Reviewerly/exHarmony huggingface dataset:
On General Data Protection Regulation Vulnerabilities and Privacy Issues, for Wearable Devices and Fitness Tracking Applications

Individual users’ sensitive information, such as heart rate, calories burned, or even sleep patterns, are casually tracked by smart wearable devices to be further processed or exchanged, utilizing the ubiquitous capabilities of Internet of Things (IoT) technologies. This work aims to explore the existing literature on various data privacy concerns, posed by the use of wearable devices, and experimentally analyze the data exchanged through mobile applications, in order to identify the underlying privacy and security risks. Emulating a man-in-the-middle attack scenario, five different commercial fitness tracking bands are examined, in order to test and analyze all data transmitted by each vendor’s suggested applications. The amount of personal data collected, processed, and transmitted for advertising purposes was significant and, in some cases, highly affected the network’s total overhead. Some of the applications examined requested access for sensitive data driven device functionalities, such as messaging, phone calling, audio recording, and camera usage, without any clear or specific reason stated by their privacy policy. This paper concludes by listing the most critical aspects in terms of privacy and security concerning some of the most popular commercial fitness tracking applications.
Apparel-Invariant Feature Learning for Person Re-Identification

With the rise of deep learning methods, person Re-Identification (ReID) performance has been improved tremendously in many public datasets. However, most public ReID datasets are collected in a short time window in which persons’ appearance rarely changes. In real-world applications such as in a shopping mall, the same person may change their wearings, and different persons may wear similar apparel. It reveals a critical problem that current ReID models heavily rely on a person’s apparel, resulting in an inconsistent ReID performance. Therefore, it is crucial to learn an apparel-invariant person representation under clothes changing or several persons wearing similar clothes cases. In this work, we tackle this problem from the viewpoint of invariant feature representation learning. The main contributions of this work are as follows. (1) We propose the semi-supervised Apparel-invariant Feature Learning (AIFL) framework to learn an apparel-invariant pedestrian representation using images of the same person wearing different clothes. (2) To obtain images of the same person wearing different clothes, we propose an unsupervised apparel-simulation GAN (AS-GAN) to synthesize cloth-changing images according to the target cloth embedding. It is worth noting that the images used in ReID tasks were cropped from real-world low-quality CCTV videos, making it more challenging to synthesize cloth-changing images. Extensive experiments demonstrate that our proposal can improve the ReID performance of the baseline models.
Low-Code Is Often High-Code, So We Must Design Low-Code Platforms to Enable Proper Software Engineering

The concept of low-code (and no-code) platforms has been around for decades, even before the term was used. The idea is that applications on these platforms can be built by people with less technical expertise than a professional programmer, yet can leverage powerful technology such as, for example, for databases, financial analysis, web development and machine learning. However, in practice, software written on such platforms often accumulates large volumes of complex code, which can be worse to maintain than in traditional languages because the low-code platforms tend not to properly support good engineering practices such as version control, separation of concerns, automated testing and literate programming. In this paper we discuss experiences with several low-code platforms and provide suggestions for directions forward towards an era where the benefits of low-code can be obtained without accumulation of technical debt. Our recommendations focus on ensuring low-code platforms enable scaling, understandability, documentability, testability, vendor-independence, and the overall user experience for developers those end-users who do some development.
Efficient machine learning approach for volunteer eye-blink detection in real-time using webcam

The progressive diminishment of motor capacities due to Amyotrophic Lateral Sclerosis (ALS) causes a severe communication deficit. The development of Alternative Communication software aids ALS patients in overcoming communication issues and the detection of communication signals plays a big role in this task. In this paper, volunteer eye-blinking is proposed as human–computer interaction signal and an intelligent Computer Vision detector was built for handling the captured data in real-time using a generic webcam. The eye-blink detection was treated as an extension of the eye-state classification, and the base pipeline used is delineated as follows: face detection, face alignment, region-of-interest (ROI) extraction, and eye-state classification. Furthermore, this pipeline was complemented with auxiliary models: a rotation compensator, a ROIs evaluator, and a moving average filter. Two new datasets were created: the Youtube Eye-state Classification (YEC) dataset, built from the AVSpeech dataset by extracting face images; and the Autonomus Blink Dataset (ABD), built completely as a result of the present work. The YEC allowed training the eye-classification task; ABD was specifically idealized taking into consideration volunteer eye-blinking detection. The proposed models, a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM), were trained by the YEC dataset and performance evaluation experiments for both models were conducted across different databases: CeW, ZJU, Eyeblink, Talking Face (public datasets) and ABD. The impact of the proposed auxiliary models was evaluated and the CNN and SVM models were compared for the eye-state classification task. Promising results were obtained: 97.44% accuracy for the eye-state classification task on the CeW dataset and 92.63% F1-Score for the eye-blink detection task on the ABD dataset.
Demystifying the Transferability of Adversarial Attacks in Computer Networks

Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry. Recent studies demonstrated that adversarial attacks against such models can maintain their effectiveness even when used on models other than the one targeted by the attacker. This major property is known as transferability, and makes CNNs ill-suited for security applications. In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. Furthermore, we investigate whether the transferability property issue holds in computer networks applications. In our experiments, we first consider five different attacks: the Iterative Fast Gradient Method (I-FGSM), the Jacobian-based Saliency Map (JSMA), the Limited-memory Broyden Fletcher Goldfarb Shanno BFGS (L-BFGS), the Projected Gradient Descent (PGD), and the DeepFool attack. Then, we perform these attacks against three well-known datasets: the Network-based Detection of IoT (N-BaIoT) dataset, the Domain Generating Algorithms (DGA) dataset, and the RIPE Atlas dataset. Our experimental results show clearly that the transferability happens in specific use cases for the I-FGSM, the JSMA, and the LBFGS attack. In such scenarios, the attack success rate on the target network range from 63.00% to 100%. Finally, we suggest two shielding strategies to hinder the attack transferability, by considering the Most Powerful Attacks (MPAs), and the mismatch LSTM architecture.
3D-PMRNN: Reconstructing three-dimensional porous media from the two-dimensional image with recurrent neural network

The accurate reconstruction of three-dimensional(3D) structure of porous media is crucial for predicting their physical or mechanical properties. Reconstructing the 3D structure from its reference two-dimensional(2D) image is an intractable inverse problem. Although many traditional methods have been proposed to address this problem, their low efficiency and poor performance limit their application. Recently, the deep learning(DL) based methods have attracted widespread attention. However, the existing DL-based methods suffer from some serious defects, including heavy demand for training samples, unstable training and high GPU memory requirement. In this study, a recurrent neural network(RNN) based model, namely 3D-PMRNN, has been proposed to overcome these weaknesses. To the best of our knowledge, it is the first time that RNN model has been applied to solve the 2D-TO-3D reconstruction problem. Benefiting from this innovative architecture, the model just requires one 3D training sample at least and reconstructs the 3D structure layer-by-layer. Furthermore, the proposed model can reconstruct a larger-scale realization(2563 or larger) due to the novel network architecture, less demand for GPU memory and stable training process. The reconstruction efficiency has also been greatly improved compared to the traditional methods. Three experiments are carried out on isotropic and anisotropic porous media to verify the model’s performance on accuracy, diversity and generalization. Experimental results indicate that the synthetic realizations have good agreement with the testing target in terms of visual observation and quantitative comparison.
Distributed Secure Storage Scheme Based on Sharding Blockchain

Distributed storage can store data in multiple devices or servers to improve data security.However, in today's explosive growth of network data, traditional distributed storage scheme is faced with some severe challenges such as insufficient performance, data tampering, and data lose.A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage.Under this scheme, the following improvements have been made in this paper.This paper first analyzes the problems faced by distributed storage.Then proposed to build a new distributed storage blockchain scheme with sharding blockchain.The proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology, which can improve the efficiency of data verification between nodes.In addition, this paper uses polynomial commitment to construct a new verifiable secret share scheme called PolyVSS.This new scheme is one of the foundations for building our improved distributed storage blockchain scheme.Compared with the previous scheme, our new scheme does not require a trusted third party and has some new features such as homomorphic and batch opening.The security of VSS can be further improved.Experimental comparisons show that the proposed scheme significantly reduces storage and communication costs.
Estimation of water level fluctuations in groundwater through a hybrid learning machine

In the present study, a new hybrid artificial intelligence-based model is presented to simulate monthly time-series data relevant to groundwater quantity collected from an observation well located in Kermanshah, Iran. To define the hybrid AI model, the differential evolutionary is applied to optimize the Extreme Learning Machine (ELM) so that the Self Adaptive ELM (SAELM) is developed and the wavelet transform is employed to decompose the input variables of the model (WSAELM). The autocorrelation function is utilized for detecting effective lags of the time-series data. Moreover, 70% of the observed data is used to train the AI techniques and the rest (i.e. 30%) is applied in test mode. Then, using these influencing lags, different models are defined for the SAELM and WSAELM models. After that, superior models for the simulation of GWL are selected by evaluating different input combinations. For instance, the values of the Nash-Sutcliffe efficiency coefficient, variance accounted for (VAF) and correlation coefficient (R) for the superior WSAELM model are obtained to be 0.973, 97.450 and 0.988, respectively. The prediction uncertainty of the developed SAELM model for GWL is quantified with a value of ±0.081. Finally, a formula is developed for computing GWL. The obtained results show that the applied hybrid machine learning model can be utilized to estimate the groundwater quantity parameter (i.e. groundwater level) in the Karnachi well and the possible applicability of the WSAELM technique to other areas.
Developing Physics Experiments Using Augmented Reality Game-Based Learning Approach: A Pilot Study in Primary School

The augmented reality game-based learning (ARGBL) approach is an advantageous pathway for the development and enhancement of teaching and learning processes. To this end, this paper presents the design and development of an ARGBL application for the implementation of physics experiments in the fifth grade of a Greek primary school. The purpose of the ARGBL system is twofold: to educate and entertain. For this reason, a treasure hunt game was implemented, which allows students to interact with a digital world and to manipulate virtual objects with the use of an augmented reality (AR) device. Then, according to the instructions, students have to collect all the materials to conduct the AR educational experiment. Overall, the evaluation of the system’s usability by 17 users (both students and teachers) was very promising, indicating that the ARGBL application has the potential to be an easy-to-use educational tool for improving not only the teaching of physics experiments in primary school but also the learning process, by positively affecting the students’ motivation and engagement.
Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)

Multiview detection incorporates multiple camera views to deal with occlusions, and its central problem is multiview aggregation. Given feature map projections from multiple views onto a common ground plane, the state-of-the-art method addresses this problem via convolution, which applies the same calculation regardless of object locations. However, such translation-invariant behaviors might not be the best choice, as object features undergo various projection distortions according to their positions and cameras. In this paper, we propose a novel multiview detector, MVDeTr, that adopts a newly introduced shadow transformer to aggregate multiview information. Unlike convolutions, shadow transformer attends differently at different positions and cameras to deal with various shadow-like distortions. We propose an effective training scheme that includes a new view-coherent data augmentation method, which applies random augmentations while maintaining multiview consistency. On two multiview detection benchmarks, we report new state-of-the-art accuracy with the proposed system. Code is available at https://github.com/hou-yz/MVDeTr.
Perceptual Quality Assessment of Internet Videos

With the fast proliferation of online video sites and social media platforms, user, professionally and occupationally generated content (UGC, PGC, OGC) videos are streamed and explosively shared over the Internet. Consequently, it is urgent to monitor the content quality of these Internet videos to guarantee the user experience. However, most existing modern video quality assessment (VQA) databases only include UGC videos and cannot meet the demands for other kinds of Internet videos with real-world distortions. To this end, we collect 1,072 videos from Youku, a leading Chinese video hosting service platform, to establish the Internet video quality assessment database (Youku-V1K). A special sampling method based on several quality indicators is adopted to maximize the content and distortion diversities within a limited database, and a probabilistic graphical model is applied to recover reliable labels from noisy crowdsourcing annotations. Based on the properties of Internet videos originated from Youku, we propose a spatio-temporal distortion-aware model (STDAM). First, the model works blindly which means the pristine video is unnecessary. Second, the model is familiar with diverse contents by pre-training on the large-scale image quality assessment databases. Third, to measure spatial and temporal distortions, we introduce the graph convolution and attention module to extract and enhance the features of the input video. Besides, we leverage the motion information and integrate the frame-level features into video-level features via a bi-directional long short-term memory network. Experimental results on the self-built database and the public VQA databases demonstrate that our model outperforms the state-of-the-art methods and exhibits promising generalization ability.
LC<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2414" altimg="si9.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>Net: Ladder context correlation complementary network for salient object detection

Currently, the existing salient object detection methods based on convolutional neural networks commonly resort to constructing discriminative networks to aggregate high level and low level features. However, the potential contextual information is always difficult to be fully and reasonably utilized, which usually causes either the absence of useful features or the contamination of redundant features. To address these issues, we propose a novel ladder context correlation complementary network (LC3Net) in this paper, which is equipped with three crucial components. At the beginning, we propose a filterable convolution block (FCB) to assist the automatic collection of information on the diversity of initial features by skillfully inserting the atrous convolutions into independent branches. Besides, we propose a dense cross module (DCM) to facilitate the intimate aggregation of different levels of features by validly integrating the semantic information and detailed information of both adjacent and non-adjacent layers. Furthermore, we propose a bidirectional compression decoder (BCD) to help the progressive shrinkage of multi-scale features from coarse to fine by elaborately leveraging multiple pairs of alternating top-down and bottom-up feature interaction flows. Extensive experiments demonstrate the superiority of our method against 20 state-of-the-art methods.
InterBN: Channel Fusion for Adversarial Unsupervised Domain Adaptation

A classifier trained on one dataset rarely works on other datasets obtained under different conditions because of domain shifting. Such a problem is usually solved by domain adaptation methods. In this paper, we propose a novel unsupervised domain adaptation (UDA) method based on Interchangeable Batch Normalization (InterBN) to fuse different channels in deep neural networks for adversarial domain adaptation.Specifically, we first observe that the channels with small batch normalization scaling factor have less influence on the whole domain adaption, followed by a theoretical proof that the scaling factors for some channels will definitely come close to zero when imposing a sparsity regularization. Then, we replace the channels that have smaller scaling factors in the source domain with the mean of the channels which have larger scaling factors in the target domain or vice versa. Such a simple but effective channel fusion scheme can drastically increase the domain adaption ability.Extensive experimental results show that our InterBN significantly outperforms the current adversarial domain adaptation methods by a large margin on four visual benchmarks. In particular, InterBN achieves a remarkable improvement of 7.7% over the conditional adversarial adaptation networks (CDAN) on VisDA-2017 benchmark.
SYNT++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition

With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthetic data is not trivial due to the gap between the synthetic and the real data distributions. Synthetic datasets may contain artifacts that do not exist in real data such as structured noise, content errors, or unrealistic speaking styles. Moreover, the synthesis process may introduce a bias due to uneven sampling of the data manifold. We propose two novel techniques during training to mitigate the problems due to the distribution gap: (i) a rejection sampling algorithm and (ii) using separate batch normalization statistics for the real and the synthetic samples. We show that these methods significantly improve the training of speech recognition models using synthetic data. We evaluate the proposed approach on keyword detection and Automatic Speech Recognition (ASR) tasks, and observe up to 18% and 13% relative error reduction, respectively, compared to naively using the synthetic data.
Applications of Swarm Intelligent and Deep Learning Algorithms for Image-Based Cancer Recognition

In this book chapter, the authors aim to deliver in-depth details about the applications of deep learning and swarm intelligent algorithms for image-based cancer recognition and diagnosis. In this study, we first describe the overview of popular architectures of deep learning and swarm intelligent algorithms used for cancer recognition. In deep learning, we talk about convolutional neural networks, fully connected convolutional networks, and auto-encoders. In swarm intelligent algorithms, we talk about architecture of genetic algorithms. Secondly, this study presents a brief survey about the research exploiting deep learning and swarm intelligent algorithms for cancer recognition.
Optimal state for a Tavis-Cummings quantum battery via the Bethe ansatz method

In this paper, we investigate the effect of different optical field initial states on the performance of the Tavis-Cummings (TC) quantum battery. In solving the dynamical evolution of the system, we found a fast way to solve the Bethe ansatz equation. We find that the stored energy and the average charging power of the TC quantum battery are closely related to the probability distribution of the optical field initial state in the number states. We define a quantity called the number-state stored energy. With this prescribed quantity, we only need to know the probability distribution of the optical field initial state in the number states to obtain the stored energy and the average charging power of the TC quantum battery at any time. We propose an equal probability and equal expected value splitting method by which we can obtain two inequalities, and the two inequalities can be reduced to Jensen's inequalities. By this method, we found the optimal initial state of the optical field. We found that the maximum stored energy and the maximum average charging power of the TC quantum battery are proportional to the initial average photon number, and the quantum battery can be fully charged when the initial average photon number is large enough. We found two phenomena, which can be described by two empirical inequalities. These two phenomena imply the hypersensitivity of the stored energy of the TC quantum battery to the number-state cavity field. Finally, we discuss the impact of decoherence on battery performance.