Reviewerly/exHarmony huggingface dataset:
Personalized Blended Mobile Learning Framework for the Koran Memorization Process

Increased interest in memorization of the Koran in Indonesia is not yet supported by appropriate educational services. A number of tahfidz schools and communities provide solutions by opening memorization and learning services through telephone communications. However, this solution is not very effective. In addition to high cost, the level of flexibility of teaching-learning is still not good considering the diversity of backgrounds, activities, and ages of the learners. This solution is also very prone to dropouts (learners quitting the program), because there is no commitment to study face to face so that learners are less motivated. This research aims to develop a framework and a software system of blended mobile learning that is able to accommodate the needs of learners in order to obtain an efficient memorization process. ADDIE and context aware framework are adopted as methods for prototype development. Evaluations are performed on the prototype through formative and summative tests. The summative testing uses statistical tests (independent t-test) and formative testing uses UAT (user acceptance test). Statistical test results obtained the significance value of 0.007 (<; 0.05), which means that there is a difference in improvement scores between experimental group and traditional group. Further results of UAT show that 77.87% of learners declare conformity, and 83.76% of learners declare high satisfaction with the learning model featured in the prototype.
An Eigenvector-Enhanced Parallel Adaptive Differential Evolution for Electric Motor Design

Differential Evolution (DE) is a well-known metaheuristic designed to solve continuous optimization problems. Its simple structure and straight forward search operators make it suitable for solving a wide range of real world problems. Despite its success, DE performance may be limited when tackling high dimensional complex problems. Therefore, its algorithmic structure can be reconsidered by adaptively controlling its parameters, and incorporating more resilient search operators. In this study, a Q-learning-based strategy is proposed to adapt DE parameters during the search process. Moreover, an eigenvector-based crossover is introduced in order to accelerate the convergence rate when ill-conditioned landscapes are treated. However, to avoid premature convergence, a simple yet efficient switching technique is proposed to choose between the normal and the eigenvector-based crossover. Due to the high computational time that might occur when applying the eigenvector-based crossover, a parallel counterpart of the algorithm has been implemented using graphics processing units (GPUs). The proposed algorithm has been applied to find the optimal mechanical structure of a recent electric motor. Its performance has been also validated by testing the proposal on CEC 2011 test suite, which contains a set of real world problems. The experimental results reveal the competetive performance of our algorithm compared to recent adaptive DE versions. Besides, the parallel version of the proposal achieved a serious speedup compared with the sequential version while keeping the same results.
Using Classical Planning in Adversarial Problems

Many problems from classical planning are applied in the environment with other, possibly adversarial agents. However, plans found by classical planning algorithms lack the robustness against the actions of other agents - the quality of computed plans can be significantly worse compared to the model. To explicitly reason about other (adversarial) agents, the game-theoretic framework can be used. The scalability of game-theoretic algorithms, however, is limited and often insufficient for real-world problems. In this paper, we combine classical domain-independent planning algorithms and game-theoretic strategy-generation algorithm where plans form strategies in the game. Our contribution is threefold. First, we provide the methodology for using classical planning in this game-theoretic framework. Second, we analyze the trade-off between the quality of the planning algorithm and the robustness of final randomized plans and the computation time. Finally, we analyze different variants of integration of classical planning algorithms into the game-theoretic framework and show that at the cost a minor loss in the robustness of final plans, we can significantly reduce the computation time.
Application of Fuzzy Neural Networks in Robotic Path Planning

This paper essentially discusses different methodologies of Fuzzy Neural Networks which are implemented to robust the functionality of mobile robots in dynamic and static environment. Fusion of algorithms is important to increase the working of any system provided. Different kind of mobile robots along with various algorithms frameworks are taken as case studies for this paper. Due to the reason that typical mathematical models used to make robots mobile in real environment were not very useful as they were not catering for the limitation of robotic system memory. Thus new methodologies are being discussed and implemented by robotics research community.
Improvement of Speech Preprocessing Model Based on Linear Prediction

With the rapid development of information society and communication technology, human-computer interaction becomes more and more important. Therefore, the recognition rate of speech signal becomes the target that people pursue. Speech preprocessing plays an important role in achieving this goal. In this paper,we firstly analyze the conventional linear prediction model and propose a new speech signal regression linear prediction model based on the linear prediction model algorithm, Type by regression forecasting model to forecast the speech signal processing, can more quickly identity, by the MATLAB simulation results show that the new type regression forecasting model performance than conventional linear prediction model is nearly 200 ms of acceleration performance, and greater effectiveness of improved prediction model, namely the pitch detection in the detection performance of speech waveform change processing, greatly improves the speech recognition rate.
Joint SPSL and CCWR for Chinese Short Text Entity Recognition and Linking

Entity Recognition Linking (ERL) is a basic task of Natural Language Processing (NLP), which is an extension of the Named Entity Recognition (NER) task. The purpose of the ERL is to detect the entity from a given Chinese short text, and the detected entity is linking to the corresponding entity in the given knowledge library. ERL’s task include two subtasks: Entity Recognition (ER) and Entity Link (EL). Due to the lack of rich context information in Chinese short text, the accuracy of ER is not high. In different fields, the meaning of the entity is different and the entity cannot be accurately linking. These two problems have brought a big challenge to the Chinese ERL task. In order to solve these two problems, this paper proposes based on neural network model joint semi-point semi-label (SPSL) and Combine character-based and word-based representations (CCWR) embedding. The structure of this model enhances the representation of entity features and improve the performance of ER. The structure of this model enhances the representation of contextual semantic information and improve the performance of EL. In summary, this model has a good performance in ERL. In the ccks2019 Chinese short text ERL task, the F1 value of this model can reach 0.7463.
A Regionalization Navigation Method Based on Deep Reinforcement Learning

Efficient navigation in complex environment is one of research hotspots in the field of robot control. In this paper, for the problems of navigation in distributed environment of a mobile robot, we propose a regionalization navigation method based on deep reinforcement learning. First of all, consider the characteristics of distributed environment, we use independent submodules learn control strategy in different region, and region model is built to integrate strategies to complete navigation in multi-area environment. Then, in order to improve learning efficiency, reward prediction and depth obstacles avoidance are added during training. Experiment result in single-area reveal the improvements of training method is helpful to enhance robot navigation performance. Moreover, by the proposed regionalization navigation studying in multi-area environment, our method shows the advantages in training time and reward that single model does not have, indicate that it can better deal with large-scale navigation.
Challenges and Approaches to Teaching Data Science Technologies in an Information Technology Program with Non-Traditional Students

Teaching Data Science to traditional CS majors presents many challenges to students, faculty and the supporting infrastructure. When the student population changes from Computer Science students to those in an Information Technology program where many of the core CS courses are not required, additional challenges arise. Couple these challenges with teaching non-traditional students who are also involved with their families and careers; the difficulty in teaching this material rises exponentially. For our experiment, we chose to teach the Hadoop Distributed File System, the Map/Reduce paradigm, and basic Hadoop programming to expose these students to Data Science technologies. We will discuss our recent experience teaching Data Science to Information Technology students and the challenges we had to overcome, including missing distributed systems knowledge, a lack of basic Unix and operating systems understanding and a complete unfamiliarity with a Distributed File System.
Interval Algorithm for Random Number Generation: Information Spectrum Approach

The problem of exactly generating a general random process (target process) by using another general random process (coin process) is studied. The performance of the interval algorithm, introduced by Han and Hoshi, is analyzed from a perspective of information spectral approach. When either the coin process or the target process has one point spectrum, asymptotic optimality of the interval algorithm among any random number generation algorithms is proved, which demonstrates utility of the interval algorithm beyond the ergodic process. The feasibility condition of exact random number generation is also elucidated.
A Composite Technique to Fortify Security for DaaS Services in Cloud Environment

Cloud computing gives an extensive amount of virtual storage to users. The demands, the importance and the usage of cloud computing are increasing day by day. Therefore, security problems and threats are also increasing. An individual and any business organization or a company may have a huge amount of data; they cannot store it physically because the cost of that huge data storage will be more. Cloud computing solves this problem by providing the platform to store huge amount of data. The data storage on the cloud can be blend with the other users’ data. This can lead to data protection issues in cloud storage. As confidentiality is the main parameter of security, if confidentiality is broken then it will result in loss of data for individuals and organizations. This data needs to be protect with confidentiality measures before being store on cloud. To provide security of this data we are using composite technique i.e. obfuscation with encryption to fortify the security level. In this paper, we propose a composite technique to increase the security level while reducing the data size, and the time of execution.
A New Clustering Algorithm Inspired by Insect Pollination

This paper proposed a novel flower pollination clustering algorithm, which called NIPC algorithm for arranging data sets into clusters. It starts from the phenomenon of plant aggregation and growth with similar characteristics inspired from insect pollination. In this context, the two position updating strategies allows plants to get the most comfortable position. The experiments show that it has a good effect on processing data sets with different sizes, shapes and density compared with other swarm intelligence algorithms.
PSI (ψ) Invariant Features for Face Recognition

Over last few decades, mathematics has played a crucial role in developing efficient algorithms for Face Recognition (FR) used in biometric systems. FR using Machine Learning (ML) techniques has impacted FR systems tremendously, towards efficient and accurate models for FR. Existing FR systems used in biometrics use ML techniques to learn patterns in the images by extracting various features from them and often require pre-processed face image data for the learning process. In this paper, we have used various pre-processing techniques and compared them in the deployed FR framework. It was observed that the Steerable Pyramid (SP) filter was the most efficient pre-processing technique among all techniques used for pre-processing in this work. Though existing feature extraction methods such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF) have been used in the past, they have not been accurate enough in various vision based biometric systems. Hence, a novel PSI (Pose Scale and Illumination) invariant SURF-RootSIFT technique is proposed by extending the well known SIFT-RootSIFT feature extraction technique which is achieved by calculating the Bhattacharya Coefficient between the feature vectors. A framework which uses the proposed novel feature extraction technique is deployed in this work. This paper demonstrates that the novel SURF-RootSIFT based framework is proven to perform more accurately and efficiently than the other techniques, with 99.65, 99.74 and 97.93% accuracy on the Grimace, Faces95 and Faces96 databases respectively.
Improvement of the Bit Plane Truncation Image Scheme for Reversible Data Hiding

Data hiding is a technique for embedding secret data into digitalized carriers, such as images, videos, and documents. With remarkable demand for quality image in the past decades, reversible data hiding (RDH) has attracted researchers' attention. In the RDH methods, not only the secret information can be extracted, but the original image can also be recovered without loss. In the present study, we attempt to improve an existing RDH scheme known as bit-plane truncation image (BPTI). The proposed method follows the block division used in the BPTI, and achieves higher embedding capacity without extra memory to store the peak point and zero point values. Experimental results show that the image visual quality of the proposed method is very satisfactory.
Distributed Electronic Data Storage and Proof System Based on Blockchain

In the context of the Internet, whether it is daily business or social networking, the penetration of electronic data is ubiquitous. Internet companies, financial institutions, government agencies and many other fields, more and more documents, notices, contracts, transaction vouchers, technology and trade secrets are stored in the form of electronic data. However, the existing traditional electronic data storage and proof systems are often encountered with third-party trust crisis and potential data security risks. To cope with these challenges, a distributed electronic data storage and proof system is designed, making use of the core features of the blockchain’s decentralization and non-tampering to effectively solve the tampering and security problems of electronic data storage and proof. The system encodes and fragments information using Reed-Solomon code. And this system provides users with data uploading, downloading, querying, comparing and authorizing services. By using the system interaction, smart contracts are compiled to anchor key data information on the main chain, ensuring the non-tampering of electronic data. In the meantime, the access rights of different users to electronic data are restricted accordingly. Finally, based on an improved RFM model, the distributed storage nodes are determined to achieve load balancing of storage nodes. It also increases the high availability of the system.
Efficient Memory Pool Allocation Algorithm for CNN Inference

Deep Learning based applications are becoming increasingly prevalent in embedded devices. There are considerable advantages in deploying the Deep Neural Network (DNN) models directly on the device rather than on the cloud. This presents new challenges as popular networks are computationally complex and have huge memory requirements. While exciting research efforts are ongoing to improve the on-device inference performance, other concerns such as reducing the memory usage of DNNs have taken a backseat. This paper focuses specifically on how to efficiently manage the memory requirements of running Convolutional Neural Networks (CNNs) on-device. We propose a novel solution consisting of 2 steps, fine-grained buffer liveness analysis followed by memory pool allocation. We calculate the lifetime of each required buffer (including internal buffers) of a network and assign an offset to each buffer for sharing a common memory pool, resulting in efficient reuse of memory across buffers. The experimental results show that the proposed solution achieves 18x better memory usage than that of opensource Caffe framework and also up-to 22% improvement over existing state-of-the-art for an individual CNN. The scalability of the solution is showcased by a further reduction in memory usage of 31% achieved for inference pipelines that use multiple neural networks. We also show that the proposed solution achieves the theoretically minimum possible memory usage for the networks considered, using a simpler method than existing solutions.
Humanoid robot shadow system based on kinect depth camera detection

The growing development of 5G technology and artificial intelligence seems to bring infinite possibilities to the robotics field. However, there is currently no humanoid robot in the world that can be completely controlled by humans and has the same mobility as humans. This paper proposes a completely new structural design designed to give the robot the same articulated joint as humans. And through the kinect depth camera, it can accurately capture the movement angle of each joint of the human body based on the human bone joint motion detection algorithm. Through the angle remapping and correction, the final angle information is sent to the steering gear through the serial port, so that the humanoid robot can perform the same action as the human being in real time.
Anonymity Mixes as (Partial) Assembly Queues: Modeling and Analysis

Anonymity platforms route the traffic over a network of special routers that are known as mixes and implement various traffic disruption techniques to hide the communicating users' identities. Batch mixes in particular anonymize communicating peers by allowing message exchange to take place only after a sufficient number of messages (a batch) accumulate, thus introducing delay. We introduce a queueing model for batch mix and study its delay properties. Our analysis shows that delay of a batch mix grows quickly as the batch size gets close to the number of senders connected to the mix. We then propose a randomized batch mixing strategy and show that it achieves much better delay scaling in terms of the batch size. However, randomization is shown to reduce the anonymity preserving capabilities of the mix. We also observe that queueing models are particularly useful to study anonymity metrics that are more practically relevant such as the time-to-deanonymize metric.
MapReduce-based Dragonfly Algorithm for large-scale Data-Clustering

The improvement in networking-technology, sensors, and availability of the internet have accelerated the huge growth to the electronic data. This immense amount of data has stimulated the development of new data analysis methods for better decision making. Clustering is an influential unsupervised data analysis approach, with wide areas of applications. K-Means is a fast and prolific approach for data-clustering present in the literature. However, the algorithm is easily-influenced by the initial positions of cluster-centroids, and the method converges to the local optimum that is nearest to the initial centroid positions. Furthermore, the algorithm cannot process large datasets within a reasonable time-period. In this work, a novel data-clustering method named MapReduce-based Dragonfly Algorithm (MR-DA) is introduced. The efficiency of MR-DA is compared with 4 other recent methods. The experimental results demonstrate that MR-DA surpassed the other considered methods on the majority of the datasets.
A Method for Massive Scientific Literature Clustering Based on Hadoop

With the development of science and technology and a large numbers of advanced vocabularies, the traditional classification of disciplines cannot meet the current needs of the subject division of scientific literature. At the same time, the clustering of the scientific literature put forward more requirements to the efficiency of the methods and the corresponding software and hardware facilities. In this paper, text features are extracted based on the TF-IDF method and the features of scientific literature. In Hadoop distributed environment, text clustering is carried out through Canopy-Kmeans algorithm, which achieved clustering of the massive scientific literature. As a result, our method proposed in this paper has improved key indicators compared to previous algorithms and greatly improved the efficiency of clustering.
Medical Information Storage Model Based on Block Chain

How to effectively control data access rights in the medical field and securely share data between hospitals is an important challenge in the standardization of medical information in China. This paper proposes a model of data access and secure sharing by using the decentralization, tamper resistance and traceability of blockchain. The system utilizes attribute-based encryption technology to ensure the authenticity of information and solve the problems of information asymmetry in the traditional medical field, difficulty in information inquiry, difficulty in positioning of responsible persons, and inability to trace information.
Inaugural Issue of the IEEE Open Journal of Intelligent Transportation Systems

Dear readers, It is with pleasure and pride that we present the first issue of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). Responding to a world-wide demand for Gold Open Access Journals, ‘OJ-ITS’ has been developed as one of seven new Open Journals published by IEEE. Published by the IEEE Intelligent Transportation System Society, OJ-ITS publishes both scientific as well as application oriented work covering the full scope of ITS. OJ-ITS is an on-line journal, with rapid publication through IEEE Xplore.
Evaluation and Mitigation of Timing Side-Channel Leakages on Multiple-Target Dynamic Binary Translators

Timing side-channel attacks are an important issue for cryptographic algorithms. If the execution time of an implementation depends on secret information, an adversary may recover the latter through measuring the former. Different approaches have emerged to exploit information leakage on cryptographic implementations and to protect them against these attacks, and recent works extend the concerns to dynamic execution systems [3, 15, 24]. However, little has been said about Cross-ISA emulation and its impact on timing leakages. In this paper, we investigate the impact of dynamic binary translators in the constant-time property of known cryptographic implementations, using different Region Formation Techniques (RFTs). We show that the emulation may have a significant impact by inserting non constant-time constructions during the translation, leading to significant timing leakages in QEMU and HQEMU emulators. These leakages are then verified using a statistical approach. In order to guarantee the constant-time property, we have implemented a solution in the QEMU dynamic binary translator, mitigating the inserted timing side-channels.