphone in agriculture, information on how the applications improve the farming value chain is still very scanty. Consequently, this study set out to assess the currently used application technologies by small-scale farmers at Miono to upgrade the user requirements for present and future applications. The study applied qualitative interview and observation approach in collecting data and thematic analysis to analyze the data. Results show that farmers at Miono used messaging applications, call applications, calculator apps and clock applications the most. On the other hand, the farmers rarely used WhatsApp, Facebook, email applications, and photo apps. Thus, for real present and future development, farmers need upgraded apps that disseminate information on local pests, pesticides, weather forecasts, and market information.
Suppression of random intensity error for the measurement of surface shape by using a novel phase shifting algorithm

research-article Suppression of random intensity error for the measurement of surface shape by using a novel phase shifting algorithm Share on Authors: Fuqing Miao Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Seokyoung Ahn Pusan National University, Korea Pusan National University, KoreaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 226–231https://doi.org/10.1145/3480571.3480611Online:29 October 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Teacher Perceptions About the Incorporation of Mobile Devices in a Costa Rican Secondary School

Despite many studies researching the increased use and potential effectiveness of mobile devices in secondary education (high school) classrooms, less research has examined teachers' perceptions around such use. Accordingly, this qualitative (phenomenological) study used semi-structured interviews to explore 10 secondary teachers' perceptions around the use of mobile devices in Costa Rican classrooms. Utilizing an interpretive phenomenological analysis (IPA) approach to analyze the data of participants' experiences, results from this study found (1) that Costa Rican secondary teachers had mixed perceptions about using mobile devices for the teaching and learning process and (2) that mobile devices seem a “double edge sword,” which, if not used appropriately in the classroom, can be more detrimental than beneficial for students' learning processes.
Epilogue: Open Science in an Open Society

Abstract The European Union has chosen Open Science as the way to do science and research based on its cultural and social values. Open Science can only really thrive in democracies and Open Societies to the benefit of humanity. This relationship between science, scientists and society is not trival and sometimes endangered, therefore we need to continuously engage in research with and for society.
Resource Allocation for Multipath Cooperative Video Transmission over 5G Networks

Recently, the continuously increasing demands for video application poses the pressing need for solutions that enable to optimize the resource allocation for video transmission, thereby improving the quality of user experience (QoE). In order to solve this problem, we first design a novel scheme with software-defined networking (SDN) architecture for multipath cooperative scalable video transmission to optimize the resource allocation over MEC-assisted 5G heterogeneous networks. And then, we conduct an effective real-time delay analysis for tracking the time-varying wireless channels and traffic congestion, and accordingly establish a long-term video quality estimation model. Finally, combined with this model, a transmission optimization algorithm is proposed to determine the optimal transmission strategy for multipath cooperative scalable video transmission over 5G heterogeneous wireless networks. The simulation results show that our proposed algorithm can effectively achieve the expected goal in playback continuity and video quality.