Reviewerly/exHarmony huggingface dataset:
Using a soft computing OSPRC risk framework to analyze multiple contaminants from multiple sources; a case study from Khoy Plain, NW Iran

Water shortage in arid and semi-arid areas like Iran makes groundwater contamination a crucial issue. In the Khoy aquifer, NW Iran, contaminants (e.g., arsenic (As), nitrate (NO3-), lead (Pb), and zinc (Zn)) may originate from both geological and anthropogenic sources. The objectives of the study are to (1) employ soft modeling framework to abstract available hydrogeochemical information into a perceptual model and (2) build a conceptual model using the risk cells (RCs) by applying the following two steps: (i) study Origin-Source-Pathways-Receptor-Consequence (OSPRC) as a risk system; and (ii) apply "soft modeling" as a set of diverse and classical tools including graphical representations, geological surveys, and multivariate statistical analysis to validate the information by evaluating their convergence or divergence behaviors among different tools used for investigating the groundwater contaminants. According to the perceptual model, the Khoy aquifer contains four RCs. RC4 (southern of plain) and RC2 (northern of the plain) contain high levels of As, while RC2 contains high amounts of Zn. In RC1 (northern of plain) and RC3 (middle of plain), a high concentration of Pb is detected, while in RC3 and RC4, there is a high concentration of NO3-. It was found that a soft modeling approach can only identify the dominant hydrogeochemical processes for each RC as a descriptive model, rather than the use of quantitative models if sufficient data are available.
Class-imbalanced positive instances augmentation via three-line hybrid

The class-imbalance problem is one of the researches of machine learning and data mining. To address the class-imbalance problem, the traditional oversampling algorithm only utilizes the information of the positive instances to generate the synthetic instances with similar characteristics to the minority instances, and there is a problem that the information of the majority instances cannot be used. When the minority instances are too few and too concentrated, such methods suffer from the problem of small disjuncts, resulting in overfitting of the training data. To solve this problem, we incorporate the genetic process of three-line hybrid rice, and a new positive instances augmentation algorithm, i.e., Three-line Hybrid Positive Instance Augmentation (THPIA) is proposed. The THPIA uses the genetic process of three-line hybrid rice to mixup the features of majority-class and minority-class to construct unlabeled instances. Then, the positive instances in the pool of the positive instances are randomly selected to hybridize with the randomly selected unlabeled instances, and the enhanced seed instances of the positive instances are obtained. Finally, a distance constraint is used to prevent the augmented positive instances from generating noisy instances in the negative region. The experimental results on 20 open datasets show that THPIA can effectively utilize the information of the majority instances to enhance the minority instances. Comparing with 7 state-of-the-art methods by Friedman test and Holm’s post-hoc test, THPIA is comparable to CDSMOTE and SMOTE-LOF, and outperforms the remaining 5 state-of-the-art algorithms.
Novel trajectory privacy protection method against prediction attacks

When the public uses Location-based Services (LBSs), their location information is constantly exposed. Owing to the spatiotemporal correlation of trajectories, it is easy for attackers to use historical trajectories and background knowledge to predict future locations of target users. We refer to this type of inference attack as a trajectory prediction attack. To address such potential but threatening attacks in a continuous location query, we propose a novel trajectory privacy protection method. The proposed algorithm aims to generate an indistinguishable perturbed location that is robust to the prediction attack, wherein the user’s real location can be replaced by a perturbed location when submitted to an untrusted server. First, a hidden Markov model-based trajectory prediction mechanism is proposed to simulate predictive attacks and compute the predictability of positions before the trajectory is released. Second, the w sliding window mechanism is designed to dynamically adjust the privacy protection degree of each location point in the trajectory according to the predictability of the location and privacy needs of users. Finally, we propose a bounded noise-adding algorithm based on the Laplace mechanism to improve the usability of data. In our experiments, mutual information, trajectory root-mean-square error, query error, and root-mean-square predictability were used as evaluation criteria, and the performance of the proposed method was comprehensively evaluated. The results show that our algorithm can reduce the trajectory predictability to 0.21 without reducing data availability, which is effective against prediction attacks.
Activity-Based Person Identification Using Multimodal Wearable Sensor Data

Wearable devices equipped with a variety of sensors facilitate the measurement of physiological and behavioral characteristics. Activity-based person identification is considered an emerging and fast-evolving technology in security and access control fields. Wearables, such as smartphones, Apple Watch, and Google glass can continuously sense and collect activity-related information of users, and activity patterns can be extracted for differentiating different people. Although various human activities have been widely studied, few of them (gaits and keystrokes) have been used for person identification. In this article, we performed person identification using two public benchmark data sets (UCI-HAR and WISDM2019), which are collected from several different activities using multimodal sensors (accelerometer and gyroscope) embedded in wearable devices (smartphone and smartwatch). We implemented eight classifiers, including an multivariate squeeze-and-excitation network (MSENet), time-series transformer (TST), temporal convolutional network (TCN), CNN-LSTM, ConvLSTM, XGBoost, decision tree, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor. The proposed MSENet can model the relationship between different sensor data. It achieved the best person identification accuracies under different activities of 91.31% and 97.79%, respectively, for the public data sets of UCI-HAR and WISDM2019. We also investigated the effects of sensor modality, human activity, feature fusion, and window size for sensor signal segmentation. Compared to the related work, our approach has achieved the state of the art.
Robust learning from noisy web data for fine-Grained recognition

Due to DNNs' memorization effect, label noise lessens the performance of the web-supervised fine-grained visual categorization task. Previous literature primarily relies on small-loss instances for subsequent training. The current state-of-the-art approach JoCoR additionally employs explicit consistency constraints to make clean samples more confident. However, a joint loss designed for both sample selection criteria and parameter updating is not competent for training a robust model in the presence of web noise. Especially, false positives are assigned with larger weights, causing the model to pay more attention to misclassified noisy images. Besides, leveraging weight decay to forget discarded noisy instances is too slow and implicit to take effect. Therefore, we propose a simple yet effective approach named MS-DeJOR (Multi-Scale training with Decoupled Joint Optimization and Refurbishment). In contrast to JoCoR, we decouple sample selection from training procedure to handle the above problems. Specifically, a negative entropy term is applied to prevent false positives from being overemphasized. The model can explicitly forget those samples identified as noise by imposing such a regularization term on all training data. Furthermore, we use accumulated predictions to refurbish the noisy labels and re-weight training images to boost the model performance. A multi-scale feature enhancement module is adopted to extract discriminative and subtle feature representations. Extensive experiments show that MS-DeJOR yields state-of-the-art performances on three web-supervised fine-grained datasets, demonstrating the effectiveness of our approach. The data and source code have been available at https://github.com/msdejor/MS-DeJOR.
TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation

• Proposed a new age estimation algorithm using Facial and Skeletal-Cosmetic graphs. • The proposed TAA-GCN includes two new modules AGCL and TMM. • The TMM capture non-ordinal temporal dependencies and the AGCL refines graphs. • Proposed a new graph representation based on face, skeleton, posture, and clothing. • The proposed algorithm outperformed SOTA methods on four public datasets. This paper proposes a novel age estimation algorithm, the Temporally-Aware Adaptive Graph Convolutional Network (TAA-GCN). Using a new representation based on graphs, the TAA-GCN utilizes skeletal, posture, clothing, and facial information to enrich the feature set associated with various ages. Such a novel graph representation has several advantages: First, reduced sensitivity to facial expression and other appearance variances; Second, robustness to partial occlusion and non-frontal-planar viewpoint, which is commonplace in real-world applications such as video surveillance. The TAA-GCN employs two novel components, (1) the Temporal Memory Module (TMM) to compute temporal dependencies in age; (2) Adaptive Graph Convolutional Layer (AGCL) to refine the graphs and accommodate the variance in appearance. The TAA-GCN outperforms the state-of-the-art methods on four public benchmarks, UTKFace, MORPHII, CACD, and FG-NET. Moreover, the TAA-GCN showed reliability in different camera viewpoints and reduced quality images.
TeSSLa – An Ecosystem for Runtime Verification

Abstract Runtime verification deals with checking correctness properties on the runs of a system under scrutiny. To achieve this, it addresses a variety of sub-problems related to monitoring of systems: These range from the appropriate design of a specification language over efficient monitor generation as hardware and software monitors to solutions for instrumenting the monitored system, preferably in a non-intrusive way. Further aspects play a role for the usability of a runtime verification toolchain, e.g. availability, sufficient documentation and the existence of a developer community. In this paper we present the TeSSLa ecosystem, a runtime verification framework built around the stream runtime verification language TeSSLa: It provides a rich toolchain of mostly freely available compilers for monitor generation on different hardware and software backends, as well as instrumentation mechanisms for various runtime verification requirements. Additionally, we highlight how the online resources and supporting tools of the community-driven project enable the productive usage of stream runtime verification.
InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction

Humans constantly interact with daily objects to accomplish tasks. To understand such interactions, computers need to reconstruct these from cameras observing whole-body interaction with scenes. This is challenging due to occlusion between the body and objects, motion blur, depth/scale ambiguities, and the low image resolution of hands and graspable object parts. To make the problem tractable, the community focuses either on interacting hands, ignoring the body, or on interacting bodies, ignoring hands. The GRAB dataset addresses dexterous whole-body interaction but uses marker-based MoCap and lacks images, while BEHAVE captures video of body-object interaction but lacks hand detail. We address the limitations of prior work with InterCap, a novel method that reconstructs interacting whole-bodies and objects from multi-view RGB-D data, using the parametric whole-body model SMPL-X and known object meshes. To tackle the above challenges, InterCap uses two key observations: (i) Contact between the hand and object can be used to improve the pose estimation of both. (ii) Azure Kinect sensors allow us to set up a simple multi-view RGB-D capture system that minimizes the effect of occlusion while providing reasonable inter-camera synchronization. With this method we capture the InterCap dataset, which contains 10 subjects (5 males and 5 females) interacting with 10 objects of various sizes and affordances, including contact with the hands or feet. In total, InterCap has 223 RGB-D videos, resulting in 67,357 multi-view frames, each containing $$6$$ RGB-D images. Our method provides pseudo ground-truth body meshes and objects for each video frame. Our InterCap method and dataset fill an important gap in the literature and support many research directions. Our data and code are available for research purposes at https://intercap.is.tue.mpg.de .
Machine Learning Model Generation With Copula-Based Synthetic Dataset for Local Differentially Private Numerical Data

With the development of IoT technology, personal data are being collected in many places.These data can be used to create new services, but consideration must be given to the individual's privacy.We can safely collect personal data while adding noise by applying differential privacy.However, because such data are very noisy, the accuracy of machine learning trained by the data greatly decreased.In this study, our objective is to build a highly accurate machine learning model using these data.We focus on the decision tree machine learning algorithm, and, instead of applying it as is, we use a preprocessing technique wherein pseudodata are generated using a copula while removing the effect of noise added by differential privacy.In detail, the proposed novel protocol consists of three steps: generating a covariance matrix from the differentially private numerical data, generating a discrete cumulative distribution function from differentially private numerical data, and generating copula-based numerical samples.Simulation results using synthetic and real datasets verify the utility of the proposed method not only for the decision tree algorithm but also for other machine learning algorithms such as deep neural networks.This method will help create machine learning models, such as recommendation systems, using differential privacy data.
Capturing Users’ Fresh Interests via Evolving Session-Based Social Recommendation

Recommendation systems play a crucial part in helping users efficiently obtain information based on users’ current preferences and discover their individual needs, but the existing works are deficient in terms of the evolution of users’ interests. In this paper, Graph Embedding with Service and User information (GESU) model is proposed to address the limitations of capturing users’ fresh interests. Graph-structured data derived from time-varying session sequences are captured via gated graph neural networks. Then, the evolving influence of different services for users is obtained through a multi-head module. At the same time, a graph attention network is applied to predict users’ fresh consumption preferences by selecting representative friends to characterize user information. Extensive experiments on three datasets show that the proposed model outperforms state-of-the-art methods consistently on various evaluation metrics. GESU provides a means to recommend services that meet current requirements in an environment where users’ interests evolve dynamically.
PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing

In autonomous and mobile robotics, one of the main challenges is the robust on-the-fly perception of the environment, which is often unknown and dynamic, like in autonomous drone racing. In this work, we propose a novel deep neural network-based perception method for racing gate detection – PencilNet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> – which relies on a lightweight neural network backbone on top of a pencil filter. This approach unifies predictions of the gates' 2D position, distance, and orientation in a single pose tuple. We show that our method is effective for zero-shot sim-to-real transfer learning that does not need any real-world training samples. Moreover, our framework is highly robust to illumination changes commonly seen under rapid flight compared to state-of-art methods. A thorough set of experiments demonstrates the effectiveness of this approach in multiple challenging scenarios, where the drone completes various tracks under different lighting conditions.
On Committing Authenticated-Encryption

We provide a strong definition for committing authenticated-encryption (cAE), as well as a framework that encompasses earlier and weaker definitions. The framework attends not only to what is committed but also the extent to which the adversary knows or controls keys. We slot into our framework strengthened cAE-attacks on GCM and OCB. Our main result is a simple and efficient construction, CTX, that makes a nonce-based AE (nAE) scheme committing. The transformed scheme achieves the strongest security notion in our framework. Just the same, the added computational cost (on top of the nAE scheme’s cost) is a single hash over a short string, a cost independent of the plaintext’s length. And there is no increase in ciphertext length compared to the base nAE scheme. That such a thing is possible, let alone easy, upends the (incorrect) intuition that you can’t commit to a plaintext or ciphertext without hashing one or the other. And it motivates a simple and practical tweak to AE-schemes to make them committing.
Blind deblurring with fractional-order calculus and local minimal pixel prior

Fractional-order calculus is an extension of integer order calculus. In signal processing, fractional-order calculus can non-linearly enhance the low-frequency signal and suppress the high-frequency signal. In this paper, a new fractional-order local minimum pixel prior (FOLMP) is proposed by combining fractional-order calculus with the local minimum pixel prior. The FOLMP of the sharp images includes fewer non-zero pixels than the blur images. A new blur kernel estimation algorithm is proposed by combining L0 regularized FOLMP with the maximum posterior probability. Furthermore, the kernel similarity is employed to adjust the iteration times to accelerate the computational efficiency. Comparative experiments show that the proposed algorithm can perform better on different types of datasets than the most advanced algorithms. In addition, non-overlapping image patches are adopted to compute the FOLMP, and the kernel similarity is used to suppress excessive iterations. Therefore, the proposed algorithm is several times or even tens of times more efficient than the classical prior-based methods.
A Combination of Dilated Self-Attention Capsule Networks and Bidirectional Long- and Short-Term Memory Networks for Vibration Signal Denoising

As scalar neurons of traditional neural networks promote dimension reduction caused by pooling, it is a difficult task to extract the high-dimensional spatial features and long-term correlation of pure signals from the noisy vibration signal. To address the above issues, a vibration signal denoising method based on the combination of a dilated self-attention capsule network and bidirectional long short memory network (DACapsNet–BiLSTM) is proposed to extract high-dimensional spatial features and learn long-term correlations between two adjacent time steps. An improved self-attention module with spatial feature extraction ability was constructed based on the random distribution of noise, which is embedded into the capsule network for the extracted spatial features and denoising. The dilated convolution is integrated into the improved capsule network to expand the receptive field to obtain the spatial features of the vibration signal. The output of the capsule network was used as the input of the bidirectional long-term and short-term memory network to obtain the timing characteristics of the vibration signal. Numerical experiments demonstrated that DACapsNet–BiLSTM performs better than other signal denoising methods, in terms of signal-to-noise ratio, mean square error, and mean absolute error metrics.
A Boundary Regression Model for Nested Named Entity Recognition

Abstract Recognizing named entities (NEs) is commonly treated as a classification problem, and a class tag for a word or an NE candidate in a sentence is predicted. In recent neural network developments, deep structures that map categorized features into continuous representations have been adopted. Using this approach, a dense space saturated with high-order abstract semantic information is unfolded, and the prediction is based on distributed feature representations. In this paper, the positions of NEs in a sentence are represented as continuous values. Then, a regression operation is introduced to regress the boundaries of NEs in a sentence. Based on boundary regression, we design a boundary regression model to support nested NE recognition. It is a multiobjective learning framework that simultaneously predicts the classification score of an NE candidate and refines its spatial location in a sentence. This model was evaluated on the ACE 2005 Chinese and English corpus and the GENIA corpus. State-of-the-art performance was experimentally demonstrated for nested NE recognition, which outperforms related works about 5% and 2% respectively. Our model has the advantage to resolve nested NEs and support boundary regression for locating NEs in a sentence. By sharing parameters for predicting and locating, this model enables more potent nonlinear function approximators to enhance model discriminability.
A New Design Identification and Control Based on GA Optimization for An Autonomous Wheelchair

The daily lifestyle of an average human has changed drastically. Robotics and AI systems are applied to many fields, including the medical field. An autonomous wheelchair that improves the degree of independence that a wheelchair user has can be a very useful contribution to society. This paper presents the design and implementation of an autonomous wheelchair that uses LIDAR to navigate and perform SLAM. It uses the ROS framework and allows the user to choose a goal position through a touchscreen or using deep learning-based voice recognition. It also presents a practical implementation of system identification and optimization of PID control gains, which are applied to the autonomous wheelchair robot. Input/output data were collected using Arduino, consisting of linear and angular speeds and wheel PWM signal commands, and several black-box models were developed to simulate the actual wheelchair setup. The best-identified model was the NLARX model, which had the highest square error (0.1259) among the other candidate models. In addition, using MATLAB, Optimal PID gains were obtained from the genetic algorithm. Performance on real hardware was evaluated and compared to the identified model response. The two responses were identical, except for some of the noise due to the encoder measurement errors and wheelchair vibration.