Reviewerly/exHarmony huggingface dataset:
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks

Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.
Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Machine learning models are biased toward data seen during the training steps. The models will tend to give good results in classes where there are many examples and poor results in those with few examples. This problem generally occurs when the classes to predict are imbalanced and this is frequent in educational data where for example, there are skills that are very difficult or very easy to master. There will be less data on students that correctly answered questions related to difficult skills and who incorrectly answered those related to skills easy to master. In this paper, we tackled this problem by proposing a hybrid architecture combining Deep Neural Network architectures- especially Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)-with expert knowledge for user modeling. The proposed solution uses attention mechanism to infuse expert knowledge into the Deep Neural Network. It has been tested in two contexts: knowledge tracing in an intelligent tutoring system (ITS) called Logic-Muse and prediction of socio-moral reasoning in a serious game called MorALERT. The proposed solution is compared to state-of-the-art machine learning solutions and experiments show that the resulting model can accurately predict the current student's knowledge state (in Logic-Muse) and thus enable an accurate personalization of the learning process. Other experiments show that the model can also be used to predict the level of socio-moral reasoning skills (in MorALERT). Our findings suggest the need for hybrid neural networks that integrate prior expert knowledge (especially when it is necessary to compensate for the strong dependency-of deep learning methods-on data size or the possible unbalanced datasets). Many domains can benefit from such an approach to building models that allow generalization even when there are small training data.
Enhancing cross-reality applications and user experiences

This paper summarizes the contributions to the AVI 2022 workshop on "Enhancing Cross-reality Applications and User Experiences".
Origin of the Bleaching in Lower Cretaceous Continental Red Beds in the Uragen Zn–Pb Deposit, Xinjiang, NW China, and Its Implications for Zn–Pb Mineralization

The Uragen giant sandstone-hosted Zn–Pb deposit has a proven reserve of 5.90 Mt metals in the southern ore zone and potentially 10 Mt metals for the whole deposit, and orebodies are strictly confined to the bleached clastic rocks of the Lower Cretaceous red beds. The bleaching has been used to guide lead–zinc exploration; however, its nature and origin, as well as the relationship with Zn–Pb mineralization, remains unclear, although it is closely related to regional oil–gas infillings. Detailed field investigation and petrographic observation, TESCAN-integrated mineral analyzer (TIMA), and X-ray fluorescence (μ-XRF) analysis of the red and bleached sandstone at the same sedimentary layer demonstrate that the bleaching is mainly caused by the reductive dissolution of hematite pigment, which probably resulted from the interaction with H2S formed by in situ sulfate reduction during hydrocarbon migration. The calcite cements in the bleached sandstones show δ13C and δ18O values of −5.36~−5.94‰ and 20.94~27.91‰, respectively, and these samples fall close to the evolution line of decarboxylation of organic matter in δ13C-δ18O diagram, also suggesting a genetic relationship between the bleaching and hydrocarbon-bearing fluids. Petrol–mineral composition changes and sulfide characteristics of red, bleached, mineralized zones, as well as pyrite locally replaced by coarse-grained galena in the mineralized zone, imply that the bleaching may occurred before Zn–Pb mineralization. Mass balance calculation and μ-XRF analysis indicate that large amounts of Fe and minor Zn were extracted from red beds with little or no sulfates; however, the red beds with abundant sulfates may be a sink for leached ore metals during the bleaching process. We therefore propose that the former accumulations of iron sulfides and reduced sulfur in the bleached zones may provide an ideal chemical trap for later Zn–Pb mineralization, and the bleached zones with high ∑S contents are the favorable prospective targets of the Uragen-style sandstone-hosted Zn–Pb deposits.
Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold

In many classification scenarios, the data to be analyzed can be naturally represented as points living on the curved Riemannian manifold of symmetric positive-definite (SPD) matrices. Due to its non-Euclidean geometry, usual Euclidean learning algorithms may deliver poor performance on such data. We propose a principled reformulation of the successful Euclidean generalized learning vector quantization (GLVQ) methodology to deal with such data, accounting for the nonlinear Riemannian geometry of the manifold through log-Euclidean metric (LEM). We first generalize GLVQ to the manifold of SPD matrices by exploiting the LEM-induced geodesic distance (GLVQ-LEM). We then extend GLVQ-LEM with metric learning. In particular, we study both 1) a more straightforward implementation of the metric learning idea by adapting metric in the space of vectorized log-transformed SPD matrices and 2) the full formulation of metric learning without matrix vectorization, thus preserving the second-order tensor structure. To obtain the distance metric in the full LEM learning (LEML) approaches, two algorithms are proposed. One method is to restrict the distance metric to be full rank, treating the distance metric tensor as an SPD matrix, and readily use the LEM framework (GLVQ-LEML-LEM). The other method is to cast no such restriction, treating the distance metric tensor as a fixed rank positive semidefinite matrix living on a quotient manifold with total space equipped with flat geometry (GLVQ-LEML-FM). Experiments on multiple datasets of different natures demonstrate the good performance of the proposed methods.
Data Profiling and Machine Learning to Identify Influencers from Social Media Platforms

Because of the numerous applications domains in which social media networks can be used, the huge volume of data and information uploaded by them is gaining significant interest. Publishing allows consumers to express their thoughts on products and services. Some feedbacks could also influence other users on those things. Therefore, extracting and identifying influencers from social media networks, also profiling their product perceptions and preferences, is critical for marketers to use efficient viral marketing and recommendation strategies. Our major goal in this research is to find the best machine learning model for characterizing influencers on social media networks. However, to achieve this objective, our strategy revolves around applying the PageRank algorithm to profile influential nodes throughout the social media network graph. The results of our experiment showed that the correlation is always different when adding a new parameter to machine learning models, also to determine the suitable model for our needs. In any event, the experiment outcomes are critical and significant to profiling influencers from social media platforms.
Deep learning for component fault detection in electricity transmission lines

Abstract Component fault detection and inventory are one of the most significant bottlenecks facing the electricity transmission and distribution utility establishments especially in developing countries for delivery of efficient services to the customers and to ensure proper asset audit and management for network optimization and load forecasting. For lack of technology and data, insecurity, the complexity associated with traditional methods, untimeliness, and general human cost, electricity assets monitoring, and management have remained a big problem in many developing countries. In view of this, we explored the use of oblique UAV imagery with high spatial resolution and fine-tuned deep Convolutional Neural Networks (CNNs) for automatic faulty component inspection and inventory in an Electric power transmission network (EPTN). This study investigated the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. Our proposed neural network model is a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults through a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision (mAP) of 89.61%. All the developed SSD-based models achieve a high precision rate and low recall rate in detecting faulty components, thus achieving acceptable balance levels of F1-score and representation. We have established in this paper that combined use of UAV imagery and computer vision presents a low-cost method for easy and timely electricity asset inventory, especially in developing countries. This study also provides the guide to various considerations when adopting this technology in terms of the choice of deep learning architecture, adequate training samples over multiple fault characteristics, effects of data augmentation, and balancing of intra-class heterogeneity.
Anti-Jamming Strategy for Federated Learning in Internet of Medical Things: A Game Approach

Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-things (IoMT) nodes. The performance of such a learning technique can be seriously hampered by the activities of a malicious jammer robot. In this paper, we study client selection and channel allocation along with the power control problem of the uplink FL process in IoMT domain under the presence of a jammer from the perspective of long-term learning duration. We map the interaction between the FL network and the jammer in each learning iteration as a Stackelberg game, in which the jammer acts as the leader and the FL network serves as the follower. We consider the client and channel selection as well as the power control jointly as the strategy of this game. Upon formulating the game, we find the joint best response strategy for both types of players by leveraging the difference of convex (DC) programming approach and the dual decomposition technique. Beside the availability of the complete information to both the players, we also study the problem from the perspective that the FL network knows the partial information of the other player. Extensive simulations have been conducted to verify the effectiveness of the proposed algorithms in the jamming game.
Emoji-SP, the Spanish emoji database: Visual complexity, familiarity, frequency of use, clarity, and emotional valence and arousal norms for 1031 emojis

Abstract This article presents subjective norms for 1031 emojis in six dimensions: visual complexity, familiarity, frequency of use, clarity, emotional valence, and emotional arousal. This is the largest normative study conducted so far that relies on subjective ratings. Unlike the few existing normative studies, which mainly comprise face emojis, here we present a wide range of emoji categories. We also examine the correlations between the dimensions assessed. Our results show that, in terms of their affective properties, emojis are analogous to other stimuli, such as words, showing the expected U-shaped relationship between valence and arousal. The relationship between affective properties and other dimensions (e.g., between valence and familiarity) is also similar to the relationship observed in words, in the sense that positively valenced emojis are more familiar than negative ones. These findings suggest that emojis are suitable stimuli for studying affective processing. Emoji-SP will be highly valuable for researchers of various fields interested in emojis, including computer science, communication, linguistics, and psychology. The full set of norms is available at: https://osf.io/dtfjv/ .
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection

Object detection technology has been widely used in medical field, such as detecting the images of blood cell to count the changes and distribution for assisting the diagnosis of diseases. However, detecting small objects is one of the most challenging and important problems especially in medical scenarios. Most of the objects in medical images are very small but influential. Improving the detection performance of small objects is a very meaningful topic for medical detection. Current researches mainly focus on the extraction of small object features and data augmentation for small object samples, all of these researches focus on extracting the feature space of small objects better. However, in the training process of a detection model, objects of different sizes are mixed together, which may interfere with each other and affect the performance of small object detection. In this paper, we propose a method called pixel level balancing (PLB), which takes into account the number of pixels contained in the detection box as an impact factor to characterize the size of the inspected objects, and uses this as an impact factor. The training loss of each object of different size is adjusted by a weight dynamically, so as to improve the accuracy of small object detection. Finally, through experiments, we demonstrate that the size of objects in object detection interfere with each other. So that we can improve the accuracy of small object detection through PLB operation. This method can perform well with blood cell detection in our experiments.
Enhanced efficient outlier detection and classification approach for WSNs

Wireless Sensor Networks (WSNs) are one of the main components of the Internet of things (IoT) for gathering information and monitoring the environment in a variety of applications (medical, agricultural, manufacturing, militarily, etc.). However, data collected by sensors and sent to the base station are susceptible to have outliers. These outliers can occur due to sensor nodes themselves or to the harsh environment where they are deployed. Thus, the WSNs have to detect the outliers and take actions to ensure network quality of service (in terms of reliability, latency, etc.) and to avoid further degradation of the application efficiency. In this paper, we propose an enhanced approach of our previous work to achieve better performance for outlier issues in WSNs. The enhancement tackles the clustering phase and the outlier detection phase. The classification phase remains the same as in the EODCA work by using the Inverse Distance Weighting (IDW) method. The enhancement is titled EEODCA for Enhanced Efficient Outlier Detection and Classification Algorithm. For evaluation, we conduct a comparison study between EEODCA, EODCA and another work from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets showed that the EEODCA outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy Rate (ACC), F1_score and Area Under the Curve (AUC).
An SKP-ABE Scheme for Secure and Efficient Data Sharing in Cloud Environments

Security threats such as data forgery and leakage may occur when sharing data in cloud environments. Therefore, it is important to encrypt your data and securely access it when sharing it with other users via a cloud server. Of the various security technologies, research on secure data sharing commonly employs Key Policy Attribute-Based Encryption (KP-ABE). However, existing KP-ABE schemes generally lack ciphertext search features. Furthermore, even if a KP-ABE scheme incorporates it, the number of searches required increases markedly by the number of attributes used in the search. It in turn proportionally increases the ciphertext size. In addition, the attribute authority (AA) could be attacked, which can result in the leakage of users’ decryption keys. AA is a server that manages user attributes and decryption keys when using attribute-based encryption in a cloud environment. If the AA is curious, it can cause problems with the key escrow with the attributes and decryption (secret) key information of the users it knows. In this paper, to solve all these problems, we present a new scheme called Searchable Key-Policy Attribute-Based Encryption (SKP-ABE) for secure and efficient data sharing in the cloud. This proposed SKP-ABE scheme allows fast ciphertext search and keeps the ciphertext of constant size. The key escrow problem is solved via user key generation.
Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

To solve the long-tail problem and improve the testing efficiency for autonomous navigation systems of unmanned surface vehicles (USVs), a visual image-based navigation scene complexity perception method is proposed. In this paper, we intend to accurately construct a mathematical model between navigation scene complexity and visual features from the analysis and processing of image textures. First, the typical complex elements are summarized, and the navigation scenes are divided into four levels according to whether they contain these typical elements. Second, the textural features are extracted using the gray level cogeneration matrix (GLCM) and Tamura coarseness, which are applied to construct the feature vectors of the navigation scenes. Furthermore, a novel paired bare bone particle swarm clustering (PBBPSC) method is proposed to classify the levels of complexity, and the exact value of the navigation scene complexity is calculated using the clustering result and an interval mapping method. By comparing different methods on the classical and self-collected datasets, the experimental results show that our proposed complexity perception method can not only better describe the level of complexity of navigation scenes but also obtain more accurate complexity values.
A critical review of the Online Safety Bill

The UK Parliament has tabled the Online Safety Bill to make the internet safer for users by requiring providers to regulate legal but harmful content on their platform. This paper critically assesses the draft legislation, surveying its rationale; its scope in terms of lawful and unlawful harms it intends to regulate; and the mechanisms through which it will be enforced. We argue that it requires further refinement if it is to protect free speech and innovation in the digital sphere. We propose four conclusions: further evidence is required to substantiate the necessity and proportionality of the Bill’s interventions; the Bill risks a democratic deficit by limiting the opportunity for parliamentary scrutiny; the duties of the bill may be too wide (in terms of burdening providers); and that enforcement of a Code of Practice will likely be insufficient.
Development of a solar energy forecasting system for two real solar plants based on WRF Solar with aerosol input and a solar plant model

Regional meteorological models are becoming a generalized tool for solar energy production forecasting, due to their capacity to simulate different types of cloud formations and their interaction with solar radiation. The greater demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting atmospheric model package designed to fulfill the needs of solar energy applications (WRF-Solar), with the solaR power plant model. This study focuses on the use and validation of this coupled tool in forecasting the energy production for two real solar plants, one in Spain and another in India. A period of one year for the Spanish emplacement and nine months for the Indian site are simulated with a daily operational forecasting set-up. Aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) are considered in the calculations, a new capability in WRF-Solar. Power predictions are obtained and compared with real data from the inverters of both plants provided by the operating company. The results show that WRF-Solar obtains accurate forecasts of global, direct, and diffuse radiation and of the ambient temperature that solaR uses as input to predict the energy production of the solar plants. The normalized Mean Annual Errors (NMAE) is 5.18% in the Spanish and 5.59% in the Indian plant for the first day of predictions, demonstrating a reliable performance of the forecasting system in different climate locations. The skill scores for the second day of prediction are also promising, with practically the same errors as the previous day (5.19% and 6.17 for Spain and India respectively). By comparing the model predictions, with and without AOD input during the dustiest days in the Spanish site, the importance of the aerosol effect inclusion is demonstrated with an improvement up to 10% in the energy forecast. These results demonstrate the system’s potential both for solar plant operation and energy market applications.
Impact of Task Performance on Job Satisfaction of Information Technology Employees in Bengaluru City

This paper empirically examined the impact of task performance on job satisfaction on Information Technology employees in Bengaluru City.It notes that Employee job satisfaction is vital for improved organizational performances.The paper tells that an employee with a high level of job satisfaction holds positive attitudes towards his job.By distributing well-structured questionnaires to 200 employees chosen at random, this descriptive research assesses and understand the depth of task performance and job satisfaction that is predominant among employees in the Software Industry, as well as investigate the impact of task performance on job satisfaction.Numerous statistical techniques such as the Mean, Independent samplest-test, ANOVA, and Regression Analysis was utilized to examine the acquired data using inferential statistics.When IT personnel incorporate task performance in the course of their work, the study found that they are quite happy with their jobs.Employees with less education, lower income levels, and those in lower job cadres report lower levels of job satisfaction, whereas those with less work experience, age,education,income,jobcadre,andmaleemployeesreportlowerlevelsoftaskperformance.TaskPerformancehasasubstant ialbeneficialinfluenceonjobsatisfaction,accordingto the findings of the study.