Reviewerly/exHarmony huggingface dataset:
To Generalize or Not to Generalize: Towards Autoencoders in One-Class Classification

In One-Class Classification (OCC), data affiliated with only one given class are accessible during training, while the trained algorithm must be capable of distinguishing the given class from all other unknown classes during test. OCC can be considered as a general case for many anomaly detection and novelty discovery problems and is more challenging than conventional binary and multi-class classification due to the absence of unknown classes. In literature, one of the most popular techniques towards OCC is autoencoder because autoencoders can capture the intrinsic structure of training data and are expected to have smaller reconstruction errors for the given class than those of unknown classes. As a result, during test, data from the known and unknown classes can be discriminated by thresholds. Despite the wide application of autoencoders in OCC, we have recently discovered that autoencoders can easily generalize to unknown classes, although they are trained on one given class only. The unexpected behavior reduces the overall OCC performance and leads to performance degradation during training. This paper proposes a novel solution to regularize the generalization ability of autoencoders and reduce performance degradation in OCC by introducing a feature weighting block in the latent space of autoencoders. Intensive experiments show that our method enables a training without performance degradation and significantly improves the OCC ability in comparison with contemporary state-of-the-art approaches.
An Approach Based on Transformer and Deformable Convolution for Realistic Handwriting Samples Generation

In the field of handwritten recognition, it usually needs to collect a large number of handwriting samples to obtain better results. However, it is so time-consuming and expensive that such a large number of samples cannot be collected manually. There is an effective solution to the above problem through synthesizing handwriting samples. In this study, an approach based on Transformer and deformable convolution has been proposed to generate much more realistic handwriting samples. To be specific, a Transformer has been utilized to capture the relations between writing style and text strings. By this way, global features (e.g. thickness of stroke, slant and so on) of writing style can be obtained. Meanwhile, a feature deformation fusion (FDF) module has been designed for obtaining local features (i.e. personalization) of writing style. Moreover, a focal frequency loss (FFL) is employed to solve the problem of pen-level artifacts. Experimental results demonstrate that the proposed approach can be competent for the task of handwriting samples generation and outperforms various baseline and state-of-the-art methods.
Boosting Black-Box Adversarial Attacks with Meta Learning

Deep neural networks (DNNs) have achieved remarkable success in diverse fields. However, it has been demonstrated that DNNs are very vulnerable to adversarial examples even in black-box settings. A large number of black-box attack methods have been proposed to in the literature. However, those methods usually suffer from low success rates and large query counts, which cannot fully satisfy practical purposes. In this paper, we propose a hybrid attack method which trains meta adversarial perturbations (MAPs) on surrogate models and performs black-box attacks by estimating gradients of the models. Our method uses the meta adversarial perturbation as an initialization and subsequently trains any black-box attack method for several epochs. Furthermore, the MAPs enjoy favorable transferability and universality, in the sense that they can be employed to boost performance of other black-box adversarial attack methods. Extensive experiments demonstrate that our method can not only improve the attack success rates, but also reduces the number of queries compared to other methods.
Rethinking Controllable Variational Autoencoders

The Controllable Variational Autoencoder (ControlVAE) combines automatic control theory with the basic VAE model to manipulate the KL-divergence for overcoming posterior collapse and learning disentangled representations. It has shown success in a variety of applications, such as image generation, disentangled representation learning, and language modeling. However, when it comes to disentangled representation learning, ControlVAE does not delve into the rationale behind it. The goal of this paper is to develop a deeper understanding of ControlVAE in learning disentangled representations, including the choice of a desired KL-divergence (i.e, set point), and its stability during training. We first fundamentally explain its ability to disentangle latent variables from an information bottleneck perspective. We show that KL-divergence is an upper bound of the variational information bottleneck. By controlling the KL-divergence gradually from a small value to a target value, ControlVAE can disentangle the latent factors one by one. Based on this finding, we propose a new DynamicVAE that leverages a modified incremental PI (proportionalintegral) controller, a variant of the proportional-integralderivative (PID) algorithm, and employs a moving average as well as a hybrid annealing method to evolve the value of KL-divergence smoothly in a tightly controlled fashion. In addition, we analytically derive a lower bound of the set point for disentangling. We then theoretically prove the stability of the proposed approach. Evaluation results on multiple benchmark datasets demonstrate that DynamicVAE achieves a good trade-off between the disentanglement and reconstruction quality. We also discover that it can separate disentangled representation learning and re-construction via manipulating the desired KL-divergence.
A Machine-Learning Based Scheme for Solar PV Detection Using Medium-Resolution Satellite Images in Vietnam

The capacity of installed solar photovoltaic (PV) panels has been increased dramatically in recent years due to the strong demand for renewable energy resources. Locations and spatial extent of those facilities are important for grid-managers, environmental monitoring purposes, and disaster mitigation works. Here, a simple machine-learning based solar PV detection in medium-resolution satellite images is proposed. The approach is cost-effective and scalable to a larger area compared to the exiting studies which usually rely on high-resolution but expensive satellite or aerial data. The proposed method is applied on a case-study site in Vietnam, and a good performance of the PV detection is achieved for the study site. The scale of the study area is currently limited to a part of Vietnam due to a lack of computation resources, but it could be possible to process and create a solar PV dataset for the whole country if enough computation power is available.
Integrated context-aware ontology for MNCH decision support

Health data are generally difficult to obtain due to ethical concerns such as data privacy and confidentiality, hence causing unresolved semantic heterogeneities in spatial-related information, and significantly altering accurate data integration and sharing. Furthermore, available health data are oftentimes highly unstructured, refining away important geolocation details. The use of ontologies therefore presents a promising resolution to the semantic heterogeneities problem through a formalized representation of geographic entities and established relation that machines understand. In this chapter, a Preposition-enabled Spatial Ontology (PeSONT) framework that utilizes available health indicators for location information extraction is proposed. Patients' clinical records were retrieved from patients' files in an ongoing project to build semistructured MNCH data sets. Using a preposition-enabled parser, locations were extracted to model context-aware information for healthcare information discovery. A visualization, evaluation, and documentation of the proposed ontology framework is provided and a discussion with reference to existing works and implications provided.
An Improved Fog Concentration Detection Method for Waterway Based on FADE

.- The frequent occurrence of foggy weather greatly impacts the water transportation system, so it is essential to detect fog concentration. Therefore, we introduce and improve an approach of Fog Aware Density Evaluator (FADE) to specifically detect fog concentration in waterways. First, we test a series of fog-related features on the synthesized foggy waterway images. Second, we propose a novel set of features with the addition of two new ones, namely color value in HSV color space, and gray standard deviation. Finally, based on the feature set, the fog concentration of the image patch and the whole image can be obtained using our proposed method, and the fog concentration distribution map can be derived from the fog concentration of image patches. Experimental results show that the method has higher accuracy than FADE in detecting the whole fog concentration of waterway images.
Task Allocation Under Geo-Indistinguishability via Group-Based Noise Addition

Locations are usually necessary for task allocation in spatial crowdsourcing, which may put individual privacy in jeopardy without proper protection. Although existing studies have well explored the problem of location privacy protection in task allocation under geo-indistinguishability, they potentially assume the workers could perform any tasks, which might not be practical in reality. Moreover, they usually adopt planar laplacian mechanism to achieve geo-indistinguishability, which will introduce excessive noise due to its randomness and boundlessness. To this end, we propose a task allo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CA</b> tio <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> approach via gr <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> up-based nois <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> addition under Geo-I, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> . Its main idea is that each worker uploads the noisy distances between his true location and the obfuscated locations of his preferred tasks instead of uploading his obfuscated location. In particular, to alleviate the total noise when conducting grouping, we put forward an optimized global grouping with adaptive local adjustment method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OGAL</i> with convergence guarantee. To collect the noisy distances which are required for subsequent task allocation, we develop a utility-aware obfuscated distance collection method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UODC</i> with solid privacy and utility guarantees. We further theoretically analyze the privacy, utility and complexity guarantees of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> . Extensive analyses and experiments over two real-world datasets confirm the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CANOE</i> .
BABOONS

BABOONS (BlAck BOx Optimization of Natural language data Summaries) optimizes text data summaries for an arbitrary, user-defined utility function. Primarily, it targets scenarios in which utility is evaluated via large language models. Users describe their utility function in natural language or provide a model, trained to score text summaries in a specific domain. BABOONS uses reinforcement learning to explore the space of possible descriptions. In each iteration, BABOONS generates summaries and evaluates their utility. To reduce data processing overheads during summary generation, BABOONS uses a proactive processing strategy that dynamically merges current with likely future queries for efficient processing. Also, BABOONS supports scenario-specific sampling and batch processing strategies. These mechanisms allow to scale processing to large data and item sets. The experiments show that BABOONS scales significantly better than baselines. Also, they show that summaries generated by BABOONS receive higher average grades from users in a large survey.
IAFFNet: Illumination-Aware Feature Fusion Network for All-Day RGB-Thermal Semantic Segmentation of Road Scenes

Semantic segmentation based on RGB and thermal images is an effective way to achieve an all-day understanding of road scenes. However, how to fuse the RGB and thermal information effectively remains an open problem. By studying fusion strategies at different stages, an illumination-aware feature fusion network has been developed for all-day semantic segmentation of urban road scenes in this paper, called IAFFNet. At the encoding stage, we introduce a bi-directional guided feature fusion module to effectively recalibrate and unify both RGB and thermal information. At the decoding stage, we have developed adaptive fusion modules to fuse low-level details and high-level semantic information. Finally, we have developed a decision-level illumination-aware strategy to achieve robust all-day segmentation. As far as we know, we are the first to incorporate illumination clues explicitly into RGB-T semantic segmentation. Extensive experimental evaluations demonstrate that the developed method can achieve remarkable performance on public datasets compared with state-of-the-art methods.
How to Agree to Disagree

Abstract The importance of taking individual, potentially conflicting perspectives into account when dealing with knowledge has been widely recognised. Many existing ontology management approaches fully merge knowledge perspectives, which may require weakening in order to maintain consistency; others represent the distinct views in an entirely detached way. As an alternative, we propose Standpoint Logic , a simple, yet versatile multi-modal logic “add-on” for existing KR languages intended for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints , which can be hierarchically organised, combined, and put in relation with each other. Starting from the generic framework of First-Order Standpoint Logic (FOSL), we subsequently focus our attention on the fragment of sentential formulas, for which we provide a polytime translation into the standpoint-free version. This result yields decidability and favourable complexities for a variety of highly expressive decidable fragments of first-order logic. Using some elaborate encoding tricks, we then establish a similar translation for the very expressive description logic $$\mathcal {SROIQ}b_s$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>SROIQ</mml:mi><mml:msub><mml:mi>b</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow></mml:math> underlying the OWL 2 DL ontology language. By virtue of this result, existing highly optimised OWL reasoners can be used to provide practical reasoning support for ontology languages extended by standpoint modelling.
Source-Free Multi-Domain Adaptation with Generally Auxiliary Model Training

Unsupervised domain adaptation transfers gained knowledge from labeled source domain(s) to a similar unlabeled target domain by eliminating the domain shifts. Most existing domain adaptation methods require the access to source data to match the source and target distributions. However, data privacy concerns make it difficult or impossible to share source data, leading to failures in existing domain adaptation methods. Admittedly, a few previous studies deal with domain adaptation without source data, but they rarely pay heed to data free domain adaptation with multiple source domains containing richer knowledge. In this paper, we propose a new multi-source data-free domain adaptation method- generally auxiliary model training (GAM)- which fits the source models to the target domain under the supervision of pseudo target labels rather than matching data distributions. To collect high-quality initial pseudo target labels, our approach learns both specific and general source models to improve the generality of source models based on auxiliary learning. Going further, we introduce a class balanced coefficient of each category based on the number of samples to reduce the misclassification often caused by data imbalance. Experiments on real-world classification datasets show that the propsosed generally auxiliary training has a superiority over the baselines.
A Revised Isolation Forest procedure for Anomaly Detection with High Number of Data Points

Anomaly Detection defines the process of identifying unusual data characterised by a different behaviour with respect to the rest of the dataset. It represents a key element for Industry 4.0: Anomaly Detection allows to automatically monitor and identify anomalous events in collected data with the purpose of preventing the occurrence of failures and malfunctions and of taking quick actions if they occur. Over the years, the amount of data to be analysed has grown enormously, posing a challenge for classical anomaly detectors originally created for small sets of data. This paper proposes a reinterpretation of the classical Isolation Forest procedure mainly suitable to tackle dataset characterised by high number of data points. We consider an alternative way to calculate the split value so to avoid to split in high density areas, increasing the isolation power of the method. The performance of the proposed approach is tested on several real dataset and some promising results emerge.
Improved Aquila Optimizer Optimization Algorithm Based on Multi-strategy Fusion

Aiming at the disadvantages of slow convergence speed and relatively weak local development ability of Aquila Optimizer optimization algorithm, a Tianying optimization algorithm based on adaptive cross mutation is proposed. Firstly, the inertia weight factor is introduced into the location update formula in the global development stage to adjust the influence degree of the optimal individual on the population. Secondly, the vertical and horizontal crossing strategy is introduced into the local development of the algorithm to exchange individual information across dimensions and improve the local development ability; Finally, Cauchy elite mutation is introduced to avoid the algorithm falling into local optimization. The function optimization simulation experiments of five representative comparison algorithms on 23 different feature benchmark functions show that the optimization accuracy and stability of the proposed improved algorithm are significantly improved. Keywords: Aquila Optimizer optimization algorithm; Inertia weight factor; Vertical and horizontal cross strategy; Cauchy elite variation
Integrating Blockchain and 5G: Advantages and Challenges of Decentralization

Trusted Third Parties or TTPs are used throughout applications for transactions, verifications and aims to provide customers with a set of convenient features. We provide certain challenges that are being faced by TTP based applications. Further we discuss about certain issues with the present 5G and beyond modelling based on TTPs. Then we discuss about some highlight characteristics and the need for decentralized Blockchain based systems. The Blockchain technology assists parties to synchronize, verify and preserve the contents of applications in a positive manner. Through this paper, we plan to lay out a review perspective on the decentralization based Blockchain technology usage in 5G and beyond networks.
Capital flows in integrated capital markets: MILA case

&lt;abstract&gt;&lt;p&gt;The FeldsteinHorioka1980 study on investment flows through the correlation of domestic saving and investment concluded that liberalization of capital markets does not necessarily lead to a movement of capital looking for a better allocation of resources, as classical theory would suggest. Ever since, literature has been prolific regarding this "puzzle", with arguments for and against this conclusion. This paper aims to analyze the issue from a different perspective. In recent years, the stock markets of Chile, Colombia, Mexico and Peru joined the Latin American Integrated Market through an agreement that allows investors in any of the participating markets to invest in the others as if they were investing locally. Compositional methods are used to assess the hypothesis of a potential flow of capital between markets generated by the creation of the joint market. First, cross-sectional methods for compositional data were used to test the hypothesis. As a result, it was not possible to find a change in the composition of the investment in the four markets produced by the creation of the joint market. Secondly, vector autoregressive models were estimated and tested for structural breaks in the parameters. However, these models were not found to be informative. In conclusion, it was not possible to reject the Felstein-Horioka hypothesis, supporting the idea that liberalization is not enough to generate capital flows between markets.&lt;/p&gt;&lt;/abstract&gt;
Document-Level Relation Extraction with Structure Enhanced Transformer Encoder

Document-level relation extraction aims at discovering relational facts among entity pairs in a document, which has attracted more and more attention in recent years. Most existing methods are mainly summarized as graph-based and transformer-based methods. However, previous transformer-based methods neglect structural information between entities, while graph-based methods are unable to extract structural information effectively on account that they isolate the en-coding stage and structure reasoning stage. In this paper, we propose an effective structure enhanced transformer encoder model (SETE), integrating entity structural information into the transformer encoder. We first define a mention-level graph based on mention dependencies and convert it to a token-level graph. Then we design a dual self-attention mechanism, which enriches the structural and contextual information between entities to increase the vanilla transformer encoder inferential capability. Experiments on three public datasets show that the proposed SETE outperforms previous state-of-the-art methods and further analyses illustrate the interpretability of our model.
Fine-grained code-comment semantic interaction analysis

Code comment, i.e., the natural language text to describe code, is considered as a killer for program comprehension. Current literature approaches mainly focus on comment generation or comment update, and thus fall short on explaining which part of the code leads to a specific content in the comment. In this paper, we propose that addressing such a challenge can better facilitate code understanding. We propose Fosterer, which can build fine-grained semantic interactions between code statements and comment tokens. It not only leverages the advanced deep learning techniques like cross-modal learning and contrastive learning, but also borrows the weapon of pre-trained vision models. Specifically, it mimics the comprehension practice of developers, treating code statements as image patches and comments as texts, and uses contrastive learning to match the semantically-related part between the visual and textual information. Experiments on a large-scale manually-labelled dataset show that our approach can achieve an F1-score around 80%, and such a performance exceeds a heuristic-based baseline to a large extent. We also find that Fosterer can work with a high efficiency, i.e., it only needs 1.5 seconds for inferring the results for a code-comment pair. Furthermore, a user study demonstrates its usability: for 65% cases, its prediction results are considered as useful for improving code understanding. Therefore, our research sheds light on a promising direction for program comprehension.