Reviewerly/exHarmony huggingface dataset:
Transitive Deficiency One Parallelisms of PG(3, 7)

Consider the n-dimensional projective space PG(n,q) over a finite field with q elements. A spread in PG(n,q) is a set of lines which partition the point set. A parallelism is a partition of the set of lines by spreads. A deficiency one parallelism is a partial parallelism with one spread less than the parallelism. A transitive deficiency one parallelism corresponds to a parallelism possessing an automorphism group which fixes one spread and is transitive on the remaining spreads. Such parallelisms have been considered in many papers. As a result, an infinite family of transitive deficiency one parallelisms of PG(n,q) has been constructed for odd q, and it has been proved that the deficiency spread of a transitive deficiency one parallelism must be regular, and its automorphism group should contain an elation subgroup of order q2. In the present paper we construct parallelisms of PG(3,7) invariant under an elation group of order 49 with some additional properties, and thus we succeed to obtain all (46) transitive deficiency one parallelisms of PG(3,7). The three parallelisms from the known infinite family are among them. As a by-product, we also construct a much bigger number (55,022) of parallelisms which have the same spread structure, but are not transitive deficiency one.
Machine-Learning-Based Ground-Level Mobile Network Coverage Prediction Using UAV Measurements

Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous in hard-to-reach areas. An alternative safe method involves using drones or unmanned aerial vehicles (UAVs). The objective of this study was to use a drone to measure signal strength at discrete points a few meters above the ground and an artificial neural network (ANN) for processing the measured data and predicting signal strength at ground level. The drone was equipped with low-cost data logging equipment. The ANN was also used to classify specific ground locations in terms of signal coverage into poor, fair, good, and excellent. The data used in training and testing the ANN were collected by a measurement unit attached to a drone in different areas of Sultan Qaboos University campus in Muscat, Oman. A total of 12 locations with different topologies were scanned. The proposed method achieved an accuracy of 97% in predicting the ground level coverage based on measurements taken at higher altitudes. In addition, the performance of the ANN in predicting signal strength at ground level was evaluated using several test scenarios, achieving less than 3% mean square error (MSE). Additionally, data taken at different angles with respect to the vertical were also tested, and the prediction MSE was found to be less than approximately 3% for an angle of 68 degrees. Additionally, outdoor measurements were used to predict indoor coverage with an MSE of less than approximately 6%. Furthermore, in an attempt to find a globally accurate ANN module for the targeted area, all zones’ measurements were cross-tested on ANN modules trained for different zones. It was evaluated that, within the tested scenarios, an MSE of less than approximately 10% can be achieved with an ANN module trained on data from only one zone.
Hybrid Character Generation via Shape Control Using Explicit Facial Features

We present a hybrid approach for generating a character by independently controlling its shape and texture using an input face and a styled face. To effectively produce the shape of a character, we propose an anthropometry-based approach that defines and extracts 37 explicit facial features. The shape of a character’s face is generated by extracting these explicit facial features from both faces and matching their corresponding features, which enables the synthesis of the shape with different poses and scales. We control this shape generation process by manipulating the features of the input and styled faces. For the style of the character, we devise a warping field-based style transfer method using the features of the character’s face. This method allows an effective application of style while maintaining the character’s shape and minimizing artifacts. Our approach yields visually pleasing results from various combinations of input and styled faces.
Underwater Image Enhancement via Triple-Branch Dense Block and Generative Adversarial Network

The complex underwater environment and light scattering effect lead to severe degradation problems in underwater images, such as color distortion, noise interference, and loss of details. However, the degradation problems of underwater images bring a significant challenge to underwater applications. To address the color distortion, noise interference, and loss of detail problems in underwater images, we propose a triple-branch dense block-based generative adversarial network (TDGAN) for the quality enhancement of underwater images. A residual triple-branch dense block is designed in the generator, which improves performance and feature extraction efficiency and retains more image details. A dual-branch discriminator network is also developed, which helps to capture more high-frequency information and guides the generator to use more global content and detailed features. Experimental results show that TDGAN is more competitive than many advanced methods from the perspective of visual perception and quantitative metrics. Many application tests illustrate that TDGAN can significantly improve the accuracy of underwater target detection, and it is also applicable in image segmentation and saliency detection.
Identifying Hate Speech and Attribution of Responsibility: An Analysis of Simulated WhatsApp Conversations during the Pandemic

During the COVID-19 pandemic, public confrontations between people who had agreed to be vaccinated and those who had not, highlighted the relevance of the deepening dissemination of violent and discriminatory expressions and determined a level of perception of hate discourses.A cross-sectional observational study was carried out, based on an innovative methodology: simulations of WhatsApp conversations. In addition, the following variables were considered among others: level of empathy, personality traits and conflict resolution.The participants were 567 nursing students (413 females, 153 males and 1 person who did not identify with any gender). The results showed that, for the most part, the participants correctly identified hate speech, but were unable to discern the frame of reference.It is necessary to implement intervention strategies to minimize the impact of hate speech, which continues to be used on many levels to harass others, justify violence or undermine rights, generating an environment of prejudice and intolerance that encourages discrimination and violent attacks against certain individuals or collectives.
ScrumOntoSPL: Collaborative Method of Agile Product Line Engineering for Software Resource Reuse

Agile Product Line Engineering (APLE), a relatively new approach combining the two successful methods of Agile Software Development (ASD) and Software Product Lines (SPLs), makes product lines more responsive to ever-changing customer needs or market changes. However, SPLs often fail to keep up with market demand due to high coordination costs, slow development processes, and long release cycles in the case of frequent changes in business requirements; in agile software projects, the lack of a unified specification for describing requirements leads to high coordination costs and inconvenient requirement management. Some studies in the literature have proposed optimized approaches to integrate ASD and SPLs, but they still have not covered all aspects of APLE’s characteristics, and software resource reuse is rarely considered in these approaches during product line development. In view of this, we propose a collaborative framework of agile product line engineering for software resource reuse, namely ScrumOntoSPL. The ScrumOntoSPL approach efficiently merges ASD and SPL based on the agile method Scrum, SPL architecture, and ontology technology. In ScrumOntoSPL, uniform requirement specification is constructed by utilizing ontology, and the Matching Requirement with Component (MRC) process is designed to match product new requirements and software resources stored in a resource pool. In addition, we evaluated the proposed framework and approach with CMMI. In the end, a case study of a software development tool called IMC-Tool based on ScrumOntoSPL for a universal Instrument Microcontroller Chip (IMC) is discussed. The IMC-Tool case illustrates that the ScrumOntoSPL has the advantages of dynamically managing demand changes, enhancing software resource reuse, reducing coordination costs, and reducing time to market.
Low-Rank and Total Variation Regularization with ℓ0 Data Fidelity Constraint for Image Deblurring under Impulse Noise

Impulse noise removal is an important problem in the field of image processing. Although many methods exist to remove impulse noise, there is still room for improvement. This paper proposes a new method for removing impulse noise that combines the nuclear norm and the detection ℓ0TV model while considering the low-rank structure commonly found in visual images. The nuclear norm maintains this structure, while the detection ℓ0TV criterion promotes sparsity in the gradient domain, effectively removing impulse noise while preserving edges and other vital features. To solve the non-convex and non-smooth optimization problem, we use a mathematical process with equilibrium constraints (MPEC) to transform it. Subsequently, the proximal alternating direction multiplication algorithm is used to solve the transformed problem. The convergence of the algorithm is proven under mild conditions. Numerical experiments in denoising and deblurring show that for low-rank images, the proposed method outperforms ℓ1TV with detection, ℓ0TV and ℓ0OGSTV.
Research of Electronic Medical Record System Based on Blockchain

The traditional medical record system is stored in the hospital side, which is not convenient for data sharing and transmission between hospitals; Patients can see the medical records data, therefore it is challenging to maintain patient privacy. Based on this, a disaster-tolerant Alliance blockchain is used to construct a distributed electronic medical record system that includes a peer-to-peer (P2P) network with mutually lengthy links and improved Practical Byzantine Fault Tolerance (I-PBFT). The system encourages medical data exchange and sharing, makes the flow of medical data transparent to patients, and transfers ownership of medical records back to patients, turning medical data into a valuable data asset for patients.
A New Home-Based Pension Mutual Aid Framework Based on Blockchain

In the context of the public’s response to home-based care, the “Time Bank” care model has attracted more and more attention as an effective supplement to home-based care. Although there are cases of Time Bank implementation in China, it still has many problems such as extreme centralization, lack of credibility, and opaque and imperfect circulation of time currency. In response to this, this paper integrates blockchain technology based on the traditional time banking model and proposes a new home-based care mutual assistance framework driven by blockchain technology, which utilizes the distributed decentralization, collective maintenance, and data immutability of the blockchain. The characteristics of the blockchain provide credit guarantees by combining the community-based management agency and the channelization of the service network. Time-based transaction process service matching, smart currency scoring module. And use their respective financing agreements and other technologies to safely circulate, jointly establish a chain-wheel interoperability chain, and cooperate with various contract models to jointly establish various on-chain contracts. Experimental analysis shows that the new framework involved can realize secure encrypted storage and transmission of information, solve the problem of cross-regional storage and exchange, and provide a new feasible solution for home care.
Optimizing Quantum Classification Algorithms on Classical Benchmark Datasets

The discovery of quantum algorithms offering provable advantages over the best known classical alternatives, together with the parallel ongoing revolution brought about by classical artificial intelligence, motivates a search for applications of quantum information processing methods to machine learning. Among several proposals in this domain, quantum kernel methods have emerged as particularly promising candidates. However, while some rigorous speedups on certain highly specific problems have been formally proven, only empirical proof-of-principle results have been reported so far for real-world datasets. Moreover, no systematic procedure is known, in general, to fine tune and optimize the performances of kernel-based quantum classification algorithms. At the same time, certain limitations such as kernel concentration effects-hindering the trainability of quantum classifiers-have also been recently pointed out. In this work, we propose several general-purpose optimization methods and best practices designed to enhance the practical usefulness of fidelity-based quantum classification algorithms. Specifically, we first describe a data pre-processing strategy that, by preserving the relevant relationships between data points when processed through quantum feature maps, substantially alleviates the effect of kernel concentration on structured datasets. We also introduce a classical post-processing method that, based on standard fidelity measures estimated on a quantum processor, yields non-linear decision boundaries in the feature Hilbert space, thus achieving the quantum counterpart of the radial basis functions technique that is widely employed in classical kernel methods. Finally, we apply the so-called quantum metric learning protocol to engineer and adjust trainable quantum embeddings, demonstrating substantial performance improvements on several paradigmatic real-world classification tasks.
Integration of local position-POS awareness and global dense connection for ABSA

Aspect-based sentiment analysis (ABSA) is a key problem in text analysis. However, previous work ignores the fact that the joint effects of local and global features affect the classification accuracy. Therefore, an ABSA model based on local position-part of speech (POS) awareness and global dense connection (LPP-GDC) is proposed to fully grasp the information from both local and global features concurrently. First, the BERT pre-trained model is used to obtain the word vectors. Second, position-POS awareness mechanism is designed to focus on words of important POS in the local context. Then, the multi-head self-attention mechanism and a densely connected graph convolutional network are constructed to capture the global information. Finally, the results are obtained by the dynamic feature fusion method. Experiments on three public datasets show that LPP-GDC obtains state-of-the-art performance.
Consortium Blockchain-Based Student Status Management Framework

Student status management has always been an indispensable part of the education system, but due to the insufficient application of information technology, there are still a series of pain points in the traditional student status management system, such as easy to falsify student status, serious information islanding, and complex management. Blockchain 3.0 is a consortium blockchain model that uses protocols and consensus mechanisms for property rights confirmation, measurement and storage, with the characteristics of decentralization, immutability, traceability, collective maintenance, security and reliability, etc., which puts forward excellent solutions for student status management. This paper uses the thinking mode and framework of the blockchain to improve the logic and storage mode of the previous student status management system, and uses the Fabric consortium blockchain framework to design and realize a student status management system that can be initially put into use. Test results show that the throughput of the student status chain system can be stabilized at 200tps, which can basically meet the actual application needs of the student status management system.
Disfluency of English Speaking Skills of Students at College Level in District Karak

The purpose of this study is to examine the speaking problems of the district Karak students at college level. Problem of speaking in target language is one of the basic problems among second language learners. This problem always handicaps the learners during the communication in the target language. Speaking is the active use of language to express meaning. The researcher tries to explore causes that handicaps these learners who cannot speak English fluently. The data were collected through semi-structured interviews. Result show that most students failed to speak English fluently due to language barriers such as vocabulary, pronunciation and fluency. Psychological factors such as anxiety, fear of making mistakes in front of the people, and lack of practice were also some important factors in this regard. It is suggested that the government must take positive steps and teachers should encourage their students to improve their speaking skills.
An Anti-saturation phase retrieval algorithm based on optimal composite fringe patterns for 3D measurement with shiny surface

Three-dimensional (3D) measurement of shiny surfaces poses a challenge to the structure light method due to camera saturation caused by the reflective light. Addressing this problem, we propose an anti-saturation retrieval algorithm based on optimal composite fringe patterns. Based on the generalized expression of composite phase-shifting profilometry, we derive a complete phase error model, determine the best phase-shifting ratio theoretically, and finalize the optimal composite fringe pattern. Then, considering the phase retrieving problem occurred in the saturated regions, we present a novel anti-saturation retrieval algorithm by introducing a rejection coefficient, and accurately retrieve the phases of saturated region only with unsaturated values. Experimental results demonstrate that our method possesses a strong anti-saturation ability for shiny surface measurement with no need of adjusting camera exposure or projector brightness.
Methodological Approach of Georesources Sustainability for Northern Portuguese Industrial Rocks

The sustainable development of mineral resources is currently one of the most significant challenges facing the global world, as it contributes significantly to the world economy and the preservation of raw materials. In order to add value to companies and promote the sustainable development of the extractive sector, several international initiatives have been trying to establish Sustainability Indexes for mining activity. Furthermore, current industrial policies seek economic goals with social and environmental rights by implementing sustainable development policies. The coordinated management of these three concerns is intended to contribute to the well-being and progressive improvement of citizens’ quality of life. The implementation of an evaluation system for quarries that produce crushed stone, based on the integrated analysis of sectoral indicators in the economic, social, and environmental areas was the main objective of this work, which resulted from the development of an evaluation method that allows a qualitative and quantitative analysis of the performance of quarries from the point of view of their sustainability, in order to help improve the economic, environmental, and social performance of the country. The creation and adoption of economic, social, and environmental indicators adapted to Portuguese aggregate-producing quarries were developed. After analysing the referred sectorial indicators, a calculation method for creating a Sustainability Index was proposed, which was applied to 50 aggregate production quarries in northern Portugal.
Publicly Auditable Functional Encryption

We introduce the notion of publicly auditable functional encryption (PAFE). Compared to standard functional encryption, PAFE operates in an extended setting that includes an entity called auditor, besides key-generating authority, encryptor, and decryptor. The auditor requests function outputs from the decryptor and wishes to check their correctness with respect to the ciphertexts produced by the encryptor, without having access to the functional secret key that is used for decryption. This is in contrast with previous approaches for result verifiability and consistency in functional encryption that aim to ensure decryptors about the legitimacy of the results they decrypt. We propose four different flavors of public auditability with respect to different sets of adversarially controlled parties (only decryptor, encryptor-decryptor, authority-decryptor, and authority-encryptor-decryptor) and provide constructions for building corresponding secure PAFE schemes from standard functional encryption, commitment schemes, and non-interactive witness-indistinguishable proof systems. At the core of our constructions lies the notion of a functional public key, that works as the public analog of the functional secret key of functional encryption and is used for verification purposes by the auditor. Crucially, in order to ensure that these new keys cannot be used to infer additional information about plaintext values (besides the requested decryptions by the auditor), we propose a new indistinguishability-based security definition for PAFE to accommodate not only functional secret key queries (as in standard functional encryption) but also functional public key and decryption queries. Finally, we propose a publicly auditable multi-input functional encryption scheme (MIFE) that supports inner-product functionalities and is secure against adversarial decryptors. Instantiated with existing MIFE using “El Gamal”-like ciphertexts and $$\varSigma $$ -protocols, this gives a lightweight publicly auditable scheme.
Explicit and Nearly Tight Lower Bound for 2-Party Perfectly Secure FSS

Function Secret Sharing (FSS) is a cryptographic tool introduced by Boyle et al. (EUROCRYPT 2015) and is useful for several applications such as private information retrieval, oblivious-RAM, multi-party computation, etc. Most of the known FSS schemes are based on a pseudorandom generator and hence with computational security. In contrast, there are only a few known constructions of information-theoretic FSS, which are just for restricted function classes. It has not been well studied how efficient information-theoretic FSS can be in general. In this paper, we focus on (2-party) perfectly secure information-theoretic FSS and prove that the key size is explicitly (i.e., not just asymptotically) bounded below by the size of the subgroup generated by the function class. To the best of our knowledge, this is the first lower bound for information-theoretic FSS for an arbitrary function class. Our result shows that for several practically meaningful function classes, perfectly secure information-theoretic FSS must be much inefficient, not only asymptotically but also in practical parameters. Furthermore, we prove that this explicit lower bound is nearly tight by constructing perfectly secure information-theoretic FSS schemes for arbitrary function classes almost achieving our lower bound.