Reviewerly/exHarmony huggingface dataset:
Out-of-Distribution Generalisation with Symmetry-Based Disentangled Representations

Learning disentangled representations is suggested to help with generalisation in AI models. This is particularly obvious for combinatorial generalisation, the ability to combine familiar factors to produce new unseen combinations. Disentangling such factors should provide a clear method to generalise to novel combinations, but recent empirical studies suggest that this does not really happen in practice. Disentanglement methods typically assume i.i.d. training and test data, but for combinatorial generalisation we want to generalise towards factor combinations that can be considered out-of-distribution (OOD). There is a misalignment between the distribution of the observed data and the structure that is induced by the underlying factors. A promising direction to address this misalignment is symmetry-based disentanglement, which is defined as disentangling symmetry transformations that induce a group structure underlying the data. Such a structure is independent of the (observed) distribution of the data and thus provides a sensible language to model OOD factor combinations as well. We investigate the combinatorial generalisation capabilities of a symmetry-based disentanglement model (LSBD-VAE) compared to traditional VAE-based disentanglement models. We observe that both types of models struggle with generalisation in more challenging settings, and that symmetry-based disentanglement appears to show no obvious improvement over traditional disentanglement. However, we also observe that even if LSBD-VAE assigns low likelihood to OOD combinations, the encoder may still generalise well by learning a meaningful mapping reflecting the underlying group structure.
Discriminative restricted Boltzmann machine with trainable sparsity

Discriminative restricted Boltzmann machine (DRBM) is a probabilistic three-layered neural network, consisting of the input, hidden, and output layers, that helps to solve classification problems. This study attempts to improve the generalization property of the DRBM. Regularization methods such as L1 or L2 regularizations can be used to control the representation power of a learning model and suppress over-fitting to a dataset. To control the representation power of the DRBM, an alternative regularization approach is proposed, in which sparse regularization is imposed on the values of the hidden variables of the DRBM. In the resultant model, the sparse regularization controls the effective size of the hidden layer of the DRBM. Unlike standard regularization methods, in the proposed model, parameters that control the sparsity strength are trainable. The method is validated through numerical experiments based on benchmark datasets.
Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL Data

In sensitive applications, such as drug development, offering experts an explanation for why data mining operations arrive at certain results adds a very valuable facet. In this work we benefit from modelling the task as a Constraint Satisfaction Problem (CSP) twice: by adding multiple constraints to the mining process and by deriving pattern failure explanations. We illustrate experimentally how to apply our method on data originally retrieved from the ChEMBL database [14]. We also report some interesting dependencies discovered by our method which are not easy to observe when analysing data manually.
User Authentication via Multifaceted Mouse Movements and Outlier Exposure

Gaining information about how users interact with systems is key to behavioural biometrics. Particularly mouse movements of users have been proven beneficial to authentication tasks for being inexpensive and non-intrusive. State-of-the-art approaches consider this problem an instance of supervised classification tasks. In this paper, we argue that the problem is actually closer to unsupervised one-class classification tasks. We thus propose to view behavioural user authentication as an unsupervised task and learn individual models using data from a single user only. We further show that, by being purely unsupervised, losses in performance can be counterbalanced by augmenting additional data into the training processes (outlier exposure). Empirical results show that our approach is very effective and outperforms the state-of-the-art in several performance metrics.
Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets

In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends on the dataset at hand and thus needs to be tailored. The quality of an anomaly detector may be assessed via confusion-based metrics such as the Matthews correlation coefficient (MCC). However, since during training only normal data is available in a semi-supervised setting, such metrics are not accessible. To facilitate automated machine learning for anomaly detectors, we propose to employ meta-learning to predict MCC scores using the metrics that can be computed with normal data only and order anomaly detectors using the predicted scores for selection. First promising results can be obtained considering the hypervolume and the false positive rate as meta-features.
Explaining Black Box Reinforcement Learning Agents Through Counterfactual Policies

Despite the increased attention to explainable AI, explainability methods for understanding reinforcement learning (RL) agents have not been extensively studied. Failing to understand the agent's behavior may cause reduced productivity in human-agent collaborations, or mistrust in automated RL systems. RL agents are trained to optimize a long term cumulative reward, and in this work we formulate a novel problem on how to generate explanations on when an agent could have taken another action to optimize an alternative reward. More concretely, we aim at answering the question: What does an RL agent need to do differently to achieve an alternative target outcome? We introduce the concept of a counterfactual policy, as a policy trained to explain in which states a black box agent could have taken an alternative action to achieve another desired outcome. The usefulness of counterfactual policies is demonstrated in two experiments with different use-cases, and the results suggest that our solution can provide interpretable explanations.
The Other Side of Compression: Measuring Bias in Pruned Transformers

Social media platforms have become popular worldwide. Online discussion forums attract users because of their easy access, speech freedom, and ease of communication. Yet there are also possible negative aspects of such communication, including hostile and hate language. While fast and effective solutions for detecting inappropriate language online are constantly being developed, there is little research focusing on the bias of compressed language models that are commonly used nowadays. In this work, we evaluate bias in compressed models trained on Gab and Twitter speech data and estimate to which extent these pruned models capture the relevant context when classifying the input text as hateful, offensive or neutral. Results of our experiments show that transformer-based encoders with 70% or fewer preserved weights are prone to gender, racial, and religious identity-based bias, even if the performance loss is insignificant. We suggest a supervised attention mechanism to counter bias amplification using ground truth per-token hate speech annotation. The proposed method allows pruning BERT, RoBERTa and their distilled versions up to 50% while preserving 90% of their initial performance according to bias and plausibility scores.
Translated Texts Under the Lens: From Machine Translation Detection to Source Language Identification

Machine Translation Systems are today used to break down linguistic barriers. People from different countries and languages can now interact with each other thanks to state-of-the-art translators from prominent software companies like Google and Microsoft. However, these tools are also used to expand the audience for phishing attacks, scam emails or to generate fake reviews to promote a product on different e-commerce platforms. In all these cases, detecting whether a text has been translated can be crucial information. In this work, we tackle the problem of the detection of translated texts from different angles. On top of addressing the classic task of machine translation detection, we investigate and find common patterns across different machine translation systems unrelated to the original text's source language. Then, we show that it is possible to identify the machine translation system used to generate a translated text with high performances (F1-score 88.5%) and that it is also possible to identify the source language of the original text. We perform our tasks over two datasets that we use to evaluate our models: Books, a new dataset we built from scratch based on excerpts of novels, and the well-known Europarl dataset, based on proceedings of the European Parliament.
Self-Paced Hard Task-Example Mining for Few-Shot Classification

In recent years, researchers have commonly employed assistant tasks to enhance the training phase of the few-shot classification models. Several methods have been proposed to exploit and optimize the training tasks, such as Curriculum Learning (CL) and Hard Example Mining (HEM). However, most of the existing strategies can not elaborately leverage the training tasks and share some common drawbacks, including 1) the ignorance of the target tasks’ properties, and 2) the neglect of sample relationships. In this work, we propose a Self-Paced Hard tAsk-Example Mining (SP-HAEM) method to solve these problems. Specifically, the SP-HAEM automatically chooses hard examples via the similarity between training and target tasks to optimize the support set. To represent the property of target tasks, SP-HAEM obtains a representation of the dataset, called “meta-task”. No need to apply an additional model to measure difficulty and choose hard examples like other HEM methods, SP-HAEM selects the tasks with large optimal transport distance to the meta-task as hard tasks. Thus, training with such hard tasks can not only enhances the generalization ability of the model but also eliminate the negative effect of redundancy tasks. To evaluate the effectiveness of SP-HAEM, we conduct extensive experiments on a variety of datasets, including MiniImageNet, TieredImageNet, and FC100. The results of the experiments show that SP-HAEM can achieve higher accuracy compared with the typical few-shot classification models, e.g., Prototypical Network, MAML, FEAT, and MTL.
Intellectual Property Protection Method Based on Block Chain Technology

In the face of the current problems of intellectual property protection, blockchain technology has shown its own advantages, relying on this technology can provide better protection of intellectual property through a new method. From this point of view, this paper first introduces the basic concept of blockchain technology and its advantages in intellectual property protection, and then designs the blockchain intellectual property protection platform, which can give full play to its technological advantages to better protect intellectual property.
Contextual Word Embeddings Clustering Through Multiway Analysis: A Comparative Study

Transformer-based contextual word embedding models are widely used to improve several NLP tasks such as text classification and question answering. Knowledge about these multi-layered models is growing in the literature, with several studies trying to understand what is learned by each of the layers. However, little is known about how to combine the information provided by these different layers in order to make the most of the deep Transformer models. On the other hand, even less is known about how to best use these modes for unsupervised text mining tasks such as clustering. We address both questions in this paper, and propose to study several multiway-based methods for simultaneously leveraging the word representations provided by all the layers. We show that some of them are capable to perform word clustering in an effective and interpretable way. We evaluate their performances across a wide variety of Transformer models, datasets, multiblock techniques and tensor-decomposition methods commonly used to tackle three-way data.
Application of Mobile Virtual Reality to Computer Graphics and Image Processing Technology

With the in-depth development of intelligent computer equipment and its related product technology, the direct interaction between human and intelligent computer software and hardware has gradually become an important part of daily life. Virtual reality technology to bring readers a new perceptual experience, can achieve that ordinary readers can directly by the body, sound and virtual reality scene in the world takes the form of digital reading content directly to communicate interaction, experience to perceive the real completely different from traditional planar read content of three dimensional interactive mode. This paper uses the mobile virtual reality technology to carry on the relevant experimental research on the computer image processing technology, and through the simulation experiment to establish the promotion of mobile virtual reality to the computer image processing technology.
Hand gesture recognition using a convolutional neural network for arabic sign language

Communication is one of the most basic human needs, but for between deaf and dumb people and normal people, communication in their daily lives is a challenge, due to the lack of reliable and easy-to-use assistive devices and skilled sign language interpreters. The need to integrate hard-of-hearing Arab individuals into their societies has recently received greater attention than many public and private institutions. Accordingly, in the fields of artificial intelligence(AI) and machine learning(ML), automating sign language recognition has become a critical technology, this paper presents a proposed system for static hand gestures recognition for the Arabic sign language (ArSL) using a Convolutional Neural Network(CNN) with accuracy 97.42 %.
Web-Based Visualization and Analysis Framework for Graph Data

Graph theory has many applications in computer science. Graphs are involved in the mathematical modeling of problems belonging to very popular computer and data science, such as data mining, data clustering, computer network, image segmentation, etc. Graph theory is used in computer science to solve problems modeled as graphs. However, many applications do not have an interface that displays and edits graphs instantly. Moreover, these applications are not supported with a web interface. In this study, a web interface has been developed that support graph theory concepts, display more than one graph simultaneously, have a user-friendly interface, and include algorithms to analyze graphs. Also, we present a web service for these graph analyzing algorithms.
Fatigue Life Prediction of Road Beams Based on Artificial Bee Colony Algorithm and Traffic Simulation

With the development of the automobile industry, the road fatigue resistance has gradually become one of the most important characteristics of the road. The road fatigue resistance is usually evaluated by field tests or computer simulation techniques. In particular, the real vehicle test cycle is long and the cost is high, and the fatigue life of the road cannot be predicted in the early stage of vehicle development, but the computer simulation technology can avoid the drawbacks of the real vehicle test. The purpose of this paper is to study the fatigue life prediction of road beams based on artificial bee colony algorithm (ABCA) and traffic simulation. In this paper, the theory of ABCA, the characteristics of ABCA and the basic process of ABCA are also analyzed. The swarm algorithm has been improved in the study of fatigue life prediction of road beams. The whole experiment gives the main parameter interval of the ABCA, the median proposed probability calculation method and the ABCA, and shows the adaptability and competitiveness of the ABCA in the fatigue life prediction of road beams, and the future needs direction of implementation. After using the ABCA and the traffic flow simulation method, the performance of the traditional road beam fatigue life prediction has been greatly improved, and we conducted a questionnaire and found that more than 89% of the people felt that the design of the road beam fatigue life prediction method was applied to human beings. The worker bee colony algorithm and the traffic flow simulation method have found that the design efficiency has become higher.
Fully and Weakly Supervised Referring Expression Segmentation With End-to-End Learning

Referring Expression Segmentation (RES), which is aimed at localizing and segmenting the target according to the given language expression, has drawn increasing attention. Existing methods jointly consider the localization and segmentation steps, which rely on the fused visual and linguistic features for both steps. We argue that the conflict between the purpose of identifying an object and generating a mask limits the RES performance. To solve this problem, we propose a parallel position-kernel-segmentation pipeline to better isolate and then interact the localization and segmentation steps. In our pipeline, linguistic information will not directly contaminate the visual feature for segmentation. Specifically, the localization step localizes the target object in the image based on the referring expression, and then the visual kernel obtained from the localization step guides the segmentation step. This pipeline also enables us to train RES in a weakly-supervised way, where the pixel-level segmentation labels are replaced by click annotations on center and corner points. The position head is fully-supervised and trained with the click annotations as supervision, and the segmentation head is trained with weakly-supervised segmentation losses. To validate our framework on a weakly-supervised setting, we annotated three RES benchmark datasets (RefCOCO, RefCOCO+ and RefCOCOg) with click annotations. Our method is simple but surprisingly effective, outperforming all previous state-of-the-art RES methods on fully- and weakly-supervised settings by a large margin. The code and dataset will be released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/detectiveli/PKS.git</uri> .
A Naive but Effective Post-processing Approach for Dark Channel Prior (DCP)

Dark Channel Prior (DCP) is originally introduced to remove the haze effects from a digital image. Though the effectiveness of the DCP approach on haze removal, the DCP approach often leads the recovered image to become darker even though the haze effects had been removed. The images with the same or similar levels of color mean are likely without the problem of color shifts. Therefore, we have created a color mean adjustment method to adjust for the color shift by balancing the means of the color channels. CLAHE is employed in this study to boost the image’s contrast, while the color mean adjustment method is utilized to smooth its final appearance. Throughout the experiments like visual comparison analysis, Peak Signal-to-Noise Ratio (PSNR), and Universal Quality Index (UQI), our proposed method proved to be a highly effective post-processing approach for the DCP approach as it suppresses more image noises, enhance image quality, and, more importantly, allows the DCP approach to be used on underwater images. Besides, our proposed method also resolves the dark look issue of the DCP approach.
A survey on data integrity verification schemes in the cloud computing environment

Cloud storage can be used for enterprises and individuals in cloud data centers to access and store data remotely anywhere, anytime without any further burden. Through outsourcing data, users can be comforted by the burden of local data storage and maintenance. The main issue with cloud storage is the security of the stored data. Moreover, cloud users should be able to use cloud storage similar to local storage. For this reason, many systems have been proposed that provide auditing schemes for cloud data, but these schemes have suffered from fundamental weaknesses that may affect the efficiency of the audit process and thus will reduce the use of cloud storage despite its attractive advantages. In this paper, we identify issues related to cloud storage with an analysis of these issues, and a general classification is provided according to the characteristics of data integrity schemas. Besides, we analyze the data integrity schemes in detail according to the most prominent key features that must be achieved in the data integrity schemes. So that the audit process is efficient, and reduces users’ worry about having to check data integrity, in addition to increasing the use of cloud storage without Any hindrance. Furthermore, we discuss future trends and challenges for research in the context of data integrity schemes in cloud computing.
Modified parallel nested-layer particle swarm optimization algorithm for fast bifurcation point detection and its software implementation

This paper reports on the development of straightforward software for bifurcation point detection in discrete-time dynamical systems with a fast and versatile algorithm. Nested-layer particle swarm optimization (NLPSO) is an effective general-purpose bifurcation point detection strategy. In contrast to traditional gradient-based methods, the NLPSO method does not require derivatives of the objective functions. Note that NLPSO can incur high time costs, and parallel computation is practical. The software reported in this study employs modified NLPSO that is optimized for parallel computing. The proposed algorithm is fast and easy to use with limited knowledge of bifurcation point detection, algorithms, or parallel computing techniques.
Two-Stage Distillation-Aware Compressed Models for Traffic Classification

Traffic classification is indispensable for the Internet of Things (IoT) in intrusion detection and resource management. Deep-learning (DL)-based strategies are the key tools for traffic classification due to high accuracy but still have some challenges: 1) it is hard to deploy complex DL models on resource-constraint IoT devices and 2) performance is limited because of the ignorance of the similarity between IoT traffic. To address these issues, we propose lightweight but accurate models for traffic classification. First, we adopt a network-in-network basic model to reduce model size. Second, the basic model is trained with self-distilled response, feature map, and similarity among traffic types to enable its identification accuracy. Next, redundant filters are removed from the basic model to achieve compressed architectures. Then, a teacher model updating scheme with knowledge distillation is proposed to train compressed models without compromising performance. Experimental results demonstrate that compared to the state-of-the-art deep packet model, the compressed model can achieve the highest accuracy, deal with imbalanced traffic, and reduce nearly 99% of computation overhead in two encrypted traffic classification scenarios, thus, emphasizing its efficiency.