Reviewerly/exHarmony huggingface dataset:
Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.
Security of Neural Networks from Hardware Perspective

Recent advances in neural networks (NNs) and their applications in deep learning techniques have made the security aspects of NNs an important and timely topic for fundamental research. In this paper, we survey the security challenges and opportunities in the computing hardware used in implementing deep neural networks (DNN). First, we explore the hardware attack surfaces for DNN. Then, we report the current state-of-the-art hardware-based attacks on DNN with focus on hardware Trojan insertion, fault injection, and side-channel analysis. Next, we discuss the recent development on detecting these hardware-oriented attacks and the corresponding countermeasures. We also study the application of secure enclaves for the trusted execution of NN-based algorithms. Finally, we consider the emerging topic of intellectual property protection for deep learning systems. Based on our study, we find ample opportunities for hardware based research to secure the next generation of DNN-based artificial intelligence and machine learning platforms.
Summary of Target Detection Algorithms

Abstract In recent years, the soaring development of CNN has facilitated the maturity of the Computer Vision Algorithm. This paper will briefly introduce some representative Target Detection Algorithm, and systematically analyze the underlying problems, the modified methods, and the prospective direction of the algorithm in accordance with its merits and demerits. It is generally divided into a single-stage detection model and a double-stage detection model in terms of whether candidate areas need to be extracted during target detection for further tasks. Featured with scale in the double-stage detection model, the algorithm is divided into single-scale detection and multi-scale detection based on whether it can appropriately integrate with a network structure, which enhances the accuracy of the network model towards small targets. Meanwhile, it can also be divided into anchor-base and anchor-free in a single-stage detection model on the basis of the anchor bolt. A predictable development of the Target Detection Algorithm will show to us in the future.
Towards Inspecting and Eliminating Trojan Backdoors in Deep Neural Networks

A trojan backdoor is a hidden pattern typically implanted in a deep neural network (DNN). It could be activated and thus forces that infected model to behave abnormally when an input sample with a particular trigger is fed to that model. As such, given a DNN and clean input samples, it is challenging to inspect and determine the existence of a trojan backdoor. Recently, researchers design and develop several pioneering solutions to address this problem. They demonstrate that the proposed techniques have great potential in trojan detection. However, we show that none of these existing techniques completely address the problem. On the one hand, they mostly work under an unrealistic assumption of assuming the availability of the contaminated training database. On the other hand, these techniques can neither accurately detect the existence of trojan backdoors, nor restore high-fidelity triggers, especially when infected models are trained with high-dimensional data, and the triggers pertaining to the trojan vary in size, shape, and position. In this work, we propose TABOR, a new trojan detection technique. Conceptually, it formalizes the detection of a trojan backdoor as solving an optimization objective function. Different from the existing technique which also models trojan detection as an optimization problem, TABOR first designs a new objective function that could guide optimization to identify a trojan backdoor more correctly and accurately. Second, TABOR borrows the idea of interpretable AI to further prune the restored triggers. Last, TABOR designs a new anomaly detection method, which could not only facilitate the identification of intentionally injected triggers but also filter out false alarms (i.e., triggers detected from an uninfected model). We train 112 DNNs on five datasets and infect these models with two existing trojan attacks. We evaluate TABOR by using these infected models, and demonstrate that TABOR has much better performance in trigger restoration, trojan detection, and elimination than Neural Cleanse, the state-of-the-art trojan detection technique.
Digital Forensic Analysis of Files Using Deep Learning

Digital forensic experts are responsible for assisting law enforcement in extracting evidence from electronic devices. Identifying a file type within digital evidence is an essential part of the forensic practice. This paper investigated the existing forensic approaches to identify the file type and developed a new approach based on deep learning and overcome previous approaches' limitations. This paper also highlighted the difference between modern and traditional methods to conduct such an analysis. Whereas, most traditional techniques have been identified to have challenges emanating from the approach structure, which influences how file types are identified, which has prompted researchers in the field to look for new systems that will address this gap. Thus, a new methodology is proposed, which will utilize deep learning techniques to provide a model able to predict corrupted files.
Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning

Automated machine learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoMLis an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout. In this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions.
The Kolmogorov–Arnold representation theorem revisited

There is a longstanding debate whether the Kolmogorov–Arnold representation theorem can explain the use of more than one hidden layer in neural networks. The Kolmogorov–Arnold representation decomposes a multivariate function into an interior and an outer function and therefore has indeed a similar structure as a neural network with two hidden layers. But there are distinctive differences. One of the main obstacles is that the outer function depends on the represented function and can be wildly varying even if the represented function is smooth. We derive modifications of the Kolmogorov–Arnold representation that transfer smoothness properties of the represented function to the outer function and can be well approximated by ReLU networks. It appears that instead of two hidden layers, a more natural interpretation of the Kolmogorov–Arnold representation is that of a deep neural network where most of the layers are required to approximate the interior function.
Holistic information research: From rhetoric to paradigm

Abstract Many researchers in library and information science have claimed that studies that are holistic are critical to understanding various phenomena. On closer examination, however, the term “holistic” is used mainly as a rhetorical device in the literature, rather than as one that embraces the epistemological tenets of a holistic paradigm, and applies these to research design. This paper examines this rhetorical use, and explores what it would mean, and why it would matter, to adopt substantively holistic approaches to research. We review relevant literature in library and information science to position past uses of holistic and compare these to the conceptual intentions of holism . We also outline the concept of holism , itself, with a focus on methodological and ontological holism , which can most deeply inform research design in our discipline. Greater methodological diversity, including much wider adoption of interpretivist and critical approaches, can address the concerns underlying the use of holistic rhetoric. We illustrate this central conceptual argument with a roadmap illustrating holistic considerations throughout the research process. The paper demonstrates that it is possible to shift away from predominantly rhetorical use of holistic , toward paradigmatically holistic research, which will provide for richer analyses of critical phenomena in the discipline.
Head Motion and Eyes Blinking Detection: a mm-Wave Radar for Assisting People with Neurodegenerative Disorders

In this paper, a millimeter-wave radar operating at the frequency of 120 GHz is employed as an aid for people with neurodegenerative disorders. In detail, an interferometric approach is exploited for detecting the movement of the head and the eyes blinking. As a matter of fact, these actions are among the very few ways to enable the communication of a person with the surrounding environment in case of heavy neurodegenerative disorder. The theoretical principles and the relevant aspects of the proposed solution are shown by simulating the entire scenario, whereas the effectiveness of the system is experimentally demonstrated.
Parallel stochastic configuration networks for large-scale data regression

Stochastic configuration network (SCN) is a new powerful approach for large-scale data processing which introduces a supervisory mechanism to configure the parameters of each hidden node stochastically. To enhance the accuracy and robustness of SCN, a parallel stochastic configuration network (PSCN) based on the beetle antennae search (BAS) and fuzzy evidence theory is presented. Firstly, we propose a fuzzy evidence theory for the data fusion of multiple neural networks; Secondly, to choose a suitable scale factor λ of weights and biases, BAS, as a meta-heuristic algorithm which only uses one individual to search for optimal parameter, it is appropriate for parameter selection of SCN, termed as BAS-SCN. Thirdly, parallel training of multiple BAS-SCNs with different objective functions, then several preliminary results of BAS-SCNs are fused by the fuzzy evidence theory to obtain the final results of PSCN. Finally, a complicated real-world dataset (IEEE 2012 PHM) is used to verify the performance of the PSCN. Numerical experimental results show that BAS-SCN performs well in parameter optimization of SCN, PSCN not only has the advantages of BAS-SCN, but also has a higher accuracy and stronger robustness. PSCN has a better performance in the prediction of bearing remaining useful life.
Knowledge Distillation for Multi-task Learning

Multi-task learning (MTL) is to learn one single model that performs multiple tasks for achieving good performance on all tasks and lower cost on computation. Learning such a model requires to jointly optimize losses of a set of tasks with different difficulty levels, magnitudes, and characteristics (e.g. cross-entropy, Euclidean loss), leading to the imbalance problem in multi-task learning. To address the imbalance problem, we propose a knowledge distillation based method in this work. We first learn a task-specific model for each task. We then learn the multi-task model for minimizing task-specific loss and for producing the same feature with task-specific models. As the task-specific network encodes different features, we introduce small task-specific adaptors to project multi-task features to the task-specific features. In this way, the adaptors align the task-specific feature and the multi-task feature, which enables a balanced parameter sharing across tasks. Extensive experimental results demonstrate that our method can optimize a multi-task learning model in a more balanced way and achieve better overall performance.
Iterative weak/self-supervised classification framework for abnormal events detection

The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et al., we introduce one iterative learning framework composed of two experts working in the weak and self-supervised paradigms and providing additional amounts of learning data to each other, where the novel instances at each iteration are filtered by a Bayesian framework that supports the iterative data augmentation task; 2) we describe a novel term that is added to the baseline loss to spread the scores in the unit interval, which is crucial for the performance of the iterative framework; 3) we propose a Random Forest ensemble that fuses at the score level the top performing methods and reduces the EER values about 20% over the state-of-the-art; and 4) we announce the availability of the ”UBI-Fights” dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/.
An Integrated CNN-LSTM Model for Micro Hand Gesture Recognition

Vision based micro gesture recognition systems enable the development of HCI (Human Computer Interaction) interfaces to mirror real-world experiences. It is unlikely that a gesture recognition method will be suitable for every application, as each gesture recognition system rely on the user cultural background and application domain. This research is an attempt to develop a micro gesture recognition system suitable for the asian culture. However, hands vary in shapes and sizes while gesture varies in orientation and motion. For accurate feature extraction, deep learning approaches are considered. Here, an integrated CNN-LSTM (Convolutional Neural Network- Long Short-Term Memory) model is proposed for building micro gesture recognition system. To demonstrate the applicability of the system two micro hand gesture-based datasets namely, standard and local dataset consisting of ten significant classes are used. Besides, the model is tested against both augmented and unaugmented datasets. The accuracy achieved for standard data with augmentation is 99.0%, while the accuracy achieved for local data with augmentation is 96.1% by applying CNN-LSTM model. In case of both the datasets, the proposed CNN-LSTM model appears to perform better than the other pre-trained CNN models including ResNet, MobileNet, VGG16 and VGG9 as well as CNN excluding LSTM.
Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.
Learning privacy-enhancing face representations through feature disentanglement

Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art.
BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems

Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predicts different sentiments for similar texts that differ only in the characteristic of individuals described. To automatically uncover bias in SA systems, this paper presents BiasFinder, an approach that can discover biased predictions in SA systems via metamorphic testing. A key feature of BiasFinder is the automatic curation of suitable templates from any given text inputs, using various Natural Language Processing (NLP) techniques to identify words that describe demographic characteristics. Next, BiasFinder generates new texts from these templates by mutating words associated with a class of a characteristic (e.g., gender-specific words such as female names, she, her). These texts are then used to tease out bias in an SA system. BiasFinder identifies a bias-uncovering test case (BTC) when an SA system predicts different sentiments for texts that differ only in words associated with a different class (e.g., male vs. female) of a target characteristic (e.g., gender). We evaluate BiasFinder on 10 SA systems and 2 large scale datasets, and the results show that BiasFinder can create more BTCs than two popular baselines. We also conduct an annotation study and find that human annotators consistently think that test cases generated by BiasFinder are more fluent than the two baselines.
FineFool: A novel DNN object contour attack on image recognition based on the attention perturbation adversarial technique

Deep neural networks (DNNs) have various applications owing to their feature learning ability. However, recent studies have shown that DNNs are vulnerable to adversarial examples. Currently, research on the generation of adversarial examples primarily focuses on improving the attack success rate (ASR) while reducing the perturbation size. By visualizing of heat maps, previous works have found that the feature extraction effect of DNNs is owing to the precise location of object contours and the provision of the correct attention to those areas. Therefore, the perturbations in adversarial examples will weaken the location of object contours in deep hidden layers and reduce the attention scope of the object area, which will lead to successful attacks. Inspired by this observation, we propose FineFool, a novel adversarial attack based on the attention perturbation adversarial technique, which includes channel-spatial attention and pixel-spatial attention. The former reduces the area of concern using DNNs while the latter achieves the error location of the object contours. By using the attention perturbation adversarial technique to target positions that are more vulnerable in legitimate examples, FineFool achieves a higher ASR with fewer perturbations compared with that of state-of-the-art adversarial attacks. Extensive experiments are carried out on MNIST, CIFAR10, and ImageNet datasets against six models. The results show that FineFool can achieve the best performance compared with the six baselines. More specifically, the mean ASR values of untargeted/targeted attack are 99.23% and 98.26% for FineFool on all datasets, respectively, which is the highest under white-box attack situations. The code of FineFool is open sourced at https://zenodo.org/record/4421611#.X.