Reviewerly/exHarmony huggingface dataset:
AdaptiveSLA: A Two-Stage Scheduling Framework for SLA Profit Maximization in Multi-tenant Database

The requirement of multi-tenant applications is continuously increasing in cloud computing epoch, and they usually have larger data volume compared with traditional applications. Considering the quality of managing these data, SLAs (Service Level Agreement) are usually defined between multi-tenant database service providers and tenants. The providers will get revenues if SLAs are met, otherwise they will pay penalties. In order to maximize the profit, this paper presents AdaptiveSLA, a two-stage scheduling policy for multi-tenant database. In the first stage, AdaptiveSLA detects performance crises and leverages sliding window algorithm to mitigate crises in distributed multitenant database. The crises may derive from heterogeneity of requests or performance degradation of nodes. In the second stage, AdaptiveSLA makes requests' execution sequence. This stage aims at completing requests as many as possible. The executing process is constrained by SLA, which is modeled as slack time. Extensive experimental results demonstrate the effectiveness and efficiency of AdaptiveSLA scheduling policy.
Trajectory Planning of a Quadrotor to Monitor Dependent People

This article introduces a framework for assisting dependent people at home through a vision-based autonomous unmanned aerial vehicle (UAV). Such an aircraft equipped with onboard cameras can be useful for monitoring and recognizing a dependent’s activity. This work is focused on the problem of planning the flight path of a quadrotor to perform monitoring tasks. The objective is to design a trajectory planning algorithm that allows the UAV to position itself for the sake of capturing images of the dependent person’s face. These images will be later treated by a base station to evaluate the persons emotional state, together with his/her behavior, this way determining the assistance needed in each situation. Numerical simulations have been carried out to validate the proposed algorithms. The results show the effectiveness of the trajectory planner to generate smooth references to our previously designed GPI (generalized proportional integral) controller. This demonstrates that a quadrotor is able to perform monitoring flights with a high motion precision.
High-Throughput Covert Channels in Adaptive Rate Wireless Communication Systems

In this paper, we outline a novel, forward error correction-based information hiding technique for adaptive rate wireless communication systems. Specifically, we propose leveraging the functionality of wireless local area network modulation and coding schemes (MCS) and link adaptation mechanisms to significantly increase covert channel throughput. After describing our generalized information hiding model, we detail implementation of this technique within the IEEE 802.11ad, directional multi-Gigabit standard. Simulation results demonstrate the potential of the proposed techniques to develop reliable, high-throughput covert channels under multiple MCS rates and embedding techniques. Covert channel performance is evaluated in terms of the observed packet error ratio of the underlying communication system as well as the bit error ratio of the hidden data.
Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules

This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.
Towards Multiverse Databases

A multiverse database transparently presents each application user with a flexible, dynamic, and independent view of shared data. This transformed view of the entire database contains only information allowed by a centralized and easily-auditable privacy policy. By enforcing the privacy policy once, in the database, multiverse databases reduce programmer burden and eliminate many frontend bugs that expose sensitive data.
EtherShare: Share Information in JointCloud Environment Using Blockchain-Based Smart Contracts

With the development of cloud computing, a great amount of information is stored on cloud, such as Facebook, Twitter, and so on. Single cloud or company is not reliable enough for permanent information storage. Once the cloud or company is in downtime, users could lost their information. Multiple clouds can store the information with more security. However, in JointCloud environment, it is a big problem for multiple clouds or companies to maintain the common rights of information for each user. In this paper, we propose EtherShare, a blockchain-based application in JointCloud environment. EtherShare enables users to share information with permanent storage and open access. It keeps the rights of users through smart contracts to make consensus between multiple clouds. And this application is open source on Github.
Quality improvement of water from post Tin mining based on a wasted cockle shell

The high Tin mining activity in the Bangka Belitung Islands caused a considerable amount of environmental damage in the province. One of the most common impacts of contamination is the poor quality of water. Kulong is a former excavation of Tin mines that may be used as a reservoir of water. But most kulong, especially young ones, store acidic water and dispersed-pollutant water so that it does not meet the minimum requirements of good water. In this study, we developed a simple method to improve the quality of water from post Tin mining using wasted cockle shell. The utilisation of wasted cockle shell because of its abundant amount in this province. Based on the results it is known that calcined cockle shell powder is sufficient to increase the pH level of the water and can coagulate the pollutants that contained in it. Through the water clarity level and the number of total dissolved particles in the water after testing, it is known that the cockle shell powders have a significant effect on improving quality water.
Initialization-similarity clustering algorithm

Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-of-norms regularization to make the clustering more robust. The experimental results on ten real-world benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.
Seeking community structure in networks via biogeography-based optimization with consensus dynamics

Uncovering the community structure hidden in networks is crucial for understanding the function of networks. In this paper, an algorithm of biogeography-based optimization with consensus dynamics for community detection is proposed. The problems of seeking community structures in networks are exquisitely embedded into the framework of biogeography-based optimization. Hence the community structure unveiled in such an evolutionary and global manner is corresponding to the habitat with maximum modularity. We present a dynamical framework for generating initial distribution of solutions for the evolutionary process using consensus dynamics, which gives a reasonably good estimate of the community structure based on the topological information. Thereof, the proposed dynamical method of initialization promotes the efficiency of optimal solution search significantly, compared with the traditional random initialization. Then, the obtained partition is refined using biogeography-based optimization. In addition, a preferential selection strategy for generating the new solutions is developed based on local network topology. Furthermore, we also proposed an adaptive mutation operator that enhances the exploration ability of our evolutionary algorithm. The experimental results on both artificial random and real-world networks indicate the effectiveness and reliability of our algorithm. These findings shed new light on the role played by topological knowledge of networks extracted from consensus dynamics in the evolving optimization processes when finding complex mesoscale structures in networks such as community structure.
A robust and accurate breakpoint detection method for line-structured laser scanner

Line-structured laser scanner has been widely applied in industrial applications, such as 3D reconstruction. Breakpoint detection of the laser stripe centerline is necessary for further analysis. The laser stripe centerline is always not smooth in industrial environments due to the inevitable noises. In this paper, a robust and accurate breakpoint detection method named Fitting-Derivative-Optimization (FDO) is proposed, which mainly includes three steps. The first step is to fit the noisy laser stripe centerline. A novel piecewise fitting method without any empirical parameters is proposed in this step. The second step is to initialize the breakpoints. The second derivative curve of the point slope angle in the fitted laser stripe centerline is calculated. The local extremum points in the second derivative curve are extracted to be the initial breakpoints. The third step is to optimize the breakpoints. In this step, a new optimization method named Neighborhood-Circle (NC) is proposed, which optimizes each breakpoint in its neighborhood circularly to weaken the influence of correlation between breakpoints. Finally, robustness and accuracy of the proposed FDO method are evaluated by simulations and experiments respectively. In simulations, different Gaussian noises are added into the laser stripe centerline for comparisons. Experiments are also implemented to detect breakpoints for two industry parts by using a line-structured laser scanning system. Both of the simulation and experiment results demonstrate that the proposed FDO method can perform well in industrial environments.
A Logic of Objective and Subjective Oughts

The relation between agentive action, knowledge, and obligation is central to the understanding of responsibility – a main topic in Artificial Intelligence. Based on the view that an appropriate formalization of said relation would contribute to the development of ethical AI, we point out the main characteristics of a logic for objective and subjective oughts that was recently introduced in the literature. This logic extends the traditional stit paradigm with deontic and epistemic operators, and provides a semantics that deals with Horty’s puzzles for knowledge and obligation. We provide an axiomatization for this logic, and address its soundness and completeness with respect to a class of relevant models.
Detection of Basic Human Physical Activities With Indoor–Outdoor Information Using Sigma-Based Features and Deep Learning

The devices created on account of the developments in wearable technology are increasingly becoming a part of our daily lives. In particular, sensors have enhanced the usefulness of such devices. The aim of this paper is to detect human physical activity along with indoor/outdoor information by using mobile phones and a separate oxygen saturation sensor. There is no relevant dataset in the literature for this type of detection. For this purpose, data from four different types of human physical activity was collected through mobile phone and oxygen saturation sensors; 12 people aged between 20-65 years participated in the study. During the data collection process, different physical activities under different environmental conditions were performed by the subjects in 10 min. As a next step, a novel deep neural network (DNN) model specifically designed for physical activity recognition was proposed. In order to improve accuracy and reduce the computational complexity, standard deviation (sigma)-based features were introduced. To evaluate its efficacy, we conducted comparisons with selected machine learning algorithms on our proposed dataset. The results on our dataset indicate that the multimodal sigma-based features give the best classification accuracy of 81.60% using our proposed DNN method. Furthermore, the accuracy of the classification made with our proposed DNN method without sigma-based features was 79.04%.
Keeping Data Inter-related in a Blockchain

Blockchains are gaining a substantial recognition as an alternative to the traditional data storage systems due to their tampering-resistant and decentralized storage, independence from any centralized authority, and low entry barriers for new network participants. Presently blockchains allow users to store transactions and data sets, however, they don't provide an easy way of creating and keeping relationships between data entities. In this paper we demonstrate a solution that helps software developers maintain relationships between inter-related data entities and datasets in a blockchain. Our solution runs over Ethereum's Go implementation. This is the first step towards a database management-like middleware system for blockchains.
Compress-store on blockchain: a decentralized data processing and immutable storage for multimedia streaming

Decentralization for data storage is a challenging problem for blockchain-based solutions as the blocksize plays a key role for scalability. In addition, specific requirements of multimedia data call for various changes in the blockchain technology internals. Considering one of the most popular applications of secure multimedia streaming, i.e., video surveillance, it is not clear how to judiciously encode incentivization, immutability, and compression into a viable ecosystem. In this study, we provide a genuine scheme that achieves this encoding for a video surveillance application. The proposed scheme provides a novel integration of data compression, immutable off-chain data storage using a new consensus protocol namely, Proof-of-WorkStore (PoWS) in order to enable fully useful work to be performed by the miner nodes of the network. The proposed idea is the first step towards achieving greener application of a blockchain-based environment to the video storage business that utilizes system resources efficiently.
Removal of tACS artefact: a simulation study for algorithm comparison

Non invasive brain stimulation is a widely used technique for several applications, generally aimed at modulating brain activity and thus behavior. While the behavioral effects can be monitored during the application of the stimulation, the electrophysiological correlates, such as electroencephalography (EEG), cannot, because the stimulation artifact dramatically affects the recorded signals. Here we addressed this problem and we analyzed the artifact that transcranial alternating current stimulation (tACS) leaves on EEG traces. We found that the stimulation noise adds itself non-linearly to the EEG signal and spectral analysis revealed a peak centered at the stimulation frequency. We then created a synthetic dataset by adding to real EEG traces numerically generated signals, matching the characteristics of the tACS artifact. We used this data to test a set of artifact removal techniques based on blind source separation (BSS) methods and wavelet decomposition and we found that the best performing technique is independent component analysis (ICA).
Discovering Correlation in Frequent Subgraphs

In today's networked world, graph data is becoming increasingly more ubiquitous as the complexities, layers and hierarchies of real life data demand to be represented in a structured manner. The goal of graph mining is to extract frequent and interesting subgraph from large graph databases. It is even more significant to perform correlation analysis within these frequent subgraphs, as such relations may provide us with valuable information. However, unfortunately much work has not been done in this field even though its necessity is enormous. In this paper, we propose two measures that help us discover such correlation among frequent subgraphs. Our measures are based on the observation that elements in graphs exhibit the tendency to occur both connected and disconnected. Evaluation results show the effectiveness and practicality of our measures in real life datasets.
Application of mineral norm calculation in the resource evaluation of the sulphide and non-sulphide Zn-Pb mineralisation of the Florida Canyon MVT deposit, Peru

Both Zn–Pb hypogene sulphide and Zn supergene non-sulphide mineralisation types occur in the Mississippi Valley-type deposit of Florida Canyon, located in northern Peru. The geometry and spatial relationship between the two ore types are very complex to interpret, when it comes to mineral resource evaluation. An algorithm using the Zn, Pb and S contents is proposed for the calculation of sphalerite, galena and non-sulphide norms in the block model after its conventional ordinary kriging estimation. Model validation was performed by comparing the results of the calculations with available SEM/EDS-MLA data. The results show good correlation between the calculated and the quantitative values, thus accrediting the proposed methodology. The algorithm is simple and fast to use and can be applied to mineral resource evaluation of Zn–Pb MVT deposits associated with non-sulphide mineralisation.
A Flexible Approach for Human Activity Recognition Based on Broad Learning System

Deep Learning (DL) based methods have recently been receiving attention in Human Activity Recognition (HAR) for their strong capability of nonlinear mapping. However, these methods suffer from high time consumption during training process due to enormous network parameters. Moreover, the DL-based scheme is less capable of incremental learning which is important for some online human activity recognition applications. In this paper, the Broad Learning System (BLS) known as a promising alternative to DL-based methods is introduced to the classification of human activities. Both the online and offline BLS-based recognition frameworks are proposed to enhance the system flexibility. Specifically, during the online training stage, the artificial hyperspherical data generation model is incorporated into the incremental BLS, enabling it to update the model to accommodate new incoming data more efficiently. Experiments are made towards the proposed BLS network based upon two public human activity datasets, namely, HART and WISDM. The results demonstrate the advantage of the proposed BLS-based scheme over the classic DL-based approaches in terms of the training speed and prediction accuracy.
Discovering The Applicability of Classification Algorithms With Arabic Poetry

The classification algorithms were developed to label the objects with the same characteristics into similar groups. In Arabic poetry, poems belong to different eras based on the time they were written in. Changes in time resulted in changes in the style of writing Arabic poetry. Hence, this study is testing the use of different classification algorithms to discover their ability to classify poems in their correct era. This process can be automated by studying the linguistic changes that happened to the Arabic poetry in terms of the words used in the hemistichs structure (part of poem line) without considering poems rhyme. In this paper, we studied applying different classification algorithms to discover poems written in the Abbasid and Andalusian eras. Data have been collected and classifiers have been trained on an overall dataset that contains 30,058 words from 10,895 poetic hemistich collected from both eras. The experiments obtained 70.50% accuracy rate using the support vector machine classifier when tested with a sample of random poem lines. The study showed that it is possible to distinguish, with decent accuracy, poems from different eras based on identifying discriminant features that can be used in the classification.
GENIE: QoS-guided Dynamic Scheduling for CNN-based Tasks on SME Clusters

Convolutional Neural Network (CNN) has achieved dramatic developments in emerging Machine Learning (ML) services. Compared to online ML services, offline ML services that are full of diverse CNN workloads are common in small and medium-sized enterprises (SMEs), research institutes and universities. Efficient scheduling and processing of multiple CNN-based tasks on SME clusters is both significant and challenging. Existing schedulers cannot predict the resource requirements of CNN-based tasks. In this paper, we propose GENIE, a QoS-guided dynamic scheduling framework for SME clusters that achieves users' QoS guarantee and high system utilization. Based on a prediction model derived from lightweight profiling, a QoS-guided scheduling strategy is proposed to identify the best placements for CNN-based tasks. We implement GENIE as a plugin of Tensorflow and experiment with real SME clusters and large-scale simulations. The results of the experiments demonstrate that the QoS-guided strategy outperforms other baseline schedulers by up to 67.4% and 28.2% in terms of QoS-guarantee percentage and makespan.
Analysis of Classification Methods Based on Radio Frequency Fingerprint for Zigbee Devices

Radio frequency fingerprint is an inherent characteristic of wireless communication devices which can be extracted from communication signals and be applied in wireless device identification for communication system security. This paper selects different characteristics of RF fingerprints and compares the identification accuracy of Zigbee devices with five classification algorithms, including support vector machine, bagging, neural network, naive Bayes, and random forest algorithms. The experimental research shows that the highest identification accuracy reaches approximately 100% by using multi-features of frequency offset, IQ offset, and circle offset based on the neural network algorithm under high SNR. With the reduction in SNR, the identification accuracy based on bagging algorithm with multi-features of frequency offset and IQ offset is the highest. The performance of support vector machine algorithm is the most stable.