Reviewerly/exHarmony huggingface dataset:
Utilising CoDA methods for the spatio-temporal geochemical characterisation of groundwater; a case study from Lisheen Mine, south central Ireland

Lisheen Mine in County Tipperary, Ireland exploited an underground Pb/Zn massive sulphide deposit hosted in Carboniferous (Mississippian) carbonates. During the extraction phase, the mine workings (located at an average depth of 170 m below ground level), were continuously pumped to lower the groundwater level. Following mine closure in 2015, pumping ceased and eight groundwater wells in the surrounding area were sampled monthly over an 11-month period to monitor the effects of groundwater rebound. These wells draw water from the upper 30 m of a limestone/dolostone aquifer and the monthly samples were analysed for the concentration of 31 elements and compounds (SO4, Cl, NO3, F, NH4, NO2, P, Ca, Na, K, Mg, Fe, Mn, Cu, Zn, Pb, Al, Ni, Ba, As, Hg, B, Cr, Cd, Mo, Ag, Co, Sr, Be, Sb and U). All of the water can be described as Ca–HCO3 type as expected. Standard methods for analysing groundwater geochemistry data (e.g. piper diagrams etc.) are useful, differentiating groundwaters with first-order contrasting chemical signatures, for example, distinguishing Ca–HCO3-type from Na–HCO3-type water. Samples from the 8 monitoring wells appear to be broadly similar, using this approach. However, these major ion methods fail to further distinguish between different groundwaters. The use of multivariate statistical analytical techniques has become more common in groundwater studies in recent years, allowing the interaction of all the elements and compounds to be considered simultaneously. Compositional Data Analysis (CoDA) was used on the Lisheen dataset to gain a better understanding of the spatial and temporal variation in groundwater geochemistry. Ilr-ion plotting, Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) through CoDA highlights the elements and compounds that account for the majority of the variance and at Lisheen these are nitrate, manganese, ammonium, sulphate and potassium. By displaying these data visually in a CoDA bi-plot, each location can be reliably ‘geochemically fingerprinted’ despite similar concentrations of major ions within a relatively small geographical area (<30 km2). Relabeling the bi-plot observations by date of recovery reveals how one particular groundwater well (PH) subtly varies over time, most likely as a result of seasonal land-use changes (input of compounds associated with fertiliser). This type of statistical analysis has broad applications in hydrology and hydrogeology including contaminant tracing and interaction, environmental studies, land-use planning and mineral exploration.
securePrune:Secure block pruning in UTXO based blockchains using Accumulators

In this paper, we propose a secure block pruning scheme called securePrune for reducing the storage space of a full node and synchronization time of bootstrapping nodes joining the Peer-to-Peer (P2P) network in an Unspent Transaction Outputs (UTXO) based blockchain like bitcoin using RSA accumulators. In our scheme, the miners periodically release a snapshot of the blockchain state (UTXO set), the other full nodes in the network, securely prune the historical blocks after attaining the required number of confirmations to the snapshot block. This is achieved through the modification of the block structure by including a representation for the state as an RSA accumulator called accumulator state in the block header and proofs of knowledge for deletion/inclusion of the current block's input/output transactions in the block. The secure and periodic pruning of the old blocks, reduce the synchronization time for a new node joining into the network. The simulation results demonstrate a significant reduction in the storage space of a full node and the bootstrapping cost of the new nodes.
Infrared salient object detection based on global guided lightweight non-local deep features

In this paper, we studied infrared (IR) maritime salient object detection based on convolutional neural networks (CNNs). There are mainly two contributions. Firstly, we constructed a large extended IR ship image dataset (ExtIRShip) for salient maritime target detection, including 9,123 labelled IR images. Secondly, we proposed a global guided lightweight non-local depth feature (DG-Light-NLDF) model. We introduced Dilated Linear Bottleneck (DLB) to replace the standard convolution and adding a simplified global module to provide the location information of the potential salient object, the proposed method can significantly improve the efficiency of Light-NLDF. Experimental results demonstrate that the proposed DG-Light-NLDF model could detect IR maritime salient objects more accurately with less parameters. In addition, comparison experiments between two datasets validated that the larger dataset is also much more beneficial in improving saliency detection performance.
Differential evolution algorithm with dynamic multi-population applied to flexible job shop schedule

This article proposes a novel differential evolution algorithm based on dynamic multi-population (DEDMP) for solving the multi-objective flexible job shop scheduling problem. In DEDMP, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of the subpopulation is dynamically adjusted based on the last search experience. Furthermore, DEDMP is adaptive based on two search strategies, one with strong exploration ability and the other with strong exploitation ability. The selection probability of each search strategy is also dynamically adjusted according to the success rate. Furthermore, the proposed algorithm adopts newly designed mutation and crossover operators and it can directly generate feasible solutions in the search space. To evaluate the performance of DEDMP, DEDMP is compared with some state-of-the-art algorithms on benchmark instances. The experimental results show that DEDMP is better than or at least competitive with other outstanding algorithms.
Login Security Using One Time Password (OTP) Application with Encryption Algorithm Performance

Abstract During the Covid-19 pandemic, almost all jobs and activities to be limited, relying on the internet network. Data security in an internet network is most important and needs to be a concern for internet users. The internet network is a public network, which is vulnerable to security attacks. Attacks may occur to retrieve user data in the form of a username and password. This study conducts system design to improve the security of the username and password data when logging into web applications. Login using One-Time Password (OTP) verification by implementing the AES algorithm and Blowfish to encrypt the verification code. The verification code uses an SMS gateway. Testing is done by looking at security performances and comparing the performance of the Blowfish and AES algorithm. The results of the design research show that OTP is running well where the AES algorithm performance has advantages in terms of encryption and decryption speed compared to the Blowfish algorithm with a difference of ± 0.015 milliseconds for encryption and ± 0.04 milliseconds for decryption.
Active Learning of Bayesian Probabilistic Movement Primitives

Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distributions. However, providing or requesting useful demonstrations is not easy, as quantifying what constitutes a good demonstration in terms of generalization capabilities is not trivial. In this letter, we propose an active learning method for contextual ProMPs for addressing this problem. More specifically, we learn the trajectory distributions using a Bayesian Gaussian mixture model (BGMM) and then leverage the notion of epistemic uncertainties to iteratively choose new context query points for demonstrations. We show that this approach reduces the required number of human demonstrations. We demonstrate the effectiveness of the approach on a pouring task, both in simulation and on a real 7-DoF Franka Emika robot.
Aggregation-Based Tag Deduplication for Cloud Storage with Resistance against Side Channel Attack

Tag deduplication is an emerging technique to eliminate redundancy in cloud storage, which works by signing integrity tags with a content-associated key instead of user-associated secret key. To achieve public auditability in this scenario, the linkage between cloud users and their integrity tags is firstly re-established in current solutions, which provides a potential side channel to malicious third-party auditor to steal the existence privacy of a certain target file. Such kind of attack, which is also possible among classic public auditing schemes, still cannot be well resisted and is now becoming a big obstacle in using this technique. In this paper, we propose a secure aggregation-based tag deduplication scheme (ATDS), which takes the lead to consider resistance against side channel attack during the process of public verification. To deal with this problem, we define a user-associated integrity tag based on the defined content-associated polynomial and devise a Lagrangian interpolation-based aggregation strategy to achieve tag deduplication. With the help of this technique, content-associated public key is able to be utilized instead of a user-associated one to achieve auditing. Once the verification is passed, the TPA is just only able to make sure that the verified data are correctly corresponding to at least a group of users in cloud storage, rather than determining specific owners. The security analysis and experiment results show that the proposed scheme is able to resist side channel attack and is more efficient compared with the state of the art.
Role of governance in open trade policies–growth nexus in ECOWAS countries: The use of extended IV approach in panel data

The work assesses whether the effect of open trade policies-OTP on growth is dependent on the level of governance quality in the Economic Community of West African States (ECOWAS) countries. We estimate a non-linear growth equation that interacts OTP with governance. Based on the cross-sectional dependence test, we use the Driscoll–Kraay approach and mainly an extended instrumental variables-IV approach (fixed effect + IV approach also controls the endogeneity issue) to control the correlation between units. The basic regressions without interaction terms indicate that good governance itself is essential for growth while economic growth decreases with the level of OTP. The findings from the non-linear growth equation indicate that while the effect of OTP on growth increases as the institutional quality improves, it changes from statistically significant in the negative direction for bad institutional quality to statistically significant in the positive direction for better institutional quality. These findings suggest that ECOWAS countries must improve their institutional quality to increase the positive effect of OTP on growth.
EEG denoising through a wide and deep echo state network optimized by UPSO algorithm

In the complex environment of telemedicine, Electroencephalogram (EEG) signals are easily overwhelmed by noise, which affects the intelligent diagnosis of diseases. Since the time-frequency domain characteristics of some noise in EEG signals are complex and the distribution is unknown, and the spectrum of some noise overlaps with the original EEG signal spectrum, it is difficult to filter those noise by traditional methods. To tackle this problem, and considering the large data characteristics of EEG signals in the context of telemedicine, a wide-deep echo state networks (WDESN) with multiple reservoirs in parallel and stacked configuration is proposed for multivariate time series denoising. Firstly, stacking and paralleling multiple reservoirs, the deep features of signals can be extracted to complete the task of reconstructing signal from the noisy signal. Then, the Uniform Search Particle Swarm Optimization (UPSO) is used to optimize reservoir parameters of WDESN. Finally, the effectiveness of UPSO-WDESN is verified through experiments. Experiment results show that compared with existing models, the proposed UPSO-WDESN model can achieve better noise removal performance, while keeping more nonlinear feature of signals.
Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms

Abstract Natural disasters are disasters caused by events or series of events caused by nature such as earthquakes, tsunamis, volcanic eruptions, floods, tornadoes, and landslides. Some of these natural disasters have taken a lot of public attention, from empathy, sadness and criticism that form an opinion on social media. One of the most popular social media used by the public is Twitter. Opinions written by Twitter users are called tweets. A collection of tweets can be processed to obtain information by using data mining techniques namely Text Mining. In this study, the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm and K-Medoids were used. The result of this study shows that DBSCAN is the best algorithm because it has the Silhouette Index (SI) validity of 0.9140 and the average execution time in RapidMiner Studio is 83.40 seconds. Meanwhile, the K-Medoids algorithm has a Silhouette Index (SI) validity of 0.2259 and an average execution time in RapidMiner Studio 849.93 seconds. The frequency of the word “earthquake” dominates for the positive category, the word “disaster” dominates the negative category, and the word “flood and earthquake” dominates the negative category.
Text GCN-SW-KNN: a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

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Chronological citation recommendation with time preference

Citation recommendation is an important task to assist scholars in finding candidate literature to cite. Traditional studies focus on static models of recommending citations, which do not explicitly distinguish differences between papers that are caused by temporal variations. Although, some researchers have investigated chronological citation recommendation by adding time related function or modeling textual topics dynamically. These solutions can hardly cope with function generalization or cold-start problems when there is no information for user profiling or there are isolated papers never being cited. With the rise and fall of science paradigms, scientific topics tend to change and evolve over time. People would have the time preference when citing papers, since most of the theoretical basis exist in classical readings that published in old time, while new techniques are proposed in more recent papers. To explore chronological citation recommendation, this paper wants to predict the time preference based on user queries, which is a probability distribution of citing papers published in different time slices. Then, we use this time preference to re-rank the initial citation list obtained by content-based filtering. Experimental results demonstrate that task performance can be further enhanced by time preference and it’s flexible to be added in other citation recommendation frameworks.
Posture Tracking Meets Fitness Coaching: A Two-Phase Optimization Approach with Wearable Devices

Fitness training is becoming an increasingly popular way of maintaining overall health and preventing illness. However, in some cases the training could be risky and fitness-related injuries have increased by 48% in the USA. The training itself will not cause hurt, but if it is performed in a crucial-improper form (using the wrong technique), it will injure the exerciser. Research has explored the potential of using wearables to monitor fitness training, but the consideration of proper/improper form is not included. In this paper, we propose WearCoach, a wearable based fitness training assistant, which acquires the user's fitness form information and generates real-time feedback during training. WearCoach differs from previous work of training assistant in 1) it employs a two-phase tracking algorithm to achieve accurate and real-time tracking of body motion, 2) it analyzes the captured arm posture and generates training guidance based on the user's form, 3) it uses joint orientation as an exercise classification feature to improve recognition accuracy. We conducted experiments with eight participants and nine exercises. Three kinds of feedback are generated, including injury alert, movement correction and symmetry analysis.
Heterogeneous model parallelism for deep neural networks

Abstract Deep neural networks (DNNs) have transformed computer vision, establishing themselves as the current state-of-the-art for image processing. Nevertheless, the training of current large DNN models is one of the main challenges to be solved. In this sense, data-parallelism has been the most widespread distributed training strategy since it is easy to program and can be applied to almost all cases. However, this solution suffers from several limitations, such as its high communication requirements and the memory constraints when training very large models. To overcome these limitations model-parallelism has been proposed, solving the most substantial problems of the former strategy. However, describing and implementing the parallelization of the training of a DNN model across a set of processes deployed on several devices is a challenging task. Current proposed solutions assume a homogeneous distribution, being impractical when working with devices of different computational capabilities, which is quite common on high performance computing platforms. To address previous shortcomings, this work proposes a novel model-parallelism technique considering heterogeneous platforms, where a load balancing mechanism between uneven devices of an HPC platform has been implemented. Our proposal takes advantage of the Google Brain’s Mesh-TensorFlow for convolutional networks, splitting computing tensors across filter dimension in order to balance the computational load of the available devices. Conducted experiments show an improvement in the exploitation of heterogeneous computational resources, enhancing the training performance. The code is available on: https://github.com/mhaut/HeterogeneusModelDNN.
Zero-shot Deep Domain Adaptation with Common Representation Learning

Domain Adaptation aims at adapting the knowledge learned from a domain (source-domain) to another (target-domain). Existing approaches typically require a portion of task-relevant target-domain data a priori. We propose an approach, zero-shot deep domain adaptation (ZDDA), which uses paired dual-domain task-irrelevant data to eliminate the need for task-relevant target-domain training data. ZDDA learns to generate common representations for source and target domains data. Then, either domain representation is used later to train a system that works on both domains or having the ability to eliminate the need to either domain in sensor fusion settings. Two variants of ZDDA have been developed: ZDDA for classification task (ZDDA-C) and ZDDA for metric learning task (ZDDA-ML). Another limitation in Existing approaches is that most of them are designed for the closed-set classification task, i.e., the sets of classes in both the source and target domains are "known." However, ZDDA-C is also applicable to the open-set classification task where not all classes are "known" during training. Moreover, the effectiveness of ZDDA-ML shows ZDDA's applicability is not limited to classification tasks. ZDDA-C and ZDDA-ML are tested on classification and metric-learning tasks, respectively. Under most experimental conditions, ZDDA outperforms the baseline without using task-relevant target-domain-training data.
Research and Development of Data Security Multidimensional Protection System in Cloud Computing Environment

In the cloud computing environment, most application software and data are transferred to the cloud computing data center, network service provider, all applications and data management and maintenance work are entrusted to the cloud computing service providers to complete. The feature of cloud computing provides convenience for users and brings many security challenges. In this paper, firstly, the architecture of cloud computing applications were discussed and studied in the organization and, for virtualization, cloud computing, large-scale, dynamic and extensible, based on cloud computing security challenges, summarizes the advantages and safety and environmental protection measures, on the basis of in this paper, we propose a dynamic hash authentication scheme based on Merkle tree. With the user dimension as the letter and the management technology as the side, the trust of empowerment management system is put forward. Focus on data encryption technology, from data, design and demotion are cloud storage security solutions. In addition, in view of the problems existing in the information security standard, we put forward the combination of the cloud security standard and the evaluation system, and improve the multi-dimensional cloud protection system and implementation.
Packaging Information as Fact Versus Opinion: Consequences of the (Information-)Structural Position of Subjective Adjectives

How do we distinguish fact from opinion? We tested whether people’s ability to detect opinion-based content—as indicated by the use of subjective adjectives (e.g., amazing, frustrating)—depends on the linguistic position of the adjective. Our results show that simply changing the linguistic structure of a sentence influences our perception of the sentence’s subjectivity: The same basic information, packaged differently in linguistic terms, yields significantly different subjectivity ratings. Specifically, our results show that texts with subjective adjectives in syntactic positions associated with new information and “main news” are rated as more opinion-based than texts conveying the same core information with the same adjective presented in a position that presents the information as already-known information or as secondary information. We also show that this information-packaging effect is independent of whether the sentence provides grounds/evidence for the opinion. More generally, our results suggest that linguistic-packaging choices can be used to blur the distinction between fact and opinion or, at least, our ability to perceive opinion-based information as such.
Shaping Concrete for Interaction

Concrete is a ubiquitous material in urban environments and increasingly used by industry and the maker movement. However, there is little research about its affordance and its potential for embedded User Interfaces (UI). In our ongoing work, we investigate different manufacturing processes and design strategies to change and adapt the affordances of concrete to make it appear interactive. We tested three interface elements, a button, a scroll wheel, and a slider, with 33 participants in a lab elicitation study. Each was created in two versions following two design strategies, one with a more natural look, the other more abstract. Five participants then bodystormed ideas with the prototypes in an outdoor environment. Based on our explorations, we discuss design considerations for creating concrete interfaces including the potential of both design strategies and present different application scenarios.