Reviewerly/exHarmony huggingface dataset:
Hybrid LSTM and GRU for Cryptocurrency Price Forecasting Based on Social Network Sentiment Analysis Using FinBERT

Cryptocurrencies are digital assets that are widely used for trading and investing. One of the characteristics that traders take advantage of for profit is the high volatility of the price. Its volatile and rapidly changing prices have made cryptocurrency price predictions a challenging and highly sought-after research topic. Cryptocurrency price predictions usually only use historical prices on the dataset, while price movements are also influenced by other aspects such as sentiment contained in social media. This study proposes a new machine learning method to predict Ethereum and Solana cryptocurrency price, which integrates cryptocurrency historical price data and social media sentiment as inputs of the prediction model. FinBERT, a pre-trained sentiment analysis model is used to extract the sentiment implied in social network tweets into daily sentiment score, which are then combined with the historical market price data. The hybrid model of LSTM-GRU model is used to train the dataset and perform cryptocurrency price prediction. The experiment results show that the presented method can successfully predict the Ethereum and Solana price movement and has superior performance than all the benchmark models.
Efficient Attention-Convolution feature extractor in semantic segmentation for autonomous driving systems

Deep learning has been widely used in computer vision applications and it has been shown to achieve state-of-the-art results in many applications including self-driving cars. Despite the great progress, less attention has been paid to the safety-level importance of different classes and the majority of the models treat all classes similarly, and only average precision is considered. However, different classes contribute differently to the reliability and safety level of an autonomous driving system i.e. the Person class should be of higher priority than the Sky class in terms of segmentation accuracy. So, in this work, we introduced a new Attention-Convolution Block (ACB) feature extractor with modified self-attention, which can extract detailed and long-range information from the input feature maps and feed the entire network with more focused feature maps. Based on this feature extractor, we developed two models for semantic segmentation that have a balanced trade-off between complexity and accuracy and can accurately distinguish important classes, like the Person class. To demonstrate the performance of our models, we run our experiments on Cityscapes datasets, and used both quantitative (mean and per-class IoU score) and qualitative (visual representation of output segmentation maps) measures and compared the results of our model with the state-of-the-art methods. The results show that our proposed model improve Per-class IoU scores for Person and Bike classes at least by 7 percent. In addition, we compared the accuracy of different models against their complexity to show despite simple structure and low parameter number, our proposed model has high IoU accuracy.
Automatic Diagnosis of Medical Conditions Using Deep Learning With Symptom2Vec

In this paper, a medical examination algorithm is proposed that can collect users’ symptoms and automatically issue a diagnosis. The proposed algorithm makes use of “Symptom2Vec” and the “analysis model of responses on self-diagnosis questions” (AMoRSD) for real-time interviews with users. Symptom2Vec can learn about the relationship between terms related to the symptoms and disease, and establish questioning criteria to be used in patient health checkups, as well as general appropriate follow-up questions based on patient symptomology. AMoRSD analyzes the patient’s emotional expressions and responses to self-diagnostic questions, classifying them into "Sick," "Not Sick," and "Neutral" categories based on patterns. Compared to traditional models, Symptom2Vec earned the highest mean symptom similarity score of 0.983. Furthermore, compared to other models that only learn from patient responses, AMoRSD demonstrates an area under curves (AUC) of 0.99%, indicating that jointly learning the relationship between emotions and patient responses improves the accuracy of user response classification. The combined algorithm of Symptom2Vec and AMoRSD enhances the efficiency and accuracy of user symptom collection and appropriate diagnosis generation. The data were collected from reliable medical sources such as WebMD Dictionary, NHS inform, Snomed Ct, and Cleveland Clinic, encompassing 526 disease names and 2078 symptoms. Additional data were obtained for AMoRSD, focusing on conversations within a hospital context, and effectively trained and evaluated the model using diverse and representative datasets. This research addresses the importance of medical history-taking and contributes to the field by providing a robust framework for real-time symptom-based diagnosis in clinical environments.
Deep Semantic Feature Reduction for Efficient Remote Sensing Image Retrieval

Content-Based Remote Sensing Image Retrieval (CBRSIR) is used to find relevant images from large collections of remote sensing images. CBRSIR works by indexing each image in the database with a feature vector. Deep semantic features generated using convolutional neural networks (CNNs) are more powerful than low-level features for CBRSIR tasks because they can comprehend the context and content within an image. However, the major problem with the deep features is its large vector size which in turn can impact the performance of the retrieval system and are more susceptible to noise and outlier data. Therefore, in this work, a modified ResNet50 architecture is proposed that serves as a powerful feature extractor, benefiting from its deep learning capabilities. Specific modifications are introduced to enhance its discriminative power and generalization ability, enabling it to extract more robust deep features for image indexing. The proposed method achieves a mean average precision (mAP) of 0.899 surpassing the popular competing methods ResNet50 and GoogleNet by a substantial margin of 22.02%, 26.79% respectively. Moreover, to address the curse of dimensionality, this study also proposes a novel approach that combines a modified ResNet50 architecture with Linear Discriminant Analysis (LDA) and Maximum Relevance and Minimum Redundancy (MRMR) technique. The proposed approach achieves 85.45% reduction in size of the feature vector using MRMR and 98.19% using LDA, thereby improving retrieval efficiency without impacting the performance.
The Evolution of Public Opinion and Its Emotion Analysis in Public Health Emergency Based on Weibo Data

Since the occurrence of the Corona Virus Disease 2019, relevant online public opinion has spread rapidly, which has had an important impact on social order. How to identify, prevent and control public opinion crisis of public health emergencies has become a practical problem that urgently needs to be studied. First data source comes from Weibo comments, comparing with the three models of Naive Bayesian Model, Support Vector Machine and Logistic Regression, Long Short Term Memory (LSTM) model based on word to vector (Word2vec) model is selected for emotion classification. Secondly, the evolution of public opinion is divided into three stages base to the Baidu search index, use visualization methods to study emotional tendencies at various stages and analyze the temporal and spatial laws of public opinion. At last, according to the evolution law and characteristics of public opinion in each stage, relevant optimization strategies are proposed. Research shows, the Word2Vec-LSTM model can effectively predict the emotional state of netizens; analyzes the law of public opinion evolution of public health emergencies, provide a basis for optimizing the network environment and preventing public opinion crisis.
Explicitly Semantic Guidance for Face Sketch Attribute Recognition With Imbalanced Data

Current facial attribute recognition (FAR) methods focus exclusively on photographs, and fail when applied to face sketches. Besides, face sketch attribute recognition (FSAR) encounters the following difficulties: the scarcity of labelled instances, the heavily imbalanced data distribution, and the inter-attribute correlations. To combat this challenge, in this paper, we propose a novel FSAR method based on the correlations between facial attributes and semantic regions. Our full model includes a shared feature extraction network, followed by several attribute-specific prediction branches. In each branch, we use the corresponding semantic mask, to select features from the associated region, for attribute prediction. Such explicitly semantic guidance (ESG) reduces the learning space, and thus alleviates the problems of limited data and imbalanced distribution. Besides, ESG decouples inter-attribute correlations, and makes the recognition process credible. Finally, we adopt the balanced cross-entropy loss during training, which further alleviates the problem of imbalanced data distribution. Experiments on the benchmark FS2K dataset demonstrate that our method significantly outperforms advanced visual recognition networks. Our codes have been released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AiArt-HDU/ESGAR</uri> .
A Pairwise DomMix Attentive Adversarial Network for Unsupervised Domain Adaptive Object Detection

Unsupervised Domain Adaptive Object Detection (DAOD) could adapt a model trained on a source domain to an unlabeled target domain for object detection. Existing unsupervised DAOD methods usually perform feature alignments from the target to the source. Unidirectional domain transfer would omit information about the target samples and result in suboptimal adaptation when there are large domain shifts. Therefore, we propose a pairwise attentive adversarial network with a Domain Mixup (DomMix) module to mitigate the aforementioned challenges. Specifically, a deep-level mixup is employed to construct an intermediate domain that allows features from both domains to share their differences. Then a pairwise attentive adversarial network is applied with attentive encoding on both image-level and instance-level features at different scales and optimizes domain alignment by adversarial learning. This allows the network to focus on regions with disparate contextual information and learn their similarities between different domains. Extensive experiments are conducted on several benchmark datasets, demonstrating the superiority of our proposed method.
Trust-Aware Scheduling for Edge Computing With Task Dependencies and Unreliable Servers

Volunteer Edge Computing (VEC) is a promising solution for addressing the challenge of high round-trip latency in traditional cloud computing systems. Leveraging distributed computing resources reduces latency and improves performance. However, resource management in VEC is challenging, first, due to the uncertain behavior of volunteers, which frequently go offline unexpectedly, and second, since sequences of tasks can be executed on different volunteers, which requires transmitting data from one to another volunteer, which can lead to processing interruptions and network overhead. To address these challenges, we propose a trust-aware scheduling procedure that consists of two stages. First, we train a regression model based on lagged data suitable to accurately predict volunteer availability. Second, we assign tasks to volunteers using a metric based on the predicted availability from the first stage. The metric assesses the likelihood that a candidate volunteer can successfully complete a task and the likelihood that nearby nodes are available for successor tasks or as replacements if the processing is not completed. Thereby, we increase the chances of assigning tasks with dependencies to nearby resources, thus reducing long-distance communication and hence latency. We evaluate our approach in a discrete-event simulation using real data from Telecom’s base stations. The results demonstrate an average reduction of 43.44% in the number of failed dependent tasks, an increase of 5% in completed tasks, a reduction of 85.68% in failed tasks, a reduction of 47.32%. in delay duration, and a reduction of 3.06% in average execution task, compared to the existing alternative algorithm.
A Framework for Modelling Enterprise Technological Innovation Knowledge System Using Semantic Ontology Representation

With the rapid development of the semantic web, ontology engineering has become an important research field of the semantic web application. The domain ontology can realize the knowledge organization and representation. Presently, the research of domain ontology is mainly concentrated in the fields of medicine, geography, agriculture and biology. In terms of enterprise technological innovation ontology establishment and knowledge acquisition, the related work has not been carried out. Since there is no mature domain ontology in the field of enterprise technological innovation, this paper uses scientometric analysis to locate relevant literature and extracts key terms to form the basic domain terms set. A domain ontology related to construction aspects of enterprises technological innovation was established based on the basic term set and combined with the Seven-step ontology methodology, which provides support for semantic knowledge mining and acquisition.
Information System Function-Data Architecture Planning Based on Subspace Partition

Under the background of big data, the development of enterprise business and the growth of data lead to the characteristics of enterprise information system with complex business functions, large amount of data, and sparse connection between functions and data. Therefore, dividing the information system structure based on the link between functions and data can effectively improve the construction and operation efficiency of enterprise information systems and facilitate system maintenance and management. However, the traditional information system planning method only focuses on the planning of the functions, and cannot plan a reasonable data structure at the same time. In order to solve the above problems, this study proposes A Information System Function-Data Architecture Planning Method that can simultaneously complete the information system function-data system planning. It takes the Function-Data Subspace Partition Algorithm as the core, and realizes the information system structure partition from the perspective of function and data at the same time.
Articulating the power of reasoning and gathering data for information security and justice

With the accumulation of big data, dramatic improvements in computing power, and continuous innovation in Machine Learning (ML) methods, Artificial Intelligence (AI) technologies such as image recognition, voice recognition, and natural language processing have become ubiquitous. Meanwhile, AI poses a significant impact on computer security: on the one hand, AI can be used to build defensive systems such as malware and network attack detection; on the other hand, AI might be exploited to launch more effective attacks. In some scenarios, the security of AI systems is a matter of life and death. Thus, building robust AI systems that are immune to external interference is essential. AI can benefit security and vice versa. Unlike security vulnerabilities in traditional systems, the root cause of security weaknesses in ML systems lies in the lack of explicability in AI systems. This lack of explicability leaves openings that can be exploited by adversarial machine learning methods such as evasion, poisoning, and backdoor attacks. These attacks are very effective and have strong transferability among different ML models, and thus pose serious security threats to Deep Neural Network (DNN)-based AI applications. There is a long way to go before the industry achieves secure and robust AI systems. On the technology side, we need to continuously research AI explicability to understand how to implement a sound AI foundation and build systematic defense mechanisms for secure AI platforms.
Self-assessment of professional competencies by students for tutor support of students with special educational needs in the conditions of inclusive educationter

The task of psychological and pedagogical support for students with special educational needs, especially those with health limitations that require the creation of special conditions, in many countries, including Russia, is solved, inter alia, through tutor support or other similar forms of individual support for such students (in foreign practice - teaching assistants, auxiliary teachers, etcA). Separate problem is the training of specialists who can effectively achieve these results and goals, which requires the formation of a set of professional competencies, the framework of which in Russia is set by the professional standard “Specialist in the field of education.” Based on this, the article aims to study the features of professional competencies of university and colleges students of various profiles of training based on scales measuring subjective readiness and ability for tutoring activities. The obtained quantitative results, as well as a qualitative analysis of the responses, showed that the training of university students in the specialty of “Teacher-Psychologist” is largely aimed at developing their professional competencies and job functions related to the development of methods and technologies of psychological and pedagogical support for children with disabilities, coordinated to a greater extent with the implementation of an individual approach to students, where the accompanied child himself is the object of accompaniment, and not its active participant. At the same time, the training in the social field of students of secondary vocational education (“Social work”) and of university students (“Psychologist in the social sphere”) is largely aimed at supporting individualization, which presupposes the subjectivity of students with disabilities when they actively participate in the educational process. The data obtained will serve to solve the problem of creating more balanced and comprehensive training programs for specialists in tutor support for persons with special educational needs.
The Design, Development and Pilot Study of a Marine Ecological Simulation for Education of Environmental Changes on Marine Life

A “serious game” simulating examples of marine life found at the artificial reef ex-HMS Scylla has been developed to demonstrate the impact of environmental changes. The game has been adapted from prior research into agent-based models of marine ecologies. The design of the game considers the abstraction and presentation of marine life, and the effect of environmental changes. Two simulations are performed: one exposing the ecology to the effects of climate change, and another which simulates the introduction of microplastics. A pilot study measured knowledge-recall for simulations with open and closed-ended questions, user awareness of environmental issues and future game design preferences. This research contributes by both demonstrating the conversion of a complex marine simulation to a game format, and also emphasises the potential of the simulation journey to learning and engagement.
The Influence of Personalized Music on Sense of Presence, Motivation, and Player Experience in Virtual Reality

The sense of presence, characterized by the feeling of “being there,” plays a crucial role in virtual reality (VR) to enhance involvement and engagement. While previous studies have examined the effects of sound or music on presence by manipulating auditory cues, little research has explored the effects of personalized music within virtual environments. Therefore, in this paper, we investigate the influence of personalized music on the sense of presence, motivation, player performance, time perception, and cybersickness. In a within-subjects design, 21 participants engaged in a VR strategy game under three randomized conditions: hearing (1) sound effects only without background music, (2) the original game music, and (3) personalized music, individually chosen by each participant. The results reveal that personalized music significantly contributes to a higher sense of presence compared to the condition with sound effects only, however with no significant difference between both music conditions. The results further show that participants are significantly more motivated when exposed to personalized music compared to the original game music. These findings provide valuable insights into how personalized music, synchronized with the gameplay elements, can enhance the sense of presence, motivation, and overall player experience within VR environments. Further investigations are necessary to examine the specific effects of personalized music within different VR games.
Artificial Intelligence and EU Design Law

This Chapter inspects the possibilities for AI-generated designs to receive protection under EU design law, paying special attention to digital designs. The Chapter notes that the concept of ‘designer’ does not necessarily prevent considering AI-generated designs as protected subject matter. However, it is the act of ‘design development’ that might be the obstacle for protecting AI-generated designs. Although the concept of ‘design development’ seems to be essential in EU design law, the meaning of this concept is unclear and scarcely researched. This Chapter brings clarity to this issue and investigates whether acts committed by or with the help of AI programs can be considered as ‘design development’ that results in protection. This Chapter concludes that protecting AI-generated designs might be in line with the aims and objectives of EU design law, if they fulfil the requirements of novelty and individual character.
Derivatives Sensitivities Computation under Heston Model on GPU

This work investigates the computation of option Greeks for European and Asian options under the Heston stochastic volatility model on GPU. We first implemented the exact simulation method proposed by Broadie and Kaya and used it as a baseline for precision and speed. We then proposed a novel method for computing Greeks using the Milstein discretisation method on GPU. Our results show that the proposed method provides a speed-up up to 200x compared to the exact simulation implementation and that it can be used for both European and Asian options. However, the accuracy of the GPU method for estimating Rho is inferior to the CPU method. Overall, our study demonstrates the potential of GPU for computing derivatives sensitivies with numerical methods.