OpenRaiser/Intern-Atlas huggingface dataset:
Assessing learned features of Deep Learning applied to EEG
Venue: arXiv cs.LG 2021
Year: 2021

Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc. These achievements benefit from the CNNs' outstanding capability to learn discriminative features with deep layers of neuron structures and iterative training process. This has inspired the EEG research community to adopt CNN in performing EEG classification tasks. However, CNNs learned features are not immediately interpretable, causing a lack of understanding of the CNNs' internal working mechanism. To improve CNN interpretability, CNN visualization methods are applied to translate the internal features into visually perceptible patterns for qualitative analysis of CNN layers. Many CNN visualization methods have been proposed in the Computer Vision literature to interpret the CNN network structure, operation, and semantic concept, yet applications to EEG data analysis have been limited. In this work we use 3 different methods to extract EEG-relevant features from a CNN trained on raw EEG data: optimal samples for each classification category, activation maximization, and reverse convolution. We applied these methods to a high-performing Deep Learning model with state-of-the-art performance for an EEG sex classification task, and show that the model features a difference in the theta frequency band. We show that visualization of a CNN model can reveal interesting EEG results. Using these tools, EEG researchers using Deep Learning can better identify the learned EEG features, possibly identifying new class relevant biomarkers.
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy
Venue: arXiv cs.CV 2021
Year: 2021

Aiming at recognizing the samples from novel categories with few reference samples, few-shot learning (FSL) is a challenging problem. We found that the existing works often build their few-shot model based on the image-level feature by mixing all local-level features, which leads to the discriminative location bias and information loss in local details. To tackle the problem, this paper returns the perspective to the local-level feature and proposes a series of local-level strategies. Specifically, we present (a) a local-agnostic training strategy to avoid the discriminative location bias between the base and novel categories, (b) a novel local-level similarity measure to capture the accurate comparison between local-level features, and (c) a local-level knowledge transfer that can synthesize different knowledge transfers from the base category according to different location features. Extensive experiments justify that our proposed local-level strategies can significantly boost the performance and achieve 2.8%-7.2% improvements over the baseline across different benchmark datasets, which also achieves state-of-the-art accuracy.
Is knowledge the key? An experiment on debiasing architectural decision-making -- a pilot study
Venue: arXiv cs.SE 2021
Year: 2021

The impact of cognitive biases on architectural decision-making has been proven by previous research. In this work, we endeavour to create a debiasing treatment that would minimise the impact of cognitive biases on architectural decision-making. We conducted a pilot study on two groups of students, to investigate whether a simple debiasing presentation reporting on the influences of cognitive biases, can provide a debiasing effect. The preliminary results show that this kind of treatment is ineffective. Through analysing our results, we propose a set of modifications that could result in a better effect.
Sequence pairs related to produced graphs from a method for dividing a natural number by two
Venue: arXiv cs.CR 2021
Year: 2021

This paper is about producing a new kind of the pairs which we call it MS-pairs. To produce these pairs, we use an algorithm for dividing a natural number $x$ by two for two arbitrary numbers and consider their related graphs. We present some applications of these pairs that show their interesting properties such as unpredictability, irreversible, aperiodicity and chaotic behavior.
Geometrically Adaptive Dictionary Attack on Face Recognition
Venue: arXiv cs.CV 2021
Year: 2021

CNN-based face recognition models have brought remarkable performance improvement, but they are vulnerable to adversarial perturbations. Recent studies have shown that adversaries can fool the models even if they can only access the models' hard-label output. However, since many queries are needed to find imperceptible adversarial noise, reducing the number of queries is crucial for these attacks. In this paper, we point out two limitations of existing decision-based black-box attacks. We observe that they waste queries for background noise optimization, and they do not take advantage of adversarial perturbations generated for other images. We exploit 3D face alignment to overcome these limitations and propose a general strategy for query-efficient black-box attacks on face recognition named Geometrically Adaptive Dictionary Attack (GADA). Our core idea is to create an adversarial perturbation in the UV texture map and project it onto the face in the image. It greatly improves query efficiency by limiting the perturbation search space to the facial area and effectively recycling previous perturbations. We apply the GADA strategy to two existing attack methods and show overwhelming performance improvement in the experiments on the LFW and CPLFW datasets. Furthermore, we also present a novel attack strategy that can circumvent query similarity-based stateful detection that identifies the process of query-based black-box attacks.
Get a Model! Model Hijacking Attack Against Machine Learning Models
Venue: arXiv cs.CR 2021
Year: 2021

Machine learning (ML) has established itself as a cornerstone for various critical applications ranging from autonomous driving to authentication systems. However, with this increasing adoption rate of machine learning models, multiple attacks have emerged. One class of such attacks is training time attack, whereby an adversary executes their attack before or during the machine learning model training. In this work, we propose a new training time attack against computer vision based machine learning models, namely model hijacking attack. The adversary aims to hijack a target model to execute a different task than its original one without the model owner noticing. Model hijacking can cause accountability and security risks since a hijacked model owner can be framed for having their model offering illegal or unethical services. Model hijacking attacks are launched in the same way as existing data poisoning attacks. However, one requirement of the model hijacking attack is to be stealthy, i.e., the data samples used to hijack the target model should look similar to the model's original training dataset. To this end, we propose two different model hijacking attacks, namely Chameleon and Adverse Chameleon, based on a novel encoder-decoder style ML model, namely the Camouflager. Our evaluation shows that both of our model hijacking attacks achieve a high attack success rate, with a negligible drop in model utility.
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points
Venue: arXiv cs.SD 2021
Year: 2021

Numerous compression and acceleration strategies have achieved outstanding results on classification tasks in various fields, such as computer vision and speech signal processing. Nevertheless, the same strategies have yielded ungratified performance on regression tasks because the nature between these and classification tasks differs. In this paper, a novel sign-exponent-only floating-point network (SEOFP-NET) technique is proposed to compress the model size and accelerate the inference time for speech enhancement, a regression task of speech signal processing. The proposed method compressed the sizes of deep neural network (DNN)-based speech enhancement models by quantizing the fraction bits of single-precision floating-point parameters during training. Before inference implementation, all parameters in the trained SEOFP-NET model are slightly adjusted to accelerate the inference time by replacing the floating-point multiplier with an integer-adder. For generalization, the SEOFP-NET technique is introduced to different speech enhancement tasks in speech signal processing with different model architectures under various corpora. The experimental results indicate that the size of SEOFP-NET models can be significantly compressed by up to 81.249% without noticeably downgrading their speech enhancement performance, and the inference time can be accelerated to 1.212x compared with the baseline models. The results also verify that the proposed SEOFP-NET can cooperate with other efficiency strategies to achieve a synergy effect for model compression. In addition, the just noticeable difference (JND) was applied to the user study experiment to statistically analyze the effect of speech enhancement on listening. The results indicate that the listeners cannot facilely differentiate between the enhanced speech signals processed by the baseline model and the proposed SEOFP-NET.
Addressing Privacy Threats from Machine Learning
Venue: arXiv cs.CY 2021
Year: 2021

Every year at NeurIPS, machine learning researchers gather and discuss exciting applications of machine learning in areas such as public health, disaster response, climate change, education, and more. However, many of these same researchers are expressing growing concern about applications of machine learning for surveillance (Nanayakkara et al., 2021). This paper presents a brief overview of strategies for resisting these surveillance technologies and calls for greater collaboration between machine learning and human-computer interaction researchers to address the threats that these technologies pose.
"If we didn't solve small data in the past, how can we solve Big Data today?"
Venue: arXiv cs.CY 2021
Year: 2021

Data is a critical aspect of the world we live in. With systems producing and consuming vast amounts of data, it is essential for businesses to digitally transform and be equipped to derive the most value out of data. Data analytics techniques can be used to augment strategic decision-making. While this overall objective of data analytics remains fairly constant, the data itself can be available in numerous forms and can be categorized under various contexts. In this paper, we aim to research terms such as 'small' and 'big' data, understand their attributes, and look at ways in which they can add value. Specifically, the paper probes into the question "If we didn't solve small data in the past, how can we solve Big Data today?". Based on the research, it can be inferred that, regardless of how small data might have been used, organizations can still leverage big data with the right technology and business vision.
Systematic Review for AI-based Language Learning Tools
Venue: arXiv cs.CY 2021
Year: 2021

The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI). Although increasingly adaptive language learning tools are being developed with the application of AI to the Computer Assisted Language Learning field, there have been concerns regarding insufficient information and teacher preparation. To effectively utilize these tools, teachers need an in-depth overview on recently developed AI-based language learning tools. Therefore, this review synthesized information on AI tools that were developed between 2017 and 2020. A majority of these tools utilized machine learning and natural language processing, and were used to identify errors, provide feedback, and assess language abilities. After using these tools, learners demonstrated gains in their language abilities and knowledge. This review concludes by presenting pedagogical implications and emerging themes in the future research of AI-based language learning tools.
Mem3DG: Modeling Membrane Mechanochemical Dynamics in 3D using Discrete Differential Geometry
Venue: arXiv cs.CE 2021
Year: 2021

Biomembranes adopt varying morphologies that are vital to cellular functions. Many studies use computational modeling to understand how various mechanochemical factors contribute to membrane shape transformations. Compared to approximation-based methods (e.g., finite element method), the class of discrete mesh models offers greater flexibility to simulate complex physics and shapes in three dimensions; its formulation produces an efficient algorithm while maintaining coordinate-free geometric descriptions. However, ambiguities in geometric definitions in the discrete context have led to a lack of consensus on which discrete mesh model is theoretically and numerically optimal; a bijective relationship between the terms contributing to both the energy and forces from the discrete and smooth geometric theories remains to be established. We address this and present an extensible framework, $\texttt{Mem3DG}$, for modeling 3D mechanochemical dynamics of membranes based on Discrete Differential Geometry (DDG) on triangulated meshes. The formalism of DDG resolves the inconsistency and provides a unifying perspective on how to relate the smooth and discrete energy and forces. To demonstrate, $\texttt{Mem3DG}$ is used to model a sequence of examples with increasing mechanochemical complexity: recovering classical shape transformations such as 1) biconcave disk, dumbbell, and unduloid and 2) spherical bud on spherical, flat-patch membrane; investigating how the coupling of membrane mechanics with protein mobility jointly affects phase and shape transformation. As high-resolution 3D imaging of membrane ultrastructure becomes more readily available, we envision Mem3DG to be applied as an end-to-end tool to simulate realistic cell geometry under user-specified mechanochemical conditions.
Virtual Reality for Emotion Elicitation -- A Review
Venue: arXiv cs.HC 2021
Year: 2021

Emotions are multifaceted phenomena that affect our behaviour, perception, and cognition. Increasing evidence indicates that induction mechanisms play a crucial role in triggering emotions by simulating the sensations required for an experimental design. Over the years, many reviews have evaluated a passive elicitation mechanism where the user is an observer, ignoring the importance of self-relevance in emotional experience. So, in response to the gap in the literature, this study intends to explore the possibility of using Virtual Reality (VR) as an active mechanism for emotion induction. Furthermore, for the success and quality of research settings, VR must select the appropriate material to effectively evoke emotions. Therefore, in the present review, we evaluated to what extent VR visual and audio-visual stimuli, games, and tasks, and 360-degree panoramas and videos can elicit emotions based on the current literature. Further, we present public datasets generated by VR and emotion-sensing interfaces that can be used in VR based research. The conclusions of this survey reveal that VR has a great potential to evoke emotions effectively and naturally by generating motivational and empathy mechanisms which makes it an ecologically valid paradigm to study emotions.
Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method
Venue: arXiv cs.LG 2021
Year: 2021

Overweight and obesity remain a major global public health concern and identifying the individualized patterns that increase the risk of future weight gains has a crucial role in preventing obesity and numerous sub-sequent diseases associated with obesity. In this work, we use a rule discovery method to study this problem, by presenting an approach that offers genuine interpretability and concurrently optimizes the accuracy(being correct often) and support (applying to many samples) of the identified patterns. Specifically, we extend an established subgroup-discovery method to generate the desired rules of type X -> Y and show how top features can be extracted from the X side, functioning as the best predictors of Y. In our obesity problem, X refers to the extracted features from very large and multi-site EHR data, and Y indicates significant weight gains. Using our method, we also extensively compare the differences and inequities in patterns across 22 strata determined by the individual's gender, age, race, insurance type, neighborhood type, and income level. Through extensive series of experiments, we show new and complementary findings regarding the predictors of future dangerous weight gains.
Beyond Participation: A Review of Co-Creation in Computing
Venue: arXiv cs.HC 2021
Year: 2021

New methods and technologies for engaging future users and other stakeholders in participatory (design) processes are being developed and proposed. Increasingly, researchers refer to co-creation in order to capture such approaches. However, how co-creation is being framed and understood across domains differs substantially. To better understand co-creation in computing, we conducted a literature review of all papers in the ACM Digital Library with co-creation or co-create in their abstracts. After an initial screening, we retained 62 for further analysis. We introduce a framework to analyze different notions of co-creation, distinguishing between co-creation target audiences, the roles of co-creators, the role of technology (as means or objective) and its results. We discuss the adoption of co-creation in domains such as learning, business, arts & culture, health, and the public sector. This paper contributes to the understanding of different approaches and conceptualizations of co-creation in computing and puts forward an agenda for future research.
D-Flow: A Real Time Spatial Temporal Model for Target Area Segmentation
Venue: arXiv cs.RO 2021
Year: 2021

Semantic segmentation has attracted a large amount of attention in recent years. In robotics, segmentation can be used to identify a region of interest, or \emph{target area}. For example, in the RoboCup Standard Platform League (SPL), segmentation separates the soccer field from the background and from players on the field. For satellite or vehicle applications, it is often necessary to find certain regions such as roads, bodies of water or kinds of terrain. In this paper, we propose a novel approach to real-time target area segmentation based on a newly designed spatial temporal network. The method operates under domain constraints defined by both the robot's hardware and its operating environment . The proposed network is able to run in real-time, working within the constraints of limited run time and computing power. This work is compared against other real time segmentation methods on a dataset generated by a Nao V6 humanoid robot simulating the RoboCup SPL competition. In this case, the target area is defined as the artificial grass field. The method is also tested on a maritime dataset collected by a moving vessel, where the aim is to separate the ocean region from the rest of the image. This dataset demonstrates that the proposed model can generalise to a variety of vision problems.
A Composite Centrality Measure for Improved Identification of Influential Users
Venue: arXiv cs.SI 2021
Year: 2021

In recent years, the problem of identifying the spreading ability and ranking social network users according to their influence has attracted a lot of attention; different approaches have been proposed for this purpose. Most of these approaches rely on the topological location of nodes and their neighbours in the graph to provide a measure that estimates the spreading ability of users. One of the most well-known measures is k-shell; additional measures have been proposed based on it. However, as the same k-shell index may be assigned to nodes with different degrees, this measure suffers from low accuracy. This paper is trying to improve this by proposing a composite centrality measure in that it combines both the degree and k-shell index of nodes. Experimental results and evaluations of the proposed measure on various real and artificial networks show that the proposed measure outperforms other state-of-the-art measures regarding monotonicity and accuracy.
AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models
Venue: arXiv cs.CL 2021
Year: 2021

This paper describes our participation in the DEtection of TOXicity in comments In Spanish (DETOXIS) shared task 2021 at the 3rd Workshop on Iberian Languages Evaluation Forum. The shared task is divided into two related classification tasks: (i) Task 1: toxicity detection and; (ii) Task 2: toxicity level detection. They focus on the xenophobic problem exacerbated by the spread of toxic comments posted in different online news articles related to immigration. One of the necessary efforts towards mitigating this problem is to detect toxicity in the comments. Our main objective was to implement an accurate model to detect xenophobia in comments about web news articles within the DETOXIS shared task 2021, based on the competition's official metrics: the F1-score for Task 1 and the Closeness Evaluation Metric (CEM) for Task 2. To solve the tasks, we worked with two types of machine learning models: (i) statistical models and (ii) Deep Bidirectional Transformers for Language Understanding (BERT) models. We obtained our best results in both tasks using BETO, an BERT model trained on a big Spanish corpus. We obtained the 3rd place in Task 1 official ranking with the F1-score of 0.5996, and we achieved the 6th place in Task 2 official ranking with the CEM of 0.7142. Our results suggest: (i) BERT models obtain better results than statistical models for toxicity detection in text comments; (ii) Monolingual BERT models have an advantage over multilingual BERT models in toxicity detection in text comments in their pre-trained language.
Time- and Space-Efficient Regular Path Queries on Graphs
Venue: arXiv cs.DB 2021
Year: 2021

We introduce a time- and space-efficient technique to solve regularpath queries over labeled graphs. We combine a bit-parallel simula-tion of the Glushkov automaton of the regular expression with thering index introduced by Arroyuelo et al., exploiting its wavelettree representation of the triples in order to efficiently reach thestates of the product graph that are relevant for the query. Ourquery algorithm is able to simultaneously process several automa-ton states, as well as several graph nodes/labels. Our experimentalresults show that our representation uses 3-5 times less space thanthe alternatives in the literature, while generally outperformingthem in query times (1.67 times faster than the next best).
Accelerating GAN training using highly parallel hardware on public cloud
Venue: arXiv cs.LG 2021
Year: 2021

With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using Tensorflow data parallel strategy. More specifically, we parallelize the training process on multiple GPUs and Google Tensor Processing Units (TPU) and we compare two algorithms: the TensorFlow built-in logic and a custom loop, optimised to have higher control of the elements assigned to each GPU worker or TPU core. The quality of the generated data is compared to Monte Carlo simulation. Linear speed-up of the training process is obtained, while retaining most of the performance in terms of physics results. Additionally, we benchmark the aforementioned approaches, at scale, over multiple GPU nodes, deploying the training process on different public cloud providers, seeking for overall efficiency and cost-effectiveness. The combination of data science, cloud deployment options and associated economics allows to burst out heterogeneously, exploring the full potential of cloud-based services.