Reviewerly/exHarmony huggingface dataset:
[Spatial Distribution Characteristics and Pollution Evaluation of Heavy Metals in Arable Land Soil of China].

Soil is an important component of the terrestrial ecosystem and an indispensable natural resource in agricultural production. Understanding the spatial distribution characteristics and pollution levels of heavy metals in arable land soil is important to safeguard the agriculture ecosystem and human health. Based on the collected data of heavy metal concentrations in arable land soil in various administrative regions of China since 2002, first, we selected and removed the abnormal values, then obtained the spatial distribution of the provinces by the Kriging method, and discussed the spatial distribution characteristics of seven heavy metals (Cr, Cd, Pb, Zn, Cu, As, and Hg) in arable land soil. Finally, we adopted the geo-accumulation index to evaluate the pollution status of arable land soil. The results showed that the spatial distribution of heavy metals Cr, Pb, Cu, Zn, and As in arable land soil of China was obvious, while the spatial distribution of soil Cd and Hg was very similar. Furthermore, the concentration of arable land soil in south China was obviously higher than that in the north. The mean value of the results showed that heavy metals are enriched to different degrees in arable land soil of China, and the average soil heavy metal concentrations of arable land are higher in Hunan, Yunnan, Guizhou, Sichuan, Fujian, Guangxi, and Shanghai. The results of geo-accumulation index pollution evaluations showed that the pollution degree of the seven heavy metals was in the order Cd > Hg > Pb > Cu > Zn > As > Cr, among which the soil heavy metals in arable land soil had a higher enrichment degree in Fujian, Guangdong, Hainan, Zhejiang, Hunan, Shaanxi, Gansu, Henan, Chongqing, Shanxi, Tianjin, Inner Mongolia, and Anhui. In general, the geo-accumulation indexes of Cd and Hg in each administrative region are higher, whereas the average land accumulation indexes also indicate that Cd and Hg in arable land shows greater pollution specially. Results from this study provide valuable scientific basis for formulating effective arable land pollution control strategies in the future.
Deep generative cross-modal on-body accelerometer data synthesis from videos

Human activity recognition (HAR) based on wearable sensors has brought tremendous benefit to several industries ranging from healthcare to entertainment. However, to build reliable machine-learned models from wearables, labeled on-body sensor datasets obtained from real-world settings are needed. It is often prohibitively expensive to obtain large-scale, labeled on-body sensor datasets from real-world deployments. The lack of labeled datasets is a major obstacle in the wearable sensor-based activity recognition community. To overcome this problem, I aim to develop two deep generative cross-modal architectures to synthesize accelerometer data streams from video data streams. In the proposed approach, a conditional generative adversarial network (cGAN) is first used to generate sensor data conditioned on video data. Then, a conditional variational autoencoder (cVAE)-cGAN is proposed to further improve representation of the data. The effectiveness and efficacy of the proposed methods will be evaluated through two popular applications in HAR: eating recognition and physical activity recognition. Extensive experiments will be conducted on public sensor-based activity recognition datasets by building models with synthetic data and comparing the models against those trained from real sensor data. This work aims to expand labeled on-body sensor data, by generating synthetic on-body sensor data from video, which will equip the community with methods to transfer labels from video to on-body sensors.
Evaluating the Effectiveness of Heuristic Worst-Case Noise Analysis in FHE

The purpose of this paper is to test the accuracy of worst-case heuristic bounds on the noise growth in ring-based homomorphic encryption schemes. We use the methodology of Iliashenko (Ph.D. thesis, 2019) to provide a new heuristic noise analysis for the BGV scheme. We demonstrate that for both the BGV and FV schemes, this approach gives tighter bounds than previous heuristic approaches, by as much as 10 bits of noise budget. Then, we provide experimental data on the noise growth of HElib and SEAL ciphertexts, in order to evaluate how well the heuristic bounds model the noise growth in practice. We find that, in spite of our improvements, there is still a gap between the heuristic estimate of the noise and the observed noise in practice. We extensively justify that a heuristic worst-case approach inherently leads to this gap, and hence leads to selecting significantly larger parameters than needed. As an additional contribution, we update the comparison between the two schemes presented by Costache and Smart (CT-RSA, 2016). Our new analysis shows that the practical crossover point at which BGV begins to outperform FV occurs for very large plaintext moduli, well beyond the crossover point reported by Costache and Smart.
A Genetic Algorithm Application in Planning Path Using B-Spline Model for Autonomous Underwater Vehicle (AUV)

This paper proposes a method of using the B-spline mathematical model to plan high smoothness curve trajectories with heading condition through given waypoints for autonomous underwater vehicles (AUVs) in particular and ships with rudder systems in general. In addition, this paper examines some of the physical limitations of this vehicle, which lead to some binding conditions of the trajectory. Besides, the paper applies B-spline approximation method to reduce the curvature of the trajectory, when waypoints are too close and we do not need to go through exactly these waypoints in the 2D plane. Finally, this paper also proposes the genetic algorithm application with some modifications to solve the optimization of B-spline path length with constraint in turning radius of the vehicle.
Loss tolerant quantum absorption measurement

Abstract The quantum statistics of entangled photon pairs promise greater performance for absorption measurements than can be achieved classically. However, the performance of a quantum approach is easily degraded under a loss of photons caused by experimental limitations. Here, we propose a loss-tolerant quantum scheme using quantum destructive interference in a nonlinear interferometer. Our theoretical results show that the loss-tolerant quantum scheme surpasses the conventional quantum scheme under photon loss at the detection stage. We demonstrate how photon losses at optical paths and mode-mismatches in a nonlinear interferometer affect the performance of our scheme. We also propose a hybrid approach to cope with the case where the quantum destructive interference is imperfect, and show that the hybrid scheme always surpasses the conventional quantum scheme.
Beyond Birthday Bound Secure Fresh Rekeying: Application to Authenticated Encryption

Fresh rekeying is a well-established method to protect a primitive or mode against side-channel attacks: an easy to protect but cryptographically not so involved function generates a subkey from the master key, and this subkey is then used for the block encryption of a single or a few messages. It is an efficient way to achieve side-channel protection, but current solutions only achieve birthday bound security in the block size of the cipher and thus halve its security (except if more involved primitives are employed). We present generalized solutions to parallel block cipher rekeying that, for the first time, achieve security beyond the birthday bound in the block size n. The first solution involves, next to the subkey generation, one multiplication and the core block cipher call and achieves $$2^{2n/3}$$ security. The second solution makes two block cipher calls, and achieves optimal $$2^n$$ security. Our third solution uses a slightly larger subkey generation function but requires no adaptations to the core encryption and also achieves optimal security. The construction seamlessly generalizes to permutation based fresh rekeying. Central to our schemes is the observation that fresh rekeying and generic tweakable block cipher design are two very related topics, and we can take lessons from the advanced results in the latter to improve our understanding and development of the former. We subsequently use these rekeying schemes in a constructive manner to deliver three authenticated encryption modes that achieve beyond birthday bound security and are easy to protect against side-channel attacks.
Cryptosystem Based on Triple Random Phase Encoding with Chaotic Henon Map

Well-known optical image encryption technique double random phase encoding scheme has been shown vulnerable to basic attacks. Thereafter triple random phase encoding scheme is proposed to endure basic attacks. Recently, triple random phase encoding scheme is also shown vulnerable by using deep learning based attack. In this paper, an image encryption scheme based on triple random phase encoding with chaotic Henon map in Fourier domain is proposed. Henon map is used to strengthen the image encryption scheme. Henon map has two parameters and two initial conditions which are highly sensitive to its original value. Experiments were carried out on grayscale images to validate the proposed scheme. Statistical attacks such as information entropy, histogram and 3-D plot analysis are successfully endured by the scheme. Performances against noise and occlusion attacks show the robustness of the scheme. The key sensitivity results indicate that the proposed scheme is highly secure.
The Onset Threshold of Cybersickness in Constant and Accelerating Optical Flow

This study investigated the principal translational or rotational axis that evokes the most severe cybersickness by detecting constant velocity and acceleration thresholds on the onset of cybersickness. This human subject experiment with 16 participants used a 3D particle field with movement directions (lateral, vertical, yaw, or pitch) and motion profiles (constant velocity or constant acceleration). The results showed that the threshold of pitch optical flow was suggestively lower than that of the yaw, and the vertical threshold was significantly lower than the lateral. Still, there was no effect of scene movement on the level of cybersickness. In four trials, the threshold increased from the first to the second trial, but the rest remained the same as the second one. However, the level of cybersickness increased significantly between the trials on the same day. The disorientation-related symptoms occurred on the first trial day diminished before the second trial day, but the oculomotor-related symptoms accumulated over the days. Although there were no correlations between the threshold and total cybersickness severity, participants with a lower threshold experienced severe nausea. The experimental findings can be applied in designing motion profiles to reduce cybersickness by controlling the optical flow in virtual reality.
Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages

State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language. However, it is still unclear to what extent neural LID models generalize to speech samples with different acoustic conditions due to domain shift. In this paper, we present a set of experiments to investigate the impact of domain mismatch on the performance of neural LID systems for a subset of six Slavic languages across two domains (read speech and radio broadcast) and examine two low-level signal descriptors (spectral and cepstral features) for this task. Our experiments show that (1) out-of-domain speech samples severely hinder the performance of neural LID models, and (2) while both spectral and cepstral features show comparable performance within-domain, spectral features show more robustness under domain mismatch. Moreover, we apply unsupervised domain adaptation to minimize the discrepancy between the two domains in our study. We achieve relative accuracy improvements that range from 9% to 77% depending on the diversity of acoustic conditions in the source domain.
Food Safety Traceability Method Based on Blockchain Technology

Abstract Food safety is a major issue related to the vital interests of the people. In recent years, food safety incidents have emerged in an endless stream in China. However, the existing food safety traceability methods lack of data sharing, which makes it difficult to conduct unified supervision on food safety. In view of the problems existing in the current food safety traceability method, this paper introduces blockchain technology in food safety traceability field, selects milk as the object of food safety traceability in this paper, analyzes the whole process of milk supply chain, designs the overall framework of milk traceability scheme according to the demand analysis and design principles of milk safety traceability scheme, and designs the scheme’s business function module, and chooses ECP-96 traceability coding scheme to ensure the uniqueness as well as security of milk traceability source. In view of the current application of blockchain technology, Hyperledger Fabric is creatively selected as the blockchain technology platform, and Go language is used to develop and implement the traceability method. Through the deployment of multiple nodes and functional testing, the expected food safety traceability goal is achieved.
A Data-Efficient Geometrically Inspired Polynomial Kernel for Robot Inverse Dynamic

In this paper, we introduce a novel data-driven inverse dynamics estimator based on Gaussian Process Regression. Driven by the fact that the inverse dynamics can be described as a polynomial function on a suitable input space, we propose the use of a novel kernel, called Geometrically Inspired Polynomial Kernel (GIP). The resulting estimator behaves similarly to model-based approaches as concerns data efficiency. Indeed, we proved that the GIP kernel defines a finite-dimensional Reproducing Kernel Hilbert Space that contains the inverse dynamics function computed through the Rigid Body Dynamics. The proposed kernel is based on the recently introduced Multiplicative Polynomial Kernel, a redefinition of the classical polynomial kernel equipped with a set of parameters that allows for a higher regularization. We tested the proposed approach in a simulated environment, and also in real experiments with a UR10 robot. The obtained results confirm that, compared to other data-driven estimators, the proposed approach is more data-efficient and exhibits better generalization properties. Instead, with respect to model-based estimators, our approach requires less prior information and is not affected by model bias.
Group Activity Prediction with Sequential Relational Anticipation Model

In this paper, we propose a novel approach to predict group activities given the beginning frames with incomplete activity executions. Existing action (We define action as the behavior performed by a single person, and define activity as the behavior performed by a group of people) prediction approaches learn to enhance the representation power of the partial observation (We define partial observation as the beginning frames with incomplete activity execution, and full observation as the one with complete activity execution). However, for group activity prediction, the relation evolution of people’s activity and their positions over time is an important cue for predicting group activity. To this end, we propose a sequential relational anticipation model (SRAM) that summarizes the relational dynamics in the partial observation and progressively anticipates the group representations with rich discriminative information. Our model explicitly anticipates both activity features and positions by two graph auto-encoders, aiming to learn a discriminative group representation for group activity prediction. Experimental results on two popularly used datasets demonstrate that our approach significantly outperforms the state-of-the-art activity prediction methods.
Perceptual Quality Assessment of Digital Images Using Deep Features

Perceptual quality assessment is a tough task especially in the absence of reference information. No-reference image quality assessment is more challenging than full-reference or reduced reference methods, as the system has to model the different image distortions in the form of a quality score. Most of the approaches are based on handcrafted features which are based on natural scene statistics and are specific to some distortion types. These approaches provide high correlation with human opinion score for datasets containing specific distortions, but they fail to generalize well in scenarios were multiple distortions or real-time distortions are present in images. Deep learning algorithms, on the other hand, demonstrated their abilities to learn expert features with better discriminatory power for various classification and regression tasks. It is a big challenge to use those deep learning methods for image quality assessment as the image datasets with human opinion score are very small and cannot be used effectively to train a deep learning algorithm. We experimented with activations of different deep layers of thirteen pre-trained models and checked for their suitability for the task of no-reference quality assessment. Fine-tuning of these models on quality assessment datasets provided even better performance. A Gaussian process regression model is trained on these activations to perform the quality assessment and it provided state-of-the-art performance. Cross-dataset validation demonstrated its performance further and also provided further prospects of research in this direction.
WITHDRAWN: The generalized non-linear fresnel transform and its application to image encryption

In this research, we proposed a new image encryption method for encrypting the input color images. In this encryption process, the Fresnel transform is used for the wavelength multiplexing process. In this method, there is no need for a lens. So this technique is also called as lens-less technique. In this process, the image or photo to be encrypted will split into three channels (RGB). For decrypting this three channels, we need to use three separate encryption keys, because each channel is encrypted individually. Here the random phase masks and Fresnel transforms are acts as the decryption as well as encryption keys. In this communication, the image encryption framework according to the optical color is described. The proposed idea is simulated using the com-puter system. From the conducted simulations the working of the proposed idea has been proved.
A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named C-EL. Combined with the Bagging method and causal Information Flow theory, the EL mechanism for BN structural learning is established. Base learners of EL are trained by using various SS algorithms. Then, a new causality-based weighted ensemble way is proposed to achieve the fusion of different BN structures. To verify the validity and feasibility of C-EL, we compare it with six different SS algorithms. The experiment results show that C-EL has high accuracy and a strong generalization ability. More importantly, it is capable of learning more accurate structures under the small training sample condition.
Data Integration as Coordination

Recent CSCW research on the collaborative design and development of research infrastructures for the natural sciences has increasingly focused on the challenges of open data sharing. This qualitative study describes and analyzes how multidisciplinary, geographically distributed ocean scientists are integrating highly diverse data as part of an effort to develop a new research infrastructure to advance science. This paper identifies different kinds of coordination that are necessary to align processes of data collection, production, and analysis. Some of the hard work to integrate data is undertaken before data integration can even become a technical problem. After data integration becomes a technical problem, social and organizational means continue to be critical for resolving differences in assumptions, methods, practices, and priorities. This work calls attention to the diversity of coordinative, social, and organizational practices and concerns that are needed to integrate data and also how, in highly innovative work, the process of integrating data also helps to define scientific problem spaces themselves.
Thermodynamic resources in continuous-variable quantum systems

Thermodynamic resources, beyond their well-known usefulness in work extraction and other thermodynamic tasks, are often important also in tasks that are not evidently thermodynamic. Here we develop a framework for identifying such resources in diverse applications of bosonic continuous-variable systems. Introducing the class of bosonic linear thermal operations to model operationally-feasible processes, we apply this model to identify uniquely quantum properties of bosonic states that refine classical notions of thermodynamic resourcefulness. Among these are (1) a suite of temperature-like quantities generalizing the equilibrium temperature to quantum, non-equilibrium scenarios; (2) signal-to-noise ratios quantifying a system's capacity to carry information in phase-space displacement; and (3) well-established non-classicality measures quantifying the resolution in sensing and parameter estimation tasks.
Exploring the Machine Learning Algorithms to Find the Best Features for Predicting the Breast Cancer and Its Recurrence

Every year around one million women are diagnosed with breast cancer. Conventionally it seems like a disease of the developed countries, but the fatality rate in low and middle-income countries is preeminent. Early detection of breast cancers turns out to be beneficial for clinical and survival outcomes. Machine Learning Algorithms have been effective in detecting breast cancer. In the first step, four distinct machine learning algorithms (SVM, KNN, Naive Bayes, Random forest) were implemented to show how their performance varies on different datasets having different set of attributes or features by keeping the same number of data instances, for predicting breast cancer and it’s recurrence. In the second step, analyzed different sets of attributes that are related to the performance of different machine learning classification algorithms to select cost-effective attributes. As outcomes, the most desirable performance was observed by KNN in breast cancer prediction and SVM in recurrence of breast cancer. Again, Random Forest predicts better for recurrence of breast cancer and KNN for breast cancer prediction, while the less number of attributes were considered in both the cases.