Reviewerly/exHarmony huggingface dataset:
Ontology-Driven Digital Profiling for Identification and Linking Evidence Across Social Media Platform

In an era in which social media platforms are proliferating and becoming primary communication channels, the identification of evidence for crimes from such platforms is crucial for digital forensics and legal proceedings. This paper presents a novel approach for systematically structuring and categorising digital attributes that are interlinked across social media platforms using digital ontologies, as well as a method for user profiling using domain-specific digital artefacts. The ontology models consist of classes with subclass distinctions for text, image, and video types of evidence. These models are flexible and can be expanded to include various social media platforms and evidence categories. Simultaneously, the user profiling method employs mathematical formulas and visual representations to develop comprehensive profiles of individuals based on extracted social media data. This methodology evaluates the relevance of a set of digital artefacts and related attributes, such as interests, location, and activities, using their weights. Additionally, the research addresses the legal and ethical considerations pertinent to the collection of data from social media. Despite the approaches’ immense potential for expediting evidence collection and developing insightful profiles, obstacles such as scalability, legal complexities, and data noise are identified. This work makes a substantial contribution to the development of digital forensics and cybercrime investigations involving social media platforms.
EwLoss: A Exponential Weighted Loss Function for Knowledge Graph Embedding Models

Knowledge graphs have been widely used in various domains such as intelligent question answering, information retrieval, transportation, medicine, e-commerce, and others, and have achieved significant success. To support these applications with a high-quality vector representation, numerous studies have proposed various knowledge graph embedding (KGE) models. However, most of these models primarily focus on the design of new scoring functions, disregarding the crucial role of the loss function during the model training stage. In light of this, we propose a new exponential weight coefficient loss function, named EwLoss, which can be incorporated into any typical KGE model. This loss function enables the model to better distinguish between triplets that are otherwise difficult to differentiate. Consequently, the model can learn the features of these challenging triplets more effectively, leading to improved embedding vectors. By conducting experiments on four commonly used data sets, where EwLoss is applied to six representative models, we demonstrate the superiority of EwLoss in distinguishing such hard-to-distinguish triplets. Moreover, we observe that the performance of the embedding models is significantly enhanced when utilizing EwLoss.
Trustworthy Healthcare Professional Credential Verification Using Blockchain Technology

Healthcare credentialing plays a vital role in ensuring the competence and integrity of healthcare professionals. However, the current credentials verification process suffers from time-consuming procedures due to the large number of intermediaries, limited information access, data fragmentation and the persistent risk of fraudulent credentials, leading to delayed hiring, increased administrative burden, and loss of trust and reputation in the healthcare system. In this paper, we utilize blockchain technology to enhance the credentialing process by streamlining the verification steps, improving data security, and providing stakeholders with confidence through secure storage of credentials. In addition, we utilize advanced security techniques, such as proxy re-encryption and cryptographic algorithms, to ensure the protection of sensitive data, facilitate secure communication, and prevent unauthorized access. We develop smart contracts which eliminate the need for intermediaries, automate the verification process, and enhance transparency and data integrity. We present system architecture, sequence diagrams, entity relationship diagrams, and the underlying algorithms of our blockchain-based solution. We discuss how our proposed solution attains the objectives outlined in the paper. We conduct cost evaluation and security analysis to validate the effectiveness of our solution. Additionally, we compare our proposed system with existing blockchain-based solutions, highlighting its novelty. The code of our smart contracts is made publicly available on GitHub.
Where2Stand: Toward a Framework for Portrait Position Recommendation in Photography

Composition layout is crucial in the portrait location recommendation of photography. The existing studies require both landscape background images and portrait foreground images, which limits the scope of practical applications. In this paper, we propose an end-to-end portrait location recommendation model, which mainly consists of three sub-networks: the first sub-networks is the portrait generation network, which generates relatively real portrait foreground images based on random input noise; the second sub-networks is the spatial transformation network, which mainly changes the size and location of the generated portrait based on the input landscape image; The third sub-networks is the compose network to generate a realistic portrait landscape image, which considers not only the correlation between the portrait foreground and the landscape background but also the overall composition aesthetics. Last, the proper standing position is obtained by computing the difference between the generated and input landscape images. We also construct a portrait landscape photo dataset PLDataset to train and verify our method. The experimental results on our dataset show that our proposed method can recommend a relatively reasonable standing position by only providing a landscape image in portrait landscape photography, which greatly increases the availability.
Anomaly Detection in Coastal Wireless Sensors via Efficient Deep Sequential Learning

Wireless Sensor Networks (WSNs) encounter a substantial challenge when it comes to energy conservation. As sensor nodes rely on battery power to operate in unattended environments, and it can be inconvenient to recharge the batteries. So, maximizing the network’s operational lifetime by minimizing energy consumption is crucial for improving the quality of service. Anomaly detection is widely used technique in WSNs to identify anomalies or unusual events. However, timely anomaly detection can be challenging to execute reliably in real-time. This study presents a methodology that focuses on energy efficiency when detecting anomalies in real-time settings for multivariate time series data by utilizing compression of measurement data and reducing operating time without compromising measurement resolution. This effectively reduces energy consumption. Instead of modeling the time series of each sensor individually, the proposed deep sequential learning approach models the time series of multiple sensors concurrently, accounting for potential interactions among them. Additionally, the proposed deep sequential learning approach eliminates the need for labeled data and can be directly applied to real-world scenarios where labeling a large data stream is impractical and time-consuming. Finally, experiments with a real-world WSN demonstrate that the proposed approach is both adequate and robust in practice.
A Binary-Directional Shape Descriptor for the Enhancement of Real-Time Automatic Headlight Control Function

Advanced Driver Assistance Systems (ADAS) are becoming more and more important in recent years. All relevant manufacturers equip their cars with various safety features and in some locations it is even mandatory to do so. One of such systems is Automatic Headlight Control (AHC) algorithm that prevents from accidental dazzling of other road users. Recognizing vehicle lights with high accuracy is crucial in this algorithm. Any improvement can result in the limitation of car accidents and dangerous situations on the road. The paper introduces a novel visual descriptor that describes previously binarized blob shapes of the analyzed objects. We also present our referenced AHC system that operates not only on the images but also on the set of other visual features. Adding new feature leads to the improvement of around 1.5 percentage point of vehicle lights classification accuracy (21% reduction of incorrectly classified light sources). It can also replace the already used features (four-time reduction from 32 to 8 feature values) and keep the classification accuracy at the similar level. The proposed solution has been tested with an extensive set of video sequences (total duration exceeding ten hours) under diverse weather conditions and from different types of roads. The proposed descriptor is shown to be far less computationally complex, and it is proved that it can outperform other state-of-the-art descriptors when analyzed individually.
ELM-Based Discriminant Auto-Encoder and Multi-Kernel Fusion for Radar Specific Emitter Identification

Radar specific emitter identification (SEI) distinguishes different radar emitters, which is the research hotspot in the fields of electronic countermeasures and intelligence reconnaissance. To enhance the identification accuracy of the real radar SEI system with limited training data, we propose the multi-kernel extreme learning machine-based discriminant auto-encoder (MK-ELM-DAE) method by combining representation learning and multi-kernel fusion in this paper. Firstly, ELM-DAE is applied to each primary feature of radar signals to extract the more discriminative low-dimensional feature representations. With the features extracted by ELM-DAE, the linear discriminant ratio-based two stage multiple kernel ELM algorithm is then employed to conduct the multi-feature fusion. The most important module of MK-ELM-DAE is ELM-DAE, which is an effective supervised dimensionality reduction method for representation learning. ELM-DAE incorporates the label information into ELM auto-encoder by introducing a supersized regularization term, so it is more suitable for classification tasks. Specifically, we use the ambiguity function (AF) to extract primary features, and subsequently design an AF-based MK-ELM-DAE method for radar SEI. Experiments show that our method has significant advantages in accuracy and testing efficiency.
Warm-Starting for Improving the Novelty of Abstractive Summarization

Abstractive summarization is distinguished by using novel phrases that are not found in the source text. However, most previous research ignores this feature in favour of enhancing syntactical similarity with the reference. To improve novelty aspects, we have used multiple warm-started models with varying encoder and decoder checkpoints and vocabulary. These models are then adapted to the paraphrasing task and the sampling decoding strategy to further boost the levels of novelty and quality. In addition, to avoid relying only on the syntactical similarity assessment, two additional abstractive summarization metrics are introduced: 1) NovScore: a new novelty metric that delivers a summary novelty score; and 2) NSSF: a new comprehensive metric that ensembles Novelty, Syntactic, Semantic, and Faithfulness features into a single score to simulate human assessment in providing a reliable evaluation. Finally, we compare our models to the state-of-the-art sequence-to-sequence models using the current and the proposed metrics. As a result, warm-starting, sampling, and paraphrasing improve novelty degrees by 2%, 5%, and 14%, respectively, while maintaining comparable scores on other metrics.
An Enhanced Threshold RSA-Based Aggregate Signature Scheme to Reduce Blockchain Size

The transformative potential of blockchain technology has resulted in its widespread adoption, bringing about numerous advantages such as enhanced data integrity, transparency, and decentralization. Blockchain has effectively proven its ability to establish trustworthy systems across a multitude of applications. As the number of transactions recorded into a blockchain grows, the blockchain’s size expands significantly posing challenges to the network, particularly in terms of storage capacity and processing power. To address this problem, we present a cryptosystem based on RSA to provide aggregate signatures in blockchains. The aggregate signature replaces all transaction signatures of a block. In this scheme, all participating blockchain nodes use the same modulus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> , each with its own private and public key pair generated from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> . Regardless of the number of transactions, nodes, and signers, the aggregate signature size is always <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(k)</i> , where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> is a security parameter. The miner that constructs a candidate block computes the aggregate signature σ, replaces all transaction signatures by σ, and transmits the block with only one aggregate signature. The proposed scheme incorporates a flexible and accountable subgroup aggregate signature mechanism, allowing any subset <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> total elements to sign data, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> is the required number of signers. To verify that a set of elements signed the block, the verifier requires the aggregate signature, the aggregate public key, and the data hash. This approach requires minimal interaction between the signers, which results in reduced network traffic. Regardless of the network size, there are always <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> exchanged messages. Experimental analysis shows the proposed aggregate signature scheme’s effectiveness in increasing security robustness and reducing block size and overall network traffic.
A Self-Organized Mapping Neural Network-Based Intelligent Evaluation Model for Business Capacity in Enterprise Management

In era of big data, the integration of deep learning with enterprise management has been an inevitable requirement. However, scenarios of enterprise management vary with ever-changing entities, without fixed problem format. This makes it difficult to form a universal golden dataset for training supervised models. To handle such challenge, this paper takes the “business capacity evaluation of human resources in the enterprise management” as the main situation, and explores unsupervised deep learning-based technical methods for solution. Specifically, this paper proposes a self-organized mapping neural network (SOMNN)-based intelligent evaluation model for business capacity in enterprise management. For one thing, the procedures of SOMNN are described using symbol representation. For another, the SOMNN is specifically embedded into the realistic situations of business capacity evaluation, so as to generate digital evaluation results. After that, we carry out a case study on data from a real-world enterprise, in order to make performance assessment for the proposed model. The simulation results show that the proposed model can make proper evaluation towards business capacity in enterprise management, under the unsupervised pattern.
Unsupervised Anomaly Detection for Rural Fixed Wireless LTE Networks

This article presents an anomaly detection (AD) algorithm, robust AD for rural fixed wireless LTE (RAINFOREST), to address the difficulty of fault detection in LTE networks, specifically those that are rural and fixed wireless. We propose a hybrid AD method that uses network key performance indicators (KPIs), historical KPI forecasts, density-based spatial clustering of applications with noise (DBSCAN), and statistical analysis to detect anomalies. RAINFOREST outperformed benchmark AD methods and was able to detect faults in a rural commercial fixed wireless network earlier than existing LTE threshold-based alarms.
Consensus Low-Rank Multi-View Subspace Clustering With Cross-View Diversity Preserving

Multi-view subspace clustering has drawn significant attentions in recent years, which significantly improves learning performance of the single-view methods. In this letter, we propose a novel multi-view subspace clustering method, which learns a consensus representation with auto-weighted local neighboring transition probability matrix fusion and preserves cross-view diversity with a matrix-induced term. The new model is convex and thus admits efficient optimization. The effectiveness is confirmed by extensive experiments.
Profit vs Accuracy: Balancing the Impact on Users Introduced by Profit-Aware Recommender Systems

This work proposes the design of a profit-aware recommender system named Multi-Aware Recommender System (MARS), where the impact on the user is subject to adjustment. The item re-ranking process is based on the profit generated for the business and other important attributes (i.e. price of the item), and it is performed using a weighted rank aggregation method. The weights are optimized iteratively using a variant of the Gradient Descent algorithm, in order train the model and control the desired impact on the user. In this way, the model controls the impact on the user so as not to compromise the customer loyalty, while increasing the profit for the company. The weights can be unique for every user, making this approach personalized. The MARS method also controls the impact on the user by adjusting the ranking threshold $$T_R$$ , which determines the minimum rating that items must have to be re-ranked. Experiments showed that MARS models have the ability to control the impact on the user while increasing the profit by varying the weights associated to the considered attributes. It is important to notice that the training of the model can be parallelized; thus, the weights and results may be obtained for each user separately. By implementing MARS on the MovieLens-1M data set and various configurations, we increased the profit generated in all the experiments and got an acceptable or desired impact on the user.
Human Actions Recognition System Based on Neural Networks

The recognition of human activities in videos is a relevant area of study due to its real-life applications, such as surveillance, security, healthcare, human-machine interaction, and monitoring. This research compares two recognition approaches, called simple and hybrid, using two specific data sets. The first set includes three classes: yoga, exercise, and dance; the second is a sample from the Kinetics-700 set, with five activities, four of which are violent. Both sets present low variability between classes and high variability within classes. To reduce computational costs, a pre-trained CNN model and simple techniques for reducing computational resources are used. The hybrid approach uses an additional model with three variants: GRU, LSTM, or BiLSTM. Even though all the models presented similar results, the simple approach, using a pre-trained architecture and a reconstructed top-head, proved to be the most effective, reaching an accuracy of 94%, while the hybrid approach using LSTM layers obtained 90%. The model demonstrated an adequate classification of violent activities, which could serve as a basis for developing a surveillance and security systems.
Exploratory Interview Study About Teacher Strategies in Encouraging Secondary Students Participation in EFL Writing Classroom

This research paper exploring teachers' strategies in encouraging EFL secondary students to participate in writing classes. The researcher conducted research in one of the public schools in district Karawang and appointed one of the teachers at the school as a participant. In this research, researcher employed qualitative design with the research method Interview Study. The researcher collects data using Semi-structured Interviews and documentation analysis. In addition, the data obtained were analyzed by researcher in several stages so that the findings could be identified. The findings show participant uses six strategies in encouraging EFL secondary students to participate in writing classes, namely, Building Motivation and Confidence in Learning Writing, Using English as a Classroom Language, Using Technology in Learning Writing, Mandatory Using Dictionary, Interweaving Assessment and Teaching, Proper Time Management.
Роль психологічного фактчекінгу в підвищенні медіаграмотності студентської молоді

The essence of psychological fact-checking and its role in improving the media literacy of student youth in Ukraine is considered in this article. Media literacy, the ability to think critically and correctly consume information is key skills of the population of the 21st century. Media literacy in Ukraine is a mandatory direction for the development of a democratic state. Military actions in Ukraine and other threats to national security emphasize the importance of media literacy of the population to fight on the information front. After studying the phenomena of «post-truth», «fake information», «media literacy», «factchecking» and «psychological fact-checking», it became clear how important this topic is. Today we live in the so-called digital age, so it is important for everyone, especially for young people, to develop together with the trends of information and communication technologies in order to understand correctly what is being spread. Each message affects the consumer of information and shapes his perception, behavior and choice. If media literacy is not implemented and regulated at the state level, it may lead to the appearance of new threats to the state from its own population. The article also provides practical recommendations for the implementation of state policy in the field of media literacy, first of all, of student youth in Ukraine, because the implementation of this policy today, more than ever before, is a necessary and important tool for the development of the self-awareness of a true Ukrainian, the Ukrainian nation, the basis of which and now, and in the future, it is the youth of Ukraine that makes up and will make up. It is proposed to implement the state policy in the field of media literacy with the involvement of the national project «Filter», which is subordinate to the Ministry of Culture and Information Policy of Ukraine.