Reviewerly/exHarmony huggingface dataset:
Data-Similarity Based Integrated Federated and Centralized Learning for 6G Communications

It is commonly believed that 6G will be established on ubiquitous artificial intelligence (AI). Federated learning is recognized as one of the vital solutions for 6G intelligent applications due to its distributed AI structure and privacy preservation nature. In this paper, we consider an integrated federated and centralized learning network that consists of multiple users and a server for acquiring perceived data from mobile devices. The data similarity rule is adopted at users, where the received data offloaded from devices is either retained at users for distributed training or relayed to server for centralized training, and thus the server also aggregates local models from users. By optimizing bandwidth resource allocation and the number of communication rounds, we formulate an optimization problem with the goal of energy consumption minimization under the constraints of model loss and communication delay. Simulation results show that the proposed scheme not only improves model performance but also reduces energy consumption.
Concept learning in a probabilistic language-of-thought. How is it possible and what does it presuppose?

Where does a probabilistic language-of-thought (PLoT) come from? How can we learn new concepts based on probabilistic inferences operating on a PLoT? Here, I explore these questions, sketching a traditional circularity objection to LoT and canvassing various approaches to addressing it. I conclude that PLoT-based cognitive architectures can support genuine concept learning; but, currently, it is unclear that they enjoy more explanatory breadth in relation to concept learning than alternative architectures that do not posit any LoT.
Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.
On optimization of coherent and incoherent controls for two-level quantum systems

This article considers some control problems for closed and open two-level quantum systems. The closed system's dynamics is governed by the Schrödinger equation with coherent control. The open system's dynamics is governed by the Gorini-Kossakowski-Sudarshan-Lindblad master equation whose Hamiltonian depends on coherent control and superoperator of dissipation depends on incoherent control. For the closed system, we consider the problem for generation of the phase shift gate for some values of phases and final times for which we numerically show that zero coherent control, which is a stationary point of the objective functional, is not optimal; it gives an example of subtle point for practical solving quantum control problems. The two-stage method, which was developed in [A. Pechen, Phys. Rev. A., 84, 042106 (2011)] for generic $N$-level open quantum systems for approximate generation of a given target density matrix, is modified here for the case of two-level systems. We modify the first ("incoherent") stage by numerically optimizing piecewise constant incoherent control instead of using constant incoherent control analytically computed using eigenvalues of the target density matrix. Exact analytical formulas are derived for the system's state evolution, the objective functions and their gradients for the modified first stage. These formulas are then applied in the two-step gradient projection method. The numerical simulations show that the modified first stage's duration can be significantly less than the unmodified first stage's duration, but at the cost of performing optimization in the class of piecewise constant controls. Bibliography: 72 titles.
Can We Counteract Hate? Effects of Online Hate Speech and Counter Speech on the Perception of Social Groups

Hate speech can increase stereotyped thinking and social distancing in a society. However, there is still a lack of variety in the social groups under study and research into possible solutions to the problem. Thus, our aim is to (1) study effects of hate speech against Chinese people and transgender people and (2) to investigate if counter speech can offset the detrimental effects of hate speech. We conducted a pre-registered online experiment with a 2 × 3 between-subject design, varying the attacked group (Chinese people/transgender people) and the type of comments (neutral/hate speech/hate speech and counter speech) for an Austrian sample ( n = 1285). Findings reveal no effect of hate speech on the dependent variables, indicating that citizens might not be as vulnerable to hate speech after all. However, counter speech has a polarizing effect: attitudinal gaps and differences for social distancing increase between left-wing and right-wing participants if hate speech is countered.
Construction of linear codes from the unit graph G(ℤn)

In this paper, we consider the unit graph [Formula: see text], where [Formula: see text] and [Formula: see text] are distinct primes. For any prime [Formula: see text], we construct [Formula: see text]-ary linear codes from the incidence matrix of the unit graph [Formula: see text] with their parameters. We also prove that the dual of the constructed codes have minimum distance either three or four. Lastly, we stated two conjectures on diameter of unit graph [Formula: see text] and linear codes constructed from the incidence matrix of the unit graph [Formula: see text] for any integer [Formula: see text].
Ontology and Nanotechnologies

Usually, ontology means the branch of metaphysics dealing with the nature of being, a set of concepts and categories in a subject area or domain that shows the properties and the relations between them (created automatically from large datasets), or a specification of a conceptualization. The article presents a brief discussion of the study undertaken to design an ontology for nanotechnology.
Еconomic development of the United Republic of Tanzania

Achieving balanced economic growth is one of the priorities of governments in developing countries. This is especially relevant for certain African countries that have such characteristics as a favorable geographical location and considerable demographic resource. The policy of the United Republic of Tanzania is an example of successful strategy, since the country not only demonstrates a dynamic growth of GDP, but also competes for regional leadership with neighboring Kenya. The object is the economic policy of Tanzania, while the subject is represented by national interests, resources, and the tools of management of processes of national economic growth. Because of this the article aims at establishing comparative advantages of Tanzania and the peculiarities of its economic advancement. The system approach is used in the article, since it analyzes the economic policy as the set of elements that interact with each other. It was found that Tanzania is a strategic player in East Africa in terms of logistics that at the same time enjoys an appropriate tourism potential, represented by Mount Kilimanjaro, the national parks of international importance, and the popular islands in the Indian Ocean. The basis of the national economic strategy is the focus on transport and tourist services, which can be considered rather unusual for the African continent. At the same time, Tanzania is trying to effectively use its opportunities in the fields of agriculture and mining industry and is also actively developing transport infrastructure in order to become a logistics hub in the region. Moreover, the country is taking steps to exploit the commercial reserves of oil and natural gas. Tanzania’s foreign economic ties play an important role. This state actor has Chinese orientation since Beijing is its main trading partner and a major investor in infrastructure projects. Tanzanian political elite is also actively diversifying its contacts by deepening the cooperation with Türkiye. The complex of these factors helps to ensure balanced economic development, as well as to gain certain advantages in the competition with Kenya for the leadership in the East African region.
Positive Business Communication: Means of Harmonizing Communication in Commercial Group Chats

Internet communication and its genres are one of the most relevant issues of modern linguistics. Genres of Internet communication are in constant development. Internet chats started as a platform of phatic communication that provided leisure activities. These days, group chats serve a lot of purposes. For instance, commercial group chats are aimed at online buying and selling. This research focused on two Viber commercial chats that united women-customers and sellers from the city of Saratov (2021–2023). The discursive analysis revealed conflict-free environment, which is not typical of phatic chats, where anonymity usually contributes to the violation of communication and ethical standards. The authors identified the means of harmonizing communication that communicants resorted to for effective interaction. The participants created a benevolent atmosphere by using hypocoristic proper names, emoticons, complementarity, etc. Etiquette genres of greetings, gratitude, compliments, congratulations, and wishes resulted in a balanced communication. Tactics of conflict prevention included solidarity, mitigation, e.g., diminutives, reminders, concern for the addressee, assessment, courtesy, humor, etc. The resulting communicative attraction contributed to effective communication between sellers and buyers. The positive communication experience in such chats may improve the real communication.
Impolite Communication Practices as a Community-Building Tool in Sports News Comments

Conflict discourse studies provide a valuable insight into linguistic and sociocultural ideologies of online communities. They also may offer effective approaches to linguistic detoxification. This research featured online communication of sport fans. Their discourse is interdiscursive, possesses a certain netiquette, and reflects the social and ideological diversity within the community. The authors explored common speech strategies aimed at threatening the social identity of outsiders or at the acquisition of power. The material involved an array of comments posted on Sports.ru : each example was tested for speech aggression, and the conflict-marked comments were described from the strategy perspective. The sampling relied on the general intention and expressive vocabulary aimed at the addressee. The frequency distribution of impoliteness strategies revealed that users often tended to assert power through personal characterization of their opponents, e.g., agism, sarcasm, hypercorrect speech behavior, identity attacks via derogatory team nominations, etc. The analysis identified various conflict strategies employed to form derogatory nominations, as well as defined the socio-cultural characteristics that marked the addressee as a target of conflictladen speech behavior.
Secondary Functions of Tenses in Italian Comedies of the XVI Century

At its present stage, the Italian language of informal communication seems to demonstrate a lot of innovations. For example, the colloquial use of verb tenses is limited to Presente Indicativo , Passato Prossimo or Passato Remoto , Imperfetto , and Trapassato Prossimo . The obvious simplification of the verb system eventually expands the functions of each tense. However, a diachronic analysis may prove that this process is not as innovative as it seems. This research featured Italian comedies of the XVI century, which saw the emergence of a unified state language. The comedies provide a valuable research material: in pursuit of comic effect, their texts often imitated the contemporary colloquial speech. As a result, they yield a lot of valuable examples of everyday communication of previous centuries. Characters that portrayed lower-class people often used tenses in their secondary functions, e.g., they replaced the subjunctive with the indicative, used the present to indicate the near future, formulated polite requests with the help of the imperfect, the functions of the future tense were narrowed down to an epistemic modality, etc. Therefore, the modern Italian language is not developing new constructions: it is undergoing a re-standardization, where colloquial phenomena are gradually becoming a norm. The prescriptive norm is being replaced by a descriptive one, which reflects the actual speech of native speakers.
A Comparative Analysis of Factors Affecting the Selection of English Grammar Checkers with FUCOM and BWM

Online grammar checkers are interactive online software that offers authors suggestions to replace incorrect sentences with grammatically correct ones, repetitive words with alternative synonyms, and misspelled words with correct ones. Besides providing grammar checks to their users, also offer various services such as a dictionary, contextual guidance, similarity check, word/sentence suggestions, and punctuation correction. In addition to the basic services offered, users can also consider different criteria such as user-friendliness of grammar checkers, ease of installation, integration into different applications, customer service, reporting, and price, while choosing the most suitable software tool for their intended use. This study aims to determine and evaluate the factors that affect the selection of English grammar checkers. The prominent factors were evaluated by the users as an expert opinion and prioritized within themselves. In this evaluation stage, the results obtained were presented comparatively by applying the multi-criteria decision-making methods FUCOM and BWM.
Access Control Strategy for the Internet of Vehicles Based on Blockchain and Edge Computing

Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV.
Image Inpainting Based on Multi-Level Feature Aggregation Network for Future Internet

(1) Background: In the future Internet era, clarity and structural rationality are important factors in image inpainting. Currently, image inpainting techniques based on generative adversarial networks have made great progress; however, in practical applications, there are still problems of unreasonable or blurred inpainting results for high-resolution images and images with complex structures. (2) Methods: In this work, we designed a lightweight multi-level feature aggregation network that extracts features from convolutions with different dilation rates, enabling the network to obtain more feature information and recover more reasonable missing image content. Fast Fourier convolution was designed and used in the generative network, enabling the generator to consider the global context at a shallow level, making it easier to perform high-resolution image inpainting tasks. (3) Results: The experiment shows that the method designed in this paper performs well in geometrically complex and high-resolution image inpainting tasks, providing a more reasonable and clearer inpainting image. Compared with the most advanced image inpainting methods, our method outperforms them in both subjective and objective evaluations. (4) Conclusions: The experimental results indicate that the method proposed in this paper has better clarity and more reasonable structural features.
DSA: Deformable Segmentation Attention for Multi-Scale Fisheye Image Segmentation

With a larger field of view (FOV) than ordinary images, fisheye images are becoming mainstream in the field of autonomous driving. However, the severe distortion problem of fisheye images also limits its application. The performance of neural networks designed for narrow FOV images degrades drastically for fisheye images, and the use of large composite models can improve the performance, but it brings huge time overhead and hardware costs. Therefore, we decided to balance real time and accuracy by designing the deformable segmentation attention(DSA) module, a generalpurpose architecture based on a deformable attention mechanism and a spatial pyramid architecture. The deformable mechanism serves to accurately extract feature information from fisheye images, together with attention to learn the global context and the spatial pyramid structure to balance multiscale feature information, thus improving the perception of fisheye images by traditional networks without increasing the amount of excessive computation. Lightweight networks such as SegNeXt equipped with the DSA module enable effective and rapid multi-scale segmentation of fisheye images in complex scenes. Our architecture achieves outstanding results on the WoodScape dataset, while our ablation experiments demonstrate the effectiveness of various parts of the architecture.
Group-Privacy Threats for Geodata in the Humanitarian Context

The role of geodata technologies in humanitarian action is arguably indispensable in determining when, where, and who needs aid before, during, and after a disaster. However, despite the advantages of using geodata technologies in humanitarianism (i.e., fast and efficient aid distribution), several ethical challenges arise, including privacy. The focus has been on individual privacy; however, in this article, we focus on group privacy, a debate that has recently gained attention. We approach privacy through the lens of informational harms that undermine the autonomy of groups and control of knowledge over them. Using demographically identifiable information (DII) as a definition for groups, we first assess how these are derived from geodata types used in humanitarian DRRM. Second, we discuss four informational-harm threat models: (i) biases from missing/underrepresented categories, (ii) the mosaic effect—unintentional sensitive knowledge discovery from combining disparate datasets, (iii) misuse of data (whether it is shared or not); and (iv) cost–benefit analysis (cost of protection vs. risk of misuse). Lastly, borrowing from triage in emergency medicine, we propose a geodata triage process as a possible method for practitioners to identify, prioritize, and mitigate these four group-privacy harms.
Improving Low-Resource Chinese Named Entity Recognition Using Bidirectional Encoder Representation from Transformers and Lexicon Adapter

Due to their individual advantages, the integration of lexicon information and pre-trained models like BERT has been widely adopted in Chinese sequence labeling tasks. However, given their high demand for training data, efforts have been made to enhance their performance in low-resource scenarios. Currently, certain specialized domains, such as agriculture, the industrial sector, and the metallurgical industry, suffer from a scarcity of data. Consequently, there is a dearth of effective models for entity relationship recognition when faced with limited data availability. Inspired by this, we constructed a suitable small balanced dataset and proposed a based-domain-NER model. Firstly, we construct a domain-specific dictionary based on mine hoist equipment and fault text and generate a dictionary tree to obtain word vector information. Secondly, we use a Lexicon Adapter to obtain the vector information of the domain-specific dictionary feature words matched using the characters and calculate the weights between their word vectors, integrating position encoding to enhance the positional information of the word vectors. Finally, we incorporate word vector information into the feature extraction layer to enhance the boundary information of domain entities and mitigate the semantic loss problem caused via using only character feature representation. Experimental results on a manually annotated dataset of mine hoist fault texts show that this method outperforms BiLSTM, BiLSTM-CRF, BERT, BERT-BiLSTM-CRF, and LEBERT, effectively improving the accuracy of named entity recognition (NER) for mine hoist faults.