Reviewerly/exHarmony huggingface dataset:
Two-stage grey cloud clustering model under the panel data and its application

Purpose The purpose of this paper is to establish a two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem with three-parameter interval grey number to evaluation of agricultural drought resistance grade of 18 cities in Henan Province. Design/methodology/approach The clustering process is divided into two stages. In the first stage: Combining variance and time degree, the time weight optimization model is established. Applying the prospect theory, the index weight optimization model is established. Then, with the help of grey possibility function, the first stage of grey cloud clustering evaluation is carried out. In the second stage: the weight vector group of kernel clustering is constructed, and the grey class of the object is determined. A two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem is proposed. Findings This paper indicates that 18 cities in Henan Province are divided into four categories. The drought capacity in Henan province is high in the east and low in the west, high in the south and low in the north and the central region is relatively stable. The drought is greatly affected by natural factors. And the rationality and validity of this model is illustrated by comparing with other methods. Practical implications This paper provides a practical method for drought resistance assessment, and provides theoretical support for farmers to grasp the drought information timely and improve the drought resistance ability. Originality/value The model in this paper solves the situation of ambiguity and randomness to some extent with the help of grey cloud possibility function. Moreover, the time weight of time degree and variance are used to reduce the volatility and the degree of subjective empowerment. Considering the risk attitude of the decision makers, the prospect theory is applied to make the index weight more objective. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.
Sequential recommendation model integrating micro-behaviors and attribute enhancement

Sequential recommendation predicts the items that the user may interact with next based on the time-series information of the user-item interactions, and learns the users’ dynamic preferences. However, most existing sequential recommendation models ignore the micro-behaviors and the importance of attribute information. Thus, we propose a new model named Sequential Recommendation Model Integrating Micro-behaviors and Attribute Enhancement (SRMA). First, we build a user-item interaction graph and a user-item-attribute interaction graph by introducing user and item attributes. In addition, we perform the temporal attention embedding propagation in the user-item interaction graph, in which the multi-head attention is used to learn the temporal neighborhood weights under micro-behaviors. Simultaneously, we perform the attribute attention embedding propagation in the user-item-attribute interaction graph, which learns the high-hop interactions among users, items and attributes under micro-behaviors, and assigns different weights to attributes through the attribute attention. Finally, the prediction is made by combining the embedding of each layer in the two graphs. Experiments on two real datasets show that the model has good performance.
Improving in-text citation reason extraction and classification using supervised machine learning techniques

In the last decade, automatic extraction and classification of in-text citations have received immense popularity and have become one of the most frequently used techniques to evaluate research. Due to the large volume of in-text citations in various digital libraries such as Web of Science, Scopus, Google Scholar, Microsoft Academic, etc., machine learning models and natural language processing techniques are being used to extract, classify, and analyze them. Typical automatic in-text classification techniques use sentiment-based classes (Positive, Negative, and Neutral). However, there are cognitive-based schemes as well that classify in-text citations based on the author’s perspective. In such schemes, extracting citation reasons with high recall is challenging. To address this challenge, we have used eight citations’ context and reason classes defined by CCRO (Citation’s Context and Reasons Ontology) to develop a machine learning model to achieve high recall without compromising on precision. We have worked on Association for Computational Linguistics Corpus with over 7000 in-text citations, randomly annotated by experts in CCRO classes. Afterwards, an array of machine-learning models is implemented on the annotated dataset: Support Vector Machine (SVM), Naïve Bayesian (NB), and Random Forest (RF). We have used various part-of-speech (Nouns, Verbs, Adverbs, and Adjectives) as novel features. Our results show that we have outperformed the three comparative models by achieving 91% accuracy.
Ability-aware knowledge distillation for resource-constrained embedded devices

Deep Neural Network (DNN) models have notably improved the efficiency of machine learning tasks. However, their high storage and computational costs restrict their deployment on resource-limited embedded devices. Knowledge distillation (KD) has emerged as a promising approach for compressing DNN models. However, two challenges in KD, namely the capacity gap problem and the time-consuming redundancy problem, have hindered its performance and efficiency in compression. To alleviate these challenges, this paper proposes a novel framework, called Ability-Aware Knowledge Distillation (AAKD). AAKD introduces a knowledge sample selection strategy and an adaptive teacher switching strategy based on the dynamic awareness of the student's ability. This enables the framework to automatically select suitable knowledge samples and teacher networks according to the increasing representation ability of students. Extensive experiments on different datasets and models have demonstrated that AAKD can enhance the performance of compact student models, significantly improve the efficiency of distillation, and lead to higher compression rates.
Polish natural language inference and factivity: An expert-based dataset and benchmarks

Despite recent breakthroughs in Machine Learning for Natural Language Processing, the Natural Language Inference (NLI) problems still constitute a challenge. To this purpose we contribute a new dataset that focuses exclusively on the factivity phenomenon; however, our task remains the same as other NLI tasks, i.e. prediction of entailment, contradiction or neutral (ECN). The dataset contains entirely natural language utterances in Polish and gathers 2,432 verb-complement pairs and 309 unique verbs. The dataset is based on the National Corpus of Polish (NKJP) and is a representative sample in regards to frequency of main verbs and other linguistic features (e.g. occurrence of internal negation). We found that transformer BERT-based models working on sentences obtained relatively good results ($\approx89\%$ F1 score). Even though better results were achieved using linguistic features ($\approx91\%$ F1 score), this model requires more human labour (humans in the loop) because features were prepared manually by expert linguists. BERT-based models consuming only the input sentences show that they capture most of the complexity of NLI/factivity. Complex cases in the phenomenon - e.g. cases with entitlement (E) and non-factive verbs - remain an open issue for further research.
Unsupervised learning for identifying silicified and dolomitized zones in pre-salt using portable XRF data

The discovery of significant oil reserves in the pre-salt region along the eastern coast of Brazil marks a significant milestone in hydrocarbon exploration and production, triggering extensive discussions on the origin and composition of non-marine carbonate rocks. Dolomitization and silicification have been identified as major processes responsible for substantial syn/post-depositional alterations in pre-salt reservoir rocks. In this study, we assess the feasibility of employing machine learning techniques to identify highly dolomitized and/or silicified intervals. A 25-m-thick rock succession from a drill core in the Tupi oil field, Santos Basin, was selected for analysis. Chemical element measurements were collected at approximately 10 cm intervals using an Olympus Vanta M Series model portable XRF analyzer. Subsequently, the acquired dataset underwent preprocessing and processing steps, including: (i) standardization of the database, (ii) data cleaning and imputation, (iii) basic statistical analysis, (iv) data transformation, and (v) outlier removal. Due to the considerable number of variables, the dataset was reduced to three principal components (PCs) using principal component analysis (PCA). Unsupervised learning was performed using the k-Means clustering algorithm, which employs Euclidean distance to determine the similarity between objects. Pearson's correlation analysis was also conducted to support the chemical interpretation of the clusters by comparing them with macroscopic and microscopic geological information. To ensure the optimal representativeness of the data, five clusters (C1, C2, C3, C4, and C5) were defined. Clusters C1, C2, and C3 exhibited increasing values on PC1, while C4 and C5 were characterized by higher values on PC2 and PC3, respectively. Based on significant correlations identified by Pearson's analysis, we can conclude that there is a progressive increase in Si content represented by C1, C2, and C3, accompanied by a relative decrease in all other chemical elements measured by the portable XRF. C1 not only emerged as the most frequently occurring cluster but also displayed the most common chemical composition in the studied interval, with Ca > Si. C2 and C3 corresponded to more intensely silicified levels. C4 exhibited a stronger affinity to Ca and Mg, suggesting intensified dolomitization. C5 stood out due to its relationship with Ba, an element associated with barite, a mineral that may result from the percolation of hydrothermal fluids depending on the associated paragenesis.
Policy ensemble gradient for continuous control problems in deep reinforcement learning

Policy gradient algorithms for reinforcement learning (RL) have successfully tackled a broad range of high-dimensional continuous RL problems, including many challenging robotic control problems. These algorithms can be largely divided into two categories, i.e., on-policy algorithms and off-policy algorithms. Off-policy deep RL (DRL) algorithms enjoy better sample efficiency than and often outperform on-policy algorithms. However, cutting-edge off-policy algorithms still suffer from the low-quality estimation of policy gradients, resulting in compromised learning performance and high sensitivity to hyper-parameter settings. To address this issue, we propose a new concept of robust policy gradient (RPG). Driven by RPG, this paper further develops a new policy ensemble gradient (PEG) algorithm for DRL, inspired by the recent success of several ensemble DRL algorithms. PEG efficiently and effectively estimates RPG by using multiple policy gradients obtained respectively from several off-policy base learners in an ensemble. The estimated RPG is then utilized for training all base learners simultaneously. Comprehensive experiments have been performed on six Mujoco benchmark problems. Compared to four state-of-the-art off-policy algorithms and four cutting-edge ensemble policy gradient algorithms, our new PEG algorithm achieved highly competitive stability, performance and sample efficiency. Further analysis shows that PEG is insensitive to varied hyper-parameter settings, confirming the positive role of RPG in building reliable and effective off-policy DRL algorithms.
A new entity prediction method for cyber threat intelligence

With the diversification and rapid development of the network, the generation of cyber threat intelligence (CTI) is more and more comprehensive, and the knowledge graph of which is also developed and updated constantly. Of course, it is inevitable to encounter the expansion of the knowledge base of threat intelligence, and Intelligence entities are closely connected. In the Knowledge Graph (KG) field, link prediction is widespread because the entities are already available. And you need to determine the relationship between the two entities somehow. However, the more complex problem is extending the entities created after establishing the knowledge base into the existing one. Dealing with these problems often relies on extracting entities and relationships from the natural language of threat intelligence, but this approach only predicts undeclared intelligence entities or relationships. Embedded models often use structural rules to predict link relationships but cannot predict missing intelligence entities. In this paper, the latest MiNer method is combined with the effective STransE model to fully invoke the neighbor information around the entity and analyze the proportion of each neighbor. At the same time, this paper uses neighbor traffic statistics to assign corresponding weights to indicate which neighbor is more important to the entity to be predicted. We show that our method can use the existing cyber threat intelligence KG database to effectively predict some unknown entities, according to the importance of neighbor information, and add them to the existing KG database. At the same time, our method’s performance is greatly improved when the specific matrix is converted to the diagonal matrix.
Intermediate deep feature coding for human–machine vision collaboration

Traditional image coding are mainly designed for human vision. While for collaborative intelligence, deep feature coding is specific for machine vision, which includes feature extraction and compression. Actually, deep features can build a bridge between human and machine vision. Therefore, we focus on generalized deep feature extraction and compression for multitask, which includes image reconstruction task for human vision and computer visual tasks for machine vision. After analyzing correlation among multitask, a reconstruction guided feature extraction strategy and feature fusion based network are proposed to get more generalized intermediate deep feature, which contains sufficient information friendly for human and machine vision. Besides, a non-uniform quantization method based on importance and a compact representation method for feature distribution information protection are proposed for high efficiency feature coding. Eventually, we come up with an entire intermediate deep feature coding framework including feature extraction and compression. Experimental results indicate the performance gains with our framework.
Advanced Predictive Tile Selection Using Dynamic Tiling for Prioritized 360° Video VR Streaming

The widespread availability of smart computing and display devices such as mobile phones, gaming consoles, laptops, and tethered/untethered head-mounted displays has fueled an increase in demand for omnidirectional (360°) videos. 360° video applications enable users to change their viewing angles while interacting with the video during playback. This allows users to have a more personalized and interactive viewing experience. Unfortunately, these applications require substantial network and computational resources that the conventional infrastructure and end devices cannot support. Recently proposed viewport adaptive fixed tiling solutions stream only relevant video tiles based on user interaction with the virtual reality (VR) space to use existing transmission resources more efficiently. However, achieving real-time accurate viewport extraction and transmission in response to both head movements and bandwidth dynamics can be challenging, which can impact the user’s Quality of Experience (QoE). This article proposes innovative dynamic tiling-based adaptive 360° video streaming solutions in order to achieve high viewer QoE. First, novel and easy-to-scale tiling layout selection methods are introduced, and the best tiling layouts are employed in each adaptation interval based on the prediction-assisted visual quality metric and the observed viewport divergence. Second, a novel proactive tile selection approach is presented, which adaptively extracts tiles for each selected tiling layout based on two low-complex viewport prediction mechanisms. Finally, a practical dynamic tile priority-oriented bitrate adaptation scheme is introduced, which uniformly distributes the bitrate budget among different tiles during 360° video streaming. Extensive trace-driven experiments are conducted to evaluate the proposed solutions using head motion traces from 48 VR users for five 360° videos with tiling layouts of 4 × 3, 6 × 4, and 8 × 6 and segment durations of 1s, 1.5s, and 2s. The experimental evaluations show that the dynamic video tiling solutions achieve up to 11.2% more viewport matches and an average improvement in QoE of 9.7% to 18% compared to state-of-the-art 360° streaming approaches.
An End-to-end Trust Management Framework for Crowdsourced IoT Services

We propose a novel end-to-end trust management framework for crowdsourced Internet of Things (IoT) services. The framework targets three main aspects: trust assessment , trust information credibility and accuracy , and trust information storage . We harness the usage patterns of IoT consumers to offer a trust assessment that adapts to IoT consumers’ uses. Additionally, our framework ascertains the credibility and accuracy of trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures the contextual fairness between IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves the integrity and availability of trust information.
XimSwap: many-to-many face swapping for TinyML

The unprecedented development of deep learning approaches for video processing has caused growing privacy concerns. To ensure data analysis while maintaining privacy, it is essential to address how to protect individuals’ identities. One solution is to anonymize data at the source, avoiding the transmission or storage of information that could lead to identification. This study introduces XimSwap, a novel deep learning technique for real-time video anonymization, which can remove facial identification features directly on edge devices with minimal computational resources. Our approach offers a comprehensive solution that guarantees privacy by design. This novel method for implementing face-swapping ensures that the pose and expression of a target face remain unchanged and can be used on embedded devices with very limited computational resources. By incorporating style transfer layers into convolutional ones and optimizing the network’s operation, we achieved a reduction of over \(98\% \) in the required operations and parameters compared to state-of-the-art architectures. Our approach also significantly reduces RAM usage, making it possible to implement the anonymization process on tiny edge devices, including microcontrollers, such as the STM32H743.
METAPHYSICS OF THE OBJECT-IMAGE DICHOTOMY IN ARCHITECTURE

The physical and mental development of the phenomena of the world presupposes the distinction between the ob-ject of knowledge and the concept of it. The discovery of the difference between a thing and the idea of a thing became an unprecedented triumph of sci-ence in ancient Greece, noted Soviet art critic A. F. Losev [1]. The «stratification» of the object of study marked the beginning of a centuries-old confrontation between ideal-ism and materialism. The thesis «the idea of a thing exists by itself» has been overcome by progressive science for a long time, but recently it has risen from the ashes by the efforts of philosophers of the XX century, who declared the priority of thinking as a creative tool for replacing reality with hyperreality, the triumph of imagination over reality – and quite reasonably. The concept of metaphysics («that which is after – or outside–physics») is addressed precisely to the idea of a thing that precedes and closes the process of perception of the object of cognition with a conditional shell of a meta-physical cloud that provides functional and aesthetic persuasiveness in this case of architectural work in the role of a synharmonic superstructure, artistic comprehension superimposed on a physical medium – a conceived or realized object. Vitruvius also considered the content of architecture as a unity of thought and practice [2]. Not to the detriment of the sacred triad «benefit–strength–beauty», the article proposes to consider the dichotomy «architectural object–its semantic image», the dynamics of its historical unfolding.
Access to the affordances of literacy: reflections on the evolving influence of ICT in Zambia

The literacy practices of a community draw on a set of affordances reflecting the particular speech varieties and orthographic conventions of a sociocultural system. Access to these affordances depends on co-ownership of a system of meanings co-constructed in the course of history. Individuals acquire this sense of ownership by participating in social activities such as personal correspondence, formal schooling, engagement with bureaucracies, etc. Modern information and communication technology (ICT) affords access to the system of meanings that constitutes literate communication in rapidly evolving ways. Thus, in contemporary Zambian society, social groups influence public opinion via social media accessed on mobile phones that reach a larger and more immediate audience than the older technologies of print, radio and TV. Economic and political processes influence, but do not fully control, the use of ICT in African countries. It is debatable whether the net impact of modern ICT on access to and uses of literate communication in Zambian society has been democratisation or the entrenchment of top-down, hegemonic control.
Diabatic quantum and classical annealing of the Sherrington-Kirkpatrick model

Quantum annealing is a contender to solve combinatorial optimization problems based on quantum dynamics. While significant efforts have been undertaken to investigate the quality of the solutions and the required runtimes, much less attention has been paid to understanding the dynamics of quantum annealing and the process leading to the solution during the sweep itself. In this comprehensive study, we investigate various aspects of the quantum annealing dynamics using different approaches. We perform quantum annealing, simulated quantum annealing, and classical annealing on several hundred instances of the Sherrington-Kirkpatrick model with intermediate system sizes up to 22 spins using numerical simulations. We observe qualitative differences between the quantum and classical methods, in particular at intermediate times, where a peak in the fidelity, also known as diabatic bump, appears for hard instances. Furthermore, we investigate the two-point correlation functions, which feature differences at intermediate times as well. At short times, however, the methods are similar again, which can be explained by relating the short-time expansion of quantum annealing to a high-temperature expansion, thus allowing in principle to find the classical solution already at short times, albeit at prohibitive sampling cost.