Reviewerly/exHarmony huggingface dataset:
An Efficient Outlier Detection Approach for Streaming Sensor Data Based on Neighbor Difference and Clustering

In wireless sensor networks (WSNs), the widely distributed sensors make the real-time processing of data face severe challenges, which prompts the use of edge computing. However, some problems that occur during the operation of sensors will cause unreliability of the collected data, which can result in inaccurate results of edge computing-based processing; thus, it is necessary to detect potential abnormal data (also known as outliers) in the sensor data to ensure their quality. Although the clustering-based outlier detection approaches can detect outliers from the static data, the feature of streaming sensor data requires the detection operation in a one-pass fashion; in addition, the clustering-based approaches also do not consider the time correlation among the streaming sensor data, which leads to its low detection accuracy. To solve these problems, we propose an efficient outlier detection approach based on neighbor difference and clustering, namely, ODNDC, which not only quickly and accurately detects outliers but also identifies the source of outliers in the streaming sensor data. Experiments on a synthetic dataset and a real dataset show that the proposed ODNDC approach achieves great performance in detecting outliers and identifying their sources, as well as the low time consumption.
Parallel fuzzy minimals on GPU

Clustering is a classification method that organizes objects into groups based on their similarity. Data clustering can extract valuable information, such as human behavior, trends, and so on, from large datasets by using either hard or fuzzy approaches. However, this is a time-consuming problem due to the increasing volumes of data collected. In this context, sequential executions are not feasible and their parallelization is mandatory to complete the process in an acceptable time. Parallelization requires redesigning algorithms to take advantage of massively parallel platforms. In this paper we propose a novel parallel implementation of the fuzzy minimals algorithm on graphics processing unit as a high-performance low-cost solution for common clustering issues. The performance of this implementation is compared with an equivalent algorithm based on the message passing interface. Numerical simulations show that the proposed solution on graphics processing unit can achieve high performances with regards to the cost-accuracy ratio.
Distributed Virtual Selective-Forwarding Units and Sdn-Assisted Edge Computing for Optimization of Multi-Party Webrtc Videoconferencing

Network service providers have growing interest in placing network intelligence and services at network edges by means of edge clouds leveraging softwaredefined network (SDN) and network function virtualization infrastructure. A selective forwarding unit (SFU) is required in multi-party WebRTC videoconferencing using scalable video coding to provide connectivity between clients with heterogeneous bandwidth and terminals. An important question is where in the network to place the SFU service in order to minimize end-to-end delay between all client pairs. Clearly, there is no optimal place for a single SFU for all possible client pairs. We propose a method for placing virtual SFUs at network edges leveraging edge datacenters to optimize end-to-end delay and usage of overall network resources and an architecture to manage multi-party WebRTC services at edge clouds. The main advantage of the proposed distributed edge SFU framework is that each peer video stream travels the shortest path similar to mesh connection model, while each peer uploads a single video stream to its edge SFU avoiding the upload bottleneck problem. Additionally, bandwidth guarantees can be provided in the access networks by means of SDN-assisted network slicing at edge clouds. The performance of the proposed service architecture and methods are demonstrated by means of experimental results.
BCoT: Concluding Remarks

The advent of cloud computing has ensured a fertile soil for efficiently handling distributed computing. However, it calls for the provisioning of high-configuration infrastructure, including advanced servers and high-bandwidth networks for augmenting storage and computation-intensive services. To address this problem, IoT services have envisaged a centralized cloud-enabled IoT framework modeled as a black box facilitating resilience, adaptability, reliability, trust, confidentiality, and integrity, reducing maintenance costs, and enabling time-efficient IoT application support. Blockchain technology stands out to be one of the most suitable candidates for enabling a secure and distributed IoT ecosystem, thereby adding a helping hand in countering these inherent challenges and issues. Blockchain technology is a conglomerate of cryptography, public key infrastructure, and economic modeling to induce distributed database synchronization in peer-to-peer networking supported by a decentralized consensus. The underlying features of decentralized architecture, immutability, verifiability, and fault-resistance make it suitable for envisaging a properly coordinated and distributed IoT environment, giving rise to a Blockchain for Internet of Things (BCoT).
Designing a Modular and Distributed Web Crawler Focused on Unstructured Cybersecurity Intelligence

There are many use cases for cybersecurity related information available on the Internet. Tasks relating to natural language processing and machine learning require large amounts of structured and labeled data. However, the availability of recent data is limited due to the difficulty in its sanitization, retrieval, and labeling. Data on the Internet is generally diverse and unstructured, and storing this information in a manner that is easily usable for research and development purposes is not an intuitive task. We propose architectural considerations when developing a distributed system consisting of web crawlers, web scrapers, and various post-processing components, as well as possible implementations of these considerations. Our team developed such a system that is capable of applying structure and storing open source intelligence data from the Internet in an easily-searchable software platform called Splunk.
Image Steganography using Pixel Value Differencing with Modulus Function and Optimization

The secure transmission of data over digital media insists the use of steganography. In this paper Pixel value differencing with modulus function (PVDMF) is used for embedding and extraction of data in image. Differential evolution optimization algorithm is used to minimize the mean square error (MSE). Optimization uses MSE as objective function in such a way that the constraints associated with PVDMF method is satisfied. Results show that proposed method is good in embedding capacity as well as visual quality.
Robot Path Planning Based on Hybrid Adaptive Dimensionality Representation with Glowworm Swarm Optimization

Nowadays, robotics occupies considerable attention in human life especially in the modern industries that are automatically running. Robot path planning is considered a key problem for the robot movement. The adaptive dimensionality (AD) concept has been successfully used to solve this type of problems. Actually, algorithms of swarm intelligence optimization are largely successful in finding solutions to the robot’s path planning problems. Based on these facts, our paper aims to develop a new approach for planning the robot’s path in a hybrid state space environment which depends on AD representation and Glowworm Swarm optimization (GOS) algorithm. Also, in this paper, the effectiveness of the proposed approach will be gaged by comparing it with some other swarm intelligence algorithms. The improvement is represented by moving the robot from the start node in its environment, where the number of neighborhoods taken into consideration will be adaptive depending on the number of Neighbors of the Current node with Specific Attributes (NCwSA) and the value of the total number of neighborhoods under consideration, or the so-called Maximum Visual Range of glowworm (MVR). Accordingly, the number of neighbors from which the next node will be selected varies from one node to another. This leads to a reduction in the required storage capacity, and hence minimizing the time occupy required for processing. Adaptive dimensionality also results in decreasing the number of iterations required to determine the path from the start state to the goal state. The results prove the efficiency of the proposed approach to move the robot from start state to goal state without collision to determine the optimal path with a minimum time occupy and a minimum iterations number.
Mobility and Learning Through Tourism

The perspective of mobile learning research has transitioned from using mobile technology for education to learning as a human and non-human practice surrounding mobility. This study exemplifies tourism in an increasingly mobile society as a mobile learning practice. Specifically, the learning of children during family travels and the mutual organization of human and non-human interactions are emphasized. Interviews with 12 Japanese parents revealed that children's touristic learning encompassed the translation of various actors in tourist destinations and the spatiotemporal creation of a knowledge network different than that of settled areas and textbooks. Family tourism suspended cultural norms by moving children to boundaries, enabling them to learn beyond the usual constraints. Moreover, this article contends that children's learning through tourism comprised negotiations with parents before tourism, accidental learning along the way, and reconfiguration of life afterward.
A Survey of Transformer and GNN for Aspect-based Sentiment Analysis

Sentiment analysis (SA) is one of the most active and rapidly developing research areas in both information retrieval and text mining fields of computer science. SA is the process of analysing, handling, generalizing, and reasoning about user sentiments, opinions, and emotions hidden within the text by using sentiment. There are various categories of levels of SA: (1) document-level, (2) sentence-level, and (3) aspect-level. This paper is focused on sorting out some Aspect-based sentiment analysis (ABSA) works. ABSA is one of the primary tasks in sentiment analysis, which involves identifying the expression of an opinion or sensation of an object in a particular dimension or feature. ABSA involves two primary subtasks: (1) aspect extraction and (2) aspect-based sentiment classification. In contrast to document-and sentence-based sentiment analysis, ABSA simultaneously considers sentiment and target information. Generally, the ABSA methods can be categorized into Transformer based ABSA and Graph Neural Network based ABSA. This paper introduces some popular frameworks, datasets, and evaluation metrics of ABSA tasks based on the above-proposed classification and methodology.
One vs. One Offline Signature Verification: A Forensic Handwriting Examiners Perspective

Verifying the authorship of a questioned signature is a common task for forensic handwriting examiners. While the automatic systems are typically orientated to improve performance, their practical utility for forensics is not always guaranteed. In this paper, we propose an offline automatic signature verifier oriented to forensic handwriting examiners. Our design is based on likelihood ratios which translate the signature verification results into objective and understandable evidence for a jury in a courtroom. The likelihood ratios depend on a universal background model build with signatures from other users and distance measures between signature handcrafted features. These features can be more interpretable for forensics, even though others can be included in our verifier, like deep learning ones. In our experiments, a single signature was used as reference. Two universal background models have been developed - the first is based on the GPDS database, and the second on synthetic signatures. The scheme is tried and tested with signatures from MCYT75 and BiosecureID databases with promising results. The outcome of this work is an offline signature verifier for forensic handwriting examiner practice.
Feature-Induced Label Distribution for Learning with Noisy Labels

It is challenging to train deep neural networks robustly with noisy labels, since the deep neural networks can totally over-fit on these noisy labels. In this paper, motivated by label distribution learning, we propose a novel method named Feature-Induced Label Distribution (FILD) to deal with noisy labels. Specifically, FILD recovers label distributions by leveraging the topological structure information of feature space, where the feature representation adjusts alternately by fitting the predictive model on the recovered label distributions. Extensive experiments on CIFAR-10, CIFAR-100, and Clothing1M clearly validate the effectiveness of FILD against other compared approaches.
Visual Cryptography Techniques: Short Survey

Visual cryptography (VC) is a mechanism to addresses problems regarding security of images. Visual cryptography splits up a picture into several shares through the encrypting phase. The expression “visual” within visual cryptography comes from verity that the recipient can understand the recovered secret image in his/her vision system during the decryption phase without computer interference. In this survey, we conducted a literature survey on current works from 2012 to 2020 that used various visual cryptography techniques. Provide readers with a schematic of visual cryptography methods is the purpose of this survey. The research summarized by analyzing the concerning methods, which range from the above-mentioned period of time according to specific characteristics and the different techniques proposed and developed from that period. This paper also includes applications focused on the principle about visual cryptography (VC).
Object Detection Method Based On Improved SSD Algorithm For Smart Grid

SSD (Single Shot Multi-Box Detector) is an object detection algorithm based on deep learning. As one of the most mainstream detection algorithms, it can greatly improve the detection speed and ensure the detection accuracy. In this paper, in order to improve the feature extraction capability of the network and the ability of the algorithm to detect small objects, the CBAM (Convolutional Block Attention Module) is introduced into the SSD network. The improved network uses DIoU (Distance-IoU) instead of IoU (Intersection over Union) in the original network. DIoU is more consistent with the mechanism of target frame regression than IoU. DIoU takes into account the distance between the target and the anchor, the overlap rate, and the scale, so that the target frame regression becomes more stable, and there will be no problems such as divergence during training like IoU and GIoU. The improved SSD algorithm was tested for comparison on the National Grid dataset. The experimental results show that the detection accuracy of the improved SSD algorithm on the test set is increased by 3.3% compared with the original SSD algorithm.
Crime Based Evaluation of GPS Network Using Machine Learning Techniques

With the increased usage of data transmission, data leakage and privacy protection are becoming increasingly critical. Data comes in a variety of forms, and the amount of protection required for each one differs. With the rapid development and demand of Global Positioning System (GPS) networks in every of big data technology, the vulnerability of the transportation system to unintentional and intentional GPS disruption is also increasing. The GPS signal is degraded and lost as a result of opposing interests' attacks. Potential assaults vary from GPS signal jamming and spoofing to GPS ground stations and satellite disruption. Security issues with respect to data increases during data transmission. Data travelling across an organization's network is frequently sensitive and internet being the storehouse of every kind of data with methods for accessing the precise information serves as an auspicious medium for the hackers [19]. Network security solutions must deal with complicated data in this setting in order to detect and minimize any threats in real time. There are several techniques to the problem of data security such as cryptographic algorithms, classification-based models, and block-chain methods [1]–[6]. Through this research, crime rates are evaluated in a GPS network data with different other parameters using machine learning (ML) algorithms. Moreover, the inferred features effecting the crimes during data transmission are extracted. Some of the techniques used includes techniques such as ‘Decision Tree (DT)’, ‘K Nearest Neighbors (KNN)’, ‘Random Forest (RF)’, ‘Multivariable Regression (MR)’ and ‘Naïve Bayes (NB)’. It was seen that the best accuracy was given by RF - of approximately 84.25%.
Projective Fisher Information for Natural Gradient Descent

Improvements in neural network optimization algorithms have enabled shorter training times and the ability to reach state-of-the-art performance on various machine learning tasks. Fisher information based natural gradient descent is one such second-order method that improves the convergence speed and the final performance metric achieved for many machine learning algorithms. Fisher information matrices are also helpful to analyze the properties and expected behavior of neural networks. However, natural gradient descent is a high complexity method due to the need to maintain and invert covariance matrices. This is especially the case with modern deep neural networks, which have a very high number of parameters, and for which the problem often becomes computationally unfeasible. We suggest using the Fisher information for analysis of parameter space of fully connected and convolutional neural networks without calculating the matrix itself. We also propose a lower complexity natural gradient descent algorithm based on the projection of Kronecker factors of Fisher information combined with recursive calculation of inverses, which is computationally less complex and more stable. We finally share analysis and results showing that all these optimizations do not impact the accuracy while considerably lowering the optimization process’s complexity. These improvements should enable applying natural gradient descent methods for optimization to neural networks with a larger number of parameters, than possible previously.
Short-term Load Forecasting Method Based on Artificial Intelligence Highway Neural Network

Comprehensively considering power load and real-time weather influence factors, this paper proposes a short-term load forecasting method based on artificial intelligence highway neural network (HNN). Firstly, through the Pearson correlation analysis, the weather influencing factors that affect the most can be obtained, which are considered as part of the input variable of the model. Then, the principle component analysis (PCA) method is used to reduce the dimension of the daily feature matrix. And the matrix is used as the input of K-means clustering to divide the operating days into three categories. Next and most importantly, the HNN models corresponding to each type of operating days are established separately. Finally, through comprehensive case studies, it is proved that the HNN load forecasting model proposed in this paper can achieve better forecasting results than support vector machine (SVM), long short-term memory (LSTM) and non-clustering HNN method.
Gesture Action Mapping for Deformation-based One-handed Landscape Mode Interaction with Flexible Smartphone-sized Devices

This paper presents a user study investigating the effect of directional cues and ease of performing deformation gestures on gesture action mappings. We selected four gestures of one-handed landscape mode interaction and twelve actions associated with different spatial directions for the mapping task. We evaluated thirty participants’ preferences and appropriateness scores for mappings and ease of performing the gestures. We found that the Top-right-corner Bend-upward gesture is the easiest to perform and it constitutes the significantly most appropriate gesture action mappings that are majority preferred. We also found that directional cue, size of deformation, and the required effort in performing the gesture play important roles in user-preferred and appropriate mapping along with ease of performing the gestures. We also noticed that bend gestures at the corners and sides allow users to perceive multiple distinct directional cues, and they are suitable for mapping with multiple actions associated with different spatial directions.
Reliable metrics‐based linear regression model for multilevel privacy measurement of face instances

Social networking sites have made photo sharing convenient and, consequently, users of those sites frequently share photos. The proliferation of these social images throughout the Internet has inadvertently exposed an increasing number of personally identifiable information, particularly from the visual information of the faces in the images. Most existing methods to de-identify faces results in an excessive loss of visual information. To solve this problem, this paper proposes a reliable metrics-based linear regression model for multilevel privacy measurement of face instances using the size information of face instances. The proposed privacy measurement model provides a novel instance-level-based solution to measure privacy levels. The paper also establishes a scientific relationship between the size information of face instances and their privacy levels, quantifying the degree to which face instances need to be de-identified. Finally, the paper proposes a novel k-Same-DT de-identification method to provide reliable metrics for a linear regression model. It is a real-time k-same-related de-identification algorithm that combines the PCA dimensionality reduction strategy and the Delaunay triangle-based face alignment algorithm.The proposed k-Same-DT method creates a de-identified face with a lower identifiable rate and higher structural similarity, and it can provide reliable de-identification metrics for privacy measurement. Experiments using the classical face dataset demonstrates the effectiveness of the proposed de-identification method. Extensive experiments and surveys on real-world social images were also conducted to verify the proposed measurement model.
Intelligent Path Planning with Improved BFS (I-BFS) Strategy for Mobile Robot in RFID Equipped Unknown Environment

This paper brings an efficient low complexity path-finding algorithm for autonomous mobile robot in a grid-based environment. Research is carried out by using a hybrid approach which combines the greedy strategy with proposed improving BFS (I-BFS) algorithm. A robot adopts localization strategy based on the environment in which it is employed. The radio frequency identification (RFID)-based localization is quite challenging and is adopted by various authors for localizing robots in indoor environment. The proposed navigation algorithm utilizes RFID technique for positioning the mobile robot which is a substitute for existing vision-based approaches. The added features like both obstacle detection and avoidance are the most promising factor of the proposed methodology. The methodology generated an optimal shortest path in an unknown grid-structured environment through repeated exploration. In this paper, both computationally effective and cost-efficient solution is made for an alternative to several mobile robot navigation algorithms. The navigation algorithm presented here can be used for autonomous vehicular robot employed in different environments like buildings, hospitals, shopping malls, etc., for effective path planning. The simulation result establishes the efficiency of the proposed navigation algorithm.