Reviewerly/exHarmony huggingface dataset:
Kebun Buah Learning Media for Early Childhood Counting Ability

Children aged 5-6 years have four types of tasks in counting ability, which are to distinguish between bigger and smaller, calculations, number identification, and simple arithmetic. This study involved 22 children aged 5-6 years in Kindergarten, Sleman, Special District, Yogyakarta. This study used the quasy experiment method by using the experimental class and control class by giving different treatments. Children who participated in using interactive learning media by using a computer called Kebun Buah media and tested whether they can count various kinds of fruit. The results of research showed that Kebun Buah media affected counting ability on children aged 5-6 years, the results of post-test counting ability of children in the experimental class using Kebun Buah media increasedÂ to 88 in average while in the control class using a number card media obtained an average 78.9. This study was conducted by utilizing technology that can increase the implications of education in learning and improving counting ability of early childhood in the use of technological means in the classroom.
Securing Cognitive Radio Vehicular Ad Hoc Network with Fog Node based Distributed Blockchain Cloud Architecture

Cognitive radio, ad hoc networks' applications are continuously increasing in wireless communication globally.In vehicles' environment, cognitive radio technology with mobile ad hoc networks (MANETs) enables vehicles to monitor the available channels and to effectively function in these frequencies through sharing ongoing information with drivers and different frameworks to enhance traffic safety on roads.To fulfill the computational storage resources' limitations of a specific vehicle, Vehicular Cloud Computing (VCC) is used by merging VANET with cloud computing.Cloud computing requires high security and protection because authenticate users and attackers have the same rights in VCC.The security is enhanced in CRVANETs, but the distributed nature of cloud unlocks a door for dissimilar attacks, such as trust modal, data security, connection fault and query tracking attacks.This paper proposes an effective and secured blockchain scheme-based distributed cloud architecture in place of conventional cloud architecture to secure the drivers' privacy with low cost and on-demand sensing procedure in CRVANETs ecosystem.
A Game-based Thermal-Aware Resource Allocation Strategy for Data Centers

Data centers (DC) host a large number of servers, computing devices and computing infrastructure, which incur significant electricity / energy. This also results in huge amount of heat produced, which if not addressed can lead to overheating of computing devices in the DC. In addition, temperature mismanagement can lead to thermal imbalance within the DC environment, which may result in the creation of hotspots. The energy consumed during the life of a hotspot is greater than the energy saved during computation. Hence, the thermal imbalance impacts on the efficiency of the cooling mechanism installed inside the DC, which can result in high energy consumption. One popular strategy to minimize energy consumption is to optimize resource allocation within the DC. However, existing scheduling strategies do not consider the ambient effect of the surrounding nodes at the time of job allocation. Moreover, thermal-aware resource scheduling as an optimization problem is a topic that is relatively understudied in the literature. Therefore, in this research, we propose a novel Game-based Thermal-Aware Resource Allocation (GTARA) strategy to reduce the thermal imbalances within the DC. Specifically, we use cooperative game theory with a Nash-bargaining solution concept to model the resource allocation as an optimization problem, where the user jobs are assigned to the computing nodes based on their thermal profiles and their potential effect on the surrounding nodes. This allows us to improve the thermal balance and avoid the hotspots. We then demonstrate the effectiveness of GTARA, TACS, TASA, and FCFS, in terms of minimizing thermal imbalance and the hotspots.
Entropy-Based Dynamic Heterogeneous Ant Colony Optimization

To solve large-scale traveling salesman problem (TSP) with better performance, this paper proposes an entropy-based dynamic heterogeneous ant colony optimization (EDHACO). The allotropic mechanism and the heterogeneous colonies model are proposed to balance the convergence and the solution diversity. First, entropy is used to measure the diversity, and the entropy-based allotropic mechanism with three communication strategies can improve the adaptability of EDHACO. Then, the heterogeneous colonies with complementary advantages are proposed to balance the convergence speed and the diversity of the algorithm. Besides, two operators are proposed to improve the performance of the algorithm. The adaptive 3-opt operator can be used to accelerate the convergence, and the dynamic-pheromone-reset operator can be introduced to avoid trapping in a local optimum. Finally, EDHACO is applied to solve TSPs, and the experimental results suggest that it has better performance with higher stability and precision in TSP instances, especially in the large-scale TSP instances.
Integrity Audit of Shared Cloud Data with Identity Tracking

More and more users are uploading their data to the cloud without storing any copies locally. Under the premise that cloud users cannot fully trust cloud service providers, how to ensure the integrity of users’ shared data in the cloud storage environment is one of the current research hotspots. In this paper, we propose a secure and effective data sharing scheme for dynamic user groups. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> In order to realize the user identity tracking and the addition and deletion of dynamic group users, we add a new role called Rights Distribution Center (RDC) in our scheme. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">2</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> To protect the privacy of user identity, when performing third party audit to verify data integrity, it is not possible to determine which user is a specific user. Therefore, the fairness of the audit can be promoted. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">3</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> Define a new integrity audit model for shared cloud data. In this scheme, the user sends the encrypted data to the cloud and the data tag to the Rights Distribution Center (RDC) by using data blindness technology. Finally, we prove the security of the scheme through provable security theory. In addition, the experimental data shows that our proposed scheme is more efficient and scalable than the state-of-the-art solution.
UAV Landing Based on the Optical Flow Videonavigation

An automatic landing of an unmanned aerial vehicle (UAV) is a non-trivial task requiringa solution of a variety of technical and computational problems. The most important is the precisedetermination of altitude, especially at the final stage of approaching to the earth. With currentaltimeters, the magnitude of measurement errors at the final phase of the descent may be unacceptablyhigh for constructing an algorithm for controlling the landing manoeuvre. Therefore, it is desirableto have an additional sensor, which makes possible to estimate the height above the surface of therunway. It is possible to estimate all linear and angular UAV velocities simultaneously with thehelp of so-called optical flow (OF), determined by the sequence of images recorded by an onboardcamera, however in pixel scale. To transform them into the real metrical values it is necessary toknow the current flight altitude and the camera angular position values. The critical feature of theOF is its susceptibility to the camera resolution and the shift rate of the observed scene. During thedescent phase of flight, these parameters change at least one hundred times together with the altitude.Therefore, for reliable application of the OF one needs to coordinate the shooting parameters withthe current altitude. However, in case of the altimeter fault presence, the altitude is also still to beestimated with the aid of the OF, so one needs to have another tool for the camera control. One of thepossible and straightforward ways is the camera resolution change by pixels averaging in computerpart which performed in coordination with theoretically estimated and measured OF velocity. Thearticle presents results of such algorithms testing from real video sequences obtained in flights withdifferent approaches to the runway with simultaneous recording of telemetry and video data.
Part-aligned pose-guided recurrent network for action recognition

Action recognition using pose information has drawn much attention recently. However, most previous approaches treat human pose as a whole or just use pose to extract robust features. Actually, human body parts play an important role in action, and so modeling spatio-temporal information of body parts can effectively assist in classifying actions. In this paper, we propose a Part-aligned Pose-guided Recurrent Network (P2RN) for action recognition. The model mainly consists of two modules, i.e., part alignment module and part pooling module, which are used for part representation learning and part-related feature fusion, respectively. The part-alignment module incorporates an auto-transformer attention, aiming to capture spatial configuration of body parts and predict pose attention maps. While the part pooling module exploits both symmetry and complementarity of body parts to produce fused body representation. The whole network is a recurrent network which can exploit the body representation and simultaneously model spatio-temporal evolutions of human body parts. Experiments on two publicly available benchmark datasets show the state-of-the-art performance and demonstrate the power of the two proposed modules.
Cryptocurrencies &amp; Initial Coin Offerings: Are they Scams? - An Empirical Study

The volume of Initial Coin Offerings (ICOs) had risen steeply with an all-time high market capitalisation of close to 1 trillion USD in December 2017.Since then the digital asset market has slumped, retreating to approximately 200 billion USD by mid-2018.Stakeholders of the crypto industry have pondered the reasons for this retrenchment and are increasingly focusing on the notion that many ICOs could be scams.A recent industry study even went as far to claim that 80% of all ICOs are indeed scams.In this paper, we investigate the question whether these scams are as common as claimed.We do so by first defining what a scam is and secondly, by drawing on empirical data to assess the number of cases fitting such a definition.Building on Principal Agent Theory and based on the statistical analysis of our empirical data set we attempt to establish the current state of affairs with regards to scams in the crypto-currency world.The results of our study divert from salient beliefs.
Improved Kalman filter based differentially private streaming data release in cognitive computing

Cognitive computing works well based on volumes of data, which offers the guarantee of unlocking novel insights and data-driven decisions. Steaming data is a major component of aggregated data, and sharing these real-time aggregated statistics has gained a lot of benefits in decision analysis, such as traffic heat map and disease outbreaks. However, original streaming data sharing will bring users the risk of privacy disclosure. In this paper, differential privacy technology is introduced into cognitive system, and an improved Kalman filter based differentially private streaming data release scheme is proposed for privacy requirement of cognitive computing system. The feasibility of the proposed scheme has been demonstrated through analysis of the utility of sanitized data from four real datasets, and the experimental results show that the proposed scheme outperforms the Kalman filter-based method at the same level of privacy preserving.
Identifying Cybersickness through Heart Rate Variability alterations

Numerous users of Head Mounted Displays develop symptoms similar to motion sickness. This phenomenon is known as cybersickness. The current standard method to evaluate it is the Simulator Sickness Questionnaire (SSQ). Additionally, detection by methods such as biosignals is possible, but it requires sophisticated equipment and expertise. In order to detect early cybersickness symptoms, the availability of real-time detection by using simple equipment, such as a 2-lead ECG, would be highly useful. This contribution aims to analyze the feasibility of detecting cybersickness with a simple 2 lead ECG. A total of 13 participants played the VR game QuakeVR wearing an Oculus Rift DK2 for 15 minutes, during which a 2-lead ECG was recorded. Addiionally, pre- and post-SSQ questionnaires were given. Four of 13 participants had to end the experiment prematurely due to cybersickness. The difference in SSQ scores is statistically significant (p&lt;.01), as is the ECG (p=.02), in these participants. This study shows the utility of a simple 2-lead ECG to detect cybersickness. These findings raise the possibility of real-time monitoring and prediction of cybersickness with simple devices and open the question of whether photoplethysmography could be used with the same purpose.
Polynomial-based modifiable blockchain structure for removing fraud transactions

Blockchain has become one of the most significant technology in financial fields. A hash-based blockchain holds the feature of strong tamper resistance. However, it is almost impossible to change fraud transactions in time, since starting a fork requires a lot of time and resources. Failing to remove fraudulent transactions in time is harmful to the entire economic environment. In order to deal with the modification problem, in this work, we propose a novel polynomial-based blockchain structure. Data segments are organized by a Lagrange interpolation method in each block. Polynomial functions are used to keep the order of blocks. The polynomial-based blockchain structure not only achieves the aim of modification but also supports differential control strategy on modification difficulty. Experimental results demonstrate that the polynomial-based blockchain structure is efficient and practical. Detailed theoretical and practical analysis showed that the polynomial-based modifiable blockchain structure has a wide range of application scenarios with the help of other techniques on cryptography and privacy preservation.
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models

Recently, a variety of unsupervised methods have been proposed that map pre-trained word embeddings of different languages into the same space without any parallel data.These methods aim to find a linear transformation based on the assumption that monolingual word embeddings are approximately isomorphic between languages.However, it has been demonstrated that this assumption holds true only on specific conditions, and with limited resources, the performance of these methods decreases drastically.To overcome this problem, we propose a new unsupervised multilingual embedding method that does not rely on such assumption and performs well under resource-poor scenarios, namely when only a small amount of monolingual data (i.e., 50k sentences) are available, or when the domains of monolingual data are different across languages.Our proposed model, which we call 'Multilingual Neural Language Models', shares some of the network parameters among multiple languages, and encodes sentences of multiple languages into the same space.The model jointly learns word embeddings of different languages in the same space, and generates multilingual embeddings without any parallel data or pre-training.Our experiments on word alignment tasks have demonstrated that, on the low-resource condition, our model substantially outperforms existing unsupervised and even supervised methods trained with 500 bilingual pairs of words.Our model also outperforms unsupervised methods given different-domain corpora across languages.Our code is publicly available 1 .
Virtual Reality Technology

This study discussed the recent application of Virtual Reality Technology (VRT) in an educational setting and self-face learning of chemistry in specific considering the need for an effective strategy in learning chemical concepts among the pre-service chemistry teachers in Nigeria. It highlighted some benefits of VRT to pre-service chemistry teachers who are faced with difficulty in content knowledge of teaching abstract chemistry concepts such organic structure, molecular structure, chemical reactions and stoichiometry through scrutinizing existing literature on VRT in chemistry. The paper further presented some empirical studies on VTR in resolving misconceptions among pre-service chemistry teachers in Nigeria. Some of the VRT benefits highlighted include; interactivity, immersability, and visualizability which are expected to enable the pre-service chemistry teacher to fully understand concepts better for effective and efficient chemistry instructional delivery after their graduation.
A Spatiotemporal Heterogeneous Two-Stream Network for Action Recognition

The method based on the two-stream networks has achieved great success in video action recognition. However, most existing methods employ the same structure for both spatial and temporal networks, leading to unsatisfied performance. In this paper, we propose a spatiotemporal heterogeneous two-stream network, which employs two different network structures for spatial and temporal information, respectively. Specifically, the Residual network (ResNet) and BN-Inception are utilized as the base networks to present the spatiotemporal characteristics of different human actions. In addition, a segmental architecture is employed to model long-range temporal structure over video sequences to better distinguish the similar actions owning sub-action sharing phenomenon. Moreover, combined with the strategy of data augment, a modified cross-modal pre-training strategy is proposed and applied to the spatiotemporal heterogeneous network to improve the final performance of human actions recognition. The experiments on UCF101 and HMDB51 datasets demonstrate the proposed spatiotemporal heterogeneous two-stream network outperforms the spatiotemporal isomorphic networks and other related methods.
Tokenization: Open Asset Protocol on Blockchain

Token represents the right to do some operations in software. In blockchain, there are two types of tokens: utility token and security token. They endow the items of token more value in blockchain world. In order to achieve asset tokenization, we propose a new kind of token in this paper, the asset-backed token, which is used for the proposed blockchain based Open Asset Protocol (OAP). Using OAP, we show to how to convert both of the real and virtual objects to asset-backed tokens on blockchain. Then we discussed a new method for data utilization and privacy-preserving based on OAP and compare with our previous scheme, the Secure Multi-Party Computation (SMPC). Additionally, we introduce the Policy-Backed Token(PBT), which is an instance implementing OAP in insurance industry. We have applied PBT in the airline travel insurance product E-life of an insurance company.
Design of IoT Blockchain Based Smart Agriculture for Enlightening Safety and Security

The Internet of Things is evolving as a complete matured technology to be used in all the Smart applications and it establishes itself in the future generations of internet. As like Internet of Things, Blockchain is the blooming technology in which each node involved in the blockchain contains the distributed ledger which enhances the security and data transparency. Illegal users are not able to perform any fault transaction in the blockchain network due to its ability of performing smart contract and consensus. The Internet of Things can be merged with the blockchain to improve the performance of the application in real time. However, managing the devices connected to the sensors in IoT environment and mining the block chain remains the technical challenge forever. With this background, we make an attempt to survey the core details of blockchain technology and its features. In this paper, we have proposed design architecture by merging IoT and BlockChain for Smart Agriculture and ended up with some new architectural framework.
CMNet: A Connect-and-Merge Convolutional Neural Network for Fast Vehicle Detection in Urban Traffic Surveillance

A wide variety of vehicle detection approaches using the deep convolutional neural network (CNN) have achieved great success in recent years. However, the existing CNN-based feature extraction algorithms, especially residual network, cannot obtain powerful semantic information in the vehicle detection task, and thus suffer from the problem of a missing detection, error detection, or repeated detection. In this paper, we present a connect-and-merge convolutional neural network (CMNet) for fast detecting vehicles in complex scenes. First, we propose a connect-and-merge residual network (CMRN) for performing feature extraction. Specifically, the CMRN assembles residual branches in parallel through a connect-and-merge mapping: Connect the input to the outputs of two residual branches separately (Connect), and merge the outputs of the connection as the input of the subsequent residual block (Merge), respectively. Second, we present a multi-scale prediction network (MSPN) to accurately regress the vehicle shape and classify vehicle fine-grained categories. In addition, the feature maps from the CMRN are merged with their corresponding upsampled features from the MSPN using concatenation. Thus, we can improve the information flow of the framework and make it easy to train. The experimental results on the KITTI dataset and the UA-DETRAC dataset demonstrate that the CMNet can achieve efficient detection performance in terms of accuracy and speed for the real-world traffic surveillance data.
Differential evolution with biological-based mutation operator

Evolutionary algorithms are swiftly gaining recognition where the application of the same in solving complex real-world problems is beneficial. One of the best well known Evolutionary algorithms is Differential Evolution (DE). It is considered the most powerful stochastic population-based optimization technique, inspired by the natural phenomenon of evolution to solve the real world problems. The performance of differential evolution has been promising, enabling researchers to develop a DE-variant that prevents premature convergence and enable delayed stagnation. The proposed method involves the implementation of new mutation vectors inspired from the biological phenomenon called Hemostasis that regulates the blood flow in the human body. The proposed bio-inspired mutation operator is termed as a Hemostatic operator that results in more promising solutions. It enhances diversity by means of good vectors during earlier stages thereby avoiding stagnation in the later stages. Actually, the process to avoid trapping in local search space pushes the proposed algorithm to perform globally in an effective manner. Specifically, it accelerates the convergence rate to enhance the search capability towards global optima and assist differential evolution to address the problem of stagnation. It has been compared with other state-of-the-art optimization algorithms on COCO (Comparing Continuous Optimizers). The simulation results indicate that the proposed Hemostasis based mutation strategy outperforms most of the state-of-the-art differential evolution variants and few population-based optimization algorithms.