Reviewerly/exHarmony huggingface dataset:
A novel ECG signal classification method using DEA-ELM

Electrocardiogram (ECG) signals represent the electrical mobility of the human heart. In recent years, computer-aided systems have helped to cardiologists in the detection, classification and diagnosis of ECG. The aim of this paper is to optimize the number hidden neurons of the traditional Extreme Learning Machine (ELM) using Differential Evolution Algorithm (DEA) and contribute to the classification of ECG signals with a higher accuracy rate. In this paper, publicly ECG records in Physionet was utilized. Pan-Tompkins technique (PTT) and Discrete Wavelet Transform (DWT) approaches were implemented to obtain characteristic properties which are PR period, QT period, ST period and QRS wave of ECG signals. Then, ELM was executed to the ECG samples. Lastly, DEA on software ELM was developed for the assign of the number of hidden neurons, which were used in the ELM algorithm. The performance criterions were used in order to compare the performance of the classification exerted. Concordantly, it was realized that the highest classification achievement values were reached to Accuracy 97.5% and values 93 of number of hidden neurons, with the practice improved with the DEA compared to conventional ELM.
User Experience with Smart Voice Assistants: The Accent Perspective

Voice assistants (VA) like Siri, Alexa, Google Assistant, Cortana, and Bixby are increasingly becoming popular among the general mass. They are being used by a large group of people who use English as their primary mode of communication (native speakers) as well as those who are secondary English language speakers (non-native speakers). However, from an end-user perspective very little is known about the usability, acceptability, satisfaction, and the usage pattern of the VA's between these two different group of users. The current study aims to identify if there exist any differences between these two group of users with respect to the overall usability and the satisfaction received after using the VA's. To answer the research questions a mixed methodology approach is undertaken comprising of an online questionnaire survey in conjunction with a real-world testing of the VA's. The experiment is conducted in Thailand with 275 users for the questionnaire phase and 52 users for the testing phase from 7 different countries. Siri and Alexa are used as the VA representatives since they are the two most popular ones in Thailand. Results suggest no significant differences in usability between the two user groups, however the same being significant for the satisfaction levels.
Cost-efficient dynamic scheduling of big data applications in apache spark on cloud

Job scheduling is one of the most crucial components in managing resources, and efficient execution of big data applications. Specifically, scheduling jobs in a cloud-deployed cluster are challenging as the cloud offers different types of Virtual Machines (VMs) and jobs can be heterogeneous. The default big data processing framework schedulers fail to reduce the cost of VM usages in the cloud environment while satisfying the performance constraints of each job. The existing works in cluster scheduling mainly focus on improving job performance and do not leverage from VM types on the cloud to reduce cost. In this paper, we propose efficient scheduling algorithms that reduce the cost of resource usage in a cloud-deployed Apache Spark cluster. In addition, the proposed algorithms can also prioritise jobs based on their given deadlines. Besides, the proposed scheduling algorithms are online and adaptive to cluster changes. We have also implemented the proposed algorithms on top of Apache Mesos. Furthermore, we have performed extensive experiments on real datasets and compared to the existing schedulers to showcase the superiority of our proposed algorithms. The results indicate that our algorithms can reduce resource usage cost up to 34% under different workloads and improve job performance.
A Multi-Scale Spatial-Temporal Attention Model for Person Re-Identification in Videos

In this paper, we propose a novel deep neural network based attention model to learn the representative local regions from a video sequence for person re-identification. Specifically, we propose a multi-scale spatial-temporal attention (MSTA) model to measure the regions of each frame in different scales from the perspective of whole video sequence. Compared to traditional temporal attention models, MSTA focuses on exploiting the importance of local regions of each frame to the whole video representation in both spatial and temporal domains. A new training strategy is designed for the proposed model by incorporating the image-to-image mode with the videoto- video mode. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed model over state-ofthe- art methods.
A fast RetinaNet fusion framework for multi-spectral pedestrian detection

At present, the mainstream visible pedestrian detector is easily affected by the ambient lighting, the complex background, and the pedestrians distance. While the infrared images (IR) can compensate for the defects of visible images because of its insensitivity to illumination conditions. Based on the Deep Convolutional Neural Network (DCNN), we proposed a multispectral pedestrian detector that combines visual-optical (VIS) image and infrared (IR) image. We carefully designed three DCNN fusion architectures to study the better fusion stages of the two-branch DCNN. In addition, we compared the three fusion strategies and found that the sum fusion strategy showed better performance to our multispectral detector. Our multispectral pedestrian detectors are more adaptable to the around-the-clock applications such as autonomous driving and unattended monitoring, by testing on the public multispectral benchmark dataset KAIST, our best fusion architectures achieved a log-average miss rate of 27.60% comparable to the state-of-the-art detector, but with half the runtime.
Exact algorithms for energy-efficient virtual machine placement in data centers

Virtual machine placement (VMP) and power management are essential topics in the development of cloud computing and data centers. The assignment of a virtual machine to physical machine impacts the energy consumption, the makespan, and the idle time of physical machines. In this paper, we formulate the problem as a three-dimension bin-packing optimization to minimize the energy cost of working machines and idle machines. By considering the CPU and memory requirements from a virtual machine, the assignment is constrained under the capacities of the physical machine. Inspired by the best-fit decreasing algorithm, four variants of this exact algorithm are developed to address the multiple-objective problem under multiple-capacity constraints. Experimental results demonstrate the effectiveness of the proposed algorithms on small-, medium- and large-scale instances profiled from data centers. The results indicate that the algorithms assigning virtual machines to the physical machines of best-fit hosting time is competitive in cases with loose capacity constraints, and the energy-efficiency best-fit algorithm produces efficient assignments when a makespan limit is required on the physical machines. The algorithm combining the fit rules has a linear computing time concerning the numbers of physical and virtual machines, and a stable performance that obtains gaps of results lower than 5.8% compared to an on-the-shelf mixed-integer linear program solver.
Evaluating the Impact of Network Latency on the Safety of Blockchain Transactions

Blockchain came to prominence as the distributed ledger underneath Bitcoin, which protects the transaction histories in a fully-connected, peer-to-peer network. For safety against double-spending, a bitcoin transaction is only considered to be unrevokable after it is within a chain of at least six mined blocks on the blockchain-termed "six confirmations" for short. Besides the use for securing financial activity, blockchain technology is now also merged with various emerging applications, including Internet of Things (IoT) and vehicular ad-hoc networks (VANET). However, such emerging applications may have comparatively low Internet connectivity due to incorporating mobile devices, that may range away from network infrastructure. This paper investigates the impact of network latency on blockchain forking behavior and possible violations of the aforementioned six confirmations convention for transaction approval. To speed up our simulations, we simplify the lockchain's data structure, and avoid the extensive computation required in proof-of-work (PoW). Through simulation, we show that the six confirmations convention is sensitive to the peer-to-peer network latency, and also show how quickly it is violated with an easier difficulty of PoW mining. Unsurprisingly, the speed at which all nodes on the blockchain converge is shown to be severely affected by the latency of the underlying peer-to-peer network. It is also shown the extent to which nodes with more frequent Internet connectivity can gain an unfair advantage in terms of proof-ofwork mining rewards. We thus recommend that networks with heterogeneous latency profiles across nodes monitor fairness, and potentially preclude some nodes from being miners.
Trajectory Planning for UAV Based on Improved ACO Algorithm

Trajectory planning is an important subject in the field of unmanned aerial vehicles (UAVs).However, existing methods do not solve some problems well, such as slow convergence speed and low searching efficiency of related algorithms and collisions between UAVs and the obstacles.Therefore, a method is proposed to solve a trajectory planning problem for multi-UAV in a static environment, which includes three main phases: the initial trajectory generation, the trajectory correction, and the smooth trajectory planning.First, the improved algorithm called MACO which introduces the metropolis criterion into the node screening mechanism of the ant colony optimization (ACO) algorithm is presented to generate the initial trajectory that can effectively avoid falling into the local optimal solution and stagnation.Then, considering size constraint of UAVs, this paper gives three trajectory correction schemes to solve the collision avoidance problem and further to optimize the initial trajectory.Last, the discontinuity originated from the sharp turn which occurred in the trajectory planning is solved by the inscribed circle (IC) smooth method, and the produced trajectory has shown better performance in reducing fuel consumption and improving the trajectory safety.Experimental results demonstrate that the proposed method has the high feasibility and effectiveness from aspects of the optimal solution, the collision avoidance, and the smooth trajectory in the trajectory planning problem for UAVs.
Preventing Sybil Attack in Blockchain using Distributed Behavior Monitoring of Miners

Blockchain technology is useful with the record keeping of digital transactions, IoT, supply chain management etc. However, we have observed that the traditional attacks are possible on blockchain due to lack of robust identity management. We found that Sybil attack can cause severe impact in public/permissionless blockchain, in which an attacker can subvert the blockchain by creating a large number of pseudonymous identities (i.e. Fake user accounts) and push legitimate entities in the minority. Such virtual nodes can act like genuine nodes to create disproportionately large influence on the network. This may lead to several other attacks like DoS, DDoS etc. In this paper, a Sybil attack is demonstrated on a blockchain test bed with its impact on the throughput of the system. We propose a solution directive, in which each node monitors the behavior of other nodes and checks for the nodes which are forwarding the blocks of only particular user. Such nodes are quickly identified, blacklisted and notified to other nodes, and thus the Sybil attack can be restricted. We analyze experimental results of the proposed solution.
Pointing and Selection Methods for Text Entry in Augmented Reality Head Mounted Displays

Augmented reality (AR) is on the rise with consumer-level head-mounted displays (HMDs) becoming available in recent years. Text entry is an essential activity for AR systems, but it is still relatively underexplored. Although it is possible to use a physical keyboard to enter text in AR systems, it is not the most optimal and ideal way because it confines the uses to a stationary position and within indoor environments. Instead, a virtual keyboard seems more suitable. Text entry via virtual keyboards requires a pointing method and a selection mechanism. Although there exist various combinations of pointing+selection mechanisms, it is not well understood how well suited each combination is to support fast text entry speed with low error rates and positive usability (regarding workload, user experience, motion sickness, and immersion). In this research, we perform an empirical study to investigate user preference and text entry performance of four pointing methods (Controller, Head, Hand, and Hybrid) in combination with two input mechanisms (Swype and Tap). Our research represents a first systematic investigation of these eight possible combinations. Our results show that Controller outperforms all the other device-free methods in both text entry performance and user experience. However, device-free pointing methods can be usable depending on task requirements and users' preferences and physical condition.
Recommendation generation using personalized weight of meta-paths in heterogeneous information networks

In today's era of electronic markets with information overload, generating personalized recommendations for e-commerce users is a challenging and interesting problem. Recommending top-N items of interest to e-commerce users is more challenging using binary implicit feedback. The training data is usually highly sparse and has binary values capturing a user's action or inaction. Due to the sparseness of data and lack of explicit user preferences, neighborhood-based and model-based approaches may not be effective to generate accurate recommendations. Of late, network-based item recommendation methods, which utilize item related meta-information, have started getting attention. In this work, we propose a heterogeneous information network-based recommendation model called HeteroPRS for personalized top-N recommendations using binary implicit feedback. To utilize the potential of meta-information related to items, we use the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback. The proposed model is experimentally evaluated and compared with various recommendation techniques for implicit feedback using real-world datasets, and the results show the effectiveness of the proposed model.
Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity

The number of received citations have been used as an indicator of the impact of academic publications. Developing tools to find papers that have the potential to become highly-cited has recently attracted increasing scientific attention. Topics of concern by scholars may change over time in accordance with research trends, resulting in changes in received citations. Author-defined keywords, title and abstract provide valuable information about a research article. This study performs a latent Dirichlet allocation technique to extract topics and keywords from articles; five keyword popularity (KP) features are defined as indicators of emerging trends of articles. Binary classification models are utilized to predict papers that were highly-cited or less highly-cited by a number of supervised learning techniques. We empirically compare KP features of articles with other commonly used journal-related and author-related features proposed in previous studies. The results show that, with KP features, the prediction models are more effective than those with journal and/or author features, especially in the management information system discipline.
Reducing Estimation Bias via Triplet-Average Deep Deterministic Policy Gradient

The overestimation caused by function approximation is a well-known property in Q-learning algorithms, especially in single-critic models, which leads to poor performance in practical tasks. However, the opposite property, underestimation, which often occurs in Q-learning methods with double critics, has been largely left untouched. In this article, we investigate the underestimation phenomenon in the recent twin delay deep deterministic actor-critic algorithm and theoretically demonstrate its existence. We also observe that this underestimation bias does indeed hurt performance in various experiments. Considering the opposite properties of single-critic and double-critic methods, we propose a novel triplet-average deep deterministic policy gradient algorithm that takes the weighted action value of three target critics to reduce the estimation bias. Given the connection between estimation bias and approximation error, we suggest averaging previous target values to reduce per-update error and further improve performance. Extensive empirical results over various continuous control tasks in OpenAI gym show that our approach outperforms the state-of-the-art methods.
Externalized domestication: smart speaker assistants, networks and domestication theory

Framed by domestication theory, affordances and use genres, this study explores early adopters’ uses of smart speaker assistants (SSAs), like Amazon Echo (Alexa) and Google Home. Based on semi-structured, in-depth interviews, we develop a typology of use genres, and describe spatially distributed uses. The interviews revealed six use genres that go beyond the well-known convenience and entertainment. Specifically, the use genres of companionship, self-control and productivity, sleep aid, health care, peace of mind and increased accessibility emerged from participants’ accounts. In addition, we found spatially distributed uses based on the users’ perception of the spatial affordances of SSAs. These spatially distributed uses lead us to propose the process of externalization as a necessary extension of domestication theory for the appropriation of networked devices.
Solar irradiance forecasting models without on-site training measurements

Much effort has been made to increase the integration of solar photovoltaic (PV) systems to reduce the environmental impacts of fossil fuels. An essential process in PV systems is the forecasting of solar irradiance to avoid safety and stability problems due to its intermittent nature. Most of the research has been focused on improving the prediction accuracy based on the assumption that enough on-site training data are available. However, in many situations, it is required for the implementation of PV systems in locations where not enough solar irradiance measurements have been collected. Our hypothesis is that measurements from other sites can be used to train accurate forecasting models, given an appropriate definition of site similarity. We propose a methodology that takes information from exogenous variables that are correlated to on-site solar irradiance and constructs a multidimensional space equipped with a metric. Each site is a point in this space, and the learned metric is used to select those sites that can provide measurements to train an accurate forecasting model on an unobserved site. We show through experiments with real data that using the learned metric provides better predictions than using the measurements collected from the whole set of available sites.
A comparison between the sampling Kantorovich algorithm for digital image processing with some interpolation and quasi-interpolation methods

In this paper we study the performance of the sampling Kantorovich (S–K) algorithm for image processing with other well-known interpolation and quasi-interpolation methods. The S-K algorithm has been implemented with three different families of kernels: central B-splines, Jackson type and Bochner–Riesz. The above method is compared, in term of PSNR (Peak Signal-to-Noise Ratio) and CPU time, with the bilinear and bicubic interpolation, the quasi FIR (Finite Impulse Response) and quasi IIR (Infinite Impulse Response) approximation. Experimental results show better performance of S-K algorithm than the considered other ones.
Feature Selection and Instance Selection from Clinical Datasets Using Co-operative Co-evolution and Classification Using Random Forest

Co-operative co-evolution approach solves problems by breaking them into subproblems. The proposed framework for Clinical Decision Support System (CDSS) uses a co-operative coevolution approach which treats Feature Selection (FS) and Instance Selection (IS) as independent subproblems. FS and IS remove less relevant features and instances, respectively, thereby improving the overall performance of the system. In this work, both Feature and Instance selection are done using the wrapper approach, which uses co-operative co-evolution and random forest classifier. The reduced dataset is used to train a random forest classifier and this trained model helped in making clinical decisions. These decisions assist physicians as the second opinion for diagnosis and treatment. Wisconsin Diagnostic Breast Cancer (WDBC), Hepatitis, Pima Indian Diabetes (PID), Cleveland Heart Disease (CHD), Statlog Heart Disease (SHD), Vertebral Column, and Hepatocellular Carcinoma (HCC) datasets from the University of California Irvine (UCI) Machine Learning repository are used for experimentation. The proposed framework achieved an accuracy of 97.1%, 82.3%, 81.01%, 93.4%, 96.8%, 91.4%, and 72.2% for datasets WDBC, Hepatitis, PID, CHD, SHD, vertebral column, and HCC, respectively. The results prove that the CDSS-developed using co-operative coevolution can efficiently assist the physicians in decision-making.
Implementing and Benchmarking Three Lattice-Based Post-Quantum Cryptography Algorithms Using Software/Hardware Codesign

It has been predicted that within the next tenfifteen years, quantum computers will have computational power sufficient to break current public-key cryptography schemes. When that happens, all traditional methods of dealing with the growing computational capabilities of potential attackers, such as increasing key sizes, will be futile. The only viable solution is to develop new standards based on algorithms that are resistant to quantum computer attacks and capable of being executed on traditional computing platforms, such as microprocessors and FPGAs. Leading candidates for new standards include lattice-based post-quantum cryptography (PQC) algorithms. In this paper, we present the results of implementing and benchmarking three lattice-based key encapsulation mechanisms (KEMs) that have progressed to Round 2 of the NIST standardization process. Our implementations are based on a software/hardware codesign approach, which is particularly applicable to the current stage of the NIST PQC standardization process, where the large number and high complexity of the candidates make traditional hardware benchmarking extremely challenging. We propose and justify the choice of a suitable system-on-chip platform and design methodology. The obtained results indicate the potential for very substantial speed-ups vs. purely software implementations, reaching 28x for encapsulation and 20x for decapsulation.