Reviewerly/exHarmony huggingface dataset:
Real-Time Mobile Robot Perception Based on Deep Learning Detection Model

The recent advances in deep learning models have enabled the robotics community to utilize their potential. The mobile robot domain on which deep learning has the most influence is scene understanding. Scene understanding enables mobile robots to exist and execute their tasks through processes such as object detection, semantic segmentation, or instance segmentation. A perception system that can recognize and locate objects in the scene is of the highest importance for achieving autonomous behavior of robotic systems. Having that in mind, we develop the mobile robot perception system based on deep learning. More precisely, we utilize an accurate and fast Convolution Neural Network (CNN) model to enable a mobile robot to detect objects in its scene in a real-time manner. The integration of two CNN models (SSD and MobileNet) is performed and implemented on mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental results show that the proposed perception system enables a high degree of object recognition with satisfying inference speed, even with limited processing power provided by Nvidia Jetson Nano integrated within RACIO.
Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods

Decision Forests have attracted the academic community’s interest mainly due to their simplicity and transparency. This paper proposes two novel decision forest building techniques, called Maximal Information Coefficient Forest (MICF) and Pearson’s Correlation Coefficient Forest (PCCF). The proposed new algorithms use Pearson’s Correlation Coefficient (PCC) and Maximal Information Coefficient (MIC) as extra measures of the classification capacity score of each feature. Using those approaches, we improve the picking of the most convenient feature at each splitting node, the feature with the greatest Gain Ratio. We conduct experiments on 12 datasets that are available in the publicly accessible UCI machine learning repository. Our experimental results indicate that the proposed methods have the best average ensemble accuracy rank of 1.3 (for MICF) and 3.0 (for PCCF), compared to their closest competitor, Random Forest (RF), which has an average rank of 4.3. Additionally, the results from Friedman and Bonferroni-Dunn tests indicate statistically significant improvement.
Multi-criteria Decision Theory-Based Cyber Foraging Peer Selection for Content Streaming

COVID-19 has made it necessary for educational institutes to make their materials available online. Having access to these vast amounts of knowledge and learning materials can benefit students outside of these institutes greatly. With that in mind, this paper proposes a cyber foraging system. The proposed system is a peer-to-peer streaming system for educational institute content streaming that selects the best peers based on eight decision criteria. Judgments from experts are used as data to assign relative weights to these criteria using the Fuzzy Analytical Hierarchy Process method. Finally, the criteria are ranked based on the assigned relative weights to find out their importance in the peer selection decision making process.
Choosing the Right Technique for the Right Restriction – A Domain-Specific Approach for Enforcing Search-Space Restrictions in Evolutionary Algorithms

Evolutionary algorithms are a well-known tool for optimising problems that are hard to solve analytically. They mirror the evolutionary approach of recombination and mutation as well as a selection process according to the fitness of an individual. Individuals who violate set search space restrictions are either killed at birth or penalised in their fitness calculation. Which possibility is best to choose depends on the problem at hand and therefore subject to change. Furthermore, restrictions can be vague, for example, when stemming from experiments. We propose a noise-sensitive penalty for violating restrictions and develop a framework where an expert might choose which penalising technique to choose for what kind of restriction. We evaluate our configurable approach against configurations where one technique is used for every type of restriction and find that our approach achieves better results than a strict configuration. Additionally, the noise-sensitive penalising method allows individuals to survive, which may only violate the given restrictions due to a noised testing environment, leading to better results.
Phishing short URL detection based on link jumping on social networks

Nowadays, a large number of people frequently use social networks. Social networks have become important platforms for people to publish and obtain information. However, social networks have also become the main venue for hackers to initiate online fraud. Phishing is a common way used by hackers to launch online fraud on social networks. This paper proposes a method for detecting phishing short URL based on the link jumping. The method uses a hierarchical hidden Markov model with two-layer structure to describe the link jumping process after user clicking on short URL, so as to identify phishing short URL on social networks. The proposed method includes a training phase and an identification phase. In the identification phase, the average log-likelihood probability of the observation sequence is calculated. An experiment based on real datasets of Weibo is conducted to evaluate this method. The experiment results validate the effectiveness of this method.
Deep Segmentation Network Without Mask Image Supervision for 2D Image Registration

Two-dimensional (2D) image registration is a conventional technique for simultaneously performing object recognition and pose estimation tasks. Deep neural-based 2D image registration techniques recently emerged and achieved high performance in both tasks. However, these 2D image registration techniques are not designed to perform the segmentation task, which is one of the significant image processing techniques. Here, we consider introducing a deep segmentation network module into the framework of the 2D image registration. Especially, we consider training the segmentation network module with no supervision cost of mask images. To do this, we exploit the idea of the canonical plane, which is one surface observed mainly for each object in an image. We train the weakly supervised segmentation network module to perform the segmentation task of the canonical plane in the query and target images using the outputs of the 2D image registration. Experimental results show that our network can accurately perform the segmentation task without mask image supervision.
Convolutional Graph Neural Networks for Hate Speech Detection in Data-Poor Settings

Hate speech detection has received a lot of attention in recent years. However, there are still a number of challenges to monitor hateful content in social media, especially in scenarios with few data. In this paper we propose HaGNN, a convolutional graph neural network that is capable of performing an accurate text classification in a supervised way with a small amount of labeled data. Moreover, we propose Similarity Penalty, a novel loss function that considers the similarity among nodes in the graph to improve the final classification. Particularly, our goal is to overcome hate speech detection in data-poor settings. As a result we found that our model is more stable than other state-of-the-art deep learning models with few data in the considered datasets.
Non-Experts' Perceptions Regarding the Severity of Different Cyber-Attack Consequences: Implications for Designing Warning Messages and Modeling Threats

Cyber-defenders must account for users’ perceptions of attack consequence severity. However, research has yet to investigate such perceptions of a wide range of cyber-attack consequences. Thus, we had users rate the severity of 50 cyber-attack consequences. We then analyzed those ratings to a) understand perceived severity for each consequence, and b) compare perceived severity across select consequences. Further, we grouped ratings into the STRIDE threat model categories and c) analyzed whether perceived severity varied across those categories. The current study’s results suggest not all consequences are perceived to be equally severe; likewise, not all STRIDE threat model categories are perceived to be equally severe. Implications for designing warning messages and modeling threats are discussed.
Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve

Purchasing actions on e-commerce sites have become very common for general consumers in recent years. Products that were used to be bought at offline shops are purchased are also handled. Such products, like gifts or durable consumer goods, are often purchased infrequently and whose prefer items change each time they are purchased. A lot of methods are proposed for analysis purchase history data in order to improve customer satisfaction. However, most of them focus on the co-occurrence relationship between customers and products and treat products purchased by the same customer as similar. Then, it is difficult to use the conventional product analysis methods that have been proposed for purchase history data is difficult for some kinds of data mentioned before. Therefore, the authors have proposed an analysis method with extracting features of products by using the coefficients of binary classifiers that discriminates product purchases or not. In this study, we conduct experiments with artificial data in order to evaluate our method. Specifically, we verify how accurately the coefficients can be estimated and under what circumstances they can be estimated more accurately.
Mobile Robot Path Planning Based on Angle Guided Ant Colony Algorithm

Ant colony algorithm is easy to fall into local optimum and its convergent speed is slow when solving mobile robot path planning. Therefore, an ant colony algorithm based on angle guided is proposed in this paper to solve the problems. In the choice of nodes, integrate the angle factor into the heuristic information of the ant colony algorithm to guide the ants' search direction and improve the search efficiency. The pheromone differential updating is carried out for different quality paths and the pheromone chaotic disturbance updating mechanism is introduced, then the algorithm can make full use of the better path information and maintain a better global search ability. According to simulations, its global search is strong and it can range out of local optimum and it is fast convergence to the global optimum. The improved algorithm is feasible and effective.
Early Prediction of Credit Card Transaction Using Local Outlier Factor and Isolation Forest Tree Machine Learning Algorithms

A credit card is one vital thing nowadays for everyone, even in developing countries. We are using a credit card to pay bills, shop both online as well as offline. With the increase in the use of credit cards, fraud with the credit card is also increasing side by side. Credit card fraud is one of the major crimes nowadays. In this study, we proposed two machine learning models to predict the fraud transaction outbreak in a credit card accurately. Machine learning models can effectively help bankers and customers accomplish this objective because of their quick and accurate recognition efficiency. Our proposed work, we used Isolation Forest (IF) tree and Local Outlier Factor (LOF) algorithms, which used for anomaly detection for detecting fraud transactions. Isolation Forest (IF) tree algorithm randomly selecting credit card features and make a decision tree from a given dataset and finally score calculated as path length of tree to isolate outlier. Local Outlier Factor (LOF) algorithm calculates outliers by computing the local density of given data concerning its neighbors. Those two models trained and tested with the dataset contain 4092 entries of customer’s credit card details made by European cardholders. The data sample is consisting of 80% of fraudulent transactions and the remaining is authenticated transaction done by the customer. We compared our two models with all the existing models that used to identify the fraud transactions, and the prediction accuracy reaches 99%.
The Inevitability of Escalating Energy Usage for Popular Proof-of-Work Cryptocurrencies

Few financial market innovations offer the opportunities to enhance transaction efficiency, as does the potential of the blockchain and cryptocurrencies. The author demonstrates that this industry's unusual demand and supply relationship results in ever-increasing energy consumption if Proof-of-Work currencies rise in price faster than mining rewards decay. This previously undocumented challenge poses global warming risk. In addition, the accessibility and opacity of many cryptocurrencies create financial risks for inexperienced speculators. Digital currencies also reduce the ability of central banks to conduct monetary policy and challenge regulatory authorities to promulgate rules to protect the economy and consumers. The author explores these various risks, demonstrates how the design of Proof-of-Work cryptocurrency mining frustrates the efficiency they promise and describes mechanisms that would allow cryptocurrencies based on the blockchain to live up to their immense potential.
Strided DMA for Multidimensional Array Copy and Transpose

Many applications require moving subsets of multidimensional arrays across memory hierarchies of a computing system (MPI ranks, DRAM, GPU, etc.). While hardware supports efficient offload of contiguous data movement, non-contiguous data requires significantly more CPU orchestration. We test a series of multidimensional array copy and transpose microbenchmarks on two platforms: NERSC Perlmutter, and ORNL Summit, and find that for some scenarios, bandwidth is impacted up to 8-fold. We emulate a multidimensional array direct memory access (DMA) copy and transpose engine using a GPU kernel. This DMA can more effectively prefetch and write-combine non-contiguous multidimensional array data, reducing latency and improving bandwidth. We propose a reconfigurable DMA engine that supports multiple strides and discuss how it can offload multidimensional array copy and transpose. Further, this DMA engine can use the stride information to better inform policies of higher level memory hierarchies to maximize bandwidth.
Signal Sorting of Unknown Radar Emitter Based on Complexity Characteristics

Radar Emitter Signal Sampling is an important electronic reconnaissance technology Signal sampling efficiency directly affects the operational efficiency of the entire radar detection system. With the rapid development of radar technology, radar systems are becoming increasingly diverse, complex and integrated. With increasing complexity of the electromagnetic environment, new radar systems emerged. The traditional signal selection algorithm cannot meet the requirements of modern electronic warfare. The scanning accuracy, real-time accuracy and reliability of the radar emitter signal are facing major challenges. In this thesis we investigate the structure and new method of signal selectivity of unknown emitters under complex conditions in radar emitter processing. Signal.
Unsupervised New-set Domain Adaptation with Self-supervised Knowledge

PDF HTML XML Export Cite reminder Unsupervised New-set Domain Adaptation with Self-supervised Knowledge DOI: 10.21655/ijsi.1673-7288.00269 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Unsupervised Domain Adaptation (UDA) aims to use the source domain with large amounts of labeled data to help the learning of the target domain without any label information. In UDA, the source and target domains are usually assumed to have different data distributions but share the same class label space. Nevertheless, in real-world open learning scenarios, label spaces are highly likely to be different across domains. In extreme cases, the domains share no common classes, i.e., all classes in the target domain are new classes. In such a case, direct transferring the class-discriminative knowledge from the source domain may impair the performance in the target domain and lead to negative transfer. For this reason, this paper proposes unsupervised new-set domain adaptation with self-supervised knowledge (SUNDA) to transfer the sample contrastive knowledge from the source domain, and use self-supervised knowledge from the target domain to guide the knowledge transfer. Specifically, the initial features of the source and target domains are learned by self-supervised learning, and some network parameters are frozen to preserve target domain information. Sample contrastive knowledge from the source domain is then transferred to the target domain to assist the learning of class-discriminative features in the target domain. Moreover, graph-based self-supervised classification loss is adopted to handle the problem of target domain classification with no inter-domain common classes. SUNDA is evaluated on tasks of cross-domain transfer for handwritten digits without any common class and cross-race transfer for face data without any common class. The experiments show that SUNDA outperforms UDA, unsupervised clustering, and new class discovery methods in learning performance. Reference Related Cited by
A Nucleus Based Feature Extraction From Histopathology Images Using CNN For Liver Cancer

The process of evaluating a surgical tissue specimen is known as histopathology. For preservation, tissue is fixed and placed on glass slides with chemicals. Whole slide imaging WSI) is the process or scanning conventional glass slides in order to produce digital slides, WSI continues to gain attraction among pathologists for diagnostic, educational, and research purposes. A WSI contains more than 100,000 pixels in any spatial dimension. Feature extraction plays an important role in the diagnosis of cancer using computer-aided methods. Various machine learning methodologies is introduced to extract features from WSI images. These approaches ignore to extract the large scale architectural relationships present in the nucleus. Nucleus in the WSI contains enormous information. For histopathology images, a nucleus-based feature extraction utilising a Convolutional neural network is proposed in this study. Initially the nucleus is detected from images which is then used in training a neural network. This helps in extracting macro and micro image level information like the spatial distribution and pattern. The extracted features are valuated using the classification approaches on a histopathology database of liver tumour. The results show a better classification performance for liver tumour when comparing other methods.
Automatic Identification of Individual Yaks in In-the-Wild Images Using Part-Based Convolutional Networks with Self-Supervised Learning

Yaks (Bos grunniens) are the most important domestic animals for people living at high altitudes. In order to implement precise livestock management for yaks, it is of significant importance to automatically identify, keep track of, and monitor yaks. Traditional animal identification methods such as ear tags, tattoos, and RFID based methods suffer from problems like animal infection, high maintenance cost, inefficiency or sensor failure. Existing biometric-based identification methods for livestock such as muzzle prints, iris patterns, and retinal vascular patterns mostly require that animals are under control, either technically or physically, and are thus costly to deploy especially for yaks which are loosely raised on the grassland pastures and migrate with the seasons. In this paper, we propose a novel method, namely part-based convolutional network with random erasure and region-visibility prediction (PCN-RERP), for identifying individual yaks in in-the-wild images captured under unconstrained conditions. We utilize the part-based convolutional network (PCN) to obtain discriminative part-level feature representations. To further enhance the feature discriminativeness and alleviate the impact of small amount of yak image data, we implement self-supervised learning strategy by proposing random erasure and region-visibility prediction (RERP) as an auxiliary learning task. Experiments performed on the YakReID-103 dataset demonstrate that the Rank-1 accuracy and mAP achieved by the proposed PCN-RERP method are up to 97.57% and 76.30%, which significantly advance the state-of-the-art.
Analysis of the Impact of Applying UX Guidelines to Reduce Noise and Focus Attention

This research work is the continuation of previous, in which a set of usability guidelines where proposed. These guidelines were obtained from various authors and recommendations. The common part of those guides was that all of them were useful to reduce the noise and focus the attention of users in web interfaces. Some of these recommendations were evaluated in very confined environments, this research tries to evaluate the possible advantage of applying every one of these recommendations, quantifying in an experiment with real users what could be the potential time gain applying every of the recommendations. We have designed a set of web applications in which the user has to complete a simple task, some of the applications fill the usability guidelines and others not. With obtained data in the experiment, the web developers could choose in a more precise way which recommendations could be implemented in their interfaces to get a more efficient user interaction.
Earthquake Protection and Disaster Reduction Technology System Based on Optical Network

The technical system of the new science and technology park for earthquake prevention and disaster reduction of Shandong Seismological Bureau includes: comprehensive wiring of the new office building, data center computer room, data communication network, basic information platform, intelligent property, etc. The technical system of this construction is a set of complex system, which not only includes computer system and other supporting equipment, but also includes redundant data communication connection, environmental control equipment, monitoring equipment and various safety devices. In the construction of the technical system of the central station, Shandong Seismological Bureau fully investigated the functional requirements and index requirements of each technical system according to the construction idea of “systematization, intelligence, modularization and integration”. The requirements of functional and performance indicators follow the principle of “practical, advanced and forward-looking”, which not only meets the needs of operation and management of existing business systems, but also meets the needs of business development for at least 10–15 years in the future. Combined with the actual needs of the industry and future business development planning, it provides reliable technical parameters, feasible technical solutions and valuable construction experience for similar projects in the same industry, and can also play a certain exemplary role in the industry.